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Memetic Heuristic Approach for Solving Truck and Trailer Routing Problems with Stochastic Demands and Time Windows

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  • Seyedmehdi Mirmohammadsadeghi
  • Shamsuddin Ahmed

Abstract

Manufacturers and service providers often encounter stochastic demand scenarios. Researchers have, thus far, considered that the deterministic truck and trailer routing problem (TTRP) cannot address the prevailing demand uncertainties and/or other complexities. The purpose of this study is to expand the deterministic TTRP model by introducing stochastic demand and time window constraints to bring the TTRP model closer to reality and solve the model in a reasonable timeframe by administering the memetic algorithm (MA). This paper presents a model that can be applied in stochastic demand conditions. To employ the MA, various crossovers, mutations and local search approaches were applied. First, two experimental tests were carried out to show the validity and consistency of the MA for solving stochastic TTRPs. The results can be compared with the VRPSDTW (vehicle routing problem with stochastic demands (VRSPD) with time windows) solution obtained using large neighbourhood search (LNS) of earlier research. The average results from Tests 1 and 2 achieved by MA are 514.927 and 516.451. However, the average result obtained by LNS is 516.877. Therefore, the MA can generate results. Thus, MA is found to be suitable for solving truck and trailer routing problem(s) under stochastic demand with a time window (TTRPSDTW). Moreover, 54 benchmark instances were modified for this case and the initial feasible solutions were generated for this purpose. The solutions were significantly improved by the MA. Also, the problems were tested using sensitivity analysis to understand the effects of the parameters and to make a comparison between the best results obtained by MA and sensitivity analysis. Since the differences between the results are small, the MA was found to be appropriate and better for solving TTRPSDTW. The paper also gives some suggestions for further research. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Seyedmehdi Mirmohammadsadeghi & Shamsuddin Ahmed, 2015. "Memetic Heuristic Approach for Solving Truck and Trailer Routing Problems with Stochastic Demands and Time Windows," Networks and Spatial Economics, Springer, vol. 15(4), pages 1093-1115, December.
    • Handle: RePEc:kap:netspa:v:15:y:2015:i:4:p:1093-1115
    DOI: 10.1007/s11067-014-9282-2
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    3. Huber, Sandra & Geiger, Martin Josef, 2017. "Order matters – A Variable Neighborhood Search for the Swap-Body Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 263(2), pages 419-445.
    4. M. Alinaghian & M. Ghazanfari & N. Norouzi & H. Nouralizadeh, 2017. "A Novel Model for the Time Dependent Competitive Vehicle Routing Problem: Modified Random Topology Particle Swarm Optimization," Networks and Spatial Economics, Springer, vol. 17(4), pages 1185-1211, December.
    5. Ann-Kathrin Rothenbächer & Michael Drexl & Stefan Irnich, 2016. "Branch-and-Price-and-Cut for the Truck-andTrailer Routing Problem with Time Windows," Working Papers 1617, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    6. Ampol Karoonsoontawong & Onwasa Kobkiattawin & Chi Xie, 2019. "Efficient Insertion Heuristic Algorithms for Multi-Trip Inventory Routing Problem with Time Windows, Shift Time Limits and Variable Delivery Time," Networks and Spatial Economics, Springer, vol. 19(2), pages 331-379, June.
    7. Surendra Reddy Kancharla & Gitakrishnan Ramadurai, 2019. "Multi-depot Two-Echelon Fuel Minimizing Routing Problem with Heterogeneous Fleets: Model and Heuristic," Networks and Spatial Economics, Springer, vol. 19(3), pages 969-1005, September.

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