Modelling and solving a bi-objective intermodal transport problem of agricultural products

Abstract

During the past few years, transportation of agricultural products is increasingly becoming a crucial problem in supply chain logistics. In this paper, we present a new mathematical formulation and two solution approaches for an intermodal transportation problem. The proposed bi-objective model is applied to the transportation of agricultural products from Morocco to Europe to minimise both the transportation cost either in the form of uni-modal or intermodal, as well as the maximal overtime to delivery products. The first solution approach is based on a non-dominated sorting genetic algorithm improved by a local search heuristic and the second one is the GRASP algorithm (Greedy Randomised Adaptive Search Procedure) with iterated local search heuristics. They are tested on theoretical and real case benchmark instances and compared with the standard NSGA-II. Results are analysed and the efficiency of algorithms is discussed using some performance metrics.

A Visual Knowledge Map Analysis of Cross-Border Agri-Food Supply Chain Research Based on CiteSpace

“…According to the MRIO model, the technology and scientific and technological means used in the cross-border agri-food supply chain are balanced with the raw materials and service requirements of each link so that each link can be integrated into the supply chain in the most appropriate state for the whole [80][81][82][83][84]. Deureret et al [85] used the LCA method to study the contribution of major life cycle stages of kiwifruit cross-border supply chain shipping, to reduce the carbon footprint of the kiwifruit cross-border supply chain; (2) Genetic algorithms (NSGA) [86][87][88], ant colony algorithms (ACO) [89,90], particle swarm optimization (PSO) [91][92][93][94], and other heuristic algorithms have attracted more and more attention in the research of cross-border agri-food supply chain. Because of the complexity and dynamics of each link of the cross-border agri-food supply chain, many scholars have begun to apply heuristic algorithm thinking to research in this field.…”

Optimization of the Collaborative Hub Location Problem with Metaheuristics

“…To solve our studied problem, it is impossible to use traditional solutions. Generally, each problem has its own data structure and individuals, in the literature, are represented either by vectors [39] or by tables [47,48]. However, in this work, a candidate individual was represented by a set of tables; each of which showed a given period.…”

“…In each iteration, a new solution S is created by a heuristic perturbation on the current solution S. The neighborhood solution S becomes a new solution if the change in an objective function is improved (i.e., ∆ = f (S ) − f (S) ≤ 0 in our case, since it is a minimization problem). In the case where this change is not improved, the neighborhood solution becomes a new solution if r, which is a random number in the interval [0, 1], is less than or equal to an acceptance probability p acc (r ≤ p acc ) (represented by (47)). Thus, it is possible to find a global optimal solution from a local optimum.…”

Modelling the Incomplete Intermodal Terminal Location Problem

“…They proposed a program reformulation of the problem and test it using randomly generated data set. Abbassi et al (2018) proposed a bi-objective model for transportation of agricultural products from Morocco to Europe and developed heuristics to solve it. Recently, Mostert et al (2018) proposed a bi-objective mathematical model minimizing the transportation and environmental costs objectives.…”