Two-machine flow shop scheduling integrated with preventive maintenance planning

Wang, Shijin; Liu, Ming

doi:10.1080/00207721.2014.900137

Cited by 23 publications

(8 citation statements)

References 43 publications

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“…For machine fault, the Weibull distribution is adopted here according to the work of Wang et al [26]. The expected number of failures when machine j processes the i-th job i can be calculated as…”

Section: Problem Descriptionmentioning

confidence: 99%

Integrated Optimization of Blocking Flowshop Scheduling and Preventive Maintenance Using a Q-Learning-Based Aquila Optimizer

Ge,

Wang

2023

Symmetry

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In recent years, integration of production scheduling and machine maintenance has gained increasing attention in order to improve the stability and efficiency of flowshop manufacturing systems. This paper proposes a Q-learning-based aquila optimizer (QL-AO) for solving the integrated optimization problem of blocking flowshop scheduling and preventive maintenance since blocking in the jobs processing requires to be considered in the practice manufacturing environments. In the proposed algorithmic framework, a Q-learning algorithm is designed to adaptively adjust the selection probabilities of four key population update strategies in the classic aquila optimizer. In addition, five local search methods are employed to refine the quality of the individuals according to their fitness level. A series of numerical experiments are carried out according to two groups of flowshop scheduling benchmark. Experimental results show that QL-AO significantly outperforms six peer algorithms and two state-of-the-art hybrid algorithms based on Q-Learning on the investigated integrated scheduling problem. Additionally, the proposed Q-learning and local search strategies are effective in improving its performance.

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Section: Problem Descriptionmentioning

confidence: 99%

Integrated Optimization of Blocking Flowshop Scheduling and Preventive Maintenance Using a Q-Learning-Based Aquila Optimizer

Ge,

Wang

2023

Symmetry

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“…For instance, the machine availability constraints must be set ahead, which is challenging to know in practice and may affect the search space where optimal solutions exist. Hence, it is more advantageous to adopt the integrated model, which considers maintenance and production scheduling simultaneously [7][8][9][10][11]. All these studies assume that the optimal PM intervals are regular, which means that after a constant period, the PM actions must take place to renew the machine.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Bee Colony Algorithm for Coordinated Scheduling of Production Jobs and Flexible Maintenance in Permutation Flowshops

Ladj,

Benbouzid-Si Tayeb,

Dahamni

et al. 2024

Technologies

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This research work addresses the integrated scheduling of jobs and flexible (non-systematic) maintenance interventions in permutation flowshop production systems. We propose a coordinated model in which the time intervals between successive maintenance tasks as well as their number are assumed to be non-fixed for each machine on the shopfloor. With such a flexible nature of maintenance activities, the resulting joint schedule is more practical and representative of real-world scenarios. Our goal is to determine the best job permutation in which flexible maintenance activities are properly incorporated. To tackle the NP-hard nature of this problem, an artificial bee colony (ABC) algorithm is developed to minimize the total production time (Makespan). Experiments are conducted utilizing well-known Taillard’s benchmarks, enriched with maintenance data, to compare the proposed algorithm performance against the variable neighbourhood search (VNS) method from the literature. Computational results demonstrate the effectiveness of the proposed algorithm in terms of both solution quality and computational times.

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“…Multi-machines studies consider more complex systems, mainly parallel systems, and aim to perform collective preventive maintenance on machines instead of individual ones to minimize the total uncertainty and unavailability of production lines (Fitouhi and Nourelfath, 2014;Xiao et al, 2016). Generally, a breakdown within a makespan is a stochastic event and therefore, the consequential corrective maintenance is also random which increases the uncertainty level of a production line Bjarnason and Taghipour, 2016;Wang and Liu, 2016). Moreover, downtime due to corrective maintenance, followed by a breakdown, is commonly lengthier than downtime due to preventive maintenance and thus, optimization of preventive maintenance within makespan reduces idle time of the system and mitigates the unavailability level of a production line (von Hoyningen-Huene and Kiesmüller, 2015).…”

Section: Introductionmentioning

confidence: 99%

Integrated Production, Quality and Inspection Optimization Models for Production Systems with Delayed Failure

Azimpoor

2023

Preprint

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Businesses are expected to meet customers’ demands, including high-quality products and timely deliveries. However, in practice, production lines are subject to unforeseen events (i.e., failures), which affect production efficiency. Despite several attempts in the literature, there is still a need for models that can minimize those issues. This dissertation contains different optimization models that can help managers deal with various unexpected events and keep manufacturing firms competent and efficient. In the first contribution (Chapter 2), the case of failures in a single machine production machine is considered. The machine’s failures are described using a two-stage delay time model. The model integrates the machine’s condition, inspection policy, and maintenance actions in a recursive equation to obtain the total expected makespan and production cost. The developed model helps managers reduce the number of failures and resource waste due to failure downtime. In the second contribution (Chapter 3), we consider the possibility of performing minimal repair or replacement depending on the machine's age and investigate how the scheduling decisions are impacted accordingly. Several managerial insights are provided regarding the amount of investment in a maintenance operation depending on the machine's failure characteristics and inspection policy. In Chapter 4, a parallel-machine scheduling problem is studied, in which decisions about inspection and production schedules are made based on the machines' stochastic failure process. The identical parallel machines are exposed to a two-stage delay time model (DTM). Due to the large-size configuration of the solution space for this problem, a branch and bound algorithm is used for optimization. Finally, in Chapter 5, we use the direct effect of a machine's health condition as one of the significant factors that cause non-conforming products. The DTM is used as an opportunity to perform periodic quality inspections of the products and to detect a higher than acceptable level of non-conformity, which is an indicator of a defective machine. We formulate the effects of the machine's condition, inspection, and maintenance activities on the proportion of non-conforming products. Apart from product quality control, inspection is also used to assess the machine's condition and perform a maintenance action, if required.

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Two-machine flow shop scheduling integrated with preventive maintenance planning

Cited by 23 publications

References 43 publications

Integrated Optimization of Blocking Flowshop Scheduling and Preventive Maintenance Using a Q-Learning-Based Aquila Optimizer

Integrated Optimization of Blocking Flowshop Scheduling and Preventive Maintenance Using a Q-Learning-Based Aquila Optimizer

An Artificial Bee Colony Algorithm for Coordinated Scheduling of Production Jobs and Flexible Maintenance in Permutation Flowshops

Integrated Production, Quality and Inspection Optimization Models for Production Systems with Delayed Failure

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