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

Abstract This paper addresses a green transportation scheduling problem with realistic constraints widely existing in make-to-order manufacturing supply chains, such as pickup time and transport mode selections. The mathematical model of this problem is presented, which is formulated as a bi-objective mixed integer nonlinear program. The problem is simplified first by converting this program to a bi-objective integer nonlinear program. A novel evolution-strategy-based memetic Pareto optimization (ESMPO) approach is then developed to handle this new program, in which a multi-objective local search process is proposed to seek promising neighboring individuals and the faster nondominated sorting procedure is introduced into the memetic algorithm to perform multi-objective sorting. The performance of the proposed ESMPO approach is evaluated by numerical experiments based on industrial data and industrial-sized problems. Experimental results demonstrate that the proposed approach can effectively solve the investigated problem by generating much better solutions than 3 other metaheuristic-based Pareto optimization approaches and the industrial method do.

[1]  Xingyu Gu,et al.  Multi-objective optimization for asphalt pavement maintenance plans at project level: Integrating performance, cost and environment , 2015 .

[2]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  Ju-Hwan Cha,et al.  A block transportation scheduling system considering a minimisation of travel distance without loading of and interference between multiple transporters , 2011 .

[5]  Michel Gendreau,et al.  A heuristic to solve the synchronized log-truck scheduling problem , 2013, Comput. Oper. Res..

[6]  Zhenzhen Zhang,et al.  A memetic algorithm for the patient transportation problem , 2015 .

[7]  Ruiyou Zhang,et al.  Heuristic-based truck scheduling for inland container transportation , 2010, OR Spectr..

[8]  Martin Grunow,et al.  Transportation Planning/Vehicle Scheduling (TP/VS) , 2012 .

[9]  Jacques Desrosiers,et al.  Survey Paper - Time Window Constrained Routing and Scheduling Problems , 1988, Transp. Sci..

[10]  Cheng Huang,et al.  Scheduling Trans-shipment Operations in Maritime Chemical Transportation , 2006 .

[11]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

[12]  Mark A. Turnquist,et al.  Integrated Routing and Scheduling in Hazardous Materials Transportation , 1997, Transp. Sci..

[13]  Thibaut Vidal,et al.  A memetic algorithm for the Multi Trip Vehicle Routing Problem , 2014, Eur. J. Oper. Res..

[14]  Seyed Hessameddin Zegordi,et al.  A Multi-Population Genetic Algorithm for Transportation Scheduling , 2009 .

[15]  Manish Verma,et al.  A bi-objective approach to routing and scheduling maritime transportation of crude oil , 2015 .

[16]  Wen Fei Wang,et al.  Scheduling for inland container truck and train transportation , 2013 .

[17]  Byung Soo Kim,et al.  Block transportation scheduling under delivery restriction in shipyard using meta-heuristic algorithms , 2014, Expert Syst. Appl..

[18]  Bjørn Nygreen,et al.  Alternative approaches to the crude oil tanker routing and scheduling problem with split pickup and split delivery , 2015, Eur. J. Oper. Res..

[19]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[20]  Gilbert Laporte,et al.  The Pollution-Routing Problem , 2011 .

[21]  Gilbert Laporte,et al.  The time-dependent pollution-routing problem , 2013 .

[22]  Kmr Kristel Hoen,et al.  Effect of carbon emission regulations on transport mode selection under stochastic demand , 2014 .

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

[24]  Teodor Gabriel Crainic,et al.  Minimizing greenhouse gas emissions in intermodal freight transport: an application to rail service design , 2010, J. Oper. Res. Soc..

[25]  Ehsan Nikbakhsh,et al.  Variable neighborhood search for the bi-objective post-sales network design problem: A fitness landscape analysis approach , 2014, Comput. Oper. Res..

[26]  Michael Forbes,et al.  An exact algorithm for multiple depot bus scheduling , 1994 .

[27]  Christos A. Kontovas.  The Green Ship Routing and Scheduling Problem (GSRSP): A conceptual approach , 2014 .

[28]  Shangyao Yan,et al.  Cash transportation vehicle routing and scheduling under stochastic travel times , 2014 .

[29]  Zhi-Hua Hu,et al.  A container multimodal transportation scheduling approach based on immune affinity model for emergency relief , 2011, Expert Syst. Appl..

[30]  Jean-Yves Potvin,et al.  State-of-the Art Review - Evolutionary Algorithms for Vehicle Routing , 2009, INFORMS J. Comput..

[31]  Chi Xie,et al.  Bicriterion discrete equilibrium network design problem , 2014, Networks.

[32]  Ziyou Gao,et al.  A green train scheduling model and fuzzy multi-objective optimization algorithm , 2013 .

[33]  Gilbert Laporte,et al.  The bi-objective Pollution-Routing Problem , 2014, Eur. J. Oper. Res..

[34]  K. Chandra Sekaran,et al.  Improved NSGA-II Based on a Novel Ranking Scheme , 2010, ArXiv.

[35]  S.Y.S. Leung,et al.  A hybrid intelligent model for order allocation planning in make-to-order manufacturing , 2013, Appl. Soft Comput..

[36]  Reza Tavakkoli-Moghaddam,et al.  The periodicity and robustness in a single-track train scheduling problem , 2012, Appl. Soft Comput..

[37]  Pyung-Hoi Koo,et al.  Transporter scheduling based on a network flow model under a dynamic block transportation environment1 , 2009, 2009 International Conference on Computers & Industrial Engineering.

[38]  Henrik Andersson,et al.  Containership Routing and Scheduling in Liner Shipping: Overview and Future Research Directions , 2014, Transp. Sci..

[39]  R. Marler,et al.  The weighted sum method for multi-objective optimization: new insights , 2010 .

[40]  Peter J. Fleming,et al.  On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers , 1996, PPSN.

[41]  Vicenç Puig,et al.  A multi-objective optimization strategy for designing aircraft noise abatement procedures. Case study at Girona airport , 2011 .

[42]  Carlos Cotta,et al.  Memetic algorithms and memetic computing optimization: A literature review , 2012, Swarm Evol. Comput..

[43]  Tatsushi Nishi,et al.  Column generation heuristics for ship routing and scheduling problems in crude oil transportation with split deliveries , 2014, Comput. Chem. Eng..

[44]  Wai Keung Wong,et al.  A Genetic-Algorithm-Based Optimization Model for Solving the Flexible Assembly Line Balancing Problem With Work Sharing and Workstation Revisiting , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[45]  Jiuping Xu,et al.  Multi-objective bilevel construction material transportation scheduling in large-scale construction projects under a fuzzy random environment , 2013 .