Solving multitrip pickup and delivery problem with time windows and manpower planning using multiobjective algorithms

The multitrip pickup and delivery problem with time windows and manpower planning ( MTPDPTW-MP ) determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP ( MO-MTPDPTWMP ) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection ( MOILS-ANS ) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.

[1]  Ulrich Derigs,et al.  A computational study comparing different multiple neighbourhood strategies for solving rich vehicle routing problems , 2016 .

[2]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[3]  Qingfu Zhang,et al.  A Two-Stage Multiobjective Evolutionary Algorithm for Multiobjective Multidepot Vehicle Routing Problem With Time Windows , 2019, IEEE Transactions on Cybernetics.

[4]  Yang Yu,et al.  Global optimum-based search differential evolution , 2019, IEEE/CAA Journal of Automatica Sinica.

[5]  Rong Qu,et al.  Solving multi-objective multicast routing problems by evolutionary multi-objective simulated annealing algorithms with variable neighbourhoods , 2011, J. Oper. Res. Soc..

[6]  Laetitia Vermeulen-Jourdan,et al.  Survey and unification of local search techniques in metaheuristics for multi-objective combinatorial optimisation , 2018, J. Heuristics.

[7]  Kai Wang,et al.  Manpower allocation and vehicle routing problem in non-emergency ambulance transfer service , 2017 .

[8]  John E. Dennis,et al.  Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems , 1998, SIAM J. Optim..

[9]  Wang Hu,et al.  Adaptive Multiobjective Particle Swarm Optimization Based on Parallel Cell Coordinate System , 2015, IEEE Transactions on Evolutionary Computation.

[10]  Andrew Lim,et al.  Pickup and Delivery Service with Manpower Planning in Hong Kong Public Hospitals , 2017, Transp. Sci..

[11]  Michel Gendreau,et al.  Hyper-heuristics: a survey of the state of the art , 2013, J. Oper. Res. Soc..

[12]  Salwani Abdullah,et al.  On the use of multi neighbourhood structures within a Tabu-based memetic approach to university timetabling problems , 2012, Inf. Sci..

[13]  Hui Li,et al.  An Adaptive Evolutionary Multi-Objective Approach Based on Simulated Annealing , 2011, Evolutionary Computation.

[14]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[15]  Jiujun Cheng,et al.  Dendritic Neuron Model With Effective Learning Algorithms for Classification, Approximation, and Prediction , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Edmund K. Burke,et al.  The influence of the fitness evaluation method on the performance of multiobjective search algorithms , 2006, Eur. J. Oper. Res..

[17]  Michel Gendreau,et al.  A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows , 2013, Comput. Oper. Res..

[18]  Qingfu Zhang,et al.  Multi-objective Local Search Based on Decomposition , 2016, PPSN.

[19]  María José del Jesús,et al.  KEEL: a software tool to assess evolutionary algorithms for data mining problems , 2008, Soft Comput..

[20]  Jiajun Wang,et al.  Parameter optimization of interval Type-2 fuzzy neural networks based on PSO and BBBC methods , 2019, IEEE/CAA Journal of Automatica Sinica.

[21]  Md. Monirul Islam,et al.  Solving Transit Network Design Problem Using Many-Objective Evolutionary Approach , 2019, IEEE Transactions on Intelligent Transportation Systems.

[22]  Zibin Zheng,et al.  Multiobjective Vehicle Routing Problems With Simultaneous Delivery and Pickup and Time Windows: Formulation, Instances, and Algorithms , 2016, IEEE Transactions on Cybernetics.

[23]  Qingfu Zhang,et al.  Multiobjective evolutionary algorithms: A survey of the state of the art , 2011, Swarm Evol. Comput..

[24]  Daniel Selva,et al.  A Classification and Comparison of Credit Assignment Strategies in Multiobjective Adaptive Operator Selection , 2017, IEEE Transactions on Evolutionary Computation.

[25]  Yew-Soon Ong,et al.  City Vehicle Routing Problem (City VRP): A Review , 2015, IEEE Transactions on Intelligent Transportation Systems.

[26]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[27]  Qingfu Zhang,et al.  PPLS/D: Parallel Pareto Local Search Based on Decomposition , 2017, IEEE Transactions on Cybernetics.

[28]  Maged Dessouky,et al.  A new regret insertion heuristic for solving large-scale dial-a-ride problems with time windows , 2004 .

[29]  Yew-Soon Ong,et al.  A generic framework for multi-criteria decision support in eco-friendly urban logistics systems , 2017, Expert Syst. Appl..

[30]  Yalan Zhou,et al.  Multiobjective Multiple Neighborhood Search Algorithms for Multiobjective Fleet Size and Mix Location-Routing Problem With Time Windows , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[31]  Hisao Ishibuchi,et al.  Use of Heuristic Local Search for Single-Objective Optimization in Multiobjective Memetic Algorithms , 2008, PPSN.

[32]  Michel Gendreau,et al.  A unified solution framework for multi-attribute vehicle routing problems , 2014, Eur. J. Oper. Res..

[33]  Xin Yao,et al.  Decomposition-Based Memetic Algorithm for Multiobjective Capacitated Arc Routing Problem , 2011, IEEE Transactions on Evolutionary Computation.

[34]  Kenneth Sörensen,et al.  Knowledge-guided local search for the vehicle routing problem , 2019, Comput. Oper. Res..

[35]  Nelishia Pillay,et al.  Hyper-Heuristics: Theory and Applications , 2018, Natural Computing Series.

[36]  Ling Wang,et al.  A memetic algorithm with competition for the capacitated green vehicle routing problem , 2019, IEEE/CAA Journal of Automatica Sinica.

[37]  Dario Landa Silva,et al.  An Efficient Application of Goal Programming to Tackle Multiobjective Problems with Recurring Fitness Landscapes , 2018, ICORES.

[38]  Jason A. D. Atkin,et al.  A technique based on trade-off maps to visualise and analyse relationships between objectives in optimisation problems , 2017 .

[39]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[40]  Michel Gendreau,et al.  Vehicle Routing Problem with Time Windows, Part I: Route Construction and Local Search Algorithms , 2005, Transp. Sci..

[41]  Qingfu Zhang,et al.  Hybridization of Decomposition and Local Search for Multiobjective Optimization , 2014, IEEE Transactions on Cybernetics.

[42]  Hong Xie,et al.  GMMA: GPU-based multiobjective memetic algorithms for vehicle routing problem with route balancing , 2018, Applied Intelligence.