Ahmed El Hilali Alaoui

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.

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.

Optimization/simulation in the supply chain context: a review

In an increasingly competitive environment, companies are led to develop their competitiveness, a development that requires the efficient management of the supply chain. Supply chain optimization has become a major challenge. Despite the Information Technologies Solutions (ITS) available, decisions about how to plan a companys supply chain still hard to make. This is due to the complexity of problems in a logistics network and to their stochastic aspect. Therefore, combined Simulation/Optimization techniques were widely used to cope with this stochasticity. This paper is a preliminary attempt to review some applications of optimization simulation in a supply chain context, state of art various algorithms and simulation tools used in this field.

Allocation of Static and Dynamic Wireless Power Transmitters Within the Port of Le Havre

The port of Le Havre, “Grand Port Maritime du Havre (GPMH)”, is the first port in France and the fifth in the Europe’s top port list in terms of container volume. This massification in container traffic has generated a large use of trucks that are a source of diesel pollution. To address the greenhouse gas emissions related to port’s last mile logistic, a collaborative relationships are established between different parties of the port. This collaboration aims to create projects that can improve the air quality such as replacing conventional trucks by electric ones. In this context, our study aims to propose a strategic allocation of the infrastructure of charge for electric trucks. To this end, we adapt the technology Wireless Power Transfer that permit to an electric truck to charge its battery statically in a set of fixed nodes (breakpoints) or dynamically in a set of segments of the route during the electric trucks mobility. To model this problem, we propose an integer non-linear programming formulation. Afterward, we investigate the effectiveness of the population based algorithm particle swarm optimization to determine the efficient allocation.

Optimal allocation of wireless power transfer system for electric vehicles in a multipath environment

This study follows the new concept of the OnLine Electric Vehicle (OLEV) using the wireless charging technology as one of contactless electric power transfer technologies for Electric Vehicle (EV) charging. This electric transport system, which has been developed by Korea Advanced Institute of Science and Technology (KAIST), allows vehicles to charge their battery while in motion. In this paper, we used integer programming to find the minimum total investment cost that considers the OLEV system with a multiple route; afterwards, we introduced the resolution of this problem with the particle swarm optimization as the particles showed their robustness against nonlinear optimization problems.

Multiobjective container barge transport problem

The need for optimization using different methods of operations research in container terminal operations has become even more important in recent years. The mean of this paper is to study the Traveling Salesman Problem (TSP) combined with the Container Stowage Problem (CSP) that is we consider one barge where the containers are stowed in stacks with a predefined order according to their fragility, weight and stability, such that a number of containers must be removed in order to reach containers below them. In other words, only the upper-most container of each stack of the barge can be directly accessed, thus, shifting containers within the barge becomes necessary if the target container is located below other ones. Our aim is to seek an optimal rotation that takes account of unloading and reloading of the containers placed in the barge. We propose a mathematical model as a binary linear program, and then we adapt the ant colony metaheuristic to solve it.