The Experts below are selected from a list of 10863 Experts worldwide ranked by ideXlab platform
Morris A Cohen - One of the best experts on this subject based on the ideXlab platform.
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joint optimization of spare parts inventory and Service Engineers staffing with full backlogging
International Journal of Production Economics, 2019Co-Authors: Sajjad Rahimighahroodi, A. Al Hanbali, Ingrid Vliegen, Morris A CohenAbstract:Abstract We consider the integrated planning of spare parts and Service Engineers that are needed for serving a group of systems. These systems are subject to different failure types, and for each failure, a Service Engineer with the necessary spare part has to be assigned to repair the system. The Service provider follows a backlogging policy with part reservations. That is, a repair request is backlogged if one of the required resources is not immediately available upon demand. Moreover, a spare part is reserved if the requested spare part is in stock but no Service Engineer is immediately available. The spare parts are typically slow-movers and are managed according to a base-stock policy. The objective is to jointly determine the stock levels and the number of Service Engineers to minimize the total Service costs subject to a constraint on the expected total waiting times of the repair calls. For the evaluation of a given setting, we present an exact method (computationally feasible for small problems) and an accurate approximation. For the joint optimization, we present a greedy heuristic that efficiently produces close-to-optimal results. We test how the heuristic performs compared to the optimal solution and the separate optimization of spare parts and Service Engineers in an extensive numerical study. In a case study with 93 types of spare parts, we show that the solution of the greedy algorithm is always within 2% of the optimal solution and is up to 20% better than a separated optimization approach encountered in practice.
Sajjad Rahimighahroodi - One of the best experts on this subject based on the ideXlab platform.
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joint optimization of spare parts inventory and Service Engineers staffing with full backlogging
International Journal of Production Economics, 2019Co-Authors: Sajjad Rahimighahroodi, A. Al Hanbali, Ingrid Vliegen, Morris A CohenAbstract:Abstract We consider the integrated planning of spare parts and Service Engineers that are needed for serving a group of systems. These systems are subject to different failure types, and for each failure, a Service Engineer with the necessary spare part has to be assigned to repair the system. The Service provider follows a backlogging policy with part reservations. That is, a repair request is backlogged if one of the required resources is not immediately available upon demand. Moreover, a spare part is reserved if the requested spare part is in stock but no Service Engineer is immediately available. The spare parts are typically slow-movers and are managed according to a base-stock policy. The objective is to jointly determine the stock levels and the number of Service Engineers to minimize the total Service costs subject to a constraint on the expected total waiting times of the repair calls. For the evaluation of a given setting, we present an exact method (computationally feasible for small problems) and an accurate approximation. For the joint optimization, we present a greedy heuristic that efficiently produces close-to-optimal results. We test how the heuristic performs compared to the optimal solution and the separate optimization of spare parts and Service Engineers in an extensive numerical study. In a case study with 93 types of spare parts, we show that the solution of the greedy algorithm is always within 2% of the optimal solution and is up to 20% better than a separated optimization approach encountered in practice.
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integrated resource planning in maintenance logistics with spare parts emergency shipment and Service Engineers backlogging
2016Co-Authors: Sajjad Rahimighahroodi, A. Al Hanbali, W. H. M. Zijm, Van J C W Ommeren, A. SleptchenkoAbstract: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 the 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 and simultaneously attain smaller errors for larger problems. Furthermore, we test how this greedy 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.
A. Al Hanbali - One of the best experts on this subject based on the ideXlab platform.
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joint optimization of spare parts inventory and Service Engineers staffing with full backlogging
International Journal of Production Economics, 2019Co-Authors: Sajjad Rahimighahroodi, A. Al Hanbali, Ingrid Vliegen, Morris A CohenAbstract:Abstract We consider the integrated planning of spare parts and Service Engineers that are needed for serving a group of systems. These systems are subject to different failure types, and for each failure, a Service Engineer with the necessary spare part has to be assigned to repair the system. The Service provider follows a backlogging policy with part reservations. That is, a repair request is backlogged if one of the required resources is not immediately available upon demand. Moreover, a spare part is reserved if the requested spare part is in stock but no Service Engineer is immediately available. The spare parts are typically slow-movers and are managed according to a base-stock policy. The objective is to jointly determine the stock levels and the number of Service Engineers to minimize the total Service costs subject to a constraint on the expected total waiting times of the repair calls. For the evaluation of a given setting, we present an exact method (computationally feasible for small problems) and an accurate approximation. For the joint optimization, we present a greedy heuristic that efficiently produces close-to-optimal results. We test how the heuristic performs compared to the optimal solution and the separate optimization of spare parts and Service Engineers in an extensive numerical study. In a case study with 93 types of spare parts, we show that the solution of the greedy algorithm is always within 2% of the optimal solution and is up to 20% better than a separated optimization approach encountered in practice.
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Joint optimization of spare parts inventory and Service Engineers staffing with full backlogging
'Elsevier BV', 2019Co-Authors: Rahimi-ghahroodi S, A. Al Hanbali, Vliegen, Imh Ingrid, Cohen Stuart, Martien AAbstract:\u3cp\u3eWe consider the integrated planning of spare parts and Service Engineers that are needed for serving a group of systems. These systems are subject to different failure types, and for each failure, a Service Engineer with the necessary spare part has to be assigned to repair the system. The Service provider follows a backlogging policy with part reservations. That is, a repair request is backlogged if one of the required resources is not immediately available upon demand. Moreover, a spare part is reserved if the requested spare part is in stock but no Service Engineer is immediately available. The spare parts are typically slow-movers and are managed according to a base-stock policy. The objective is to jointly determine the stock levels and the number of Service Engineers to minimize the total Service costs subject to a constraint on the expected total waiting times of the repair calls. For the evaluation of a given setting, we present an exact method (computationally feasible for small problems) and an accurate approximation. For the joint optimization, we present a greedy heuristic that efficiently produces close-to-optimal results. We test how the heuristic performs compared to the optimal solution and the separate optimization of spare parts and Service Engineers in an extensive numerical study. In a case study with 93 types of spare parts, we show that the solution of the greedy algorithm is always within 2% of the optimal solution and is up to 20% better than a separated optimization approach encountered in practice.\u3c/p\u3
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integrated resource planning in maintenance logistics with spare parts emergency shipment and Service Engineers backlogging
2016Co-Authors: Sajjad Rahimighahroodi, A. Al Hanbali, W. H. M. Zijm, Van J C W Ommeren, A. SleptchenkoAbstract: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 the 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 and simultaneously attain smaller errors for larger problems. Furthermore, we test how this greedy 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.
Eun Suk Suh - One of the best experts on this subject based on the ideXlab platform.
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field Service Engineer replenishment policy assessment using a hybrid simulation model a case study
Journal of the Korean Institute of Industrial Engineers, 2015Co-Authors: Eun Suk SuhAbstract:In this paper, a hybrid simulation model for assessing the impact of alternative field Service Engineer replenishment policies is introduced. The end-to-end supply chain simulation model is created using a discrete-event and agent-based model, which enables accurate description of key individual entities in the investigated supply chain, such as field Service Engineers. Once the model is validated with the historical data, it is used to assess the impacts of field Service Engineer replenishment policies for a major system manufacturing firm. In the case study, newly proposed replenishment policies for post-sale distribution supply chain are assessed for the level of Service improvement to end customers.
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field Service Engineer replenishment policy assessment using a discrete event and agent based simulation model a case study
Journal of Korean Institute of Industrial Engineers, 2015Co-Authors: Eun Suk SuhAbstract:In this paper, a simulation model for assessing the impact of alternative field Service Engineer replenishment policies is introduced. The end-to-end supply chain simulation model is created using a discrete-event and agent-based simulation model, which enables accurate description of key individual entities in the investigated supply chain, such as field Service Engineers. Once the model is validated with the historical data, it is used to assess the impacts of field Service Engineer replenishment policies for a major printing equipment manufacturing firm.In the case study, newly proposed replenishment policies for post-sale distribution supply chain are assessed for the level of Service improvement to end customers.
Ingrid Vliegen - One of the best experts on this subject based on the ideXlab platform.
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joint optimization of spare parts inventory and Service Engineers staffing with full backlogging
International Journal of Production Economics, 2019Co-Authors: Sajjad Rahimighahroodi, A. Al Hanbali, Ingrid Vliegen, Morris A CohenAbstract:Abstract We consider the integrated planning of spare parts and Service Engineers that are needed for serving a group of systems. These systems are subject to different failure types, and for each failure, a Service Engineer with the necessary spare part has to be assigned to repair the system. The Service provider follows a backlogging policy with part reservations. That is, a repair request is backlogged if one of the required resources is not immediately available upon demand. Moreover, a spare part is reserved if the requested spare part is in stock but no Service Engineer is immediately available. The spare parts are typically slow-movers and are managed according to a base-stock policy. The objective is to jointly determine the stock levels and the number of Service Engineers to minimize the total Service costs subject to a constraint on the expected total waiting times of the repair calls. For the evaluation of a given setting, we present an exact method (computationally feasible for small problems) and an accurate approximation. For the joint optimization, we present a greedy heuristic that efficiently produces close-to-optimal results. We test how the heuristic performs compared to the optimal solution and the separate optimization of spare parts and Service Engineers in an extensive numerical study. In a case study with 93 types of spare parts, we show that the solution of the greedy algorithm is always within 2% of the optimal solution and is up to 20% better than a separated optimization approach encountered in practice.