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

Abstract

This paper investigates a fuzzy intercontinental multimodal routing problem with uncertainties in time and train capacity. The transport network has characteristics of a long distance across continents and a road-rail multimodal routing, with four kinds of nodes and three kinds of arcs. Based on a variant of the vehicle routing problem, the tractor and semi-trailer routing problem is considered for the freight collection part for intercontinental trains. Additionally, the rail routing problem includes domestic direct trains, and intercontinental trains with hard time windows of departure. To make this problem more applicable to realworld circumstances, we describe two types of uncertainty parameters, including time and train uncertainties. Based on the transport conditions of stations, the time uncertainty is considered. Due to the multimodal transport stations' operating capacity and container collection methods, train capacity uncertainty is taken into account. Furthermore, we use solution methods based on the defuzzification approach to solve a fuzzy mixed integer linear programming model and generate a series of instances to verify the fuzzy model. We perform sensitivity analyses of the parameters. The results show that different quantities of intercontinental trains can change the performance by 10% to 20%. The objective function may decrease by more than 20% when the service level increases by 0.1. A sensitivity analysis of the time satisfaction confidence level also shows the trends of fuzzy time and the objective function. These analyses can give reference results about timeliness, transport resource allocation and other suggestions for the intercontinental multimodal transport routing problem. INDEX TERMS Intercontinental road-rail multimodal transport, routing problem, time uncertainty, capacity uncertainty, fuzzy programming.

Fuzzy Approaches and Simulation-Based Reliability Modeling to Solve a Road–Rail Intermodal Routing Problem with Soft Delivery Time Windows When Demand and Capacity are Uncertain

“…After developed by Zheng and Liu [60] in optimizing a vehicle routing problem with fuzzy travel times, the fuzzy chance-constrained programming method has been widely employed to model the fuzzy constraints in the current literature on the routing problem [6,[40][41][42] and network design problem [53,[61][62][63]. Based on one of the three fuzzy measures, i.e., possibility, necessity and credibility measures, a fuzzy constraint that represents a fuzzy event can be reformulated as a fuzzy chance constraint that ensures that the possibility, necessity, or credibility that the fuzzy event holds is not lower than a prescribed confidence level.…”

“…With the help of a stochastic routing model, this study found that the total cost of the transportation system increases with the level of capacity uncertainty. Lu et al [41] discussed a multimodal routing problem in the Euro-China Expressway, and the train capacity was formulated as uncertain parameters from a fuzzy viewpoint. Their study also revealed that the capacity uncertainty significantly influences the routing optimization, and the logistics cost increases with the confidence level in the fuzzy chance constraints improves.…”

“…Another issue in the current studies is on the fuzzy approaches that are applied to solve the fuzzy optimization problem. In the articles reviewed above, the authors usually use only one fuzzy approach to obtain the optimum solutions to the fuzzy intermodal routing problem, for which the fuzzy chance-constrained programming method is commonly employed, e.g., by Sun [6], Sun and Li [40], and Lu et al [41]. In a study by Fazayeli et al [8], the fuzzy ranking method was used by the authors.…”

On understanding the impacts of shared public transportation on urban traffic and road safety using an agent-based simulation with heterogeneous fleets: a case study of Casablanca city

“…Operational management of transportation is typically about optimizing the flow routing of commuters and goods within a set of constraints so as to reduce vehicle miles, travel time, and the number of required vehicles (Schenekemberg et al 2020). In addition, the goal is to determine the optimal routes for fleets considering a level of service performance (Lu et al 2020). This implies ensuring a capacity to carry in good conditions given a type of traffic, in terms of availability, throughput, transmission delays while considering the routing limitations (Ouadi et al 2020b).…”

Türkiye’nin Demiryolu Yük Taşımacılığı Talebinin Zaman Serisi Analizi ile Tahmini

“…Yerel demiryolu ağları ile karşılaştırıldığında, uluslararası faaliyette bulunan demiryolu hatları için çeşitli belirsizlikler söz konusudur. Bunlar genel olarak farklı ülkelerdeki gümrük politikaları, ulaşım tıkanıklığı, kargo acenteleri tarafından konteyner montajı ve kalkış istasyonları seviyesi olarak gruplandırılabilir (Lu, Lang, Sun ve Li, 2020). Buna rağmen Türkiye Cumhuriyeti demiryolu taşımacılık sektörü özellikle yük taşımacılığında Avrupa ve Asya küresel ekonomilerinin taşımacılık hizmetleri içinde gelişmekte olan bir sektördür.…”