Modelling and optimization of a tri-objective Transportation-Location-Routing Problem considering route reliability: using MOGWO, MOPSO, MOWCA and NSGA-II

  • سال انتشار: 1400
  • محل انتشار: دوفصلنامه بهینه سازی در مهندسی صنایع، دوره: 14، شماره: 2
  • کد COI اختصاصی: JR_JOIE-14-2_007
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 633
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نویسندگان

Fariba Maadanpour Safari

Faculty of Mechanic and Industrial Engineering, Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Farhad Etebari

Faculty of Mechanic and Industrial Engineering, Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Adel Pourghader Chobar

Faculty of Mechanic and Industrial Engineering, Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

چکیده

 In this research, a tri-objective mathematical model is proposed for the Transportation-Location-Routing problem. The model considers a three-echelon supply chain and aims to minimize total costs, maximize the minimum reliability of the traveled routes and establish a well-balanced set of routes. In order to solve the proposed model, four metaheuristic algorithms, including Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Water Cycle Algorithm (MOWCA), Multi-objective Particle Swarm Optimization (MOPSO) and Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) are developed. The performance of the algorithms is evaluated by solving various test problems in small, medium, and large scale. Four performance measures, including Diversity, Hypervolume, Number of Non-dominated Solutions, and CPU-Time, are considered to evaluate the effectiveness of the algorithms. In the end, the superior algorithm is determined by Technique for Order of Preference by Similarity to Ideal Solution method.

کلیدواژه ها

Transportation-Location-Routing, reliability, Multi-Objective Grey Wolf Optimizer, Multi-Objective Water Cycle Algorithm, Multi-objective particle swarm optimization, Non-Dominated Sorting Genetic Algorithm- II

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