Minimizing greenhouse gas emissions in long haul transportation by synchronization, consolidation and coordination

(1)

© Catherine Gaudreault, 2019

Minimizing greenhouse gas emissions in long haul

transportation by synchronization, consolidation and

coordination

Mémoire

Catherine Gaudreault

Maîtrise en sciences de l'administration - avec mémoire

Maître ès sciences (M. Sc.)

(2)

Minimizing greenhouse gas emissions in long haul

transportation by synchronization, consolidation and

coordination

Mémoire

Catherine Gaudreault

Sous la direction de:

(3)

iii

Résumé

Ce mémoire vise à définir et quantifier les émissions de gaz à effet de serre (GES) émises par le réseau de transport logistique de notre partenaire industriel . En parallèle, nous détaillons plusieurs scénarios d'optimisation possibles afin de réduire son empreinte carbone. Cela se fait par optimisation mathématique, par laquelle les déplacements entre l'entreprise et ses différents partenaires, de l'approvisionnement à la livraison au client final, pour différents types de produits et différents transporteurs avec différents types de véhicules sont considérés.

Plus précisément, notre objectif est de décrire et de représenter la différence entre la situation actuelle et la solution obtenue en optimisant le réseau en termes de distance parcourue, de GES émis, de consolidation des livraisons ainsi que de production et de stocks nécessaires. Suite à l'analyse quantitative et qualitative des résultats, nous sommes en mesure de fournir de nombreuses suggestions d'amélioration à l'entreprise en ce qui concerne la gestion de son transport interne et externe. Un certain nombre d'indicateurs de performance clés sont également évalués, les plus importants étant l'inventaire et le nombre de voyages effectués. Ceux-ci sont considérablement réduits dans notre scénario optimisé.

Pour garantir des résultats commerciaux optimaux, nous proposons un modèle de résolution en deux étapes comprenant une modélisation mathématique du problème suivie d'une amélioration manuelle de la solution. De plus, les méthodes de calcul utilisées pour mesurer les émissions de GES sont basées sur la distance parcourue ainsi que sur la capacité utilisée de chaque véhicule, attribuant ainsi l’utilisation du véhicule à l’entreprise (tandis que la capacité restante est utilisée par d’autres compagnies lorsque le transporteur consolide ses opérations). Cela nous permet d'estimer les émissions générées même lorsque la construction des routes de différents transporteurs n'est pas exactement connue.

La coordination, la consolidation et la synchronisation des différents voyages liés aux activités de l’entreprise nous ont permis de réduire les émissions de GES jusqu’à 23%, soit 3,438.64 tonnes de CO2e économisées sur

une base annuelle, soit 2,733,354 km. De plus, nos observations des résultats ont mis en évidence une multitude de recommandations concernant l’utilisation des transporteurs, la réduction des stocks et le contrôle des flux de transport au sein de l’entreprise.

(4)

iv

Abstract

This thesis aims to define and quantify the greenhouse gas (GHG) emission emitted by our industrial partner’s logistics transportation network. Next to that, we detail several possible optimization scenarios in order to reduce its carbon footprint. This is done via mathematical optimization, in which the trips between the company and its various partners, from supply to delivery to the end customer, for different types of products and different carriers with different types of vehicles are considered.

More specifically, our purpose is to describe and represent the difference between the current situation and the solution obtained by optimizing the network in terms of distance traveled, GHG emitted, consolidation of deliveries as well as production and stock needed. Following the quantitative and qualitative analysis of the results, we are able to provide numerous suggestions for improvements to the company with regard to the management of its internal and external transport. A number of key performance indicators are also evaluated, most importantly inventory and the number of trips. These are drastically reduced in our optimized scenario.

To ensure optimal business results, we propose a two-step resolution model that includes mathematical modeling of the problem followed by manual improvement of the solution. In addition, the calculation methods used to measure GHGs emitted are based on the distance traveled as well as the capacity used of each vehicle, thus assigning vehicle usage to the company (while the remaining vehicle space is to be used by other companies when the carrier consolidates its operation). This allows us to estimate the emissions generated even when the construction of routes of different carriers is not exactly known.

The coordination, consolidation and synchronization of the various trips related to the company’s activities allowed us to reduce the GHGs emitted by up to 23%, which translates into 3,438.64 tons of CO2e saved on a

yearly basis, or 2,733,354 km. In addition, our observations of the results highlighted a multitude of recommendations regarding the use of carriers, the reduction of inventory and the control of transport flows within the company.

(5)

v

Résumé ... iii Abstract ... iv Table of Contents... v List of tables ... ix List of figures ... xi Thanks ... xii Introduction ... 1 1.1 Context ... 1 1.2 Types of products ... 2 1.3 Types of vehicles ... 3 1.4 Problem description ... 5 1.5 Methodology ... 7 1.5.1 Data collection ... 7 1.5.2 Model development ... 8

1.5.3 Validation of the model and the solution ... 8

1.6 Thesis organization ... 8 Chapter 2 ... 8 Literature Review ... 8 2.1 Green Logistics... 9 2.2 Integrated Logistics ... 12 2.3 Literature Summary ... 14 Chapter 3 ... 21

The company’s logistics network ... 21

3.1 Overview of the company ... 21

3.2 The company’s plants ... 21

3.2.1 The first processing plant ... 22

3.2.2 The B plant ... 26

3.2.3 The A plant ... 30

3.3 Overall logistics network ... 34

Chapter 4 ... 36 Experimental data ... 36 4.1 Type of products ... 36 4.2 Production capacities ... 37 4.3 Storage capacities ... 37 4.4 Plants demand... 38 4.5 Suppliers offer ... 38 4.5.1 Product 8 suppliers ... 38 4.5.2 Product 4 suppliers ... 39 4.5.3 Product 3 suppliers ... 39 4.5.4 Product 5 suppliers ... 40 4.6 Customers demand ... 41 4.6.1 Product 1 customers ... 41 4.6.2 Product 4 customers ... 42 4.6.3 Product 6 customers ... 42 4.6.4 Product 9 customers ... 43

4.7 Vehicles loading constraints ... 44

4.8 Vehicle loading capacities ... 44

4.9 Carriers and their fleet ... 45

(6)

vi Chapter 5 ... 47 Mathematical formulation ... 47 5.1 Problem formulation ... 49 5.2 Model ... 51 5.3 Valid Inequalities ... 56 5.4 Implementation details ... 57 Chapter 6 ... 58 Results analysis ... 58 6.1 Current situation ... 60

6.1.1 Carriers to suppliers flows ... 61

6.1.2 Carriers to plants flows ... 63

6.1.3 Suppliers to plants flows ... 64

6.1.4 Plants to plants flows ... 69

6.1.5 Plants to customers flows ... 74

6.1.6 Customers to carriers flows ... 78

6.1.7 Plants to carriers flows ... 79

6.1.8 Production at plants ... 79

6.1.9 Overall review ... 82

6.2 Solution obtained by optimizing the model ... 82

6.2.1 Carriers to suppliers flows ... 83

6.2.2 Carriers to plants flows ... 83

6.2.3 Suppliers to plants flows ... 84

6.2.4 Plants to plants flows ... 89

6.2.5 Plants to customers flows ... 92

6.2.6 Customers to carriers flows ... 97

6.2.7 Plants to carriers flows ... 98

6.2.8 Production at plants ... 98

6.2.9 Overall results ...101

6.3 Postoptimization solution phase – multi-trip routing ... 103

6.3.1 Carriers to suppliers flows ...103

6.3.2 Carriers to plants flows ...104

6.3.3 Suppliers to plants flows ...104

6.3.4 Plants to suppliers flows ...104

6.3.5 Plants to plants flows ...105

6.3.6 Plants to customers flows ...108

6.3.7 Customers to plants flows ...108

6.3.8 Customers to carriers flows ...109

6.3.9 Plants to carriers flows ...109

6.3.10 Production at plants ...109 6.3.11 Overall results ...110 6.4 Quantitative analysis ... 111 6.5 Qualitative analysis ... 112 Conclusion ...115 Bibliography ...117

Annex A: Detailed data tables ...121

Table 1: Volume of weekly receptions at the first processing plant of product 8 in UN by state ... 121

Table 2: Volume of weekly transfers of product 4 between the first processing plant and the A plant in UN ... 123

Table 3: Volume of weekly transfers of product 4 between the first processing plant and the B plant in UN ... 125

(7)

vii

Table 5: Volume of weekly sales of product 6 to external customers in UN ... 128

Table 6: Volume of weekly truckloads sale of product 9 from the first processing plant ... 130

Table 7: Weekly receptions of product 4 from external suppliers at the B plant ... 132

Table 8: Weekly receptions of product 5 from external suppliers to the B plant ... 134

Table 9: Weekly transfers of product 2 between the B plant and the A plant ... 136

Table 10: Volume of weekly transfers of product 3 from the B plant to the A plant in UN ... 138

Table 11: Weekly volume of product 9 picked-up at the B plant ... 139

Table 12: Volume of weekly receptions of product 3 at the A plant in UN (part one) ... 141

Table 13: Volume of weekly receptions of product 3 at the A plant in UN (part two) ... 143

Table 14: Volume of weekly receptions of product 4 at the A plant in UN (part three) ... 145

Table 15: Average cost of transport of product 3 to the A plant by suppliers and carrier ... 147

Table 16: Volume of weekly receptions of product 4 from external suppliers to the A plant in UN ... 148

Table 17: Sales of product 1 by state (customer) from January to October 2017 ... 151

Table 18: Weekly volume of product 9 sales from the A plant in truckloads ... 152

Annex B: Detailed results ...154

Table 1: Total distance travelled by all the empty trucks travelling from the carriers bases to suppliers in the current scenario ... 154

Table 2: Total distance travelled by all the empty trucks travelling from the carriers bases to the plants in the current scenario ... 158

Table 3: Volume of product 8 in UN delivered to the first processing plant by period by supplier in the current scenario ... 160

Table 4: Volume of product 1 in UN delivered by the A plant by period by customer in the current scenario ... 166

Table 5: Volume of product 9 in UN delivered by A plant by period by customer in the current scenario .. 175

Table 6: Volume of product 9 delivered by the first processing plant by period by customer in the current scenario ... 177

Table 7: Total distance travelled by all the empty trucks travelling from customers back to the carriers bases in the current scenario ... 179

Table 8: Total distance travelled by empty trucks travelling back from the plants to the carriers bases in the current scenario ... 183

Table 9: Total distance travelled by all the empty trucks travelling from the carriers bases to suppliers in the model solution ... 186

Table 10: Total distance travelled by all the empty trucks travelling from the carriers bases to the plants in the model solution ... 189

Table 11: Volume of product 8 in UN delivered to the first processing plant by period by supplier in the model solution ... 191

Table 12: Volume of product 4 in UN transferred from the first processing plant to the A plant by period in the model solution ... 197

Table 13: Volume of product 1 in UN delivered by the A plant by period by customer in the model solution ... 199

Table 14: Volume of product 9 in UN delivered by the A plant by period by customer in the model solution ... 203

Table 15: Volume of product 9 delivered by the first processing plant by period by customer in the model solution ... 205

Table 16: Total distance travelled by all the empty trucks travelling from customers back to the carriers bases in the model solution ... 207

Table 17: Total distance travelled by empty trucks travelling back from the plants to the carriers bases in the model solution ... 212

Table 18: Total distance travelled by all the empty trucks travelling from the carriers bases to suppliers in the optimized solution ... 215

(8)

viii

Table 19: Total distance travelled by all the empty trucks travelling from the carriers bases to the plants in the optimized solution ... 218 Table 20: Total distance travelled by all the empty trucks travelling back from the plants to the suppliers in the optimized solution ... 220 Table 21: Total distance travelled by all the empty trucks travelling back from the customers to the plants in the optimized solution ... 223 Table 22: Total distance travelled by all the empty trucks travelling from customers back to the carriers bases in the optimized solution ... 226 Table 23: Total distance travelled by empty trucks travelling back from the plants to the carriers bases in the optimized solution ... 231

(9)

ix

List of tables

Table 1: Classification of our literature review on Green Logistics and Integrated Logistics ... 15

Table 2: Average transportation cost of product 8 trips to the first processing plant by state ... 24

Table 3: Average of transport cost by carrier for the product 4 suppliers of the B plant ... 28

Tableau 4: Average cost of transport for product 4 to the A plant by supplier and by carrier ... 32

Table 5: The weekly production capacity in UN of each plant by type of product ... 37

Table 6: The storage capacity in UN of each plant by type of product ... 37

Table 7: Weekly plants need for each type of product in UN ... 38

Table 8: Weekly volume of product 8 supplied by each state in UN ... 38

Table 9: Weekly volume of product 4 supplied by each external supplier in UN ... 39

Table 10: Weekly volume of product 3 supplied by each external supplier in UN ... 40

Table 11: Weekly volume of product 5 supplied by external suppliers in UN ... 40

Table 12: Weekly demand of product 1 by state in UN ... 41

Table 13: Weekly demand of product 4 by customer in UN ... 42

Table 14: Weekly demand of product 6 by external customers in UN ... 42

Table 15: Weekly demand for product 9 by external customers in UN ... 43

Table 16: Loading capacities by vehicle by type of product in UN ... 44

Table 17: Number of vehicles of each type made available by each carrier ... 45

Tableau 18: Notation for sets ... 49

Table 19: Parameters ... 49

Table 20: Decision variables ... 50

Table 21: Liters per km-UN by type of vehicle and type of product ... 59

Table 22: Kg of CO2 e per km-UN by type of vehicle and type of product... 60

Table 23: Index of each carrier ... 61

Table 24: Index of each type of vehicle ... 61

Table 25: Index of each supplier ... 62

Table 26: Index of each plant ... 63

Table 27: Index of each type of product ... 64

Tableau 28: Volume of product 4 in units delivered to the A plant by period by external supplier in the current situation ... 65

Table 29: Volume of product 4 in units delivered to the B plant by period by external supplier in the current situation ... 66

Table 30: Volume of product 3 delivered to the A plant by period by external supplier in the current situation 68 Table 31: Volume of product 5 in UN delivered to the B plant by period by external supplier in the current situation ... 69

Table 32: Volume of product 4 in UN transferred from the first processing plant to the B plant by period in the current situation ... 70

Table 33: Volume of product 4 in UN transferred from the first processing plant to the A plant by period in the current situation ... 71

Table 34: Volume in UN of flows from the B plant to the A plant by period by type of product in the current situation ... 72

Table 35: Volume of product 7 in UN transferred from the A plant to the B plant by period in the current situation ... 73

Table 36: Index number of each customer ... 74

Table 37: Volume of product 4 in UN delivered by the first processing plant by period by customer in the current situation ... 75

Table 38: Volume of product 6 in UN delivered by the first processing plant by period by customer in the current situation ... 76

(10)

x

Table 40: Volume of production and inventory at the end of the period in UN at the first processing plant by period by type of product in the current situation ... 80 Table 41: Volume of production and inventory at the end of the period in UN at the B plant by period by type of product in the current situation ... 80 Table 42: Volume of production and inventory at the end of the period in UN at the A plant by period by type of product in the current situation ... 81 Table 43: Summary of the current situation global results ... 82 Table 44: Volume of product 4 in UN delivered to the A plant by period by external suppliers in the model solution ... 85 Table 45: Volume of product 4 in UN delivered to the B plant by period by external suppliers in the model solution ... 85 Table 46: Volume of product 3 delivered to the A plant by period by external supplier in the model solution .. 87 Table 47: Volume of product 5 in UN delivered to the B plant by period by external supplier in the model solution ... 88 Table 48: Volume of product 4 in UN transferred from the first processing plant to the B plant by period in the model solution ... 89 Table 49: Volume in UN of flows from the B plant to the A plant by period by type of product in the model solution ... 90 Table 50: Volume of product 7 in UN transferred from the A plant to the B plant by period in the model solution ... 92 Table 51: Volume of product 4 in UN delivered by the first processing plant by period by customer in the model solution ... 93 Table 52: Volume of product 6 in UN delivered by the first processing plant by period by customer in the model solution ... 95 Table 53: Volume of product 9 delivered by the B plant to customer 15 by period in the model solution ... 97 Table 54: Volume of production and inventory at the end of the period in UN at the first processing plant by period by type of product in the model solution... 98 Table 55: Volume of production and inventory at the end of the period in UN at the B plant by period by type of product in the model solution ... 99 Table 56: Volume of production and inventory at the end of the period in UN at the A plant by period by type of product in the model solution ...100 Table 57: Summary of the current situation and model solution global results ...101 Table 58: Total distance traveled by all the empty trucks traveling from the A plant to the first processing plant in the optimized solution ...106 Table 59: Volume in UN of flows from the A plant to the B plant by period by type of product in the optimized solution ...107 Table 60: Summary of the global results ...110

(11)

xi

List of figures

Figure 1: Mixed charge truck ... 3

Figure 2: Type BB of B-train ... 4

Figure 3: Flatbed trailer ... 5

Figure 4: Details of the incoming and outgoing flows of the first processing plant ... 23

Figure 5: Percentage of volume (UN) of product 8 purchased by state by the company ... 24

Figure 6: Details of the incoming and outgoing flows of the B plant ... 27

Figure 7: Details of incoming and outgoing flows from the A plant ... 31

Figure 8: Input and output flows of our industrial partner supply chain ... 35

Figure 9: Route from a carrier to a supplier to a plant and back to the carrier ... 47

Figure 10: Route from a carrier to a plant to a customer and back to the carrier ... 48

(12)

xii

Thanks

First and foremost, I would like to thank my research director, M. Leandro C. Coelho for the opportunity to work on this project and the support thorough it. This could have not been done without you. I will forever be gratefull for the help you’ve been and the knowledge you passed down to me. Secondly, thank you to all the OSD department and its amazing professors, M. Jacques Renaud, M. André Gascon, M. Jean-Philippe Gagliardi, M. Fayez Boctor, Mme Edith Brotherton and M. Jean-François Côté to name a few, who made me love this field even more and always pushed me to do my best.

I would also like to mention the collaboration of our industrial partner’s employees. Thank you to the CEO, for your hands-on help and the trust you have shown for the entire duration of the project. I also want to give thanks to the vice president of finances who provided me with all the data he could find, the company buyer who answered the many, many questions I had, the in-house truck driver who always took time to answer my calls and specific questions about his trucks and routes. I wouldn’t want to forget all the team from the second plant. Thank you for your warm welcome and quick replies to my many emails.

I would like to give a warm thanks to all my friends and family. A special thanks to Pierre, my partner in crime. Thank you for always being there without really being there. Thank you for making me smile and laugh when I stressed about my master’s thesis, for supporting me the way you did. You always believe in me more than I could believe in myself and for that I love you.

Thank you to my mom and dad who with their love, compassion and understanding gave me the skills and the strength to go this far in life. Thank you for your constant interest in my studies and for being the only two other people who will probably read this. Thank you to my brother too, because I kind have to. Just kidding Math, thank you for making me always want to be better than you in at least something, it’s why I am here today.

I would also like to give thanks to my gang, my other family, my amazing friends: Aurélie, Myriam, Yasmina, Pascal, Guillaume, Karine, Bastien, Andrée-Anne, Claudia, Pierrick, Vincent, Thomas, Sarah, Jean-Michel, J-S, Audrey and I pray I didn’t forget anyone. Thank you guys for understanding when I didn’t have time to go out on the weekends, for making me forget the stress I was under, for being there for me when a certain person

(13)

xiii

was away, for being the bests friends a person could ask for. I love you all and can’t wait to celebrate the end of my master’s degree with you!

I also couldn’t have done it all until now if it wasn’t for Marie-Pier, my sweat buddy and fellow organization groupie. Thank you for making my years at university so fun and paving the way for me in the thesis department. You might not know, but I owe you a lot. Because you do so, so much and always have energy (which I still don’t know how you do), you pushed me to do more every day.

(14)

1

Introduction

1.1 Context

As the second-largest country in the world, Canada needs to be served by a well-developed and efficient transportation system. Indeed, with an area of nearly 10 million square meters, the country’s economy depends on the ability of people and goods to travel from coast to coast ("Découvrir le Canada," 2012). It also makes the transportation sector a major economic driver in the country. In 2016, it accounted for 4.5% of Canada’s gross domestic product (Transports Canada, 2017). This is why the sector is the subject of several strategic and political orientations. For example, in its 2030 vision for transportation, the Canadian government aims, among other things, to reduce air pollution by encouraging environmentally friendly and innovative transportation (Les transports au Canada 2016, 2017; "Transport routier au Québec : portrait de l’industrie," 2016; Transports Canada, 2017). Considering that this sector is the second-largest GHG emitter with 24% of the country’s total emissions (173 Mt of CO2 eq), behind the oil and gas industry (26% of total emissions), this will not be an easy

task. As a result, transportation is becoming an important area of study where optimization opportunities are highlighted by the government targets (Indicateurs canadiens de durabilité de l'environnement : Émissions de gaz à effet de serre, 2017).

It is also important to note that with 1.13 million road kilometers spread throughout the country, the road network is the most widely used mode of transportation in Canada for moving people and goods (Les transports au Canada 2016, 2017). Indeed, because of its high flexibility, accessibility, short transit times and above all its competitive prices, road transport is generally preferred to the air, rail and maritime modes. That's why, in Canada, 90% of freight transportation is done via the road network ("Transport routier au Québec : portrait de l’industrie," 2016). Thus, tackling the improvement of the use and operation of the latter is key in the fight against greenhouse gases (GHG). In fact, the emissions emitted by road transport alone amounted in 2014 to 142.6 Mt of CO2 eq., or 83.2% of the GHG emissions of the transportation sector and 19.5% of Canada's total GHG

emissions (Les transports au Canada 2016, 2017).

Fortunately, in recent years, more and more companies are embracing the concept of green and sustainable supply chains whether for environmental or financial reasons (Beckwith, 2016). This has resulted in several companies setting policies to tackle air pollution through road transport. Johnson & Johnson, for example, which was recognized in 2008 as the company with the largest hybrid fleet in the United States ("Greener Boxes,

(15)

2

Bottles and Buildings," 2017) and Pfizer, which has introduced a green packaging policy to optimize space and reduce the number of trucks needed on the road ("Product Packaging," 2017).

Thus, although solutions such as the use of hybrid vehicles, hydrogen or optimization of packaging are often considered, another option exists. Few companies optimize the resources already available to them while it is a generally less expensive option that can generate impressive results. Improving the planning of road transport operations within a company, whether through better routing or consolidation, can reduce both the costs incurred and the GHGs emitted.

Indeed, for some years now, operational research, commonly called The Science of Better, contributes to the fight against climate change (Dekker, Bloemhof, & Mallidis, 2011). By prioritizing a more efficient use of resources, operational research applied to road transport is not only economical but also ecological. The following brief develops in this light.

The topic studied in this dissertation involves a Canadian industry and its various transportation activities.

1.2 Types of products

The company on which this work is based on specializes in the transformation of a raw material into a high-end product. It is also vertically integrated and therefore includes a first processing plant supplying it with semi-transformed products. This means several types of products flow through its supply chain. For ease of understanding, here are some key definitions for product types. For the purpose of anonymity, the volume of products will be measured in general units identified as UN. The list of products is as follows:

•

Product 1:

Finished products, ready to go on the market.

•

Product 2:

Semi-finished products used in the in the final transformation into finished products. •

Product 3: Semi-finished products used to make product 2.

•

Product 4:

Semi-finished products obtain after the first transformation of raw materials. •

Product 5:

Materials used in the production of product 2.

(16)

3

•

Product 7:

Materials used in the production of product 2. •

Product 8:

Raw materials.

•

Product 9:

Residues of the transformation process.

1.3 Types of vehicles

In a large supply chain, with several different types of products to be transported, several types of trucks are used, and several types of capacities have to be taken into account. This section details the vehicles used and their capacity depending on the products they transport. It is important to note that given the different transportation laws between the United States and Canada, allowable charges also differ. Thus, for a 2-axle in the United States the maximum load is 44,000 pounds while in Canada it is 44,000 pounds in thaw period and 55,000 pounds in normal time.

•

Mixed charge trucks:

Used for the transport of products 1, 2, 5, 7 and sometimes 9, trucks with mixed loading trailer are the most widespread type of semi-trailers. Those found in this analysis are 48 or 53 feet.

http://www.larryssales.info/default.htm Figure 1: Mixed charge truck

(17)

4

•

B-train:

A B-train, commonly known as a road train, consists of two trailers linked together by a fifth wheel. There are several types of road train configurations. Types AB, BB or even triple B, depending on the size of the trailers used. In the present case, only the BB type of B-train with two trailers of the same size are considered.

http://www.flickriver.com/groups/1805004@N20/pool/interesting/ Figure 2: Type BB of B-train

•

Residues trailer:

For the transport of product 9.

•

Raw materials trailers:

For product 8, the trucks used are sometimes specific to this raw material. Otherwise carriers can use «Flatbed» types of truck (described below). The capacity of the trucks depends on their number of axles. Thus, a truck with two axles will be able to carry 4 000 feet of product 8, a truck of three axles about 5 500 feet and a truck of four axles 6 500 feet.

•

Flatbed trailers:

For the transport of product 3 or 4, flatbed trailers are generally used. In 4-axle version, this type of trailer can carry from 13 500 to 14 000 UN of product 4 per trip, while in B-train version the capacity is 16 000 to 16 500 UN. A trip of product 3 can contain up to between 20 000 UN and 26 000 UN depending on how it is loaded. For product 6, a trailer can hold about 15 000 UN.

(18)

5

https://chameleon.ca/rolling-tarps/flatbeds/ Figure 3: Flatbed trailer

1.4 Problem description

Our industrial partner has an extensive supply and distribution chain in North America. With two plants, a first processing plant and a large distribution network, it results in a high number of trips to manage. Indeed, the company must manage the input of raw materials, inter-site trips and delivery to various end customers.

As previously seen, the company’s logistics managers must also consider several different types of products that can travel in different types of vehicles. Given the market and the raw material used, they must also juggle with several constraints such as the seasonality of the demand, the variation of the prices and the quality of the raw material, different constraints related to the storage and transportation as well as supply and demand forecasting. In addition, some suppliers take care of the transportation of the goods while others do not, the same goes for customers.

In order to understand the full complexity of the task, here is a brief description of the operation of the logistics network at this company. Chapter 3 of this thesis presents the detailed description of logistic flows and a simplified map of its network.

(19)

6

The process begins with the arrival of raw materials, product 8, at the first processing plant. These are stored there and then transformed into three types of products: product 4, product 6 and product 9. Product 4 is partly transferred to the plants which have dedicated productions, what remains is sold to external customers. For product 6 and 9, these are sold strictly to external customers.

For production purposes, the B plant requires product 5, 7 and 3. The latter is dedicated to the production of a type of finished products. Product 5 is acquired from external suppliers and part of the delivery is redirected to the A plant. For product 7, these are provided by the A plant. Product 4 from the first processing plant is matched with product 4 purchased from external suppliers and then transformed into product 3. Like product 5, some of the product 3 is sent to the A plant while the other is kept at the B plant for production purposes. When product 3, 5 and 7 are assembled, they are sent to the A plant in the form of product 2 since the finishing touches are done only at this plant. All this production generates processing residues, product 9, at the B plant, which will be sold to external customers.

The A plant includes part of the production, the finishing stage and the distribution center. Thus, it receives materials such as product 4 from the first processing plant and external suppliers, product 5, product 2 and product 3 from the B plant, but also product 3 from external suppliers if necessary. Product 3 at the A plant can be transformed either into product 7 which, as previously indicated, go partly to the B plant, or into product 1 in combination with different materials. When these finished products are ready for sale, they are sent to different customers from this factory. It is understood that this production also generates processing residues, product 9, which are also sold to external customers.

In recent years, with the arrival of the new Chief Executive Officer, the company has taken several steps to increase its growth. Indeed, the product offering is constantly growing, they automate manufacturing processes and use technology to expand their customer base. With all these new initiatives, the company expects annual growth of about 15 to 20% over the next four years. This will obviously lead to an increase in product traffic within the company and with its external partners, and therefore GHGs emitted by the company.

The purpose of this logistics innovation study is to significantly reduce the number of trucks and GHG emissions in transportation generated by our industrial partner’s operations, as well as to revise the company's transportation management. By keeping only the real constraints of the network such as production, storage and

(20)

7

transport capacities, we will be able to create a new transport model optimized for the company, a model that will reduce both GHGs emitted and transport costs generated.

1.5 Methodology

To arrive at an optimized model of the transport network, we had to go through three decisive steps: data collection, model development and validation of the model and the solution.

1.5.1 Data collection

In order to draw a picture of the current situation of the company's logistics network and the constraints it faces in terms of transportation, we had to conduct a number of interviews with the various actors in the logistics chain. In fact, we met the CEO, the Vice-President of Finance, the former Transportation and Logistics Manager, the first processing plant Manager, the Director of procurement and several other employees of the company. All these meetings allowed us to obtain electronic data, but also practical information on the functioning of the supply chain.

Since the company does not have any integrated software for tracking inventory, the data has sometimes been very difficult to obtain. In fact, apart from an accounting system that makes it possible to know what is bought and sold by the company, the data is generally recorded in Excel files for personal use. This makes it necessary to search in several files to obtain the information sought. In addition, little information is collected on inter-site transfers since all sites are practically managed independently.

The broad spectrum of data needed did not facilitate the task either. Indeed, since such an optimization of the company logistics network presupposes a precise knowledge of the flows, we needed the volumes of the flows, their costs, the type of transport used, the transport, storage and production capacities of the plants, distances and other constraints. For all these reasons, the collection of information was a process stretching over several months.

(21)

8

1.5.2 Model development

We modeled the company’s network and available resources mathematically to fully describe the interconnections between the sites, demand and supply, capacities and cost structures. The problem is modeled using mixed-integer linear programming, and solved exactly by mathematical programming. More details are provided in Chapter 5.

1.5.3 Validation of the model and the solution

In order to validate the optimized scenario the model proposed, we had to first check if it fitted all constraints the company had provided us. This process was done multiple times until the solution was dimmed acceptable to be presented to the company. They in turn suggested some adjustments in order to improve the solution. The final scenario was then presented in details to the company and its logistics managers for final approval. It is worth mentioning that this project will be continued and further explored with the company in the following years in order to perfect our approach and combine the routing aspect to other dimensions of the company reality.

1.6 Thesis organization

The following chapter reviews the existing literature on green logistics and integrated problems like the one at hand. Subsequently, Chapter 3 presents an overview of the company and its logistics network via a description of the different plants and the links that bind them. Chapter 4 then details all the data collected about our industrial partner flows of products from the suppliers offer to the customers demand. The mathematical model is later presented in Chapter 5. Afterwards, Chapter 6 presents the results analysis comparing the actual scenario with the optimized one based on distance and GHG emissions. Finally, Chapter 7 concludes this thesis.

Chapter 2 Literature Review

This chapter is dedicated to reviewing the literature on our research topic which is integration of production, inventory and distribution routing with the objective of GHG emissions minimization. We have divided this review into two sections. We will first explore the body of research on logistics regarding GHG emissions minimization.

(22)

9

The second part is dedicated to the integration of several types of decisions in the supply chain. A table can be found at the end resuming the studied literature.

2.1 Green Logistics

Operations research has been a part of the fight against climate change for some time. Indeed, the integration of ecological aspects in logistics, also known as Green Logistics, is a growing body of research (Dekker, Bloemhof, & Mallidis, 2012). Aiming to reduce environmental externalities of logistics operations, green logistics focus on noise, accidents and mainly GHG emissions. To lower those emissions, many types of approaches have been studied as we review next.

Barth and Boriboonsomsin (2009) studied the impacts of eco-driving on GHG emissions, which consist in changing a driver’s behavior on the road thru static or dynamic feedback and found that providing advice to drivers in real time could amount up to 20% in fuel savings without significant increase in travel time. Since emissions can also be lowered by improving traffic operations, specifically through the reduction of traffic congestion, they also tested, in a subsequent paper, the impact of real-word transportation policies such as congestion mitigation strategies and speed management techniques on emissions minimization and found they could reduce CO2 emissions to up to 20% (Barth & Boriboonsomsin, 2008).

In a more theoretical approach, Absi et al. (2013) studied the use of carbon emissions capacity constraints in a lot-sizing problem, which helps in planning production batches, considering only unit carbon emissions. They further extended the problem taking into consideration a fixed carbon emission associated with different production modes (Nabil Absi, Dauzère-Pérès, Kedad-Sidhoum, Penz, & Rapine, 2016). Since capacity constraints for GHG emissions reduction are mostly used in lot-sizing problems, their real-life applications concern carbon quotas in production or the carbon exchange which are not really the case at hands (Benjaafar, Li, & Daskin, 2013; Retel Helmrich, Jans, van den Heuvel, & Wagelmans, 2015). However, several of papers also consider introducing the minimization of emissions directly in the objective function of the model. This is the case for a handful of road freight transportation problems like ours (Bektaş, Demir, & Laporte, 2016; Ehmke, Campbell, & Thomas, 2016; Lin, Choy, Ho, Chung, & Lam, 2014).

(23)

10

Considering that transportation accounts for almost a quarter of Canada’s GHG emissions and that road freight accounts for more than 80% of the transport-related GHG emissions, road transportation optimization problems involving reduction of emissions are at the core of climate change efforts in operational research (Transports Canada, 2017). This is why a myriad of new versions of the classical Vehicle Routing Problem, which determines the optimal set of routes for a fleet of vehicles to traverse in order to deliver to a given set of customers, now involving emissions reduction goals have appeared in the last two decades (Coelho, Renaud, & Laporte, 2016; Lin et al., 2014). A majority of those extensions can be categorized in the Green Vehicle Routing branch. Green Vehicle Routing refers to Vehicle Routing problems where externalities such as GHG emissions, and not just the traditional distance and cost, are taken into account so that they are reduced by better planning (Bektaş et al., 2016). One of the most studied problems in Green Vehicle Routing is the Pollution Routing Problem introduced by Bektaş and Laporte (2011). By incorporating a fuel consumption model in the Vehicle Routing Problem, Bektaş and Laporte aimed to serve a set of customers within predefined time windows while determining the speed taken on each arc, so as to minimize fuel, emissions and drivers’ costs. The Pollution Routing Problem has since been revised in multiple papers (Dabia, Demir, & Van Woensel, 2017; Demir, Bektaş, & Laporte, 2012, 2014; Kramer, Maculan, Subramanian, & Vidal, 2015).

That being said, solving Pollution Routing problems also requires using an appropriate vehicle emission model in order to estimate fuel consumption accurately (Demir, Bektaş, & Laporte, 2011). Demir et al. (2014) identified five categories of factors contributing to fuel consumption: vehicle, environment, traffic, drivers and operations. These categories contain factors such as engine type, roadway gradients, wind conditions, speed, congestion, gear selection and number of stops to name a few. These factors are used in different vehicle emissions models and these models can be divided in two main categories: the macroscopic and microscopic models (Bektaş et al., 2016). Macroscopic models, such as the Methodology for calculating transportation emissions and energy consumption (MEET), consider an average speed in order to estimate vehicle emissions whereas the microscopic models, like the popular Comprehensive Modal Emissions Model (CMEM), take an instantaneous approach in measuring emissions according to varying speed, type of vehicle and load (Turkensteen, 2017). While those are widely used models, a new study shows that machine learning methods can beat their accuracy in estimating GHG emissions (Heni, Diop, Coelho, & Renaud, 2018). For our work, we estimate emissions with the help of data from Transports Canada (2014) as seen in Chabot, Bouchard, Legault-Michaud, Renaud, and Coelho (2018).

(24)

11

Fuel consumption models aside, Vehicle Routing problems and Green Vehicle Routing problems, can be applied to a multitude of contexts from urban logistics to long-haul freight. However urban freight can pose a particular challenge to carriers. Even if city logistics is not a recent phenomenon, the explosion of e-commerce in recent years has led to a significant increase in urban traffic and concerns over noise and air pollution in these densely populated areas (Savelsbergh & Van Woensel, 2016). To address these concerns, researchers considered the Time-Dependent Vehicle Routing Problem which takes into account that links in a network have different speeds during the day depending on traffic congestion (Figliozzi, 2011). By adding minimization of emissions costs directly in the objective function of this problem, like previously stated, studies propose different solution models for the emissions reduction problem in urban areas also known as the Time-Dependent Pollution-Routing Problem (Franceschetti et al., 2017; Franceschetti, Honhon, Van Woensel, Bektaş, & Laporte, 2013; Jabali, Van Woensel, & de Kok, 2012; Qian & Eglese, 2016). Some have pushed this problem a step further adding path flexibility, the ability to choose the road taken between multiple options to go from node to node (Ehmke et al., 2016; Heni, Coelho, & Renaud, 2017, 2018; Huang, Zhao, Van Woensel, & Gross, 2017). This extension of the Time-Dependent Vehicle Routing Problem is known as the Time-Dependent Quickest Path or Shortest Path Problem (Calogiuri, Ghiani, & Guerriero, 2015). Behnke and Kirschstein (2017) even studied the effects on path selection considering different types of roads, urban or highways, and different types of vehicles. A similar problem was considered by Xiao and Konak (2016), who were aiming to minimize emissions in a logistic system including a heterogeneous fleet, time-varying traffic conditions and delivery time windows.

While those three studies are great examples of the extended applications of the Time-Dependent Vehicle Routing Problem, they also introduce the use of a heterogeneous fleet of vehicles, which opens up an enormous potential in emissions minimization and describe best the situation at hand. Indeed, other studies incorporating different classes of vehicles have shown reduction of GHG emissions up to 20% (Kopfer & Kopfer, 2013; Kwon, Choi, & Lee, 2013; Pitera, Sandoval, & Goodchild, 2011). While different types of vehicles can add flexibility to a fleet, alternative fuel-powered vehicles can also be considered to further reduce GHG emissions in the supply chain (S. Erdoğan & Miller-Hooks, 2012). However, due to the uncertain efficiency of these technologies especially in the trucking industry, the problem has not yet been thoroughly studied.

In recent years a new type of problem has emerged combining Pollution Routing Problem models and Production, Inventory and Routing Problem models. It is called the Pollution Production-Routing Problem and it aims to incorporate emissions minimization objectives in the decision-making process for integrated problems (Kumar et al., 2016; Qiu, Qiao, & Pardalos, 2017).

(25)

12

2.2 Integrated Logistics

In integrated logistics decision-making, we can distinguish four categories: one-dimension planning problems which include only one department, two-dimensional planning problems which include two departments, three dimensions planning problems which include three departments or more and overall collaboration which include partners of the supply chain.

When decisions involve only one department, like production, integration can take the form of integrating long-term and short-long-term decisions (Vogel, Almada-Lobo, & Almeder, 2016). For example, Bouchard, D’Amours, Rönnqvist, Azouzi, and Gunn (2017) obtain a profit increase of up to 13% when integrating long term and short term planning in forest harvesting. On the other hand, it can also be viewed has collaboration between different actors in that sector. In distribution for example, integration of decisions can happen when shippers or carriers decide to join forces. This is called Collaborative Logistics, where different parties in the supply chain aim to decrease the logistics related costs by better utilizing their resources (Yilmaz & Savasaneril, 2012). While carrier collaboration seems to be more studied in the literature, shipper’s collaboration has received more attention in the recent years (Chabot et al., 2018; Ergun, Kuyzu, & Savelsbergh, 2007; Günther & Seiler, 2010).

Integrated Logistics can also concern two departments or more of an organization. Since our primary concern here is the distribution network, we focus on studies involving the integration of transport decisions with other disciplines such as inventory, production or procurement. First and foremost, a great deal of the studies analyzed in this review involving distribution decisions were linked to production only (Amorim, Günther, & Almada-Lobo, 2012). Generally known as the Production Routing Problem, it is the combination of the Lot-Sizing Problem, which determines the size and quantity of production batches, and the Vehicle Routing Problem previously mentioned (Adulyasak, Cordeau, & Jans, 2015). This problem has been studied from quite some time now and is the primary subject of its share of reviews (Adulyasak et al., 2015; Barbarosoglu & Özgür, 1999; Fahimnia, Farahani, Marian, & Luong, 2013; Sarmiento & Nagi, 1999). The Production Routing Problem has been considered from many angles, from single item to multiple products, with multiple productions sites and multiple customers (Melo & Wolsey, 2012; Park, 2005).

Similarly, the Inventory Routing Problem, which combines vehicle routing and inventory management, is a well-documented problem dating back more than 30 years (Coelho, Cordeau, & Laporte, 2014). However, it was

(26)

13

further developed in recent years due to its complexity and the fact that it is based on Vendor-Managed Inventory (VMI), a business practice that as grown in the past decade.

While two-dimensional planning problems have been thoroughly studied, three-dimensional problems still leave place for further exploration. The integrated Production, Inventory, and Distribution Routing Problem (PIDRP) who coordinate production, inventory, and delivery operations to minimize total costs is the most researched of them all (N. Absi, Archetti, Dauzère-Pérès, & Feillet, 2015; Fumero & Vercellis, 1999; Lei, Liu, Ruszczynski, & Park, 2006). Accordingly, big companies like Kellogg base their planning system on problems involving production, inventory and distribution decisions (Brown, Keegan, Vigus, & Wood, 2001). They are also used to help organizations make strategic decisions, like shown by Darvish and Coelho (2018) who extended the PIDRP to include location decisions for future plants. Nonetheless, Kanyalkar and Adil (2007) also studied the integration of production, procurement and distribution.

Finally, pushing integration of logistics decisions a step forward we can find overall collaboration; the internal, vertical and horizontal integration of decision-making in the supply chain, from the supplier to the customer. Despite overall integration in an organization and its partners being the ideal situation, to the best of our knowledge it has only been studied in the form of qualitative analysis. Caputo and Mininno (1996), for example, identified organizational and managerial solutions for improving interfunctional and interorganizational coordination.

Even though integrated logistics has been around for several years now, it is just now starting to enter the field of Green Logistics. Leading up to the Pollution Production-Routing Problem in 2017, an adaptation of the Inventory Routing Problem was done by Soysal, Bloemhof-Ruwaard, Haijema, and van der Vorst (2016) who introduced collaboration between the shippers in a many-to-many structure with the objective of minimizing fuel costs, and so GHG emissions. They were able to accomplish a 29% reduction of GHG emissions with the proposed approach. For their part, Darvish, Archetti, and Coelho (2017) tested an minimization of GHG emissions objective and compared it to the traditional minimization of cost objectives in a Production-Routing Problem and an Inventory-Routing Problem. In short, Green Integrated Logistics is a vast field full of opportunities that still need to be explored.

(27)

14

This is why we propose a real-life application of an integrated green logistics problem taking account inventory and production capacities in order to minimize GHG emissions emitted by the distribution network of the studied organization. To the best of our knowledge this type of problem has never been studied in this light. As a matter of fact, few studies in Green Logistics or Integrated Logistics have been based on a real-life case study. From all the papers presented in this review, only seven use this type of data (Brown et al., 2001; Figliozzi, 2011; Günther & Seiler, 2010; Kanyalkar & Adil, 2007; Lei et al., 2006; Pitera et al., 2011; Soysal et al., 2016).

2.3 Literature Summary

In this section, we provide a table summarizing the papers stated below. Those papers are categorized by type of problem, the objective(s) of the paper, the fleet composition if applicable, the type of data the study is based on, the type of model of emissions is used if applicable. We also denoted if the problems contained time-dependency or path flexibility is their composition.

(28)

15

Table 1: Classification of our literature review on Green Logistics and Integrated Logistics

Publication Problem Objectives

Time-Dependency Flexibility Path Composition Fleet Type of Data Emission Model

Demir et al. (2012) Pollution Routing

Problem —cost of fuel minimization —cost of driver wages minimization

Homogenous Instances CMEM

S. Erdoğan and Miller-Hooks (2012)

Green Vehicle Routing Problem

—total distance traveled minimization

Homogenous fleet of alternative fuel-powered vehicles

Instances

Bektaş and Laporte

(2011) Pollution Routing Problem —cost of fuel minimization —cost of driver wages minimization

Jabali et al. (2012) Time Dependent Vehicle Routing Problem

—cost of fuel minimization —cost of driver wages minimization

X Homogenous Instances MEET

Franceschetti et al.

(2017) Time Dependent Pollution Routing Problem

—cost of fuel minimization —cost of driver wages

minimization X

Demir et al. (2014) Pollution Routing

Benjaafar et al.

(2013) Lot-Sizing Problem —cost minimization with carbon emission constraint Instances

Nabil Absi, Dauzère-Pérès, Kedad-Sidhoum, Penz, and Rapine (2013)

Lot-Sizing Problem —production and transportation cost minimization with carbon emission constraint

Instances

Retel Helmrich et al.

(2015) Lot-Sizing Problem —Set-up, production and inventory cost minimization with carbon emission constraint

Instances

Nabil Absi et al.

(2016) Lot-Sizing Problem —Production, transportation and inventory costs minimization with carbon emissions constraints

Instances

Qiu et al. (2017) Pollution Production

Routing Problem —Production, inventory and routing costs minimization with carbon cap

(29)

16

Time-Dependency Path Flexibility Fleet Composition Type of Data Emission Model

Soysal et al. (2016) Inventory Routing

Problem —Inventory, waste, fuel and driver costs minimization Homogenous Real Case Study CMEM

Kumar et al. (2016) Pollution Production

Routing Problem —Total operational cost minimization —Total fuel consumption minimization

Barth and Boriboonsomsin (2009)

Eco-Driving

Problem —Changing a person’s driving behavior by providing general dynamic advice to the driver

Real-life

Data CMEM

Figliozzi (2011) Time Dependent Vehicle Routing Problem

—Fleet and distance minimization

X

Homogenous Real Case

Study Fuel consumption is a function of travel speed and distance traveled

Qian and Eglese (2016) Time Dependent Vehicle Routing Problem —GHG emissions minimization X X Homogenous Real-life Data New heuristic approach referred to as NHA

Ehmke et al. (2016) Time Dependent Shortest Path Problem

—GHG emissions minimization

X X Homogenous Real-life Data MEET

Turkensteen (2017) Green Vehicle Routing Problem

—Test the suitability of CMEM models for actual driving conditions with fluctuating speeds

Homogenous Driving Cycles

CMEM

Calogiuri et al. (2015) Time Dependent

Quickest Path Problem

—Traveled time minimization

X X Homogenous Instances

Barth and Boriboonsomsin (2008)

Green Logistics —CO2 emissions minimization by

transportation policies Real-life Data CMEM

Franceschetti et al. (2013)

Time Dependent Pollution Routing Problem

minimization X

Wen, Çatay, and

Eglese (2014) Minimum Cost Path Problem —total cost minimization X Homogenous Real-life Data

(30)

17

Huang et al. (2017) Time Dependent Vehicle Routing Problem with Path Flexibility

—fuel consumption cost minimization

—vehicle depreciation cost minimization

X X

Kramer et al. (2015) Pollution Routing

Homogenous Instances Based on fuel properties, vehicle and network characteristics

Xiao and Konak

(2016) Green Vehicle Routing and Scheduling Problem

—total CO2 emissions

minimization X Heterogeneous Instances CMEM

Behnke and

Kirschstein (2017) Emission Minimizing Vehicle Routing Problem

—GHG emissions minimization

X X Heterogeneous Instances and Real-world Data

Kirschstein and Meisel (2015) approach

Chabot et al. (2018) Shippers Collaboration Tests 4 objectives: 1) Shipping Cost minimization 2) Carrier Cost minimization 3) Total distance minimization 4) A mix of the 3 others

Homogenous Real-world Data

Based on Transport Canada (2014) Data

Pitera et al. (2011) Green Vehicle

Routing Problem —cost of fuel minimization —cost of driver wages minimization

Heterogeneous Real Case

Study MOVES model

Kwon et al. (2013) Vehicle Routing Problem with carbon emission

—operation cost minimization —carbon emission trading cost minimization

Heterogeneous Instances Fuel based method

Kopfer and Kopfer

(2013) Green Vehicle Routing Problem Tests 2 objectives: 1) Total travel distance minimization 2) Total emissions

minimization

Heterogeneous Instances Empirical values based on information of carriers and of test reports for vehicles

(31)

18

Darvish et al. (2017) Production Routing Problem and Inventory Routing Problem

Tests 3 objectives:

1) Total cost minimization 2) Routing costs

minimization 3) Emissions costs

minimization

Homogenous Instances Load-distance objective function

Heni, Coelho, et al.

(2018) Time Dependent Vehicle Routing Problem with Emission Minimization

minimization X X

Heni et al. (2017) Time Dependent Quickest Path Problem with Emission Minimization

minimization X X Real-world Data The designed algorithms combine pre-existing CMEM and FSM models

Heni, Diop, et al.

(2018) Green Logistics Test the accuracy of new emissions models X Homogenous Real-world Data Compares Machine Learning to CMEM and MEET models

Fumero and Vercellis

(1999) Integrated Production and Distribution Planning

—Production costs minimization

—Logistics costs minimization Homogenous Instances

Vogel et al. (2016) Integrated

Production Planning Test the Integrated Production Planning approach versus the Hierarchical approach

Instances

Bouchard et al.

(2017) Integrated Production Planning Test the Integrated Production Planning approach versus the non-integrated approach in long term and short term forest planning

Instances

Brown et al. (2001) Integrated Production and Distribution Planning

—Total cost minimization Homogenous Real Case Study

(32)

19

Amorim et al. (2012) Integrated Production and Distribution Planning

—Total cost minimization —Freshness of products maximization

Homogenous Instances

Barbarosoglu and

Özgür (1999) Integrated Production and Distribution Planning

—Total cost minimization Homogenous Instances

Park (2005) Integrated Production and Distribution Planning

—Total net profit maximization Homogenous Instances

Melo and Wolsey (2012)

Integrated Production and Distribution Planning

Ergun et al. (2007) Shippers

Collaboration —Asset repositioning costs minimization Homogenous Instances

Yilmaz and

Savasaneril (2012) Shippers Collaboration —Maximizes the total utility of the arriving shippers over time Heterogeneous Instances

N. Absi et al. (2015) Integrated Production, Distribution and Inventory Planning

Günther and Seiler

(2010) Transportation Planning Problem —Potential savings maximization Heterogeneous Real Case Study

Kanyalkar and Adil

(2007) Integrated Production, Procurement and Distribution Planning

—Total cost minimization Homogenous Real Case Study

(33)

20

Lei et al. (2006) Integrated Production, Inventory and Distribution Routing Problem

—Total cost minimization Heterogeneous Real Case Study

Caputo and Mininno (1996)

Overall Collaboration

Analyze technological and managerial solutions for vertical and horizontal collaboration in the supply chain

Darvish and Coelho

(2018) Integrated Production, Inventory and Distribution Problem

—Total cost minimization Homogenous Real-world Data

(34)

21

Chapter 3 The company’s logistics network

3.1 Overview of the company

Founded in the eighties, our industrial partner is a leader in the manufacturing of high-end products. The company, account to date for approximately 310 employees distributed through four sites and it offers an extended product range to its customers.

It is by taking advantage of the technological innovation and the irreproachable quality of its products that the organization has experienced extraordinary growth since its creation. The company has effectively moved from a rather artisanal production to a turnover close to $ 100M, and this, in less than thirty years. To support such growth, the company relies on the continuous improvement of its production techniques and an increase in the choice offered to customers.

3.2 The company’s plants

As previously mentioned, the company has four sites: a first processing plant, two factories and a workshop. The main plant, which also includes the head office and the distribution center, is designated the A plant. Close by, there is a small workshop used for special orders, the only relic of the company's artisanal past.

The second plant, purchased in 2002, is designated as the B plant. Finally, the fourth site is the first processing plant acquired in 2009. All three main sites operate on a two-shift schedule. The day shift is eight hours Monday to Thursday and five hours on Friday since the production ends at 12:00 PM that day. The evening shift is 10 hours a day from Monday to Thursday only.

It should be noted that the organization owns a truck and 12 trailers (five of 48 feet and seven of 53 feet) which are operated by a driver employed by the company who works from Monday to Thursday. The latter has booked trips which will be described in this report. For transportation not being filled by it, the company deals with a multitude of different carriers as needed.

(35)

22

The following sections will describe in detail the production, flows and constraints of the two factories and the first processing plant. Indeed, the workshop will be excluded from our logistic analysis since the production made there and the associated flows are sporadic and exceptional.

3.2.1 The first processing plant

The company’s first processing plant supplies approximately 45% of the product 4 required for production, the 55% remaining being purchased directly from external suppliers. The first processing plant’s production capacity is 275,000 UN per week, of which 20% (55,000 UN) of product 6, and its storage capacity is of 1.6 million UN of product 8, approximately 250,000 UN of product 4 and 75,000 UN of product 6.

Since the company is a top-of-the-line manufacturer and the raw material, product 8, has its share of uncertainty about the quality received, part of the first processing plant’s production does not meet the required quality criteria. That’s why the first processing plant sells about 44% of its product 4 production to external customers. Details will be provided in what follows.

(36)

23

As shown in Figure 7, the incoming flows to the first processing plant are the product 8, hence the raw materials from different suppliers.

3.2.1.1 Product 8 entries from external suppliers

The first processing plant buys product 8 in many different locations in North America. In order to facilitate the resolution of the problem, we have grouped these locations by state to create eight large groups of suppliers. Some of these groupings are large suppliers with a fairly similar volume of weekdays during the week, while others only provide by periods.

In addition, as shown in Figure 8, nearly 60% of the volume of product 8 comes from state supplier 5, averaging 121,059 UN per week. For details of weekly purchases of product 8 by state refer to Table 1 of Annex A.

First processing plant

A Plant B Plant Product 4 clients(6) Product 9 clients (3) Legend Product 1 Product 2 Product 3 Product 4 Product 5 Product 6 Product 7 Product 8 Product 9 Capacity of production Product 4: 220 000 UN/week Product 6: 55 000 UN/week Storage capacity Product 4: 250 000 UN Product 6: 75 000 UN Product 9: 47 000 UN Product 6 clients (18) Product 6 Product 8 suppliers (8)

(37)

24

These purchases operate on a principle where the supply generates demand. When the supplier calls to offer a trip, the company arranges for a carrier and so pays for the transportation as well. They regularly use about 12 carriers. Of those 12, only two, carrier 1 and 2, account for 63% of trips. The average cost of a trip by state in Canadian dollar is shown in Table 2. The weighted transportation cost is proportional to the number of trips by state. All costs in this thesis have been extracted from the company’s data and are in Canadian dollar.

Figure 5: Percentage of volume (UN) of product 8 purchased by state by the company

Table 2: Average transportation cost of product 8 trips to the first processing plant by state State Number of trips by state Average cost of transport per trip Weighted transportation _cost

1 48 $727.22 $28.66 2 82 $672.80 $45.30 3 13 $744.84 $7.95 4 86 $606.44 $42.82 5 643 $912.05 $481.48 6 140 $962.48 $110.63 7 149 $314.13 $38.43 8 57 $718.31 $33.62 Grand total 1218 $831.54 $788.88 State 1 5% State 2 6% State 3 1% State 4 5% State 5 57% State 6 10% State 7 12% State 8 4%