The route problem of multimodal transportation with timetable: stochastic multi-objective optimization model and data-driven simheuristic approach. Issue 2 (12th July 2021)

• Record Type: Journal Article
• Authors: Peng, Yong; Luo, Yi Juan; Jiang, Pei; Yong, Peng Cheng
• Is Part Of: Engineering computations. Volume 39:Issue 2(2022)
• Page Start: 587
• Page End: 608
• Publication Date: 2021-07-12
• Subjects: Multimodal transportation -- Simulation optimization -- Simheuristic -- Data-driven -- NSGA-II; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-10-2020-0587
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: Distribution of long-haul goods could be managed via multimodal transportation networks where decision-maker has to consider these factors including the uncertainty of transportation time and cost, the timetable limitation of selected modes and the storage cost incurred in advance or delay arriving of the goods. Considering the above factors comprehensively, this paper establishes a multimodal multi-objective route optimization model which aims to minimize total transportation duration and cost. This study could be used as a reference for decision-maker to transportation plans. Design/methodology/approach: Monte Carlo (MC) simulation is introduced to deal with transportation uncertainty and the NSGA-II algorithm with an external archival elite retention strategy is designed. An efficient transformation method based on data-drive to overcome the high time-consuming problem brought by MC simulation. Other contribution of this study is developed a scheme risk assessment method for the non-absolutely optimal Pareto frontier solution set obtained by the NSGA-II algorithm. Findings: Numerical examples verify the effectiveness of the proposed algorithm as it is able to find a high-quality solution and the risk assessment method proposed in this paper can provide support for the route decision. Originality/value: The impact of timetable on transportation duration is analyzed and making a detailed description in the mathematical model. The uncertain transportation duration and cost are represented by random number that obeys a certain distribution and designed NSGA-II with MC simulation to solve the proposed problem. The data-driven strategy is adopted to reduce the computational time caused by the combination of evolutionary algorithm and MC simulation. The elite retention strategy with external archiving is created to improve the quality of solutions. A risk assessment approach is proposed for the solution scheme and in the numerical simulation experiment.