Integral inventory control in spare parts networks with capacity restrictions

Integral inventory control of repairable items in service networks can result in a significant gain compared to traditional local control mechanisms, in terms of both efficiency and customer service. Research on quantitative decision support models has yielded various useful results. However, in many of these models, the random components such as demand and lead times are modelled as black boxes.In this thesis, the author focuses on the modelling of the lead times in repair facilities with limited capacities and reasonable repair priority settings. To this end, several kinds of multi-class, multi-server queueing models with different priority settings have been developed. The resultant queueing models are plugged into a well-known spare parts supply model, which usually assumes ample server capacity. The multi-class nature of the queueing models, which means that items with different arrival and service rates share the same queueing process, makes it possible to obtain more natural spare part models and more flexibility in optimisation

Integrated planning of spare parts and service engineers with partial backlogging

In this paper, we consider the integrated planning of resources in a service maintenance logistics system in which spare parts supply and service engineers deployment are considered simultaneously. The objective is to determine close-to-optimal stock levels as well as the number of service engineers that minimize the total average costs under a maximum total average waiting time constraint. When a failure occurs, a spare part and a service engineer are requested for the repair call. In case of a stock-out at spare parts inventory, the repair call will be satisfied entirely via an emergency channel with a fast replenishment time but at a high cost. However, if the requested spare part is in stock, the backlogging policy is followed for engineers. We model the problem as a queueing network. An exact method and two approximations for the evaluation of a given policy are presented. We exploit evaluation methods in a greedy heuristic procedure to optimize this integrated planning. In a numerical study, we show that for problems with more than five types of spare parts it is preferable to use approximate evaluations as they become significantly faster than exact evaluation. Moreover, approximation errors decrease as problems get larger. Furthermore, we test how the greedy optimization heuristic performs compared to other discrete search algorithms in terms of total costs and computation times. Finally, in a rather large case study, we show that we may incur up to 27% cost savings when using the integrated planning as compared to a separated optimization. , The Author(s).This publication was made possible by the NPRP award [NPRP 7-308-2-128] from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Exact and Approximate Analysis of Multi-Echelon, Multi-Indenture Spare Parts Systems with Commonality

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Simulation Results for Multi-Class Multi-Server Queueing Systems with Cross-Training

The JSON file contains a number of cases for multi-class multi-server systems with partial class-server assignments (cross-training). For each recorded systems all possible (feasible) class-server assignments were generated, and simulation results were recorded. The simulation results (for each assignment) include: (i) marginal probabilities that each class has the corresponding number of items in the system, (ii) class-server utilizations (utilization of server time capacity), (iii) class server distribution with percentages of class flows assigned to each server. Maximum numbers of servers and classes were limited to 6. Please note that NOT ALL possible combinations for numbers of servers and classes were generated, due to the exponential growth of possible class-server assignments. However, for each presented case (with certain numbers of servers, classes, and certain arrival/service rates), ALL possible class-server assignments were generated and simulated. The presented results allow easy analysis and optimization of systems where such queue occurs (call-centers, production facilities, maintenance systems) and benchmarking with possible optimization algorithms for class-server assignments

Simulation Results for Multi-Class Multi-Server Queueing Systems with Cross-Training

The JSON file contains a number of cases for multi-class multi-server systems with partial class-server assignments (cross-training). For each recorded systems all possible (feasible) class-server assignments were generated, and simulation results were recorded. The simulation results (for each assignment) include: (i) marginal probabilities that each class has the corresponding number of items in the system, (ii) class-server utilizations (utilization of server time capacity), (iii) class server distribution with percentages of class flows assigned to each server. Maximum numbers of servers and classes were limited to 6. Please note that NOT ALL possible combinations for numbers of servers and classes were generated, due to the exponential growth of possible class-server assignments. However, for each presented case (with certain numbers of servers, classes, and certain arrival/service rates), ALL possible class-server assignments were generated and simulated. The presented results allow easy analysis and optimization of systems where such queue occurs (call-centers, production facilities, maintenance systems) and benchmarking with possible optimization algorithms for class-server assignments