Michel Gourgand

A contribution to the stochastic flow shop scheduling problem

Abstract This paper deals with performance evaluation and scheduling problems in m machine stochastic flow shop with unlimited buffers. The processing time of each job on each machine is a random variable exponentially distributed with a known rate. We consider permutation flow shop. The objective is to find a job schedule which minimizes the expected makespan. A classification of works about stochastic flow shop with random processing times is first given. In order to solve the performance evaluation problem, we propose a recursive algorithm based on a Markov chain to compute the expected makespan and a discrete event simulation model to evaluate the expected makespan. The recursive algorithm is a generalization of a method proposed in the literature for the two machine flow shop problem to the m machine flow shop problem with unlimited buffers. In deterministic context, heuristics (like CDS [Management Science 16 (10) (1970) B630] and Rapid Access [Management Science 23 (11) (1977) 1174]) and metaheuristics (like simulated annealing) provide good results. We propose to adapt and to test this kind of methods for the stochastic scheduling problem. Combinations between heuristics or metaheuristics and the performance evaluation models are proposed. One of the objectives of this paper is to compare the methods together. Our methods are tested on problems from the OR-Library and give good results: for the two machine problems, we obtain the optimal solution and for the m machine problems, the methods are mutually validated.

A new heuristic procedure for the single-row facility layout problem

This paper is concerned with the single-row facility layout problem arising in Flexible Manufacturing Systems. We describe an iterative construction procedure such that the total time required by material handling systems to transport the part types between machines, is minimized. At the first stage, the procedure uses the particular structure of the problem to construct an initial feasible layout. For the next stages, it exploits the current feasible layout to construct another layout, that should be better than the previous layout. The proposed procedure is compared with existing methods for two sets of problems from the literature and is shown to be better.

A new heuristic based on a hypergraph representation for the tool switching problem

Abstract This paper introduces a new approach for the tool switching problem arising in flexible manufacturing systems. We formulate the problem using a particular hypergraph representation and we propose an efficient heuristic to solve it. The performance of the heuristic is compared with the heuristics developed by Crama et al. (The International Journal of Flexible Manufacturing Systems 6 (1994) 33–54). The results show that our heuristic performs well in terms of computational efficiency and solution quality.

A polynomial algorithm for multi-robot 2-cyclic scheduling in a no-wait robotic cell

This paper addresses the multi-robot 2-cyclic scheduling problem in a no-wait robotic cell where exactly two parts enter and leave the cell during each cycle and multiple robots on a single track are responsible for transporting parts between machines. We develop a polynomial algorithm to find the minimum number of robots for all feasible cycle times. Consequently, the optimal cycle time for any given number of robots can be obtained with the algorithm. The proposed algorithm can be implemented in O(N^7) time, where N is the number of machines in the considered robotic cell.

Markovian analysis for performance evaluation and scheduling in m machine stochastic flow-shop with buffers of any capacity

Abstract This paper deals with performance evaluation and scheduling in m machine static stochastic permutation flow-shop with buffers of any capacity (unlimited, limited or null). The processing time of a given job for a given machine is assumed to be exponentially distributed with a known rate. We propose a theorem which provides recursive scheme based on Markov chains and Chapman–Kolmogorov equations to compute the expected completion time of the last job for any sequence of jobs. This scheme is combined with metaheuristics based on simulated annealing for the scheduling problem. Computational results are given.

Genetic algorithms applied to workshop problems

We evaluate in this paper the qualities of stochastic algorithms, mainly genetic and simulated annealing-type algorithms, against heuristic methods, in the scheduling of workshops. We are particularly interested in flow-shops (minimizing makespan) and one machine schedules (minimizing total tardiness, or minimizing total flow time). Many numerical results for various samples are given, and our conclusions are supported by statistical tests. When the initial population is randomly generated, genetic algorithms are shown to be statistically less efficient than annealing-type algorithms, and better than heuristic methods. But, as soon as at least one good item (e.g.,heuristicallyfound) belongs to the initial population, genetic algorithms become as good, or better than annealing-type algorithms. The resolution methods we propose are evaluated and can be used for when scheduling more complicated real workshops.

Un environnement de modélisation pour le système d'information de la Supply Chain : Application au Nouvel Hôpital Estaing

This paper objective is to propose an approach and a modelling environment for the design of Supply Chain Information System and decisional tools. This software environment is an integrated set of tools and methods organized in order to model and evaluate Supply Chain performance. The interactions between Supply Chain entities and the problems complexity for Supply Chain Manager and Data Processing Specialist show that it is necessary to conceive and implement a modelling environment for Supply Chain. The method is applied on a real case study, the New Hospital Estaing.

A review of the static stochastic flow-shop scheduling problem

We consider a m machine stochastic flow-shop and a set of n jobs, with known release dates, to be processed. In this paper, we distinguish two types of random events: processing times (job processing times are random variables which follow a probability distribution function) and breakdowns (time between failure and repair time are random variables). The main purpose of this paper is to review and to propose a classification for some developments about the static stochastic flow-shop scheduling problem. We propose also an extension of the notation of Graham, Lawler, Lenstra, and Rinnooy Kan in order to take into account random events.

Multi-agent approach and stochastic optimization: random events in manufacturing systems

We propose a method to solve industrial problems and to take into account random events. It is called the triple coupling. It is based on stochastic algorithms, a simulation model and the multi-agents model of artificial intelligence. The method we propose is easy to use and allows us to take into account most of the constraints found in manufacturing systems. Experts look for solutions to increasing the capacity of production. But the production can be disturbed by random events experienced by the system. Industrial experts need schedules which prevent the consequences of random events. Minimizing such consequences is very important to increasing system delivery. Capital investment is often very high in factories and the cost of the investment goes on regardless of whether the resources are running or not. The multi-agent approach is used to determine schedules for which the consequences of random events are low, and a stochastic algorithm is proposed which permits us to optimize a random variable. We prove that this algorithm finds, with probability one, the schedule of the production for which the consequences of random events are the lowest. We propose to measure the consequences of random events using an influence ratio. Our approach has been used to study the consequences of random events in Peugeot sand foundries of Sept-Fons (France). A benchmark test is presented to prove the efficiency of our solution. For the Peugeot sand foundry of Sept-Fond, random events increase the production time by about 20% compared with the production time without any random events occurring. We have determined schedules of production for which the consequences of random events are about 0.5%.

Design of a monitoring environment for manufacturing systems management and optimization

Because manufacturing systems are highly computer-based, efficient monitoring tools are required to achieve various goals: The reactivity of the system, the optimization of the production rate and the quality follow-up. A monitoring environment is defined as a set including: a programmable controller for the physical system handling, a monitoring control system for the production management and a decisional system for the computation of control operations. This paper focuses on a generic framework to facilitate the design of any specific monitoring environment and to permit the integration of optimization tools. We propose a methodology that can be used either to build this generic framework, or to create an instance of a specific monitoring environment. The generic framework is a generic monitoring environment composed of three generic models: a target-shaped model for the static view, a SA/RT (Structured Analysis method for Real Time systems) model to identify clearly the environment processes as well as the data flows and the control flows they exchange, and a Petri net for the dynamic issues. An industrial application is presented concerning the monitoring of an industrial surface treatment line being implemented on site, for which specific optimization tools have been realized and integrated.