A Metaheuristic Based Approach for the Customer-Centric Perishable Food Distribution Problem

The CNRST has awarded H. El Raoui an excellence scholarship. D. Pelta acknowledges support from projects TIN2017-86647-P (Spanish Ministry of Economy, Industry, and Competitiveness. Including FEDER funds) and PID2020-112754GB-I00 (Spanish Ministry of Science and Innovation).High transportation costs and poor quality of service are common vulnerabilities in various logistics networks, especially in food distribution. Here we propose a many-objective Customercentric Perishable Food Distribution Problem that focuses on the cost, the quality of the product, and the service level improvement by considering not only time windows but also the customers’ target time and their priority. Recognizing the difficulty of solving such model, we propose a General Variable Neighborhood Search (GVNS) metaheuristic based approach that allows to efficiently solve a subproblem while allowing us to obtain a set of solutions. These solutions are evaluated over some non-optimized criteria and then ranked using an a posteriori approach that requires minimal information about decision maker preferences. The computational results show (a) GVNS achieved same quality solutions as an exact solver (CPLEX) in the subproblem; (b) GVNS can generate a wide number of candidate solutions, and (c) the use of the a posteriori approach makes easy to generate different decision maker profiles which in turn allows to obtain different rankings of the solutions.CNRSTSpanish Ministry of Economy, Industry, and Competitiveness TIN2017-86647-PEuropean Commission TIN2017-86647-PSpanish Government PID2020-112754GB-I0

Coupling soft computing, simulation and optimization in supply chain applications : review and taxonomy

Supply chain networks are typical examples of complex systems. Thereby, making decisions in such systems remains a very hard issue. To assist decision makers in formulating the appropriate strategies, robust tools are needed. Pure optimization models are not appropriate for several reasons. First, an optimization model cannot capture the dynamic behavior of a complex system. Furthermore, most common practical problems are very constrained to be modeled as simple tractable models. To fill in the gap, hybrid optimization/simulation techniques have been applied to improve the decision-making process. In this paper we explore the near-full spectrum of optimization methods and simulation techniques. A review and taxonomy were performed to give an overview of the broad field of optimization/simulation approaches applied to solve supply chain problems. Since the possibilities of coupling them are numerous, we launch a discussion and analysis that aims at determining the appropriate framework for the studied problem depending on its characteristics. Our study may serve as a guide for researchers and practitioners to select the suitable technique to solve a problem and/or to identify the promising issues to be further explored

Optimization of multimodal transportation problems

Cette thèse est une contribution aux travaux de recherche sur l’optimisation des problèmes du transport multimodal. Les principaux concepts clé de la multimodalité dans les réseaux du transport intermodal et l’état de l’art des travaux scientifique du domaine y sont présentés. Le problème de la localisation des terminaux du transport combiné est ensuite étudié. Nous proposons un algorithme génétique à codage mixte pour la résolution de ce problème et nous comparons nos résultats avec ceux de la littérature. Un ensemble de problèmes posés dans le cadre de notre travail sur le projet DCAS (Direct Cargo Axe Seine), porté par le Grand Port Maritime du Havre, y est décrit et modélisé par des outils de programmation mathématique. Ainsi, nous avons étudié le problème du transfert de navettes ferroviaires qui consiste à optimiser le transfert d’un ensemble de conteneurs entre des terminaux maritimes et un terminal multimodal. Ensuite, nous avons modélisé le problème d’ordonnancement des trains de grandes de lignes pour le placement sur les voies de la cour ferroviaire du terminal multimodal du Havre. Ces problèmes sont résolus en utilisant une approche combinée optimisation-simulation. Une première application est basée sur un algorithme génétique couplé avec la simulation multi agents pour l’affectation des voies aux trains. Une deuxième, consiste à optimiser la manutention des conteneurs lors d’un transbordement rail-rail en utilisant un algorithme de colonie de fourmis intégré dans le modèle de simulation et une stratégie de collaboration agents pour minimiser les temps d’attente des portiques et ainsi augmenter leurs productivités.This thesis is a contribution to research on the optimization of multimodal transport problems. The main key concepts of multimodality in the intermodal transportation networks and the state of the art of scientific works in the field are presented. The intermodal terminal location problem is then studied. We propose a genetic algorithm with mixed encoding for solving this problem and we compare our results with those of literature. A set of problems in the framework of our work on the project DCAS (Direct Cargo Axe Seine), carried by the Grand Port Maritime du Havre, are described and modeled by mathematical programming tools. Thus, we studied the problem of the transfer of rail shuttles which is to optimize the transfer of a set of containers between maritime terminals and a multimodal terminal. We then modeled the scheduling problem of freight trains for placement on rail tracks. These problems are solved by using combined optimization simulation approaches. A first application is based on a genetic algorithm coupled with the multi agent’s simulation. A second is to optimize a rail-rail transshipment of containers using an ant colony algorithm embedded in the simulation model and an agent’s collaboration strategy to minimize waiting times and increase cranes productivity

Optimisation des problèmes de transport multimodal

This thesis is a contribution to research on the optimization of multimodal transport problems. The main key concepts of multimodality in the intermodal transportation networks and the state of the art of scientific works in the field are presented. The intermodal terminal location problem is then studied. We propose a genetic algorithm with mixed encoding for solving this problem and we compare our results with those of literature. A set of problems in the framework of our work on the project DCAS (Direct Cargo Axe Seine), carried by the Grand Port Maritime du Havre, are described and modeled by mathematical programming tools. Thus, we studied the problem of the transfer of rail shuttles which is to optimize the transfer of a set of containers between maritime terminals and a multimodal terminal. We then modeled the scheduling problem of freight trains for placement on rail tracks. These problems are solved by using combined optimization simulation approaches. A first application is based on a genetic algorithm coupled with the multi agent’s simulation. A second is to optimize a rail-rail transshipment of containers using an ant colony algorithm embedded in the simulation model and an agent’s collaboration strategy to minimize waiting times and increase cranes productivity.Cette thèse est une contribution aux travaux de recherche sur l’optimisation des problèmes du transport multimodal. Les principaux concepts clé de la multimodalité dans les réseaux du transport intermodal et l’état de l’art des travaux scientifique du domaine y sont présentés. Le problème de la localisation des terminaux du transport combiné est ensuite étudié. Nous proposons un algorithme génétique à codage mixte pour la résolution de ce problème et nous comparons nos résultats avec ceux de la littérature. Un ensemble de problèmes posés dans le cadre de notre travail sur le projet DCAS (Direct Cargo Axe Seine), porté par le Grand Port Maritime du Havre, y est décrit et modélisé par des outils de programmation mathématique. Ainsi, nous avons étudié le problème du transfert de navettes ferroviaires qui consiste à optimiser le transfert d’un ensemble de conteneurs entre des terminaux maritimes et un terminal multimodal. Ensuite, nous avons modélisé le problème d’ordonnancement des trains de grandes de lignes pour le placement sur les voies de la cour ferroviaire du terminal multimodal du Havre. Ces problèmes sont résolus en utilisant une approche combinée optimisation-simulation. Une première application est basée sur un algorithme génétique couplé avec la simulation multi agents pour l’affectation des voies aux trains. Une deuxième, consiste à optimiser la manutention des conteneurs lors d’un transbordement rail-rail en utilisant un algorithme de colonie de fourmis intégré dans le modèle de simulation et une stratégie de collaboration agents pour minimiser les temps d’attente des portiques et ainsi augmenter leurs productivités

An Efficient Genetic Algorithm to Solve the Intermodal Terminal Location problem

The exponential growth of the flow of goods and passengers, fragility of certain products and the need for the optimization of transport costs impose on carriers to use more and more multimodal transport. In addition, the need for intermodal transport policy has been strongly driven by environmental concerns and to benefit from the combination of different modes of transport to cope with the increased economic competition. This research is mainly concerned with the Intermodal Terminal Location Problem introduced recently in scientific literature which consists to determine a set of potential sites to open and how to route requests to a set of customers through the network while minimizing the total cost of transportation. We begin by presenting a description of the problem. Then, we present a mathematical formulation of the problem and discuss the sense of its constraints. The objective function to minimize is the sum of road costs and railroad combined transportation costs. As the Intermodal Terminal Location Problemproblem is NP-hard, we propose an efficient real coded genetic algorithm for solving the problem. Our solutions are compared to CPLEX and also to the heuristics reported in the literature. Numerical results show that our approach outperforms the other approaches

ABM-GIS simulation for urban freight distribution of perishable food

Freight transport is essential to modern urban civilization. No urban area could exist without a powerful freight transport system. However, the distribution of perishable foods in urban areas is seen as a source of problems, due to traffic congestion, time pressures, and environmental impact. In this paper, an Agent-Based Model integrated with Geographic Information Systems (ABM-GIS) is designed for a time-dependent vehicle routing problem with time windows. This simulation model consists of determining the quickest routes to transport fresh products, estimating Vehicle kilometer traveled VKT and vehicle hour traveled VHT where speeds and travel times depend on the time of the day. Based on a case study, analyses of changes on traffic condition were conducted to get an insight into the impact of these changes on cost, service quality represented by the respect of time windows, and carbon emissions. The results reveal that traffic jams and restrictive time windows lead to additional cost, cause delays, and increase co2 emission. As for a short-term planning, time-dependent scheduling algorithm was proposed and assessed while extending time windows. Results have proved the potential saving in cost, travel time, and carbon emission

Intermodal Green p-Hub Median Problem with Incomplete Hub-Network

In the literature, hub-networks have often been modeled such as only one mode is considered for all transportation. Also, the consolidated demand flows are assumed to be transferred directly between each origin-destination hub pairs. The previous assumptions impose restrictions on the practical applications of such hub-networks. In fact, various transport modes are usually retained for freight transport, and intermodal terminals (e.g., rail terminals) may not realistically be fully connected. Thus, to assist decision makers, we investigate if the appropriate use of more eco-friendly transportation modes in incomplete networks may contribute to the accomplishment of the significant global reduction goals in carbon emissions. In this paper, we study the intermodal green p-hub median problem with incomplete hub-network. For each p located hub nodes, the hub-network is connected by at most q hub-links. The objective is to minimize the total transportation-based CO2 emission costs incurred through the road- and rail-transportation of each o-d demand flows. We present a MILP formulation for the studied problem and propose a novel genetic algorithm to solve it. A penalty cost is considered on solutions where train capacity is exceeded. Additionally, we present a best-path construction heuristic to generate the initial population. Furthermore, we develop a demand flows routing heuristic to efficiently determine the partition of demand flows in the incomplete road-rail network. And we implement novel crossover and mutation operators to produce new off-springs. Extensive computational experiments show that the proposed solution approach outperforms the exact solver CPLEX. Also, we perform a comparison between the unimodal and intermodal cases, and offer a discussion on the tuning of freight trains

Intermodal Green p-Hub Median Problem with Incomplete Hub-Network

In the literature, hub-networks have often been modeled such as only one mode is considered for all transportation. Also, the consolidated demand flows are assumed to be transferred directly between each origin-destination hub pairs. The previous assumptions impose restrictions on the practical applications of such hub-networks. In fact, various transport modes are usually retained for freight transport, and intermodal terminals (e.g., rail terminals) may not realistically be fully connected. Thus, to assist decision makers, we investigate if the appropriate use of more eco-friendly transportation modes in incomplete networks may contribute to the accomplishment of the significant global reduction goals in carbon emissions. In this paper, we study the intermodal green p-hub median problem with incomplete hub-network. For each p located hub nodes, the hub-network is connected by at most q hub-links. The objective is to minimize the total transportation-based CO2 emission costs incurred through the road- and rail-transportation of each o-d demand flows. We present a MILP formulation for the studied problem and propose a novel genetic algorithm to solve it. A penalty cost is considered on solutions where train capacity is exceeded. Additionally, we present a best-path construction heuristic to generate the initial population. Furthermore, we develop a demand flows routing heuristic to efficiently determine the partition of demand flows in the incomplete road-rail network. And we implement novel crossover and mutation operators to produce new off-springs. Extensive computational experiments show that the proposed solution approach outperforms the exact solver CPLEX. Also, we perform a comparison between the unimodal and intermodal cases, and offer a discussion on the tuning of freight trains