Laurent Deroussi

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This paper presents a Discrete Particle Swarm Optimization (DPSO) approach for the Multi-Level Lot-Sizing Problem (MLLP), which is an uncapacitated lot sizing problem dedicated to materials requirements planning (MRP) systems. The proposed DPSO approach is based on cost modification and uses PSO in its original form with continuous velocity equations. Each(More)
The studied problem is an extension of the well known job shop scheduling problem, in which the material handling system is considered as an overall part of the scheduling. This problem has many applications in flexible manufacturing systems. In these systems, jobs are transported from any machine to any other machine by automated guided vehicles. This(More)
This contribution presents a Discrete Particle Swarm Optimization (DPSO) approach for the Multi-Level Lot-sizing Problem (MLLP), which is an uncapacitated lot sizing problem dedicated to materials requirements planning (MRP) systems. The originality of the proposed DPSO approach is that it is based on cost modification. By the way, we use PSO for that it(More)
This paper deals with a new problem: the conjoint solution of machine assignment, job scheduling and vehicle scheduling in an FMS environment. This problem arises when we wish to reconsider the design of an FMS by evaluating its functioning with a higher model granularity. The experimental results obtained show that, in many cases, a simple reorganization(More)
Particle swarm optimization is an optimization method based on a simulated social behavior displayed by artificial particles in a swarm, inspired from bird flocks and fish schools. An underlying component that influences the exchange of information between particles in a swarm, is its topological structure. Therefore, this property has a great influence on(More)
This paper addresses the job and device scheduling problems in flexible manufacturing systems (FMS) using an automated guided vehicle system (AGV) by simultaneously dealing with material processing and transportation functions. The problem is solved using a two stage iterative approach which includes optimization and computer simulation. An iterative(More)
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