Fuzzy multi-objective chance-constrained programming model for hazardous materials transportation

Hazardous materials transportation is an important and hot issue of public safety. Based on the shortest path model, this paper presents a fuzzy multi-objective programming model that minimizes the transportation risk to life, travel time and fuel consumption. First, we present the risk model, travel time model and fuel consumption model. Furthermore, we formulate a chance-constrained programming model within the framework of credibility theory, in which the lengths of arcs in the transportation network are assumed to be fuzzy variables. A hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm is designed for finding a satisfactory solution. Finally, some numerical examples are given to demonstrate the efficiency of the proposed model and algorithm.

[1]  Erhan Erkut,et al.  Transport risk models for hazardous materials: revisited , 2005, Oper. Res. Lett..

[2]  Daniele Pretolani,et al.  Ranking paths in stochastic time-dependent networks , 2014, Eur. J. Oper. Res..

[3]  Li Jun,et al.  Study on Logistics Distribution Vehicle Routing Problem with Fuzzy Due Time , 2007, 2007 International Conference on Management Science and Engineering.

[4]  Xiang Li,et al.  A Sufficient and Necessary Condition for Credibility Measures , 2006, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[5]  Hanif D. Sherali,et al.  Low Probability - High Consequence Considerations in Routing Hazardous Material Shipments , 1997, Transp. Sci..

[6]  H. Frank,et al.  Shortest Paths in Probabilistic Graphs , 1969, Oper. Res..

[7]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[8]  Konstantinos G. Zografos,et al.  Solving the bicriterion routing and scheduling problem for hazardous materials distribution , 2010 .

[9]  Jianjun Zhang,et al.  Using GIS to assess the risks of hazardous materials transport in networks , 2000, Eur. J. Oper. Res..

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

[11]  Erhan Erkut,et al.  Catastrophe Avoidance Models for Hazardous Materials Route Planning , 2000, Transp. Sci..

[12]  Mario Vanhoucke,et al.  A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem , 2010, Eur. J. Oper. Res..

[13]  Rajan Batta,et al.  A network-based model for transporting extremely hazardous materials , 1993, Oper. Res. Lett..

[14]  Xiang Li,et al.  A Cooperative Scheduling Model for Timetable Optimization in Subway Systems , 2013, IEEE Transactions on Intelligent Transportation Systems.

[15]  Kostas Zografos,et al.  A heuristic algorithm for solving hazardous materials distribution problems , 2004, Eur. J. Oper. Res..

[16]  S. Kar,et al.  Multi-objective multi-item solid transportation problem in fuzzy environment , 2013 .

[17]  Pawan Lingras,et al.  Genetic algorithms for rerouting shortest paths in dynamic and stochastic networks , 2003, Eur. J. Oper. Res..

[18]  William C. Rhodes.  In hazardous environments. , 1967 .

[19]  F F Saccomanno,et al.  ECONOMIC EVALUATION OF ROUTING STRATEGIES FOR HAZARDOUS ROAD SHIPMENTS , 1985 .

[20]  Rajan Batta,et al.  Optimal Obnoxious Paths on a Network: Transportation of Hazardous Materials , 1988, Oper. Res..

[21]  Reza Zamani,et al.  A competitive magnet-based genetic algorithm for solving the resource-constrained project scheduling problem , 2013, Eur. J. Oper. Res..

[22]  F. FRANK SACCOMANNO.  Economic Evaluation of Routing Strategies , 2018 .

[23]  Mark D. Abkowitz,et al.  SELECTING CRITERIA FOR DESIGNATING HAZARDOUS MATERIALS HIGHWAY ROUTES , 1992 .

[24]  T. Lindvall.  ON A ROUTING PROBLEM , 2004, Probability in the Engineering and Informational Sciences.

[25]  B. Y. Karaa,et al.  Accurate calculation of hazardous materials transport risks , 2003 .

[26]  Esmaile Khorram,et al.  Preemptive priority-based algorithms for fuzzy minimal cost flow problem: An application in hazardous materials transportation , 2009, Comput. Ind. Eng..

[27]  L. B. Fu,et al.  Expected Shortest Paths in Dynamic and Stochastic Traf c Networks , 1998 .

[28]  Baoding Liu.  Uncertainty Theory: An Introduction to its Axiomatic Foundations , 2004 .

[29]  Changhyun Kwon,et al.  Routing hazardous materials on time-dependent networks using conditional value-at-risk , 2013 .

[30]  Konstantinos G. Zografos,et al.  Solving the k-shortest path problem with time windows in a time varying network , 2008, Oper. Res. Lett..

[31]  Stuart E. Dreyfus,et al.  An Appraisal of Some Shortest-Path Algorithms , 1969, Oper. Res..

[32]  Sankaran Mahadevan,et al.  Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment , 2012, Appl. Soft Comput..

[33]  Mitsuo Gen,et al.  Vehicle Routing Problem with Fuzzy Due-Time Using Genetic Algorithms , 1995 .

[34]  Gino J. Lim,et al.  Solution time reduction techniques of a stochastic dynamic programming approach for hazardous material route selection problem , 2013, Comput. Ind. Eng..

[35]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[36]  C ReVelle,et al.  Linear, non-approximated models for optimal routing in hazardous environments , 1998, J. Oper. Res. Soc..

[37]  Xiaoyu Ji,et al.  New models for shortest path problem with fuzzy arc lengths , 2007 .

[38]  Jared L. Cohon,et al.  Simultaneous Siting and Routing in the Disposal of Hazardous Wastes , 1991, Transp. Sci..

[39]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[40]  Shoudong Huang,et al.  A new crossover approach for solving the multiple travelling salesmen problem using genetic algorithms , 2013, Eur. J. Oper. Res..

[41]  Erhan Erkut,et al.  On finding dissimilar paths , 2000, Eur. J. Oper. Res..

[42]  Xiang Li.  Credibilistic Programming: An Introduction to Models and Applications , 2013 .

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

[44]  Maw-Sheng Chern,et al.  The fuzzy shortest path problem and its most vital arcs , 1993 .

[45]  Michel Gendreau,et al.  Vehicle routing with soft time windows and stochastic travel times: A column generation and branch-and-price solution approach , 2014, Eur. J. Oper. Res..

[46]  Xiang Li,et al.  A Two-Objective Timetable Optimization Model in Subway Systems , 2014, IEEE Transactions on Intelligent Transportation Systems.

[47]  K. Cooke,et al.  The shortest route through a network with time-dependent internodal transit times , 1966 .

[48]  Baoding Liu,et al.  Chance constrained programming with fuzzy parameters , 1998, Fuzzy Sets Syst..