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A hybrid priority-based genetic algorithm for simultaneous pickup and delivery problems in reverse logistics with time windows and multiple decision-makers

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Abstract

This article puts forward a hybrid priority-based nested genetic algorithm with fuzzy logic controller and fuzzy random simulation (hpn-GA with FLC–FRS) for solving a variant of the vehicle routing problem. To meet all the complex restrictions contained in practical reverse logistics, a new mathematical model is developed for simultaneous pickup and delivery problems with time windows and multiple decision-makers (SPDTW–MDM). Then, a hpn-GA with FLC–FRS is proposed, where the priority-based initializing method makes the initializing more applicable, a nested procedure structure handles multiple decision-makers, a fuzzy logic controller helps adjust the mutation rate, and a fuzzy random simulation is used to deal with uncertainties. Finally, in the case study, GA parameters are tuned by Taguchi method and result analyses are presented to highlight the performance of the optimization method for the SPDTW–MDM, while algorithm comparisons by instance applications in different scales show its efficiency and effectiveness.

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References

  • Abo-Sinna MA, Baky IA (2007) Interactive balance space approach for solving multi-level multi-objective programming problems. Inf Sci 177(16):3397–3410

    Article  MATH  Google Scholar 

  • Ai TJ, Kachitvichyanukul V (2009) Particle swarm optimization and two solution representations for solving the capacitated vehicle routing problem. Comput Ind Eng 56(1):380–387

    Article  Google Scholar 

  • Alves MJA, Antunes CH (2018) A semivectorial bilevel programming approach to optimize electricity dynamic timeof-use retail pricing. Comput Oper Res 92:130–144

    Article  MathSciNet  MATH  Google Scholar 

  • Amanna AE, Ali D, Gadhiok M, Price M, Reed JH (2012) Cognitive radio engine parametric optimization utilizing Taguchi analysis. EURASIP J Wirel Commun Netw 1:5

    Article  Google Scholar 

  • Avci M, Topaloglu S (2015) An adaptive local search algorithm for vehicle routing problem with simultaneous and mixed pickups and deliveries. Comput Ind Eng 83:15–29

    Article  Google Scholar 

  • Belgin O, Karaoglan I, Altiparmak F (2017) Two-echelon vehicle routing problem with simultaneous pickup and delivery: mathematical model and heuristic approach. Comput Ind Eng 115:1–16

    Article  Google Scholar 

  • Bertazzi L, Secomandi N (2018) Faster rollout search for the vehicle routing problem with stochastic demands and restocking. Eur J Oper Res 270(2):487–497

    Article  MathSciNet  MATH  Google Scholar 

  • Bialas WF, Karwan MH (1984) Two-level linear programming. Manag sci 30(8):1004–1020

    Article  MathSciNet  MATH  Google Scholar 

  • Chami ZA, Manier H, Manier MA, Chebib E (2018) An advanced grasp-hga combination to solve a multi-period pickup and delivery problem. Expert Syst Appl 105(1):262–272

    Article  Google Scholar 

  • Ding S, Chen C, Xin B, Pardalos PM (2018) A bi-objective load balancing model in a distributed simulation system using NSGA-II and MOPSO approaches. Appl Soft Comput 63:249–267

    Article  Google Scholar 

  • Fazayeli S, Eydi A, Kamalabadi IN (2018) Location-routing problem in multimodal transportation network with time windows and fuzzy demands: presenting a two-part genetic algorithm. Comput Ind Eng 119:233–246

    Article  Google Scholar 

  • Gen M, Cheng R (2000) Genetic algorithms and engineering optimization, vol 7. Wiley, London

    Google Scholar 

  • Gen M, Altiparmak F, Lin L (2006) A genetic algorithm for two-stage transportation problem using priority-based encoding. OR Spectr 28:337–354

    Article  MathSciNet  MATH  Google Scholar 

  • Goksal FP, Karaoglan I, Altiparmak F (2013) A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery. Comput Ind Eng 65:39–53

    Article  Google Scholar 

  • Gong F, Kung DS, Zeng T (2018) The impact of different contract structures on it investment in logistics outsourcing. Int J Prod Econ 195:158–167

    Article  Google Scholar 

  • Gschwind T, Irnich S, Rothenbächer AK, Tilk C (2018) Bidirectional labeling in column generation algorithms for pickup-and-delivery problems. Eur J Oper Res 266(2):521–530

    Article  MathSciNet  MATH  Google Scholar 

  • Kwakernaak H (1978) Fuzzy random variables part I: definitions and theorems. Inf Sci 15:1–29

    Article  MATH  Google Scholar 

  • Kwakernaak H (1979) Fuzzy random variables part II: algorithms and examples for the discrete case. Inf Sci 17:253–278

    Article  MATH  Google Scholar 

  • Letchford AN, Salazar-González JJ (2018) The capacitated vehicle routing problem: stronger bounds in pseudo-polynomial time. Eur J Oper Res. 272:24. https://doi.org/10.1016/j.ejor.2018.06.002

    Article  MathSciNet  MATH  Google Scholar 

  • Liao TY (2018) Reverse logistics network design for product recovery and remanufacturing. Appl Math Model 60:145–163

    Article  MathSciNet  Google Scholar 

  • Liu B (2001) Fuzzy random chance-constrained programming. IEEE Trans Fuzzy Syst 9(5):713–720

    Article  Google Scholar 

  • Liu CS, Kou G, Huang FH (2016) Vehicle coordinated strategy for vehicle routing problem with fuzzy demands. Math Probl Eng 2016(1):1–10

    MathSciNet  MATH  Google Scholar 

  • Ma Y, Xu J (2015) A cloud theory-based particle swarm optimization for multiple decision maker vehicle routing problems with fuzzy random time windows. Eng Optim 47(6):825–842

    Article  MathSciNet  Google Scholar 

  • Marinakis Y, Marinaki M (2010) A hybrid genetic-particle swarm optimization algorithm for the vehicle routing problem. Expert Syst Appl 37(2):1446–1455

    Article  MATH  Google Scholar 

  • Mishra S (2007) Weighting method for bi-level linear fractional programming problems. Eur J Oper Res 183(1):296–302

    Article  MATH  Google Scholar 

  • Mohammed MA, Gani MKA, Hamed RI, Mostafa SA, Ahmad MS, Ibrahim DA (2017) Solving vehicle routing problem by using improved genetic algorithm for optimal solution. J Comput Sci 21:255–262

    Article  Google Scholar 

  • Mousavi SM, Niaki STA (2013) Capacitated location allocation problem with stochastic location and fuzzy demand: a hybrid algorithm. Appl Math Model 37(7):5109–5119

    Article  MathSciNet  MATH  Google Scholar 

  • Osaba E, Yang XS, Diaz F, Onieva E, Masegosa AD, Perallos A (2017) A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy. Soft Comput 21(18):5295–5308

    Article  Google Scholar 

  • Pereira AH, Urrutia S (2018) Formulations and algorithms for the pickup and delivery traveling salesman problem with multiple stacks. Comput Oper Res 93:1–14

    Article  MathSciNet  MATH  Google Scholar 

  • Reil S, Bortfeldt A, Mönch L (2018) Heuristics for vehicle routing problems with backhauls, time windows, and 3d loading constraints. Eur J Oper Res 266(3):877–894

    Article  MathSciNet  MATH  Google Scholar 

  • Shapiro AF (2009) Fuzzy random variables. Insur Math Econ 44(2):307–314

    Article  MathSciNet  MATH  Google Scholar 

  • Soleimani H, Govindan K, Saghafi H, Jafari H (2017) Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Comput Ind Eng 109:191–203

    Article  Google Scholar 

  • Soleimani H, Chaharlang Y, Ghaderi H (2018) Collection and distribution of returned remanufactured products in a vehicle routing problem with pickup and delivery considering sustainable and green criteria. J Clean Prod 172:960–970

    Article  Google Scholar 

  • Tang J, Pan Z, Fung RY, Lau H (2009) Vehicle routing problem with fuzzy time windows. Fuzzy Sets Syst 160(5):683–695

    Article  MathSciNet  MATH  Google Scholar 

  • Wang P (1997) Speeding up the search process of genetic algorithm by fuzzy logic. In: Proceedings of the 5th European congress on intelligent techniques and soft computing, pp 665–671

  • Wang C, Mu D, Zhao F, Sutherland JW (2015) A parallel simulated annealing method for the vehicle routing problem with simultaneous pickupcdelivery and time windows. Comput Ind Eng 83:111–122

    Article  Google Scholar 

  • Wang G, Ma L, Chen J (2017a) A bilevel improved fruit fly optimization algorithm for the nonlinear bilevel programming problem. Knowl Based Syst 138:113–123

    Article  Google Scholar 

  • Wang K, Lan S, Zhao Y (2017b) A genetic-algorithm-based approach to the two-echelon capacitated vehicle routing problem with stochastic demands in logistics service. J Oper Res Soc 68(11):1409–1421

    Article  Google Scholar 

  • Wang J, Yu Y, Tang J (2018) Compensation and profit distribution for cooperative green pickup and delivery problem. Transp Res Part B Methodol 113:54–69

    Article  Google Scholar 

  • Wassan NA, Nagy G, Ahmadi S (2008) A heuristic method for the vehicle routing problem with mixed deliveries and pickups. J Sched 11(2):149–161

    Article  MathSciNet  MATH  Google Scholar 

  • Zheng JN, Chien CF, Gen M (2015) Multi-objective multi-population biased random-key genetic algorithm for the 3-d container loading problem. Comput Ind Eng 89:80–87

    Article  Google Scholar 

  • Zheng Y, Zhang G, Zhang Z, Lu J (2018) A reducibility method for the weak linear bilevel programming problems and a case study in principal-agent. Inf Sci 454:46–58

    Article  MathSciNet  Google Scholar 

  • Zhu W, Ng SCH, Wang Z, Zhao X (2017) The role of outsourcing management process in improving the effectiveness of logistics outsourcing. Int J Prod Econ 188:29–40

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by Natural Science Foundation of China (Grant Nos. 71640013, 71601134, 71401020, and 71702167) and China Postdoctoral Science Foundation (Grant No. 2018T110609).

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Authors and Affiliations

  1. School of Economics and Management, Hebei University of Technology, Tianjin, 300401, People’s Republic of China

    Yanfang Ma

  2. Business School, Sichuan University, Chengdu, 610064, People’s Republic of China

    Zongmin Li

  3. School of Economics and Management, Chongqing Jiaotong University, Chongqing, 40074, People’s Republic of China

    Fang Yan

  4. College of Economics and Management, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, People’s Republic of China

    Cuiying Feng

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  1. Yanfang Ma
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  2. Zongmin Li
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  3. Fang Yan
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  4. Cuiying Feng
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Correspondence to Fang Yan.

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Ma, Y., Li, Z., Yan, F. et al. A hybrid priority-based genetic algorithm for simultaneous pickup and delivery problems in reverse logistics with time windows and multiple decision-makers. Soft Comput 23, 6697–6714 (2019). https://doi.org/10.1007/s00500-019-03754-5

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