Green transportation scheduling with pickup time and transport mode selections using a novel multi-objective memetic optimization approach

Transportation Research Part D: Transport and Environment

Volumn 60, Issue , 2018, Pages 137-152

  Guo, Zhaoxia ^a   Zhang, Dongqing ^a   Liu, Haitao ^a   He, Zhenggang ^b   Shi, Leyuan ^c  

^a Sichuan University   (China)

^b SOUTHWEST JIAOTONG UNIVERSITY   (China)

^c UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

Evolutionary optimization; Green transportation; Multi objective optimization; Transportation scheduling

Indexed keywords

EVOLUTIONARY ALGORITHMS; INTEGER PROGRAMMING; NONLINEAR PROGRAMMING; OPTIMIZATION; PARETO PRINCIPLE; PICKUPS; PROBLEM SOLVING; SCHEDULING; SORTING; SUPPLY CHAINS; TRANSPORTATION;

EVOLUTIONARY OPTIMIZATIONS; GREEN TRANSPORTATIONS; INTEGER NONLINEAR PROGRAM; MAKE-TO-ORDER MANUFACTURING; MIXED INTEGER NONLINEAR PROGRAM; NON-DOMINATED SORTING; NUMERICAL EXPERIMENTS; OPTIMIZATION APPROACH;

MULTIOBJECTIVE OPTIMIZATION;

ALGORITHM; NUMERICAL MODEL; OPTIMIZATION; SUSTAINABILITY; TRANSPORTATION MODE; TRANSPORTATION SYSTEM; TRAVEL TIME;

EID: 84964805924     PISSN: 13619209     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trd.2016.02.003     Document Type: Article

References (48)

1. Bauer, J., Bektaş, T., Crainic, T.G., Minimizing greenhouse gas emissions in intermodal freight transport: an application to rail service design. J. Oper. Res. Soc. 61 (2010), 530–542.
2. Bektas, T., Laporte, G., The pollution-routing problem. Transport. Res. B – Meth. 45:8 (2011), 1232–1250.
3. Cattaruzza, D., Absi, N., Feillet, D., Vidal, T., A memetic algorithm for the multi-trip vehicle routing problem. Eur. J. Oper. Res. 236:3 (2014), 833–848.
4. Curry, G., Schuerma, A., A simulation solution of a transportation scheduling problem. J. Ind. Eng., 19(11), 1968, 550.
5. D'Souza, R.G.L., Sekaran, K.C., Kandasamy, A., Improved NSGA-II based on a novel ranking scheme. J. Comput. 2:2 (2010), 91–95.
6. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolut. Comput. 6:2 (2002), 182–197.
7. Demir, E., Bektas, T., Laporte, G., The bi-objective pollution-routing problem. Eur. J. Oper. Res. 232:3 (2014), 464–478.
8. ECOFYS, 2010. World GHG Emissions Flow Chart 2010, Kanaalweg 15-G, 3526 KL Utrecht, The Netherlands.
9. Eskandarpour, M., Nikbakhsh, E., Zegordi, S.H., Variable neighborhood search for the bi-objective post-sales network design problem: a fitness landscape analysis approach. Comput. Oper. Res. 52 (2014), 300–314.
10. Fonseca, C.M., Fleming, P.J., On the performance assessment and comparison of stochastic multiobjective optimizers. Lec. Notes Comput. Sci. 1141 (1996), 584–593.
11. Forbes, M., Holt, J.N., Watts, A.M., An exact algorithm for multiple depot bus scheduling. Eur. J. Oper. Res. 72 (1994), 115–124.
12. Franceschetti, A., Honhon, D., Van Woensel, T., Bektas, T., Laporte, G., The time-dependent pollution-routing problem. Transport. Res. B – Meth. 56 (2013), 265–293.
13. Grunow, M., Stefansdottir, B., Transportation planning/vehicle scheduling (TP/VS). Manage. Prof., 2012, 249–285.
14. Guo, Z., Wong, W., Leung, S., A hybrid intelligent model for order allocation planning in make-to-order manufacturing. Appl. Soft. Comput. 13:3 (2013), 1376–1390.
15. Guo, Z.X., Wong, W.K., Leung, S.Y.S., Fan, J.T., Chan, S.F., A genetic-algorithm-based optimization model for solving the flexible assembly line balancing problem with work sharing and workstation revisiting. IEEE Trans. Syst. Man Cyber. C 38:2 (2008), 218–228.
16. Hachemi, N.E., Gendreau, M., Rousseau, L.-M., A heuristic to solve the synchronized log-truck scheduling problem. Comput. Oper. Res. 40:3 (2013), 666–673.
17. Hennig, F., Nygreen, B., Furman, K.C., Song, J., Alternative approaches to the crude oil tanker routing and scheduling problem with split pickup and split delivery. Eur. J. Oper. Res. 243:1 (2015), 41–51.
18. Hoen, K., Tan, T., Fransoo, J., van Houtum, G., Effect of carbon emission regulations on transport mode selection under stochastic demand. Flex. Serv. Manuf. J. 26:1–2 (2014), 170–195.
19. Holland, J.H., Adaptation in Natural and Artificial Systems. 1975, University of Michigan Press, Michigan.
20. Hu, Z., A container multimodal transportation scheduling approach based on immune affinity model for emergency relief. Expert Syst. Appl. 38:3 (2011), 2632–2639.
21. Huang, C., Karimi, I., Scheduling trans-shipment operations in maritime chemical transportation. Ind. Eng. Chem. Res. 45:6 (2006), 1955–1973.
22. Joo, C., Kim, B., Block transportation scheduling under delivery restriction in shipyard using meta-heuristic algorithms. Expert Syst. Appl. 41:6 (2014), 2851–2858.
23. Kontovas, C.A., The green ship routing and scheduling problem (GSRSP): a conceptual approach. Transp. Res. Part D: Trans. Environ. 31 (2014), 61–69.
24. Lee, W., Lim, W., Koo, P., 2009. Transporter scheduling based on a network flow model under a dynamic block transportation environment. CIE: 2009 International Conference on Computers and Industrial Engineering, vol. 1–3, pp. 311–316.
25. Li, X., Wang, D., Li, K., Gao, Z., A green train scheduling model and fuzzy multi-objective optimization algorithm. Appl. Math. Model. 37:4 (2013), 2063–2073.
26. Marler, R.T., Arora, J.S., The weighted sum method for multi-objective optimization: new insights. Struct. Multidis. Optim. 41:6 (2010), 853–862.
27. Meng, Q., Wang, S., Andersson, H., Thun, K., Containership routing and scheduling in liner shipping: overview and future research directions. Transport. Sci. 48:2 (2013), 265–280.
28. Moscato, P., 1989. On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Caltech Concurrent Computation Program (report 826).
29. Neri, F., Cotta, C., Memetic algorithms and memetic computing optimization: a literature review. Swarm Evol. Comput. 2:1 (2012), 1–14.
30. Nishi, T., Izuno, T., Column generation heuristics for ship routing and scheduling problems in crude oil transportation with split deliveries. Comput. Chem. Eng. 60 (2014), 329–338.
31. Nozick, L., List, G., Turnquist, M., Integrated routing and scheduling in hazardous materials transportation. Transport. Sci. 31:3 (1997), 200–215.
32. Potvin, J., State-of-the art review: evolutionary algorithms for vehicle routing. INFORMS J. Comput. 21:4 (2009), 518–548.
33. Prats, X., Puig, V., Quevedo, J., A multi-objective optimization strategy for designing aircraft noise abatement procedures: case study at Girona airport. Transp. Res. Part D: Trans. Environ. 16:1 (2011), 31–41.
34. Roh, M., Cha, J., A block transportation scheduling system considering a minimisation of travel distance without loading of and interference between multiple transporters. Int. J. Prod. Res. 49:11 (2011), 3231–3250.
35. Schwefel, H.-P., Evolution and Optimum Seeking. 1995, Wiley & Sons, New York.
36. Scott, J., A transportation model, its development and application to a ship scheduling problem. Asia Pac. J. Oper. Res. 12:2 (1995), 111–128.
37. Shafia, M.A., Sadjadi, S.J., Jamili, A., Tavakkoli-Moghaddam, R., Pourseyed-Aghaee, M., The periodicity and robustness in a single-track train scheduling problem. Appl. Soft. Comput. 12:1 (2012), 440–452.
38. Siddiqui, A.W., Verma, M., A bi-objective approach to routing and scheduling maritime transportation of crude oil. Transp. Res. Part D: Trans. Environ. 37 (2015), 65–78.
39. Solomon, M.M., Desrosiers, J., Time window constrained routing and scheduling problems. Transport. Sci. 22:1 (1988), 1–13.
40. Wang, W., Yun, W., Scheduling for inland container truck and train transportation. Int. J. Prod. Econ. 143:2 (2013), 349–356.
41. Xie, C., Bicriterion discrete equilibrium network design problem. Networks 63:4 (2014), 286–305.
42. Xu, J., Gang, J., Multi-objective bilevel construction material transportation scheduling in large-scale construction projects under a fuzzy random environment. Transport. Plan. Technol. 36:4 (2013), 352–376.
43. Yan, S., Wang, S., Chang, Y., Cash transportation vehicle routing and scheduling under stochastic travel times. Eng. Optim. 46:3 (2014), 289–307.
44. Yu, B., Gu, X., Ni, F., Guo, R., Multi-objective optimization for asphalt pavement maintenance plans at project level: integrating performance, cost and environment. Transp. Res. Part D: Trans. Environ. 41 (2015), 64–74.
45. Zegordi, S., Nia, M., A multi-population genetic algorithm for transportation scheduling. Transport. Res. E – Log 45:6 (2009), 946–959.
46. Zhang, R., Yun, W., Kopfer, H., Heuristic-based truck scheduling for inland container transportation. OR Spectrum 32:3 (2010), 787–808.
47. Zhang, Z., Liu, M., Lim, A., A memetic algorithm for the patient transportation problem. OMEGA 54 (2015), 60–71.
48. Zitzler, E., Thiele, L., Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3:4 (1999), 257–271.