A new operator that combines artificial immune systems and multi-agent systems for addressing flow shop scheduling problems
DOI:
https://doi.org/10.38152/bjtv7n4-048Abstract
This paper introduces a novel mutation operator that integrates concepts from artificial immune systems and multi-agent systems to enhance systematic preventive maintenance policies within hybrid flow shop scheduling. The mutation operator is designed to make slight adjustments to the sequence, generating a new yet similar sequence. Its primary goal is to prevent convergence to a local optimum and to increase diversity within the population. Additionally, the mutation operator can be viewed as a straightforward form of local search. The proposed method draws inspiration from human body behavior, leading to the implementation of a problem-solving strategy focused on optimizing processing time through metaheuristic techniques. This approach combines multi-agent systems with elements inspired by the artificial immune system. The effectiveness of our method is consistently demonstrated throughout the paper. We apply this approach to three preventive maintenance policies aimed at maximizing availability and maintaining a minimum level of reliability in the production chain. The results indicate that our algorithm surpasses existing algorithms, taking into account the potential periodic unavailability of machines during production scheduling.
References
BOKRANTZ, J. & SKOOGH, A. (2020). Smart Maintenance: A Research Agenda For Industrial Maintenance Management. International Journal of Production Economics, vol. 224, Elsevier.
LEE, D. & PAN, R. (2017). Evaluating reliability of complex systems for Predictive maintenance. International Journal of Production Research, vol. 55, Taylor & Francis.
HYUNJU, I. & HWAN, J. (2020). New stochastic models for preventive maintenance and maintenance optimization. European Journal of Operational Research, Vol 255, Elsevier.
KUMAR, R. & GURTU,A. ( 2021). Prioritizing success factors for implementing total productive maintenance (TPM). Journal of Quality in Maintenance Engineering, Vol 28 Issue 4.
WANG, J. & QIU, Q. ( 2021). Optimal condition-based preventive maintenance policy for balanced systems. Reliability Engineering & System Safety, Vol 211, Elsevier.
CELEUX, G. CORSET, F. LANNOY, A. & RICARD, B. ( 2021). Designing a Bayesian network for preventive maintenance from expert opinions in a rapid and reliable way. Reliability Engineering and System Safety, vol. 91, N°7, PP. 849-856.
KARAR, A.N. LABIB, A. & JONES, D.(2024). A resilience-based maintenance optimisation framework using multiple criteria and Knapsack methods. Reliability Engineering & System Safety, Vol 241, Elsevier.
WANG,D. LIU, F. & ZHANG, Y. ( 2024). Integrated rescheduling and preventive maintenance for arrival of new jobs through evolutionary multi-objective optimization. Methodologies and Application, Vol 20, PP 1635–1652.
DABIRI, M. YAZDANI, M. NADERI, B. & HALEH, H. (2022). Modeling and solution methods for hybrid flow shop scheduling problem with job rejection Operational Research, Vol 22, PP 2721–2765, Elsevier.
BENDA, F. BRAUNE, R. DOERNER, KF. & HARTL, RF. (2022). A machine learning approach for flow shop scheduling problems with alternative resources, sequence-dependent setup times, and blocking. Or Spectrum, Vol 41, PP 871–893.
TADAYONIRAD, S. SEIDGAR, H. FAZLOLLAHTABAR, H. & SHAFAEI, R. (2019). Robust scheduling in two-stage assembly flow shop problem with random machine breakdowns: integrated meta-heuristic algorithms and simulation approach. Assembly Automation, vol. Vol 39 Issue 5.
COSTA, A. CAPPADONNA , FA. & FICHERA, S. ( 2019). A hybrid genetic algorithm for minimizing makespan in a flow-shop sequence-dependent group scheduling problem. Journal of Intelligent Manufacturing, Vol 28, PP 1269–1283, Springer.
WU, W. CHEN, J. & LIN, W. (2015). A heuristic-based genetic algorithm for the two-machine flowshop scheduling with learning consideration. Journal of Manufacturing Systems, Vol 35, PP 223-233, Elsevier.
BENTTALEB, M. HNAIEN, F. & YALAOUI, F. ( 2019). Minimising the makespan in the two-machine job shop problem under availability constraints. International Journal of Production Research, Vol 57,Issue 5.
HU, Z. & HU, G. (2016). A two-stage stochastic programming model for lot-sizing and scheduling under uncertainty. International Journal of Production Economics, Vol 180, PP 198-207, Elsevier..
MOSHEIOV, G. SARIG, A. STRUSEVICH, V. & MOSHEIFF, J. (2018). Two-machine flow shop and open shop scheduling problems with a single maintenance window. European Journal of Operational Research, Vol 271, PP 388-400, Elsevier.
CHEN, L. WANG, J. & YANG, W. (2021). A single machine scheduling problem with machine availability constraints and preventive maintenance.
International Journal of Production Research, Vol 59, Issue 9.
VRIGNAT, P. KRATZ, F. & AVILA, M. (2022). Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review. Reliability Engineering & System Safety, Vol 218, Part A.
FAYYAZI, E. GHOBADIAN, B. NAJAFI, G. HOSSEINZADEH, B. MAMAT, R. & HOSSEINZADEH, J. (2017). An ultrasound-assisted system for the optimization of biodiesel production from chicken fat oil using a genetic algorithm and response surface methodology. Ultrasonics Sonochemistry, Vol 26, PP 312-320.
ABDELHADI, A. MOUSS, LH. & O KADRI (2020). Hybrid Multi-Agent And Immune Algorithm Approach To Hybrid Flow Shops Scheduling With SDST. Academic Journal Of Manufacturing Engineering, Vol 18, Issue 3, PP 120-130.
ZANDIEH, M. GHOMI, S.M.T. F. & HUSSEINI, S.M. M. (2020). An immune algorithm approach to hybrid flow shops scheduling with sequence dependent setup times. Journal of Applied Mathematics and Computation, vol. 180, pp. 111-127, Elsevier.
BRITO, E.S, TOMAZELLA, V.L.D & FERREIRA, P.H. (2022) .Statistical modeling and reliability analysis of ultiple repairable systems with dependent failure times under perfect repair. Reliability Engineering & System Safety, Vol 222, Elsevier.
