Youssef Benadada

A metaheuristic approach for solving the airline maintenance routing with aircraft on ground problem

In the airline industry, the Aircraft Maintenance Routing (AMR) problem has been one of the great successes of operations research. The AMR problem is to determine a particular route for each aircraft to undergo different levels of maintenance checks. The objective is to minimize the total maintenance costs. In this study, our aim is to present a mathematical formulation for the AMR problem which takes into account the case of Aircraft On Ground (AOG). We develop solution approaches based on Particle Swarm Optimization algorithm and Genetic algorithm for solving the problem. The results show the effectiveness of this solution in reducing computational time.

Particle swarm optimization with a mutation operator for solving the preventive aircraft maintenance routing problem

Aircraft Maintenance Routing (AMR) is one of the major optimization problems in the airline industry. In this study, we present a mathematical formulation for the daily AMR problem which aims to minimize the risk of both scheduled and non-scheduled maintenance costs. Exact methods may fail to deal with such problems. Our contribution is then to examine the use of an improved particle swarm optimization (PSO) algorithm by a uniform mutation operator for solving this probabilistic problem. Computational results show that our hybrid approach gives competitive results comparing to the native binary PSO.

Particle swarm optimization algorithm for solving airline crew scheduling problem

In air transport, the cost related to crew members presents one of the most important cost supported by airline companies. The objective of the crew scheduling problem is to determine a minimum-cost set of pairings so that every flight leg is assigned a qualified crew and every pairing satisfies the set of applicable work rules. In this paper, we propose a solution for the crew scheduling problem with Particle Swarm Optimization (PSO) algorithm, this solution approach is compared with the Genetic Algorithm (GA) for both crew pairing and crew assignment problems which are the two part of crew scheduling problem.

Ant colony algorithm for the multi-depot vehicle routing problem in large quantities by a heterogeneous fleet of vehicles

Abstract This article describes a heuristic method based on an ant colony algorithm for the multi-depot vehicle routing problem in large quantities by a heterogeneous fleet of vehicles. Test results on different problem instances are presented and compared with those obtained by CPLEX and by a previous constructive heuristic.

Evolutionary algorithm for a Green vehicle routing problem with multiple trips

This paper deals with a variant of vehicle routing problem where vehicles are allowed to take more than one route during the working day. The depreciation of the vehicle may be a bad investment for green transportation because it could generate more emissions. Hence, it is necessary to satisfy green transportation requirements by reducing the CO2 emissions from road transportation. The objective is to optimize the amount of greenhouse gas emissions. A restricted fleet size is used to serve demands, so the vehicles could exceed the time horizon. It is subject also to minimize the maximum overtime to find feasible solutions. A mathematical model has been proposed for the Green Vehicle Routing Problem with multiple trips (GVRPM). An evolutionary algorithm has been developed to solve it by combining a genetic algorithm with a local search procedure. The effectiveness of our approach is tested on a set of benchmarks. Comparing with existing algorithm, our approach shows competitive performance and contributes many new best solutions.

Preventive Maintenance with Aircraft on Ground Case Consideration, and Airline Crew Scheduling Problem: A Meta-Heuristics Approaches

This study formulates an innovative aircraft preventive maintenance model by taking into account the aircraft on ground (AOG) problem. The proposed model is solved by using binary particle swarm optimization (BPSO) and Genetic Algorithm (GA). It also proposes a methodology solution based on BPSO and GA to solve the airline crew scheduling problem. Additionally, a study of computational results is given to improve the quality of the solutions and the performance of the proposed algorithms.

Multi-capacitated Location Problem: A New Resolution Method Combining Exact and Heuristic Approaches Based on Set Partitioning

In this paper, we present one generalization of the famous capacitated p-median location problem, called budget constraint multi-capacitated location problem (MCLP). This new generalization is characterized by allowing each facility to be used with different capacity levels. The MCLP solution can be represented as a set of disjoint clusters (pair of one facility and a subset of customers). Creating these clusters satisfies implicitly some constraints of the problem. In this work, we present the new formulation of the MCLP based on set partitioning, then we suggest an adapted solving method, which will be called NFF (Nearest Facility First). This new method can be used in two ways: as a heuristic by taking only the first solution found or exact approach when waiting finished the execution. Computational results are presented at the end using instances that we have created under some criteria of difficulties or adapted from those of p-median problems available in literature. The NFF method provides very good results for low and medium difficulty instances, but it is less effective for the more complex ones. To remedy this problem, the method will be supplemented by column generation approach.

Adapted Bin-Packing Algorithm for the Yard Optimization Problem

Given the importance of the Maritime transportation to move goods between continent, optimizing their processes becomes the objective of many types of research. In this paper, we present a new model of the yard optimization problem which contains three important components: unloading/loading, transfer and storage process. Our proposed method called Adapted Bin Packing Algorithm for the yard optimization problem (ABPAYOP) focus on using the approach of the Bin packing algorithm to build Bins (free positions in the yard, subgroup of containers) this will be a generalization of the container stacking problem as it will include the use of yard cranes, quay cranes and internal trucks. the ABPAYOP solutions can be represented as a set of disjoint clusters satisfying of given number of constraints (yard bays, subgroup of containers). In this work, we present the new formulation of the yard optimization problem, then we will apply our heuristic ABPAYOP to solve it. Computational results are presented at the end using instances created and adapted to the ones existing in the literature. Our results illustrate the performance of the applied method for the medium and big instances.

Ant Colony system approach for dynamic vehicles routing problem multi tours

In reality, many transport companies give the possibility to use an overtime to complete the service. However, this concept is less treated in the literature of the VRP in general and of the DVRP in particular. This paper presents an approach, based on the ant colony system, which treats this concept for multi-tours DVRP. A mathematical model of this variable is then proposed. Computational results are given in a version without overtime in addition to some first results of the version with overtime.

Optimization and Simulation Approach for Empty Containers Handling

Container handling problems at the container terminals are NP-hard problems. In this paper, we propose a new handling operation’s design and simulation of empty containers, taking into account the interrelated activities at the container terminal. This simulation have been built using a doubled trailer. It moves containers from quayside to yard side or the opposite depending on the flow in container terminal, and it is used to optimize the cycle time and to improve the efficiency of the other equipment. Our interest is to test this new model first for empty containers. The proposed model is applied on a real case study data of the container terminal at Tanger Med port. This new design was developed using Arena software and verifying the strength of materials constraint for the loaded containers. The computational results show the effectiveness of the proposed model, where the cycle time of the port equipment have been reduced by -58%, and the efficiency has been increased where +47% of the moves in container terminal was achieved.