Fatima Benbouzid-Si Tayeb

Towards an artificial immune system for scheduling jobs and preventive maintenance operations in flowshop problems

This paper investigates permutation flowshop problem with preventive maintenance (PM). The objective functions are to minimize the total completion time for the production part and the total earliness/tardiness for PM part. The resolution consists of two steps: the one consists on scheduling production jobs using an artificial immune algorithm (AIA); the second one consists on deploying PM operations, taking the production schedule as a mandatory constraint of resources unavailability in the resolution of the problem. Furthermore, we use the principles of vaccination and receptor editing in order to strengthen search ability. The efficiency of the proposed AIAs with respect to minimization of makespan for the production part and performance loss after PM insertion, is compared to some referred in the related scheduling literature metaheuristics. Simulation results on both standard PFSP problems and non- standard integrated PFSP with PM problems show the superiority of our proposed algorithms.

Research on Permutation Flow-shop Scheduling Problem based on Improved Genetic Immune Algorithm with vaccinated offspring

Abstract This work proposes a hybrid of GA and immune algorithm for permutation flowshop scheduling problems to overcome the problem of GAs early convergence during the evolutionary processes. The proposed algorithm, called VacGA, introduces vaccination into the field of GAs based on the theory of immunity in biology. VacGA employs a GA to perform global search and an artificial immune system to perform local search. VacGA has been tested on Taillard’s benchmarks, and compared with standard GA and the best existing hybrid GAs. The obtained results shed light on the efficiency of our new hybrid method. Furthermore, the effects of some parameters are discussed.

A multi-agent approach for scheduling jobs and maintenance operations in the flowshop sequencing problem

Many works refer to the scheduling problem of both preventive maintenance and production activities. Few works concern the dynamic scheduling problem of these two activities. This aspect is mainly concerned by corrective maintenance activities equipment failure and by condition-based maintenance degradation. Decision-making tools are very helpful to schedule these activities. In this regard, the proposed multi-agent system provides a cooperation medium for scheduling independent jobs and maintenance operations in the flowshop sequencing problem based on the sequential and integrated strategies. The objective is then to optimise an objective function which takes into account both maintenance and production criterion. It also provides a framework in order to react to the disturbances occurring in the workshop. Moreover, it introduces a dialogue between two communities of agents production and maintenance leading to a high level of cooperation. A comparison of the solutions yielded by the strategies developed in this paper with the heuristic solutions given by Taillard 1993 is undertaken with respect to the minimisation of performance loss after maintenance insertion. The main point is to show how the proposed multi-agent system is used to generate a joint production and maintenance schedule that provides a better compromise between the satisfactions of respective objectives of these two functions.

Game theory-based integration of scheduling with flexible and periodic maintenance planning in the permutation flowshop sequencing problem

We introduce a novel game theory approach to the problem of integrating periodic and flexible preventive maintenance and production scheduling for permutation flowshops. Our main contribution is to propose game modeling that allows decision maker to have compromise solutions meeting at best production and maintenance criteria. To achieve this, two new games formulations for the problem are proposed, and a stable solution for the players is obtained through the concept of equilibrium. The first game follows a constructive approach while the second one follows an improvement one. Moreover, to cope with the concept of game types, we proposed two player’s behaviors: rational and non-rational. Extensive experiments are carried out to validate our proposed approaches and developed methods. Besides, the results of the proposed methods are compared against the state of the art, highlighting our contributions and showing improvements.

A Hybrid of Variable Neighbor Search and Fuzzy Logic for the permutation flowshop scheduling problem with predictive maintenance

Abstract This study focuses on permutation flowshop scheduling problem (PFSP) under availability constraints with makespan and maintenance cost optimization criteria. Machines unavailabilities are due to predictive maintenance interventions scheduled based on Prognostics and Health Management (PHM) results. Hence, we deal with the post prognostic decision making in order to improve system safety and avoid downtime and inopportune maintenance spending. For this reason, we propose a new interpretation of PHM outputs to define machines degradations corresponding to each job. Moreover, to take into account the several sources of uncertainty in the prognosis process, we choose to model PHM outputs using fuzzy logic. Motivated by the computational complexity of the problem, Variable Neighborhood Search (VNS) methods are developed including well designed local search procedures. Computational experiments carried out on well known benchmark sets for permutation flowshop show that the proposed algorithms seems to be efficient and effective.

Game theoretic modelling of the integrated production and preventive maintenance scheduling problem in permutation flowshops

In this paper, the main purpose and work is to propose scheduling algorithm based on the non-cooperative game theory for the integrated permutation flowshop problem with preventive maintenance. The goal is to optimize two criteria simultaneously: minimization of total completion time (makespan) for production part and minimization of total Earliness/Tardiness of PM activities for maintenance part. Moreover, to cope with the concept of game types we proposed two player behaviors: non-rational and rational behavior. In this research, the Nash equilibrium in game theory based approach is used to deal with the multiple objectives. Computational experiment on both standard PFSP problems and non- standard PM instances shows the effectiveness of the proposed solving algorithms.

An effective heuristic for the single-machine scheduling problem with flexible maintenance under human resource constraints

Abstract In this paper, we study a new scheduling problem that considers both production and flexible preventive maintenance on a single machine where the human resource constraints (the availability and the competence) are taken into account. The objective function involves both the tardiness and the earliness resulting from production and maintenance tasks. We propose a mathematical formulation of the studied problem that is expressed in the constraint programming (CP) paradigm as a set of linear constraints. This CP modeling had been implemented in ILOG OPL language and the exact method Cplex is applied on it to compute the optimal solutions of relatively small instances of the problem. Further, a heuristic algorithm is provided to deal with lager instances of the problem. Computational experiments demonstrate that the proposed heuristic performs well and is able to find good solutions to instances up to 700 jobs in a reasonable CPU time.

An integrated prognostic based hybrid genetic-immune algorithm for scheduling jobs and predictive maintenance

Recently, Prognostics and Health Management (PHM) reveals to be a key feature for industrials as it should improve manufacturing system availability and allow avoiding downtime as well as inopportune maintenance spending. In this context, we investigate the problem of scheduling several jobs on a single machine subjected to predictive maintenance based on PHM. We propose a new hybrid genetic immune algorithm, called IPro-HGIA, which incorporates an emulation of GA with artificial immune system, to create an integrated prognostic based scheduling for planning both production and predictive maintenance interventions under the total interventions cost minimization criterion. Furthermore, we use the principals of vaccination and receptor editing in order to strengthen search ability. Computational results show the efficiency of our hybrid scheme.