This paper deals with joint optimization of production scheduling and group maintenance planning in single machine system with multi units arranged in series. The objective is to achieve optimum production sequence, preventive maintenance (PM) intervals and grouping of units which minimize the total integrated cost per unit time of the system. Teaching-Learning Based Optimization (TLBO) algorithm is applied to optimize the objective function. The peculiarity of TLBO as against other algorithms is that it is independent of algorithm specific parameters. The largest order value rule is utilized to retrieve the permutation vector while grouping of units is performed under PM intervals. Computational results reveal the effectiveness of the proposed approach. © 2020 The Authors.

Divya Shrivastava

Mechanical Engineering

(SoE) School of Engineering

Mishra A.K.

Fulltext

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Shiv Nadar University

Procedia CIRP

Opportunistic maintenance approaches deal with performing group preventive maintenance (PM) on the other units in a series system due to the intervention of any scheduled PM of a component. Simultaneous maintenance actions show better economic performance due to the direct reduction of downtime costs and production losses. However, it is uneconomical to perform maintenance on all units simultaneously. To address this issue, various simulation and optimization approaches including Markov chains, genetic algorithm etc. have been applied in order to achieve optimum solutions in group maintenance models. However, most of these strategies suffer from intractability as the problem size increases. In the present paper, we develop an efficient opportunistic grouping methodology for the multi-unit series system while considering imperfect preventive maintenance. The aim is to obtain an optimum PM interval and grouping of units to minimize the expected total system maintenance cost per unit time during the mission. A recently developed meta-heuristic named ‘Jaya algorithm’ is applied to optimize the objective function. The effectiveness of the proposed approach is examined with three maintenance models: single unit model, mono-group model and the proposed opportunistic group model. Results reveal that the proposed group maintenance model results in 19\% cost savings as compared to the mono-group model and 71\% compared to the single component maintenance model. © 2019, Operational Research Society of India.

OPSEARCH

An opportunistic group maintenance model for the multi-unit series system employing Jaya algorithm

This paper deals with permutation flow shop scheduling problem in which an integrated cost model consisting of work-in-process inventory carrying cost and penalty cost due to batch delay is proposed. The objective is to obtain an optimum production schedule which minimizes the expected total cost per unit time of scheduling. To optimize the objective function, we apply two new metaheuristic optimization techniques namely TLBO (teaching-learning based optimization) and Jaya and two traditional algorithms: PSO (particle swarm optimization) and SA (simulated annealing). The problem is solved for several instances ranging from 8 jobs and 5 machines to 500 jobs and 20 machines. Computational results show that for small instances, all algorithms performed equally good when compared with the exact solution (total enumeration method). However, for medium and large size problems, enumeration method was unable to give the results in a reasonable computation time period. Therefore the results of all four algorithms are compared among themselves and found that Jaya outperforms all algorithms. However, for a few large instances, SA yields better results in less computation time as against other heuristics. The overall performance of all algorithms reveals that TLBO and Jaya have considerable potential to solve discrete combinatorial problems such as permutation flow-shop scheduling problems. © 2018 Elsevier Ltd

Computers and Industrial Engineering

A TLBO and a Jaya heuristics for permutation flow shop scheduling to minimize the sum of inventory holding and batch delay costs

Neurocomputing

A survey of teaching–learning-based optimization

The International Journal of Advanced Manufacturing Technology

Production and maintenance planning: an integrated approach under uncertainties

Information Sciences

Teaching–Learning-Based Optimization: An optimization method for continuous non-linear large scale problems

Integration of production scheduling and group maintenance planning in multi-unit system employing TLBO algorithm