Optimization Models and Solution Algorithms for Freight Routing Planning Problem in the Multi-Modal Transportation Networks: A Review of the State-of-the-Art

Abstract

Abstract:With the remarkable development of international trade, global commodity circulation has grown significantly. To accomplish commodity circulation among various regions and countries, multi-modal transportation scheme has been widely adopted by a large number of companies. Meanwhile, according to the relevant statistics, the international logistics costs reach up to approximate 30-50% of the total production cost of the companies 1 . Lowering the transportation costs has become one of the most important sources for a company to raise profits and maintain competitiveness in the global market. Thus, how to optimize freight routes selection to move commodities through the multi-modal transportation network has gained great concern of both the decision makers of the companies and the multi-modal transport operators. In this study, we present a systematical review on the multi-modal transportation freight routing planning problem from the aspects of model formulation and algorithm design. Following contents are covered in this review: (1) distinguishing the formulation characteristics of various optimization models; (2) identifying the optimization models in recent studies according to the formulation characteristics; and (3) discussing the solution approaches that are developed to solve the optimization models, especially the heuristic algorithms.

Bi-objective optimization for multi-modal transportation routing planning problem based on Pareto optimality

“…Thus its routing planning problem has been paid great attention in the research field (Sun, Lang & Wang, 2015). Generally, this problem is addressed by two kinds of studies.…”

Bi-Objective Modelling for Hazardous Materials Road–Rail Multimodal Routing Problem with Railway Schedule-Based Space–Time Constraints

“…The combination of Equations (15) and (16) ensures that each hazardous materials flow is non-bifurcated [31]. $\underset{h\in {\mathrm{sans-serif\delta }}^{-}\left(i\right)}{140\%true\sum }\underset{r\in {\mathrm{sans-serif\Omega }}_{hi}}{140\%true\sum }{X}_{hir}^k+\underset{j\in {\mathrm{sans-serif\delta }}^{+}\left(i\right)}{140\%true\sum }\underset{s\in {\mathrm{sans-serif\Omega }}_{ij}}{140\%true\sum }{X}_{ijs}^k\le 1\forall k\in K,\forall i\in N\backslash \left\{o_k,d_k\right\}$…”

How Uncertain Information on Service Capacity Influences the Intermodal Routing Decision: A Fuzzy Programming Perspective

“…The intermodal routing decision aims at selecting the best routes to move containers from their origins to destinations through an intermodal service network to fulfill customers' transportation demands. With the rapid development and promotion of intermodal transportation in international trade, the intermodal routing problem is now at the forefront of transportation planning [3].…”

“…Therefore, the intermodal service network is capacitated. Thus the essence of the intermodal routing decision is the effective utilization of the limited resources of transportation services [3]. The service capacity is an important issue that should not be neglected during the routing decision.…”