The Experts below are selected from a list of 7032 Experts worldwide ranked by ideXlab platform
Dirk Van Oudheusden - One of the best experts on this subject based on the ideXlab platform.
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stocking decisions for repairable spare parts pooling in a multi hub system
International Journal of Production Economics, 2005Co-Authors: Hartanto Wong, Dirk Cattrysse, Dirk Van OudheusdenAbstract:Abstract This paper presents an analytical model for determining spare parts stocking levels in a single-item, multi-hub, multi-company, repairable Inventory system in which complete pooling of stock is permitted among the hubs and companies. The objective is to minimize the total system Cost which consists of Inventory Holding Cost, downtime Cost and transshipment Cost. We develop an approximation method to compute the logistical system performance measures needed for calculating the Cost function. To find the optimal stocking levels, a two-stage solution is proposed. In the first stage, the demands at all hubs are aggregated and treated as if occurring at a single location. The optimal number of total spare parts is determined by minimizing the sum of Inventory Holding Cost and downtime Cost. In the second stage, a heuristic procedure is developed to find the optimal allocation of the total spare parts to minimize the total transshipment Cost.
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modeling the interaction between operational and financial decisions in the Inventory pooling of repairable spare parts problem
Proceedings of the International Conference on Operations Research, 2003Co-Authors: Hartanto Wong, Dirk Cattrysse, Dirk Van OudheusdenAbstract:Equipment-intensive industries such as airlines, nuclear power plants, various process and manufacturing plants using complex machineries are often confronted with the difficult task of maintaining a high system performance while controlling their Inventory Holding Cost. An important type of components in such industries are called repairable items. The typical problem is to determine the optimal stocking level of spare parts. An insufficient stock of spare parts can lead to an excessive downtime Cost. On the other hand, maintaining an excessive number of spare parts increases the Cost of tying up capital in non-revenue-generating spare parts inventories.
Heris Golpira - One of the best experts on this subject based on the ideXlab platform.
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optimal integration of the facility location problem into the multi project multi supplier multi resource construction supply chain network design under the vendor managed Inventory strategy
Expert Systems With Applications, 2020Co-Authors: Heris GolpiraAbstract:Abstract Using an expert system, this study is a pioneer in the formulation of an original Mixed Integer Linear Programming to integrate Vendor Managed Inventory strategy into the general multi-project multi-resource multi-supplier Construction Supply Chain (CSC) network design and facility location problems in a minimum Cost. The framework is capable of dynamically scheduling resources in terms of timing and delivery as well as selecting appropriate suppliers and suitable candidate locations restricted to only authorized facilities in a capacitated network. Results show that there are different distributions for the different Cost components in response to the different network sizes. Since changes in ratios of transportation Cost and Inventory Holding Cost to the total Cost are mirroring each other, the total transportation and Inventory Costs in proportion to the total network's Cost does not depend on the problem's size. In other point of view, when the network is large enough, changing its size results in a very little change in the Cost components, hence better controlling the Cost variability is achieved. Besides, increasing the number of projects may increase the total Cost of the CSC with increasing rates, and the disparity between the number of projects and the number of suppliers increases the Cost of the network, nonlinearly. Further, if the duration of the projects is given fixed, the greater the number of time periods for providing resources, the lower the transportation Costs. Finally, the higher replenishment frequency results in the lower Inventory Cost and brings benefits for both sides of the CSC.
M B Aryanezhad - One of the best experts on this subject based on the ideXlab platform.
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a multi objective robust optimization model for multi product multi site aggregate production planning in a supply chain under uncertainty
International Journal of Production Economics, 2011Co-Authors: S Mirzapour M J Alehashem, H Malekly, M B AryanezhadAbstract:Abstract Manufacturers need to satisfy consumer demands in order to compete in the real world. This requires the efficient operation of a supply chain planning. In this research we consider a supply chain including multiple suppliers, multiple manufacturers and multiple customers, addressing a multi-site, multi-period, multi-product aggregate production planning (APP) problem under uncertainty. First a new robust multi-objective mixed integer nonlinear programming model is proposed to deal with APP considering two conflicting objectives simultaneously, as well as the uncertain nature of the supply chain. Cost parameters of the supply chain and demand fluctuations are subject to uncertainty. Then the problem transformed into a multi-objective linear one. The first objective function aims to minimize total losses of supply chain including production Cost, hiring, firing and training Cost, raw material and end product Inventory Holding Cost, transportation and shortage Cost. The second objective function considers customer satisfaction through minimizing sum of the maximum amount of shortages among the customers’ zones in all periods. Working levels, workers productivity, overtime, subcontracting, storage capacity and lead time are also considered. Finally, the proposed model is solved as a single-objective mixed integer programming model applying the LP-metrics method. The practicability of the proposed model is demonstrated through its application in solving an APP problem in an industrial case study. The results indicate that the proposed model can provide a promising approach to fulfill an efficient production planning in a supply chain.
Nezam Mahdaviamiri - One of the best experts on this subject based on the ideXlab platform.
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comprehensive fuzzy multi objective multi product multi site aggregate production planning decisions in a supply chain under uncertainty
Applied Soft Computing, 2015Co-Authors: Navid Gholamian, Reza Tavakkolimoghaddam, Iraj Mahdavi, Nezam MahdaviamiriAbstract:Presenting a fuzzy multi-objective multi-period mixed-integer non-linear aggregate production planning model.Considering various sources of uncertainty in a multi-product multi-site multi-customer supply chain.Developing a fuzzy programming method to solve the presented model.Considering a real-world case study and using some comparing measures. The main focus of this paper is to develop a model considering some significant aspects of real-world supply chain production planning approved by industries. To do so, we consider a supply chain (SC) model, which contains multiple suppliers, multiple manufactures and multiple customers. This model is formulated as a fuzzy multi objective mixed-integer nonlinear programming (FMOMINLP) to address a comprehensive multi-site, multi-period and multi-product aggregate production planning (APP) problem under uncertainty. Four conflicting objectives are considered in the presented model simultaneously, which are (i) to minimize the total Cost of the SC (production Costs, workforce wage, hiring/firing and training Costs, transportation Cost, Inventory Holding Cost, raw material purchasing Cost, and shortage Cost), (ii) to improve customer satisfaction, (iii) to minimize the fluctuations in the rate of changes of workforce, and (iv) to maximize the total value of purchasing in order to consider the impact of qualitative performance criteria. This model is converted to multi-objective mixed-integer linear programming (MOMILP) through three steps of the developed method and then the MOMILP model is solved by two different methods. Additionally, comparison of these two methods is presented and the results are analyzed. Finally, the efficiency of the model is investigated by a real industry SC case study.
Hartanto Wong - One of the best experts on this subject based on the ideXlab platform.
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stocking decisions for repairable spare parts pooling in a multi hub system
International Journal of Production Economics, 2005Co-Authors: Hartanto Wong, Dirk Cattrysse, Dirk Van OudheusdenAbstract:Abstract This paper presents an analytical model for determining spare parts stocking levels in a single-item, multi-hub, multi-company, repairable Inventory system in which complete pooling of stock is permitted among the hubs and companies. The objective is to minimize the total system Cost which consists of Inventory Holding Cost, downtime Cost and transshipment Cost. We develop an approximation method to compute the logistical system performance measures needed for calculating the Cost function. To find the optimal stocking levels, a two-stage solution is proposed. In the first stage, the demands at all hubs are aggregated and treated as if occurring at a single location. The optimal number of total spare parts is determined by minimizing the sum of Inventory Holding Cost and downtime Cost. In the second stage, a heuristic procedure is developed to find the optimal allocation of the total spare parts to minimize the total transshipment Cost.
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modeling the interaction between operational and financial decisions in the Inventory pooling of repairable spare parts problem
Proceedings of the International Conference on Operations Research, 2003Co-Authors: Hartanto Wong, Dirk Cattrysse, Dirk Van OudheusdenAbstract:Equipment-intensive industries such as airlines, nuclear power plants, various process and manufacturing plants using complex machineries are often confronted with the difficult task of maintaining a high system performance while controlling their Inventory Holding Cost. An important type of components in such industries are called repairable items. The typical problem is to determine the optimal stocking level of spare parts. An insufficient stock of spare parts can lead to an excessive downtime Cost. On the other hand, maintaining an excessive number of spare parts increases the Cost of tying up capital in non-revenue-generating spare parts inventories.
