Name
Abstract | ||
|---|---|---|
An effective spare part supply system planning is essential to achieve a high capital asset availability. We investigate the design problem of a repair shop in a single echelon repairable multi-item spare parts supply system. The repair shop usually consists of several servers with different skill sets. Once a failure occurs in the system, the failed part is queued to be served by a suitable server that has the required skill. We model the repair shop as a collection of independent sub-systems, where each sub-system is responsible for repairing certain types of failed parts. The procedure of partitioning a repair shop into sub-systems is known as pooling, and the repair shop formed by the union of independent sub-systems is called a pooled repair shop. Identifying the best partition is a challenging combinatorial optimization problem. In this direction, we formulate the problem as a stochastic nonlinear integer programming model and propose a sequential solution heuristic to find the best-pooled design by considering inventory allocation and capacity level designation of the repair shop. We conduct numerical experiments to quantify the value of the pooled repair shop designs. Our analysis shows that pooled designs can yield cost reductions by 25% to 45% compared to full flexible and dedicated designs. The proposed heuristic also achieves a lower average total system cost than that generated by a Genetic Algorithm (GA)-based solution algorithm. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-16035-7_6 | international conference on operations research and enterprise systems |
Field | DocType | Citations |
Mathematical optimization,Heuristic,Business system planning,Spare part,Nonlinear integer programming,Combinatorial optimization problem,Computer science,Server,Pooling,Genetic algorithm | Conference | 0 |
PageRank | References | Authors |
0.34 | 28 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hasan Hüseyin Turan | 1 | 10 | 5.22 |
| Shaligram Pokharel | 2 | 114 | 11.78 |
| Tarek ElMekkawy | 3 | 7 | 1.80 |
| Andrei Sleptchenko | 4 | 83 | 10.64 |
| Maryam Al-Khatib | 5 | 1 | 0.69 |