Jaouad Boukachour

Improved Ant Colony Algorithm to Solve the Aircraft Landing Problem

Scheduling aircraft landing is a complex task encountered by most of the control towers. In this paper, we study the aircraft landing problem (ALP) in the multiple runway case. We present in the first part, a mathematical formulation of the problem with a linear and nonlinear objective function. In the second part, we consider the static case of the problem where all data are known in advance and we present a new heuristic for scheduling aircraft landing on a single runway, this heuristic is incorporated into an ant colony algorithm to solve the multiple runway case.

The Dual-Ants Colony

We deal with the partial FJSSP with Preventives Maintenances to optimize the Makespan.We propose an adapted MIP model and a bi-level Disjunctive/Conjunctive graph.We develop a novel hybrid ACO approach with a dynamic history and dual tasks ants.Our approach provides an integration of a local search and a set of dispatching rules.Experiments carried out; we introduce new instances and a set of performance measures. Due to their importance in the fields of both manufacturing industries and operations research, production scheduling and maintenance planning have received considerable attention both in academia and in industry. This paper investigates the Flexible Job Shop Scheduling Problem (FJSSP) with machine unavailability constraints due to Preventive Maintenance (PM) activities, under the objective of minimizing the makespan. We propose two new formulations: the first one in the form of a Mixed Integer Nonlinear Program (MINLP) and the second corresponding to a bi-level disjunctive/conjunctive graph. To deal with this variant FJSSP with PMs (FJSSP/PM), we develop the Dual-Ants Colony (DAC), a novel hybrid Ant Colony Optimization (ACO) approach with dynamic history, based on an ants system with dual activities. This optimization provides an effective integration of a local search and a set of dispatching rules. Three regular performance measures are also implemented. To show the efficiency of the DAC algorithm, computational experiments are carried out on a large range of well-known benchmarks from the literature and others newly generated. We address first the classical JSSP case, then the flexible FJSSP for partial flexibility. Finally, we study the case with preventive maintenance based on well-chosen PM periods. Obtained results demonstrate the viability and performance of the proposed approach, especially for the FJSSP/PM.

Optimization-Simulation for Maritime Containers Transfer

This paper proposes a simulation model to analyze the handling and the transfer system of containers in Le Havre seaport. The decision variables of simulation are determined by using the CPLEX optimization software. The goal is to determine the least expensive strategy for the transfer of a set of containers between container terminals. The simulation model is developed using an object-oriented approach and Flexsim CT simulation software. The objective is to obtain an efficient operating process for the multimodal terminal of Le Havre which is an intermediate platform for transferring containers (collection and delivery) by rail and river (trains and barges). The goal is to evaluate the performance of the containers transfer by rail shuttles between the future multimodal terminal and the maritime terminals. The aim is to analyze and to evaluate performance indicators of port logistics chain (costs, resource occupancy rate, service rate), and to test several management strategies.

Building an Expert-System for Maritime Container Security Risk Management

Until lately, transportation risk management has mostly dealt with natural or man-made accidental disasters. The September 11th tragedy has made transportation operators, as well as shippers and public authorities, aware of a new type of risk, man-made and intentional. Securing global transportation networks has become an important concern for governments, practitioners and academics. In the current time-based competition context, securing transportation operations should not be sought at the expense of time effectiveness in physical and informational flow processing. In this paper, the authors describe a project for the design of an expert-system dedicated to maritime container security risk management, present a literature review on decision-support systems dedicated to transportation risk management, and discuss the various steps of expertise modeling in a transportation risk management context.

Hybridation de l’algorithme de colonie de Fourmis avec l’algorithme de recherche à grand Voisinage pour la résolution du VRPTW statique et dynamique

Résumé - Le problème de tournées de véhicules (Vehicle Routing Problem – VRP) est l’un des problèmes d’optimisation combinatoire les plus étudiés dans le domaine du transport. Il consiste à visiter des clients à partir d’un ou de plusieurs dépôts au moyen d’une flotte de véhicules, avec un coût minimal. Le VRP est un problème NP-complet, pour lequel il n’existe à l’heure actuelle aucun algorithme connu capable de le résoudre en un temps polynomial; c’est la raison fondamentale pour laquelle les métaheuristiques ont été fortement sollicitées. Dans ce papier, nous étudions le problème VRP avec fenêtre de temps (VRP with Time Windows – VRPTW), auquel on impose une fenêtre de temps dans laquelle la livraison doit être effectuée. Nous nous intéressons au cas statique et au cas dynamique dans lequel nous prenons en compte l’apparition de nouveaux clients au cours du temps. Après avoir présenté les caractéristiques propres au problème dynamique traité dans ce papier, nous proposons une approche de résolution basée sur l’utilisation de l’algorithme d’optimisation par colonie de fourmis (Ant Colony Optimization – ACO) hybridé avec l’algorithme de recherche à grand voisinage (Large Neighborhood Search – LNS) dans le cas statique. Ensuite, nous adaptons cette approche afin de résoudre le problème dans un contexte dynamique. Finalement, nous présentons les résultats numériques qui confirment la pertinence de l’approche que nous développons dans cet article.

Ant Colony Algorithm for Just-in-Time Job Shop Scheduling with Transportation Times and Multirobots

Handling rapidly evolving technology and almost daily changes in demand and customer satisfaction, while maintaining competitiveness in a highly competitive environment, requires good coordination and planning of both production and logistics activities on the shop floor, namely: machines and tools. The goal is to optimize costs and reduce delivery lead times in order to provide the customer just in time; we focus on the job shop scheduling problem (JSSP), which is one of the most complex problems encountered in real shop floor. In this paper, we study a generalized (JSSP) including transportation times and a set of additional constraints on the number of transporter vehicles and their multiple transfer capabilities and also on the limited capacity of input/output of machines. The objective is to minimize in one hand tardiness and earliness penalties on delays and advances compared to the lead-time delivery of finished jobs and on the other hand the number of empty moves of transporter vehicles.

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 problem is NP-hard, we propose an efficient real coded genetic algorithm for solving. 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. A realistic cost approximation of intermodal transportation is used to validate the experimental results.

Gestion du transfert interne de conteneurs : le cas du port du Havre

RÉSUMÉ L’objectif principal de cette étude est de déterminer et d’évaluer les indicateurs de performance du transfert de conteneurs par navettes ferroviaires entre le futur terminal multimodal et les terminaux maritimes du port du Havre. Différents modes de transfert ont été comparés par rapport à la minimisation des retards, des coûts et des émissions de CO2. En se fondant sur la méthode ECOGRAI, nous avons proposé une démarche ECOGRAISIM ; les variables de décision et les indicateurs de performance sont déterminés à l’aide de la méthode ECOGRAI et sont évalués par simulation.

Production, supply, and traffic systems: A unified modelling using Petri Nets

The modelling of production, supply, and distribution systems or quite simply supply chain constitutes an object of search always for topicality, because of its great complexity due mainly to the mutual interaction of multi-natural phenomena and are subject to numerous constraints and requirements. This multi-fields character returns the analysis of these systems often to realize according to each discipline in an isolated way, thus masking the interactions between the fields and complexes the total analysis of this supply chain. From where the idea to introduce new approaches particularly conceived to model all fields according to a whole coherent and unified, and brings legibility in term of interactions analysis, which gives a clearer global vision of chain. It is in this context that we propose to apply the Petri Net in the supply chain while we extended the Petri Net traffic model to study the dynamic behavior of physical flow on supply chain. We have transposed the concepts developed for the traffic to the supply chain.

Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU

This chapter presents a parallel GPU implementation for solving the Uncapacitated Single Allocation p-Hub Median Problem with genetic algorithm. Starting from an initial solution that locates hubs at center nodes, our GPU implementation quickly reaches optimal or near-optimal solutions. It efficiently exploits the computing power of the GPU and outperforms, in cost and in computing time, the other approaches proposed in the literature for this problem. We compare it to the best recent solutions on the benchmarks up to 1000 nodes and on large instances up to 6000 nodes generated by us. Moreover, we solved instance problems so far unsolved.