Uncertain multiobjective traveling salesman problem

•Consider uncertainty and multiple objectives in traveling salesman problem first time.•Propose a new approach to obtain Pareto efficient route in the uncertain multiobjective TSP, which converts the uncertain multiobjective TSP into an uncertain single objective TSP.•Present a variant ABC algorithm...

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Bibliographic Details
Published in:European journal of operational research Vol. 241; no. 2; pp. 478 - 489
Main Authors: Wang, Zutong, Guo, Jiansheng, Zheng, Mingfa, Wang, Ying
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.03.2015
Elsevier Sequoia S.A
Subjects:
Artificial bee colony algorithm
Benchmarks
Comparative analysis
Multiobjective optimization
Optimization algorithms
Pareto optimum
Route optimization
Studies
Traveling salesman problem
Uncertainty modeling
Traveling salesman problem
Artificial bee colony algorithm
Uncertainty modeling
Multiobjective optimization
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Summary:•Consider uncertainty and multiple objectives in traveling salesman problem first time.•Propose a new approach to obtain Pareto efficient route in the uncertain multiobjective TSP, which converts the uncertain multiobjective TSP into an uncertain single objective TSP.•Present a variant ABC algorithm specified for solving uncertain multiobjective TSP, and it outperforms other classical algorithms on the benchmark TSPs, such as GA, PSO, ACO, etc. Traveling salesman problem is a fundamental combinatorial optimization model studied in the operations research community for nearly half a century, yet there is surprisingly little literature that addresses uncertainty and multiple objectives in it. A novel TSP variation, called uncertain multiobjective TSP (UMTSP) with uncertain variables on the arc, is proposed in this paper on the basis of uncertainty theory, and a new solution approach named uncertain approach is applied to obtain Pareto efficient route in UMTSP. Considering the uncertain and combinatorial nature of UMTSP, a new ABC algorithm inserted with reverse operator, crossover operator and mutation operator is designed to this problem, which outperforms other algorithms through the performance comparison on three benchmark TSPs. Finally, a new benchmark UMTSP case study is presented to illustrate the construction and solution of UMTSP, which shows that the optimal route in deterministic TSP can be a poor route in UMTSP.
Bibliography:SourceType-Scholarly Journals-1
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2014.09.012