Meriem Ennigrou

New local diversification techniques for flexible job shop scheduling problem with a multi-agent approach

The Flexible Job Shop problem is among the hardest scheduling problems. It is a generalization of the classical Job Shop problem in that each operation can be processed by a set of resources and has a processing time depending on the resource used. The objective is to assign and to sequence the operations on the resources so that they are processed in the smallest time. In our previous work, we have proposed two Multi-Agent approaches based on the Tabu Search (TS) meta-heuristic. Depending on the location of the optimisation core in the system, we have distinguished between the global optimisation approach where the TS has a global view on the system and the local optimisation approach (FJS MATSLO) where the optimisation is distributed among a collection of agents, each of them has its own local view. In this paper, firstly, we propose new diversification techniques for the second approach in order to get better results and secondly, we propose a new promising approach combining the two latter ones. Experimental results are also presented in this paper in order to evaluate these new techniques.

Combining Tabu Search and Genetic Algorithm in a Multi-agent System for Solving Flexible Job Shop Problem

The Flexible Job Shop problem (FJSP) is an important extension of the classical job shop scheduling problem, in that each operation can be processed by a set of resources and has a processing time depending on the resource used. The objective is to minimize the make span, i.e., the time needed to complete all the jobs. This works aims to propose a new promising approach using multi-agent systems in order to solve the FJSP. Our model combines a local optimization approach based on Tabu Search (TS) meta-heuristic and a global optimization approach based on genetic algorithm (GA).

Scheduling problems under learning effects: classification and cartography

Traditionally, the processing times of jobs are assumed to be fixed and known throughout the entire process. However, recent empirical research in several industries has demonstrated that processing times decline as workers improve their skills and gain experience after doing the same task for a long time. This phenomenon is known as learning effects. Recently, several researchers have devoted a lot of effort on scheduling problems under learning effects. Although there is increase in the number of research in this topic, there are few review papers. The most recent one considers solely studies on scheduling problems with learning effects models prior to early 2007. For that, this paper focuses on reviewing the most recent advances in this field. First, we attempt to present a concise overview of some important learning models. Second, a new classification scheme for the different model of scheduling under learning effects is proposed and discussed. Next, a cartography showing the relation between some well-known models is proposed. Finally, our viewpoints and several areas for future research are provided.

Particle Swarm Optimization Combined with Tabu Search in a Multi-agent Model for Flexible Job Shop Problem

Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine and has a processing time depending on the machine used. The objective is to minimize the makespan, i.e., the total duration of the schedule. In this article, we propose a multi-agent model based on the hybridization of the tabu search (TS) method and particle swarm optimization (PSO) in order to solve FJSP. Different techniques of diversification have also been explored in order to improve the performance of our model. Our approach has been tested on a set of benchmarks existing in the literature. The results obtained show that the hybridization of TS and PSO led to promising results.

Diversifying TS using GA in Multi-agent System for Solving Flexible Job Shop Problem

No doubt, the flexible job shop problem (FJSP) has an important significance in both fields of production management and combinatorial optimization. For this reason, FJSP continues to attract the interests of researchers both in academia and industry. In this paper, we propose a new multi-agent model for FJSP. Our model is based on cooperation between genetic algorithm (GA) and tabu search (TS). We used GA operators as a diversification technique in order to enhance the searching ability of TS. The computational results confirm that our model MAS-GATS provides better solutions than other models.

Flexible Job-shop Scheduling Problem with Sequence-dependent Setup Times using Genetic Algorithm

Job shop scheduling problems (JSSP) are among the most intensive combinatorial problems studied in literature. The flexible job shop problem (FJSP) is a generalization of the classical JSSP where each operation can be processed by more than one resource. The FJSP problems cover two difficulties, namely, machine assignment problem and operation sequencing problem. This paper investigates the flexible job-shop scheduling problem with sequence-dependent setup times to minimize two kinds of objectives function: makespan and bi-criteria objective function. For that, we propose a genetic algorithm (GA) to solve this problem. To evaluate the performance of our algorithm, we compare our results with other methods existing in literature. All the results show the superiority of our GA against the available ones in terms of solution quality.

A self-adaptive evolutionary algorithm for solving flexible job-shop problem with sequence dependent setup time and learning effects

Flexible job shop problems (FJSP) are among the most intensive combinatorial problems studied in literature. These latters cover two main difficulties, namely, machine assignment problem and operation sequencing problem. To reflect as close as possible the reality of this problem, two others constraints are taken into consideration which are: (1) The sequence dependent setup time and (2) the learning effects. For solving such complex problem, we propose an evolutionary algorithm (EA) based on genetic algorithm (GA) combined with two efficient local search methods, called, variable neighborhood search (VNS) and iterated local search (ILS). It is well known that the performance of EA is heavily dependent on the setting of control parameters. For that, our algorithm uses a self-adaptive strategy based on: (1) the current specificity of the search space, (2) the preceding results of already applied algorithms (GA, VNS and ILS) and (3) their associated parameter settings. We adopt this strategy in order to detect the next promising search direction and maintain the balance between exploration and exploitation. Computational results show that our algorithm is more effective and robust with respect to other well known effective algorithms.

A self-adaptive hybrid algorithm for solving flexible job-shop problem with sequence dependent setup time

Abstract The flexible job shop problem (FJSP) has an important significance in both fields of production management and combinatorial optimization. This problem covers two main difficulties, namely, machine assignment problem and operation sequencing problem. To reflect as close as possible the reality of this problem, the sequence dependent setup time is taken into consideration. For solving such a complex problem, we propose a hybrid algorithm based on a genetic algorithm (GA) combined with iterated local search (ILS). It is well known that the performance of an algorithm is heavily dependent on the setting of control parameters. For that, our algorithm uses a self-adaptive strategy based on : (1) the current specificity of the search space, (2) the preceding results of already applied algorithms (GA and ILS) and (3) their associated parameter settings. We adopt this strategy in order to detect the next promising search direction and maintain the balance between exploration and exploitation. Computational results show that our algorithm provides better solutions than other well known algorithms.

How to Schedule a Job Shop Problem through Agent Cooperation

Scheduling is an important aspect of automation in manufacturing systems. It consists in allocating a finite set of resources or machines over time to perform a collection of tasks or jobs while satisfying a set of constraints. One of the most known and hardest scheduling problems is the Job Shop, to which a distributed approach is proposed in this paper based on agent cooperation. There are essentially two types of agents: Job agents and Resource agents. Different agent behaviours based on heuristics are proposed and experimentally compared on randomly generated examples.