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Evolutionary Multiobjective Optimization for the Pickup and Delivery Problem with Time Windows and Demands

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Abstract

This paper studies an evolutionary algorithm to solve a new multiobjective optimization problem, the Pickup and Delivery Problem with Time Windows and Demands (PDP-TW-D), which is applicable to operational optimization in various mobile network systems. With respect to multiple optimization objectives, PDP-TW-D is to find a set of Pareto-optimal routes for a fleet of vehicles (e.g., mobile robots, drones and autonomous heavy-haulage trucks) in order to serve given transportation requests. The proposed algorithm uses a population of individuals, each of which represents a solution candidate, and evolves them through generations to seek the Pareto-optimal solutions. In addition to the evolution-based global search process, the proposed algorithm allows individuals to improve their optimality in each generation with a local search process, which is designed based on iterative neighborhood search. Experimental results demonstrate that the integration of global and local search processes improves the optimality of individuals and expedites convergence speed. The proposed algorithm outperforms two well-known existing EMOAs, NSGA-II and MOEA/D, in relatively large-scale problems that have up to 400 pickup and delivery locations.

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References

  1. Ackerman E (2015) Fetch robotics introduces fetch and freight: your warehouse is now automated. IEEE Spectrum

  2. Auger A, Bader J, Brockhoff D, Zitzler E (2009) Theory of the hypervolume indicator: optimal μ-distributions and the choice of the reference point. In: Proceedings of ACM workshop on foundations of genetic algorithms

  3. Baker SF, Morton DP, Rosenthal RE, Williams LM (2002) Optimizing military airlift. Oper Res 50(4):582– 602

    Article  MathSciNet  MATH  Google Scholar 

  4. Bent R, Hentenryck PV (2006) A two-stage hybrid algorithm for pickup and delivery vehicle routing problems with time windows. Comput Oper Res 33(4):875–893

    Article  MATH  Google Scholar 

  5. Brockhoff D, Wagner T, Trautmann H (2012) On the properties of the R2 indicator. In: Proceedings of ACM international genetic and evolutionary computation conference

  6. Brown C (2012) Autonomous vehicle technology in mining. Eng Min J 213(1):30–32

  7. Christiansen M (1999) Decomposition of a combined inventory routing and time constrained ship routing problem. Transp Sci 33(1):3–16

    Article  MATH  Google Scholar 

  8. Creput J-C, Koukam A, Kozlak J, Lukasik J (2004) An evolutionary approach to pickup and delivery problem with time windows. In: Proceedings of international conference on computational science

  9. Dantzig G, Hubert J (1959) The truck dispatching problem. Manag Sci 6(1):80–91

    Article  MathSciNet  MATH  Google Scholar 

  10. Deb K, Agrawal S, Pratap A, Meyarivan T (2001) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii. In: Proceedings of international conference on parallel problem solving from nature

  11. Ding G, Li L, Ju Y (2009) Multi-strategy grouping genetic algorithm for the pickup and delivery problem with time windows. In: Proceedings of ACM international genetic and evolutionary computation conference

  12. D’Souza C, Omkar SN, Senthilnath J (2012) Pickup and delivery problem using metaheuristic techniques. Expert Syst Appl Int J 39(1):328–334

    Article  Google Scholar 

  13. Durillo J, Nebro A, Alba E (2010) The jMetal framework for multi-objective optimization: design and architecture. In: Proceedings of IEEE congress on evolutionary computation

  14. Fisher ML, Rosenwein MB (1989) An interactive optimization system for bulk-cargo ship scheduling. Naval Res Logist Q 35:27–42

    Article  Google Scholar 

  15. Hansen MP, Jaszkiewicz A (1998) Evaluating the quality of approximations of the non-dominated set. Technical Report IMM-REP-1998-7, Technical University of Denmark

  16. Knight W (2015) Inside amazon’s warehouse, human-robot symbiosis. MIT Technology Review

  17. Lau HC, Liang Z (2001) Pickup and delivery with time windows: algorithms and test case generation. In: Proceedings of IEEE international conference on tools with artificial intelligence

  18. Li H, Lim A (2001) A metaheuristic for the pickup and delivery problem with time windows. In: Proceedings of IEEE international conference on tools with artificial intelligence

  19. Liang C, Chee KJ, Zou Y, Zhu H, Causo A, Vidas S, Teng T, Chen IM, Low KH, Cheah CC (2015) Automated robot picking system for e-commerce fulfillment warehouse application. In: International federation for the promotion of mechanism and machine science

  20. Lien L (2011) Mining’s new future: how the industry will change in the next decade. Min Eng 63(2):41–46

  21. Madsen OBG, Ravn HF, Rygaard JM (1995) A heuristic algorithm for a dial-a-ride problem with time windows, multiple capacities and multiple objectives. Ann Oper Res 60(1):193–208

    Article  MATH  Google Scholar 

  22. Najera AG, Bullinaria JA (2009) Bi-objective optimization for the vehicle routing problem with time windows: using route similarity to enhance performance. In: Proceedings of international conference on evolutionary multi-criterion optimization

  23. Nanry WP, Barnes J (2000) Solving the pickup and delivery problem with time windows using reactive tabu search. Transp Res B Methodol 34(2):107–121

    Article  Google Scholar 

  24. Ombuki B, Ross BJ, Hanshar F (2006) Multi-objective genetic algorithms for vehicle routing problem with time windows. Appl Intell 24(1):17–30

    Article  Google Scholar 

  25. Pankratz G (2005) A grouping genetic algorithm for the pickup and delivery problem with time windows. OR Spectr 27(1):21–41

    Article  MathSciNet  MATH  Google Scholar 

  26. Parragh S, Doerner K, Hartl R (2008) A survey on pickup and delivery problems: part I: transportation between customers and depot. Journal für Betriebswirtschaft 58(2):21–51

    Article  Google Scholar 

  27. Parragh S, Doerner K, Hartl R (2008) A survey on pickup and delivery problems: Part II: Transportation between pickup and delivery locations. Journal für Betriebswirtschaft 58(2):81–117

    Article  Google Scholar 

  28. Phan D, Suzuki J (2015) R2 indicator based multiobjective memetic optimization for the pickup and delivery problem with time windows and demands (PDP-TW-D). In: International conference on bio-inspired information and communications technologies

  29. Psaraftis HN (1980) A dynamic programming solution to the single-vehicle many-to-many dial-a-ride problem with time windows. Transp Sci 14(2):130–154

    Article  Google Scholar 

  30. Psaraftis HN (1983) An exact algorithm for the single-vehicle many-to-many dial-a-ride problem with time windows. Transp Sci 17(3):351–357

    Article  Google Scholar 

  31. Psaraftis HN, Orlin JB, Bienstock D, Thompson PM (1985) Analysis and solution algorithms of sealift routing and scheduling problems. Technical Report 1700-85, Massachusetts Institute of Technology, Sloan School of Management

  32. Rappoport HK, Levy LS, Golden BL, Toussaint K (1992) A planning heuristic for military airlift. Interfaces 22(3):73–87

    Article  Google Scholar 

  33. Rappoport HK, Levy LS, Toussaint K, Golden BL (1994) A transportation problem formulation for the mac airlift planning problem. Ann Oper Res 50(1):505–523

    Article  MATH  Google Scholar 

  34. Ropke S, Pisinger D (2006) An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transp Sci 40(4):455–472

    Article  Google Scholar 

  35. Solanki RS, Shouthworth F (1991) An execution planning algorithm for military airlift. Interfaces 21 (4):121–131

    Article  Google Scholar 

  36. Solomon MM (1987) Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper Res 35(2):254–265

    Article  MathSciNet  MATH  Google Scholar 

  37. Solomon MM, Chalifour A, Desrosiers J, Boisvert J (1992) An application of vehicle routing methodology to large-scale larvicide control programs. Interfaces 22(3):88–99

    Article  Google Scholar 

  38. Takoudjou RT, Deschamps JC, Dupas R (2012) A hybrid multi-start heuristic for the pickup and delivery problem with and without transshipment. In: Proceedings of international conference of modeling, optimization and simulation

  39. Tan KC, Chew YH, Lee LH (2006) A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem with time windows. Comput Optim Appl 34:115–151

    Article  MathSciNet  MATH  Google Scholar 

  40. Toth P, Vigo D (1997) Heuristic algorithms for the handicapped persons transportation problem. Transp Sci 31(1):60–71

    Article  MathSciNet  MATH  Google Scholar 

  41. Trautmann H, Wagner T, Brockhoff D (2013) R2-EMOA: focused multiobjective search using R2-indicator-based selection. In: Proceedings of learning and intelligent optimization conference

  42. Velasco N, Castagliola P, Dejax P, Guret C, Prins C (2009) A memetic algorithm for a pick-up and delivery problem by helicopter. In: Pereira F, Tavares J (eds) Bio-inspired algorithms for the vehicle routing problem. Springer, pp 173–190

  43. Wang H, Lee D-H, Cheu R (2009) PDPTW based taxi dispatch modeling for booking service. In: Proceedings of international conference on natural computation

  44. Wang HF, Chen YY (2012) A genetic algorithm for the simultaneous delivery and pickup problems with time window. Comput Ind Eng 62(1):84–95

    Article  Google Scholar 

  45. Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731

    Article  Google Scholar 

  46. Zitzler E, Knowles J, Thiele L (2008) Quality assessment of pareto set approximations. In: Branke J, Deb K, Miettinen K, Slowinski R (eds) Multiobjective optimization: interactive and evolutionary approaches. Springer, pp 373–404

  47. Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms: a comparative study. In: Proceedings of international conference on parallel problem solving from nature

  48. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput 3(4):257–271

    Article  Google Scholar 

  49. Zitzler E, Thiele L, Bader J (2008) SPAM: set preference algorithm for multiobjective optimization. In: Proceedings of international conference on parallel problem solving from nature

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Author notes

  1. Dũng H. Phan

    Present address: , 100 Morrissey Blvd., Boston, MA, 02125, USA

Authors and Affiliations

  1. Department of Computer Science, University of Massachusetts Boston, Boston, MA, USA

    Dũng H. Phan & Junichi Suzuki

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  1. Dũng H. Phan
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  2. Junichi Suzuki
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Correspondence to Junichi Suzuki.

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Phan, D.H., Suzuki, J. Evolutionary Multiobjective Optimization for the Pickup and Delivery Problem with Time Windows and Demands. Mobile Netw Appl 21, 175–190 (2016). https://doi.org/10.1007/s11036-016-0709-5

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