Machine maintenance with workload considerations

Kaufman, David L.; Lewis, Michael M.

doi:10.1002/nav.20248

Cited by 21 publications

(30 citation statements)

References 37 publications

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“…Their work investigated the suitability of genetic algorithm as a solution method for technicians' parametric optimisation. Kaufman and Lewis (2007) considered the application of repair and replacement models for maintenance workload management. In their study, information on service rates and failures, fixed maintenance times and random replacement times was generated.…”

Section: Literature Reviewmentioning

confidence: 99%

A reliability-based maintenance technicians’ workloads optimisation model with stochastic consideration

2015

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The growing interest in technicians' workloads research is probably associated with the recent surge in competition. This was prompted by unprecedented technological development that triggers changes in customer tastes and preferences for industrial goods. In a quest for business improvement, this worldwide intense competition in industries has stimulated theories and practical frameworks that seek to optimise performance in workplaces. In line with this drive, the present paper proposes an optimisation model which considers technicians' reliability that complements factory information obtained. The information used emerged from technicians' productivity and earned-values using the concept of multi-objective modelling approach. Since technicians are expected to carry out routine and stochastic maintenance work, we consider these workloads as constraints. The influence of training, fatigue and experiential knowledge of technicians on workload management was considered. These workloads were combined with maintenance policy in optimising reliability, productivity and earned-values using the goal programming approach. Practical datasets were utilised in studying the applicability of the proposed model in practice. It was observed that our model was able to generate information that practicing maintenance engineers can apply in making more informed decisions on technicians' management.

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Section: Literature Reviewmentioning

confidence: 99%

A reliability-based maintenance technicians’ workloads optimisation model with stochastic consideration

2015

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“…The proof approach for Proposition 2.1 follows closely that of Kaufman and Lewis [15] (Proposition 3.1) and is done in several steps. Letπ be a policy that selects a constant rateμ ∈ ( L s=1 p s λ s ,ū] whenever the queue is nonempty.…”

Section: Appendixmentioning

confidence: 99%

Dynamic service rate control for a single-server queue with Markov-modulated arrivals

Kumar

Lewis

Topaloğlu

2013

Naval Research Logistics

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Abstract:We consider the problem of service rate control of a single-server queueing system with a finite-state Markov-modulated Poisson arrival process. We show that the optimal service rate is nondecreasing in the number of customers in the system; higher congestion levels warrant higher service rates. On the contrary, however, we show that the optimal service rate is not necessarily monotone in the current arrival rate. If the modulating process satisfies a stochastic monotonicity property, the monotonicity is recovered. We examine several heuristics and show where heuristics are reasonable substitutes for the optimal control. None of the heuristics perform well in all the regimes and the fluctuation rate of the modulating process plays an important role in deciding the right heuristic. Second, we discuss when the Markov-modulated Poisson process with service rate control can act as a heuristic itself to approximate the control of a system with a periodic nonhomogeneous Poisson arrival process. Not only is the current model of interest in the control of Internet or mobile networks with bursty traffic, but it is also useful in providing a tractable alternative for the control of service centers with nonstationary arrival rates.

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“…Kaufman and Lewis [8]. Figure 2 shows the eect of dierent degradation structures, i.e., p deg is varied from 0.3 to 0.9 with an increment of 0.2.…”

Section: Optimal Policy Examples -Discounted Costsmentioning

confidence: 99%

“…The expressions for these costs-to-go are slight modications of the discounted cost expressions and are not detailed here. We follow closely the arguments used in [8] to move from the discounted cost case to the average cost case. In particular, we need to verify the so-called SENSM [13] conditions for SMDP's, as given below:…”

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confidence: 99%

Single-Machine Multiple-Recipe Predictive Maintenance

Cai¹,

Hasenbein

Kutanoğlu

et al. 2013

Prob. Eng. Inf. Sci.

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This paper studies a multiple-recipe predictive maintenance problem with M/G/1 queueing eects. The server degrades according to a discrete-time Markov chain and we assume that the controller knows both the machine status and the current number of jobs in the system. The controller's objective is to minimize total discounted costs or long-run average costs which include preventative and corrective maintenance costs, holdings costs, and possibly production costs. An optimal policy determines both when to perform maintenance and which type of job to process. Since the policy takes into account the machine's degradation status, such control decisions are known as predictive maintenance policies. In the single-recipe case, we prove that the optimal policy is monotone in the machine status, but not in the number of jobs in the system. A similar monotonicity result holds in the two-recipe case. Finally, we provide computational results indicating that signicant savings can be realized when implementing a predictive maintenance policies instead of a traditional job-based threshold policy for PMs.

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Machine maintenance with workload considerations

Cited by 21 publications

References 37 publications

A reliability-based maintenance technicians’ workloads optimisation model with stochastic consideration

A reliability-based maintenance technicians’ workloads optimisation model with stochastic consideration

Dynamic service rate control for a single-server queue with Markov-modulated arrivals

Single-Machine Multiple-Recipe Predictive Maintenance

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