A model for distribution centers location-routing problem on a multimodal transportation network with a meta-heuristic solving approach

Abstract

Nowadays, organizations have to compete with different competitors in regional, national and international levels, so they have to improve their competition capabilities to survive against competitors. Undertaking activities on a global scale requires a proper distribution system which could take advantages of different transportation modes. Accordingly, the present paper addresses a location-routing problem on multimodal transportation network. The introduced problem follows four objectives simultaneously which form main contribution of the paper; determining multimodal routes between supplier and distribution centers, locating mode changing facilities, locating distribution centers, and determining product delivery tours from the distribution centers to retailers. An integer linear programming is presented for the problem, and a genetic algorithm with a new chromosome structure proposed to solve the problem. Proposed chromosome structure consists of two different parts for multimodal transportation and location-routing parts of the model. Based on published data in the literature, two numerical cases with different sizes generated and solved. Also, different cost scenarios designed to better analyze model and algorithm performance. Results show that algorithm can effectively solve large-size problems within a reasonable time which GAMS software failed to reach an optimal solution even within much longer times.

Location‐routing problem: a classification of recent research

“…As presented in Table 7, 67.12% of the classified articles developed at least one heuristics technique based on a metaheuristic framework. Figures 3 and 4 showed that the variants of simulated annealing (e.g., Yu and Lin, 2015;Ferreira and Queiroz, 2018) and genetic algorithm (e.g., Hiassat et al, 2017;Fazayeli et al, 2018aFazayeli et al, , 2018b are favored for single-objective LRP, while nondominated sorting genetic algorithm II (NSGA-II) (e.g., Wang et al, 2018b;Rabbani et al, 2019) and multiobjective particle swarm optimization (e.g., Tang et al, 2016;Vahdani et al, 2018a) are the preferred frameworks for MOLRP. This is admittedly reasonable since the aforementioned metaheuristics are historically classical (Sörensen et al, 2018) and commonly used as a benchmark for proposing a new algorithm.…”

A Time‐Dependent Fuzzy Programming Approach for the Green Multimodal Routing Problem with Rail Service Capacity Uncertainty and Road Traffic Congestion

“…The second is that they can provide an exact benchmark to verify the efficiency and accuracy of the heuristic algorithms [67]. Fazayeli et al [68] also highlighted that the development of an exact algorithm and the comparison between the algorithms will be their future work for the location-routing problem on a multimodal transportation network.…”

A Fuzzy Intercontinental Road-Rail Multimodal Routing Model With Time and Train Capacity Uncertainty and Fuzzy Programming Approaches

“…Recent research has enriched and expanded the LRP. Fazayeli et al [25] presented an LRP with time windows and fuzzy demand, involving a plan for depot establishment in customer regions. Our study has similarities with the LRP, involving multiple multimodal stations and the selection of multiple intercontinental train departure times.…”

“…In this problem, tractors depart from the DMS without containers. Constraints (20) to (25) show the relationship between the departure time in the DMS and the arrival time in the next station. According to the constraint where a tractor cannot cover two continuous routes without a semitrailer, the next station should be a DHS.…”

“…Constraints (22) and (23) are the cases for the median bound t 2 . Constraints (24) and (25) are the cases for the higher bound t 3 .…”