University Of Southampton Research Repository Eprints Soton-Books Pdf

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UNIVERSITY of SOUTHAMPTON, FACULTY OF BUSINESS AND LAW. SOUTHAMPTON BUSINESS SCHOOL,Heterogeneous Location and. Pollution Routing Problems, by, C ag r Koc , Thesis for the degree of Doctor of Philosophy. September 2015, UNIVERSITY of SOUTHAMPTON, FACULTY OF BUSINESS AND LAW. SOUTHAMPTON BUSINESS SCHOOL, Heterogeneous Location and.
Pollution Routing Problems, by, C ag r Koc , Main supervisor Professor Tolga Bektas . Supervisor Dr Ola Jabali, Supervisor Professor Gilbert Laporte. Internal Examiner Professor Chris Potts, External Examiner Professor Tom Van Woensel. Thesis for the degree of Doctor of Philosophy in Management Science. September 2015, UNIVERSITY of SOUTHAMPTON, Abstract. FACULTY OF BUSINESS AND LAW, SOUTHAMPTON BUSINESS SCHOOL.
Doctor of Philosophy in Management Science, Heterogeneous Location and Pollution Routing Problems. by C ag r Koc , This thesis introduces and studies new classes of heterogeneous vehicle routing problems. with or without location and pollution considerations It develops powerful evolutionary. and adaptive large neighborhood search based metaheuristics capable of solving a wide. variety of such problems with suitable enhancements and provides several important. managerial insights It is structured into five main chapters After the introduction. presented in Chapter 1 Chapter 2 classifies and reviews the relevant literature on het . erogeneous vehicle routing problems and presents a comparative analysis of the available. metaheuristic algorithms for these problems Chapter 3 describes a hybrid evolutionary. algorithm for four variants of heterogeneous fleet vehicle routing problems with time. windows The algorithm successfully combines several metaheuristics and introduces a. number of new advanced efficient procedures Extensive computational experiments on. benchmark instances show that the algorithm is highly competitive with state of the art. methods for the three variants New benchmark results on the fourth problem are also. presented In Chapter 4 the thesis introduces the fleet size and mix location routing. problem with time windows FSMLRPTW which extends the classical location routing. problem by considering a heterogeneous fleet and time windows The main objective of. the FSMLRPTW is to minimize the sum of depot cost vehicle fixed cost and routing. cost The thesis presents integer programming formulations for the FSMLRPTW along. with a family of valid inequalities and an algorithm based on adaptation of the hybrid. evolutionary metaheuristic The strengths of the formulations are evaluated with respect. to their ability to yield optimal solutions Extensive computational experiments on new. benchmark instances show that the algorithm is highly effective Chapter 5 introduces. ii, the fleet size and mix pollution routing problem FSMPRP which extends the previ . ously studied pollution routing problem PRP by considering a heterogeneous vehicle. fleet The main objective is to minimize the sum of vehicle fixed costs and routing cost . where the latter can be defined with respect to the cost of fuel and CO2 emissions and. driver cost An adaptation of the hybrid evolutionary algorithm is successfully applied. to a large pool of realistic PRP and FSMPRP benchmark instances where new best so . lutions are obtained for the former Several analyses are conducted to shed light on the. trade offs between various performance indicators The benefit of using a heterogeneous. fleet over a homogeneous one is demonstrated In Chapter 6 the thesis investigates. the combined impact of depot location fleet composition and routing decisions on vehi . cle emissions in urban freight distribution characterized by several speed limits where. goods need to be delivered from a depot to customers located in different speed zones . To solve the problem an adaptive large neighborhood search algorithm is successfully. applied to a large pool of new benchmark instances Extensive analyses are conducted. to quantify the effect of various problem parameters such as depot cost and location . customer distribution and fleet composition on key performance indicators including. fuel consumption emissions and operational costs The results illustrate the benefits of. locating depots located in suburban areas rather than in the city centre and of using a. heterogeneous fleet over a homogeneous one The conclusions presented in Chapter 7 . summarize the results of the thesis provide limitations of this work as well as future. research directions , Keywords Operational research combinatorial optimisation logistics city logistics . transportation vehicle routing location routing heterogeneous fleet fleet size and mix . fuel consumption CO2 emissions sustainability evolutionary metaheuristic adaptive. large neighborhood search , UNIVERSITY of SOUTHAMPTON.
O zet, FACULTY OF BUSINESS AND LAW, SOUTHAMPTON BUSINESS SCHOOL. Doktora Yo neylem Aras t rmas , Heterojen Yer Sec imi ve C evre Kirlilig i Rotalama. Problemleri, by C ag r Koc , Bu c al s mada heterojen arac rotalama problemlerinin yer sec imi ve c evre kirlilig i o zellikle . rinin oldug u ve olmad g yeni c es itleri tan mlanm s oldukc a gu c lu etkili ve birc ok prob . lem c es idini bas ar yla c o zebilen evrime dayal ve uyarlanabilir bu yu k koms uluk arama. metasezgiselleri gelis tirilmis ve c es itli idari bak s ac lar sag lanm s t r C al s ma bes . ana bo lu mden olus maktad r Birinci bo lu mde sunulan giris k sm n n ard ndan ikinci. bo lu mde heterojen arac rotalama problemleri ile ilgili literatu r taranm s ve s n fland r lm s . devam nda bu problemler ic in literatu rde o nerilmis metasezgisel algoritmalar kars las t r l . m s t r U c u ncu bo lu mde heterojen filolu ve zaman pencereli arac rotalama problem . lerinin do rt farkl c es idi ic in evrime dayal karma bir algoritma gelis tirilmis tir O nerilen. algoritma c es itli metasezgiselleri biraraya getirmekle birlikte yeni ve etkili yo ntemler. ic ermektedir Test problemleri u zerinde gerc ekles tirilen genis kapsaml deneysel c al s malar . gelis tirilen algoritmanin literatu rde bu tu r problemler ic in gelis tirilen en etkili yo ntemlerle. oldukc a s k bir s ekilde rekabet edebildig ini go stermis tir Do rdu ncu bo lu mde biles ik. yer sec imi ve rotalama problemleminin genelles tirilmis bir c es idi olan heterojen filolu. ve zaman pencereli yer sec imi rotalama problemi tan mlanm s ve incelenmis tir Bu. problemin temel amac depo arac ve rotalama maliyetleri toplam n enku c u klemektir . Problemin c o zu mu ic in gec erli es itsizliklerle kuvvetlendirilmis tamsay l programlama. formu lasyonlar o nerilmis ayr ca karma evrime dayal algoritman n bir bas ka c es idi. gelis tirilmis tir O nerilen formu lasyonlar n etkinlikleri eniyi c o zu me ulas ma yetenek . leri ac s ndan deneyler ile deg erlendirilmis tir Yeni u retilen test problemleri u zerinde. gerc ekles tirilen genis kapsaml deneyler gelis tirilen metasezgisel algoritman n oldukc a. iv, bas ar l oldug unu gostermis tir Bes inci bo lu mde biles ik c evre kirlilig i ve rotalama prob . leminin genelles tirilmis bir c es idi olan heterojen filolu c evre kirlilig i rotalama problemi. tan mlanm s t r Bu problemin temel amac arac rotalama yak t CO2 sal n m ile. su ru cu maliyetleri toplam n enku c u klemektir Problemin c o zu mu ic in evrime dayal . algoritmanin bas ka bir c es idi gelis tirilmis tir Hem go zo nu ne al nan problem hem de. problemin homojen filolu c es idi ic in genis kapsaml gerc ekc i test problemleri u zerinde. deneyler gerc ekles tirilmis tir Deneyler sonucunda problemin homojen filolu c es idi ic in. literatu rde varolan test problemleri u zerinde yeni en iyi c o zu m deg erleri elde edilmis tir . Ayr ca problem parametrelerinin c es itli performans go stergeleri u zerindeki etkilerine s k. tutmak ic in ek analizler gerc ekles tirilmis bu analizler sonucunda homojen arac filosu. yerine heterojen arac filosu kullan m n n faydalar ac kc a ortaya konulmus tur Alt nc . bo lu mde c es itli h z bo lgelerine ayr lm s olan s ehiric i yu k tas mac l g ndaki depo yerinin . arac filosunun ve rotalama kararlar n n arac CO2 sal n m u zerindeki bu tu nles ik etkisi. analiz edilmis tir Problemde u ru nlerin s ehir ic inde bulunan depolardan yine s ehir ic inde. yer alan mu s terilere ulas t r lmas amac lanmaktad r Problemi c o zmek ic in uyarlanabilir. bir bu yu k koms uluk arama metasezgiseli gelis tirilmis ve c es itli yeni test problemleri. u zerinde etkinlig i incelenmis tir Depo yeri ve maliyeti mu s teri dag l m ve heterojen. arac filosu gibi problem parametrelerinin yak t tu ketimi CO2 sal n m ve operasyonel. maliyetler gibi performans go stergeleri u zerindeki deg is imlerinin etkisini analiz etmek. ic in genis kapsaml deneyler gerc ekles tirilmis tir Elde edilen sonuc larda depolar n s ehir. ic i yerine banliyo lere yerles tirilmesinin ve homojen arac filosu yerine heterojen arac . filosu kullan m n n faydalar nu merik sonuc larla go sterilmis tir Yedinci bo lu mde tez. c al s mas nda elde edilen sonuc lar k saca o zetlenmis c al s man n s n rlar ortaya konulmus . ve gelecek c al s malar ic in c es itli o neriler sunulmus tur . Anahtar kelimeler Yo neylem aras t rmas kesikli eniyileme lojistik s ehir lojistig i . ulas t rma arac rotalama yer sec imi rotalama heterojen filo yak t tu ketimi CO2 sal n m . su rdu rebilirlik evrime dayal metasezgisel uyarlanabilir bu yu k koms uluk arama metasez . giseli , Contents,Abstract i,O zet iii,Contents v,List of Figures ix.
List of Tables x,Declaration of Authorship xii,Acknowledgements xiii. List of Abbreviations xvi,1 Introduction 1, 1 1 Context of the Research Problems 2. 1 2 Illustration The FedEx Global Distribution Network 4. 1 3 Context of the Methodology 5, 1 4 General Research Contributions 6. 1 5 Specific Objectives 7, 2 Thirty Years of Heterogeneous Vehicle Routing 10. 2 1 Introduction 11, 2 2 Classification of the Heterogeneous Vehicle Routing Problem 12.
2 2 1 Problem definition and classification 12, 2 2 1 1 Objectives 13. 2 2 1 2 Time windows 13, 2 2 1 3 Other variants 14. 2 2 2 Mathematical formulations 14, 2 2 2 1 Single commodity flow formulation 15. 2 2 2 2 Two commodity flow formulation 16, 2 2 2 3 Set partitioning formulation 17. 2 3 The Fleet Size and Mix Vehicle Routing Problem 18. 2 3 1 Lower bounds and exact algorithms 18, 2 3 2 Continuous approximation models 20.
2 3 3 Heuristics 20, 2 3 3 1 Population search heuristics 20. v, Contents vi, 2 3 3 2 Tabu search heuristics 21. 2 3 3 3 Other heuristics 22, 2 4 The Heterogeneous Fixed Fleet Vehicle Routing Problem 24. 2 4 1 Tabu search heuristics 24, 2 4 2 Other heuristics 24. 2 5 The fleet size and mix vehicle routing problem with time windows 25. 2 5 1 Tabu search heuristics 26, 2 5 2 Other heuristics 26.
2 6 Variants and Extensions 28, 2 6 1 The multi depot HVRP 28. 2 6 2 The green HVRP 31, 2 6 3 The HVRP with backhauls 32. 2 6 4 The HVRP with external carriers 33, 2 6 5 The HVRP with container loading 34. 2 6 6 The HVRP with split deliveries 35, 2 6 7 The HVRP with pickup and delivery 35. 2 6 8 The open HVRP 36, 2 6 9 Other HVRP variants and extensions 36.
2 7 Case Studies 40, 2 8 Summary and Computational Comparisons 42. 2 8 1 Summary 43, 2 8 2 Metaheuristics computational comparisons 44. 2 8 2 1 Comparison of recent metaheuristics on the FSM 48. 2 8 2 2 Comparison of recent metaheuristics on the HF 48. 2 8 2 3 Comparison of recent metaheuristics on the FSMTW 51. 2 9 Conclusions and Future Research Directions 51. 3 A Hybrid Evolutionary Algorithm for Heterogeneous Fleet Vehicle. Routing Problems with Time Windows 55, 3 1 Introduction 56. 3 2 Description of the Hybrid Evolutionary Algorithm 59. 3 2 1 Overview of the hybrid evolutionary algorithm 60. 3 2 2 Education 61, 3 2 2 1 Removal operators 64, 3 2 2 2 Insertion operators 66. 3 2 2 3 Adaptive weight adjustment procedure 66, 3 2 3 Initialization 67.
3 2 4 Parent selection 67, 3 2 5 Crossover 68, 3 2 6 Split algorithm 69. 3 2 7 Intensification 72, 3 2 8 Survivor selection 72. 3 2 9 Diversification 73, 3 3 Computational Experiments 74. 3 3 1 Data sets and experimental settings 75, 3 3 2 Comparative analysis 76. 3 4 Conclusions 82, Contents vii, 4 The Fleet Size and Mix Location Routing Problem with Time Win .
dows Formulations and a Heuristic Algorithm 83, 4 1 Introduction 84. 4 2 Formulations for the Fleet Size and Mix Location Routing Problem with. Time Windows 86, 4 2 1 Notation and problem definition 86. 4 2 2 Integer programming formulations 87, 4 2 3 Valid inequalities 91. 4 3 Description of the Hybrid Evolutionary Search Algorithm 92. 4 3 1 Initialization 93, 4 3 2 Partition 94, 4 3 3 Education 94. 4 3 3 1 Diversification based removal operators 95. 4 3 3 2 Intensification based removal operators 97. 4 3 3 3 Insertion operators 98, 4 3 4 Mutation 98.
4 4 Computational Experiments 98, 4 4 1 Benchmark instances 99. 4 4 2 Sensitivity analysis of method components 100. 4 4 3 Performance of the formulations 102, 4 4 4 Comparative analysis 105. 4 5 Conclusions 106, 5 The Fleet Size and Mix Pollution Routing Problem 110. 5 1 Introduction 111, 5 2 Background on Vehicle Types and Characteristics 114. 5 3 Mathematical Model for the Fleet Size and Mix Pollution Routing Problem119. 5 4 Description of the Hybrid Evolutionary Algorithm 121. 5 4 1 Speed optimization algorithm 123, 5 4 2 The Split algorithm with the speed optimization algorithm 125.
5 4 3 Higher Education and Intensification 125, 5 5 Computational Experiments and Analyses 128. 5 5 1 Sensitivity analysis on method components 128. 5 5 2 Results on the PRP and on the FSMPRP 129, 5 5 3 The effect of cost components 132. 5 5 4 The effect of the heterogeneous fleet 133, 5 6 Conclusions 137. 6 The Impact of Location Fleet Composition and Routing on Emissions. in Urban Freight Distribution 140, 6 1 Introduction 141. 6 1 1 A brief review of the literature 142, 6 1 2 Scientific contributions and structure of the paper 145.
6 2 General Description of the Problem Setting 145. 6 2 1 Fuel consumption and CO2 emissions 146, 6 2 2 Vehicle types and characteristics 147. 6 2 3 Speed zones 149, Contents viii, 6 2 4 Network structure 150. 6 2 5 Depot costs 152, 6 3 Formal Problem Description and Mathematical Formulation 152. 6 4 Description of the ALNS Metaheuristic 155, 6 4 1 Cheapest path calculation 156. 6 4 2 Overview of the metaheuristic 157, 6 5 Computational Experiments and Analyses 160.
6 5 1 Results obtained on the test instances 161, 6 5 2 The effect of the various cost components of the objective function 163. 6 5 3 The effect of variations in depot and customer locations 165. 6 5 4 The effect of variations in depot costs 171. 6 5 5 The effect of fleet composition 173, 6 6 Conclusions and Managerial Insights 176. 7 Conclusions 179, 7 1 Overview 180, 7 2 Summary of the Main Scientific Contributions 180. 7 3 Research Outputs 182, 7 4 Limitations of the Research Results 184. 7 5 Future Research Directions 185, 7 6 Excitement 185.
A Supplement to Chapter 2 187,B Supplement to Chapter 3 193. C Supplement to Chapter 4 201,D Supplement to Chapter 5 211. E Supplement to Chapter 6 216,Bibliography 221, List of Figures. 1 1 The FedEx global distribution network FedEx 2015 5. 1 2 A schematic representation of the thesis structure 7. 2 1 A classification of HVRP variants 29, 3 1 Illustration of the Education procedure 62. 3 2 Illustration of Ordered Crossover 69, 3 3 Illustration of procedure Split 71.
3 4 Illustration of the diversification stage 74, 4 1 Illustration of the L HALNS procedure 96. 5 1 Three vehicle types MAN 2014a 116, 5 2 Illustration of the HALNS procedure 126. 6 1 Fuel consumption as a function of speed 149, 6 2 Grid city examples Google Maps 2015 151. 6 3 Illustration of speed zones 152, 6 4 Illustration of the three speed zones 158. 6 5 Geographical customer distribution in the benchmark instances 162. ix, List of Tables, 2 1 Literature on the FSM 45, 2 2 Literature on the HF 46.
2 3 Literature on the FSM and the HF 47, 2 4 Average comparison of recent metaheuristics on the FSM 49. 2 5 Average comparison of recent metaheuristics on the HF 50. 2 6 Comparison of recent metaheuristics on the FSMTW T 52. 2 7 Comparison of recent metaheuristics on the FSMTW D 53. 3 1 Average percentage deviations of the solution values found by the HEA. from best known solution values with varying np and no 76. 3 2 Average results for FT 78, 3 3 Average results for FD 79. 3 4 Results for HT 80, 3 5 Results for HD 81, 4 1 The intervals for depot capacities 100. 4 2 Sensitivity analysis experiment setup 100, 4 3 Sensitivity analysis of the HESA components 101. 4 4 Average results of the formulations 103, 4 5 Effect of the valid inequalities 104.
4 6 Average results on small size instances 107, 4 7 Average results on medium and large size instances 108. 5 1 Vehicle common parameters 115, 5 2 Vehicle specific parameters 117. 5 3 Sensitivity analysis experiment setup 129, 5 4 Sensitivity analysis of the HEA components 130. 5 5 Computational results on the 100 node PRP instances 131. 5 6 Computational results on the 200 node PRP instances 132. 5 7 Average results on the FSMPRP instances 132, 5 8 The effect of cost components objective function values 134. 5 9 The effect of cost components percent deviation from the minimum value 135. 5 10 The effect of the speed 136, 5 11 The effect of using a heterogeneous fleet 138.
5 12 Capacity utilization rates 139, 6 1 Vehicle common parameters 147. 6 2 Vehicle specific parameters 148, 6 3 Parameters used in the P L HALNS 163. x, List of Tables xi, 6 4 Average results on the instances 164. 6 5 The effect of cost components objective function values 166. 6 6 The effect of cost components percent deviation from the minimum value 167. 6 7 The effect of variations in depot location 168. 6 8 The effect of variations in customer location 170. 6 9 The effect of same depot costs on opened depots 172. 6 10 The effect of decreasing the depot costs 174. 6 11 The effect of using a heterogeneous fleet 175. 6 12 Capacity utilization rates 177, A 1 Comparison of recent metaheuristics on the FSM F V 188. A 2 Comparison of recent metaheuristics on the FSM F 189. A 3 Comparison of recent metaheuristics on the FSM V 190. A 4 Comparison of recent metaheuristics on the HF F V 191. A 5 Comparison of recent metaheuristics on the HF V 192. B 1 Sensitivity analyis experiment setup 194, B 2 Sensitivity analyis of the HEA components 194.
B 3 Number of iterations as a percentage by education operators 194. B 4 Results for FT for cost structure A 195, B 5 Results for FT for cost structure B 196. B 6 Results for FT for cost structure C 197, B 7 Results for FD for cost structure A 198. B 8 Results for FD for cost structure B 199, B 9 Results for FD for cost structure C 200. C 1 The FSMLRPTW benchmark instances 202, C 2 Results on the 10 customer instances 203. C 3 Results on the 15 customer instances 204, C 4 Results on the 20 customer instances 205.
C 5 Results on the 25 customer instances 206, C 6 Results on the 30 customer instances 207. C 7 Results on the 50 customer instances 208, C 8 Results on the 75 customer instances 209. C 9 Results on the 100 customer instances 210, D 1 Computational results on the 75 node FSMPRP instances 212. D 2 Computational results on the 100 node FSMPRP instances 213. D 3 Computational results on the 150 node FSMPRP instances 214. D 4 Computational results on the 200 node FSMPRP instances 215. E 1 Computational results on the 25 customer instances 217. E 2 Computational results on the 50 customer instances 218. E 3 Computational results on the 75 customer instances 219. E 4 Computational results on the 100 customer instances 220.

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