The route problem of multimodal transportation with timetable: stochastic multi-objective optimization model and data-driven simheuristic approach

Abstract

PurposeDistribution 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/approachMonte 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.FindingsNumerical 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/valueThe 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.

Geometric routing model as a new approach in mathematical modeling of wagon flows rational distribution

“…Usually, various modeling methods are tried to be applied to the analysis of complex systems in these nondeterministic environments. In [10], an optimization model for multimodal transportation was developed, in which the total time of transportation and its cost are minimized. In [11], the aim of the research is to find the optimal transportation scheme under the assumption of the uncertainty of the possible multimodal transport network used.…”

Multi-Objective Optimization for Multimodal Transportation Routing Problem with Stochastic Transportation Time Based on Data-Driven Approaches

“…Ziaei and Jabbarzadeh [26] proposed a robust counterpart optimization approach to deal with the uncertainties of accident probabilities, emission factors, and costs of establishing transfer points with polyhedral uncertainty sets in their study on the similar issue of location routing decision-making. Monte Carlo simulation is often used to study the transportation time uncertainty in the multimodal transportation problem, but it may significantly increase calculation time [27,41].…”

“…Lou et al [40] combined Tabu Search Algorithm (TSA) with Support Vector Machine (SVM) and used the data-driven optimization process to increase the algorithm efficiency. Peng et al [41] derived a simple datadriven approach to solving the stochastic multi-objective multimodal transportation routing problem with a schedule. They applied historical data to the iterative process to reduce the calculation times, which overcomes the time-consuming problem of the Monte Carlo simulation.…”

Simheuristics: An Introductory Tutorial

“…Hence, the range of optimization areas for simheuristics was expanded to waste collections services in smart cities, according to work led by Yazdani et al (2021), who made a painstaking implementation of a simheuristic procedure to solve a set of waste collection problems in Sydney (Australia). Recently, Peng et al (2022) analyzed the management of multimodal transportation networks in order to deliver long-haul merchandise considering uncertainty in transportation cost and time. All these applications can be completed with agile optimization of Unmanned Aerial Vehicles routes for different logistic purposes , which reveals a fruitful future for simheuristics in urban and interurban mobility.…”