Cross-training policies for repair shops with spare part inventories

Sleptchenko, Andrei; Turan, Hasan Hüseyin; Pokharel, Shaligram; ElMekkawy, Tarek Y.

doi:10.1016/j.ijpe.2017.12.018

Cited by 27 publications

(14 citation statements)

References 32 publications

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“…Those assets include SKUs, and SKUs are subject to random failures. When a part fails, an order is immediately placed for a ready-for-use part of the same type at the stock point, and the failed part is sent to the repair shop (Turan et al, 2018;Sleptchenko et al, 2019).…”

Section: Problem Definition and Mathematical Modelmentioning

confidence: 99%

“…Otherwise, the demand is backordered and fulfilled as soon as a ready-for-use part of the demanded SKU type becomes available. In case of unavailability of the ready-for-use part, the capital asset goes down, and downtime cost starts occurring until the requested ready-for-use part is delivered (Turan et al, 2018;Sleptchenko et al, 2019).…”

Section: Problem Definition and Mathematical Modelmentioning

confidence: 99%

“…parts supply system includes: (a) the inventory policy (e.g., inventory levels and replenishment strategies), (b) the repair capacities (e.g., workforce level, amount of service machinery and tools) available to repair failed parts and (c) the scheduling method (e.g., repair priorities) to control the flow of work in the repair shop. An execution of optimal strategies regarding these decisions enables faster repairs and leads to a reduction in the downtime of capital assets (Turan et al, 2018;Sleptchenko et al, 2019). These decisions are interrelated; for example, the required amount of repairable spare parts kept on the stock depends on the repair capacity (i.e., the rate of repair), and isolated optimization of a single decision would result in suboptimal solutions.…”

Section: Introductionmentioning

confidence: 99%

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Repair priorities in repairable spare part supply systems: a simulation-optimization approach

2019

El Sawah, S. (Ed.) MODSIM2019, 23rd International Congress on Modelling and Simulation.

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Industries employing expensive assets maintain extensive repair facility operations and keep spare stocks. This type of logistics system, where both forward and reverse logistics systems are required, in addition to repair facilities, is known as a repairable system. We study a repairable spare part supply system consisting of one repair facility and one stock point, where repairables are kept on the stock to serve expensive capital assets in order to prevent downtime. We set up the objective of our model to minimize the expected total inventory holding costs of spare parts and costs for the downtime of assets over an infinite h orizon. In this study, we particularly analyze the effect of static repair priorities on the expected total cost. To achieve this, we seek optimal values of the repairable spare parts stocks and the assignment of different repairable types into priority classes. We model the repair facility as a multi-server multi-class queue, where failed repairable parts are repaired based on priority classes. It is generally difficult to analyze this type of queuing systems with analytical methods even for a small size problem with the limited number of priority classes and repairable types. Therefore to alleviate this difficulty, we develop a two-stage sequential simulation-optimization a lgorithm. In the first stage, the set of all feasible priority assignments is searched by a Genetic Algorithm (GA) meta-heuristic to find an assignment that achieves the minimum c ost. In the second s tage, a discrete event simulation (DES) is run for the given priority assignment provided by the GA to analyze the multi-class multi-server queueing model. The probability distribution for the number of failed spare parts in the repair facility is obtained as an output of the DES. We use probability distributions to calculate the optimal level of repairable spare part stocks to keep in the inventory. We compare the performance of the simulation-optimization algorithm with a First-Come First-Served (FCFS) service discipline since FCFS reflects the common way of working in p ractice. The conducted computational experiments show that the proposed approach yields a significant a mount o f t otal c ost r eduction i n some extreme cases reaching up to 90%.

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Section: Problem Definition and Mathematical Modelmentioning

confidence: 99%

Section: Problem Definition and Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Repair priorities in repairable spare part supply systems: a simulation-optimization approach

2019

El Sawah, S. (Ed.) MODSIM2019, 23rd International Congress on Modelling and Simulation.

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“…It is generally assumed that all workforce in the maintenance facility has the same skill set and can repair all types of failed parts (i.e., full cross-training) in the system (Sleptchenko et al 2019). Nevertheless, limited workforce flexibility with appropriate cross-training can offer most of the benefits of full cross-training (Jordan and Graves 1995).…”

Section: Introductionmentioning

confidence: 99%

A deep reinforcement learning assisted simulated annealing algorithm for a maintenance planning problem

2022

Self Cite

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Maintenance planning aims to improve the reliability of assets, prevent the occurrence of asset failures, and reduce maintenance costs associated with downtime of assets and maintenance resources (such as spare parts and workforce). Thus, effective maintenance planning is instrumental in ensuring high asset availability with the minimum cost. Nevertheless, to find such optimal planning is a nontrivial task due to the (i) complex and usually nonlinear inter-relationship between different planning decisions (e.g., inventory level and workforce capacity), and (ii) stochastic nature of the system (e.g., random failures of parts installed in assets). To alleviate these challenges, we study a joint maintenance planning problem by considering several decisions simultaneously, including workforce planning, workforce training, and spare parts inventory management. We develop a hybrid solution algorithm ($$\mathcal {DRLSA}$$ DRLSA ) that is a combination of Double Deep Q-Network based Deep Reinforcement Learning (DRL) and Simulated Annealing (SA) algorithms. In each episode of the proposed algorithm, the best solution found by DRL is delivered to SA to be used as an initial solution, and the best solution of SA is delivered to DRL to be used as the initial state. Different from the traditional SA algorithms where neighborhood structures are selected only randomly, the DRL part of $$\mathcal {DRLSA}$$ DRLSA learns to choose the best neighborhood structure to use based on experience gained from previous episodes. We compare the performance of the proposed solution algorithm with several well-known meta-heuristic algorithms, including Simulated Annealing, Genetic Algorithm (GA), and Variable Neighborhood Search (VNS). Further, we also develop a Machine Learning (ML) algorithm (i.e., K-Median) as another benchmark in which different properties of spare parts (e.g., failure rates, holding costs, and repair rates) are used as clustering features for the ML algorithm. Our study reveals that the $$\mathcal {DRLSA}$$ DRLSA finds the optimal solutions for relatively small-size instances, and it has the potential to outperform traditional meta-heuristic and ML algorithms.

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“…Driven by the low demand environment of the military, he developed the METRIC method to optimize repairable spare parts stock levels. Since then, the optimization of spares parts inventory has been widely studied, up to including the use of additive manufacturing as alternative supply (Knofius et al, 2019) or evaluating the impact of workforce training policies on spare parts inventory (Sleptchenko et al, 2019). We refer the interested reader to Basten and van Houtum (2014) and Hu et al (2018) for comprehensive literature reviews on system-oriented inventory models.…”

Section: Introductionmentioning

confidence: 99%

Spare parts inventory control under a fixed-term contract with a long-down constraint

Lamghari-Idrissi

Basten

Houtum

2020

International Journal of Production Economics

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Cross-training policies for repair shops with spare part inventories

Cited by 27 publications

References 32 publications

Repair priorities in repairable spare part supply systems: a simulation-optimization approach

Repair priorities in repairable spare part supply systems: a simulation-optimization approach

A deep reinforcement learning assisted simulated annealing algorithm for a maintenance planning problem

Spare parts inventory control under a fixed-term contract with a long-down constraint

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