The Experts below are selected from a list of 72177 Experts worldwide ranked by ideXlab platform
Ickhyun Kwon - One of the best experts on this subject based on the ideXlab platform.
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situation reactive approach to vendor managed inventory problem
Expert Systems With Applications, 2009Co-Authors: Choonjong Kwak, Chang Ouk Kim, Jin Sung Choi, Ickhyun KwonAbstract:In this research, we deal with VMI (Vendor Managed Inventory) problem where one supplier is responsible for managing a retailer's inventory under unstable Customer Demand situation. To cope with the nonstationary Demand situation, we develop a retrospective action-reward learning model, a kind of reinforcement learning techniques, which is faster in learning than conventional action-reward learning and more suitable to apply to the control domain where rewards for actions vary over time. The learning model enables the inventory control to become situation reactive in the sense that replenishment quantity for the retailer is automatically adjusted at each period by adapting to the change in Customer Demand. The replenishment quantity is a function of compensation factor that has an effect of increasing or decreasing the replenishment amount. At each replenishment period, a cost-minimizing compensation factor value is chosen in the candidate set. A simulation based experiment gave us encouraging results for the new approach.
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an adaptive inventory control model for a supply chain with nonstationary Customer Demands
Lecture Notes in Computer Science, 2006Co-Authors: Jungeol Baek, Chang Ouk Kim, Ickhyun KwonAbstract:In this paper, we propose an adaptive inventory control model for a supply chain consisting of one supplier and multiple retailers with nonstationary Customer Demands. The objective of the adaptive inventory control model is to minimize inventory related cost. The inventory control parameter is safety lead time. Unlike most extant inventory control approaches, modeling the uncertainty of Customer Demand as a statistical distribution is not a prerequisite in this model. Instead, using a reinforcement learning technique called action-reward based learning, the control parameter is designed to adaptively change as Customer Demand pattern changes. A simulation based experiment was performed to compare the performance of the adaptive inventory control model.
Dmitry Krass - One of the best experts on this subject based on the ideXlab platform.
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profit maximizing distributed service system design with congestion and elastic Demand
Transportation Science, 2012Co-Authors: Robert Aboolian, Oded Berman, Dmitry KrassAbstract:In this paper we develop a service network design model that explicitly takes into account the elasticity of Customer Demand with respect to travel distance and congestion delays. The model incorporates a feedback loop between Customer Demand and congestion at the facilities. The problem is to determine the number of facilities, their locations, their service capacity, and the assignment of Customers to facilities so as to maximize the overall profit of the system. Two versions of the problem are presented. In one, each facility is modeled as an M/M/1 queuing system where the service rate is a decision variable; in the other one, the facility is modeled as an M/M/k queuing model where the service rate is given, but the number k is a decision variable. An exact algorithm and heuristics are developed and tested via computational experiments. Although our model is of the “directed choice” type where the assignment of Customers to facilities is controlled by the decision maker, computational results show that in the vast majority of cases the Customers are assigned to the utility-maximizing facility, indicating that there is no conflict between the Customers' and decision makers' goals. A case study of locating preventive medicine clinics in Toronto, Ontario, illustrates the model.
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competitive facility location and design problem
European Journal of Operational Research, 2007Co-Authors: Robert Aboolian, Oded Berman, Dmitry KrassAbstract:Abstract We develop a spatial interaction model that seeks to simultaneously optimize location and design decisions for a set of new facilities. The facilities compete for Customer Demand with pre-existing competitive facilities and with each other. The Customer Demand is assumed to be elastic, expanding as the utility of the service offered by the facilities increases. Increases in the utility can be achieved by increasing the number of facilities, design improvements, or locating facilities closer to the Customer. We show that our model is able to capture some of the principal trade-offs involved in facility location and design decisions, including Demand cannibalization, market expansion, and design/location trade-offs. Managerial insights are obtained through sensitivity analysis of the model and through several illustrative examples. An efficient near-optimal solution approach, with adjustable error bound, is developed for the special case where only a finite number of design alternatives are available. Several heuristic approaches capable of handling large instances are also presented.
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competitive facility location model with concave Demand
European Journal of Operational Research, 2007Co-Authors: Robert Aboolian, Oded Berman, Dmitry KrassAbstract:Abstract We consider a spatial interaction model for locating a set of new facilities that compete for Customer Demand with each other, as well as with some pre-existing facilities to capture the “market expansion” and the “market cannibalization” effects. Customer Demand is assumed to be a concave non-decreasing function of the total utility derived by each Customer from the service offered by the facilities. The problem is formulated as a non-linear Knapsack problem, for which we develop a novel solution approach based on constructing an efficient piecewise linear approximation scheme for the objective function. This allows us to develop exact and α-optimal solution approaches capable of dealing with relatively large-scale instances of the model. We also develop a fast Heuristic Algorithm for which a tight worst-case error bound is established.
Chang Ouk Kim - One of the best experts on this subject based on the ideXlab platform.
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situation reactive approach to vendor managed inventory problem
Expert Systems With Applications, 2009Co-Authors: Choonjong Kwak, Chang Ouk Kim, Jin Sung Choi, Ickhyun KwonAbstract:In this research, we deal with VMI (Vendor Managed Inventory) problem where one supplier is responsible for managing a retailer's inventory under unstable Customer Demand situation. To cope with the nonstationary Demand situation, we develop a retrospective action-reward learning model, a kind of reinforcement learning techniques, which is faster in learning than conventional action-reward learning and more suitable to apply to the control domain where rewards for actions vary over time. The learning model enables the inventory control to become situation reactive in the sense that replenishment quantity for the retailer is automatically adjusted at each period by adapting to the change in Customer Demand. The replenishment quantity is a function of compensation factor that has an effect of increasing or decreasing the replenishment amount. At each replenishment period, a cost-minimizing compensation factor value is chosen in the candidate set. A simulation based experiment gave us encouraging results for the new approach.
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an adaptive inventory control model for a supply chain with nonstationary Customer Demands
Lecture Notes in Computer Science, 2006Co-Authors: Jungeol Baek, Chang Ouk Kim, Ickhyun KwonAbstract:In this paper, we propose an adaptive inventory control model for a supply chain consisting of one supplier and multiple retailers with nonstationary Customer Demands. The objective of the adaptive inventory control model is to minimize inventory related cost. The inventory control parameter is safety lead time. Unlike most extant inventory control approaches, modeling the uncertainty of Customer Demand as a statistical distribution is not a prerequisite in this model. Instead, using a reinforcement learning technique called action-reward based learning, the control parameter is designed to adaptively change as Customer Demand pattern changes. A simulation based experiment was performed to compare the performance of the adaptive inventory control model.
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adaptive inventory control models for supply chain management
The International Journal of Advanced Manufacturing Technology, 2005Co-Authors: Chang Ouk Kim, Jin Jun, J K Baek, R L Smith, Yeongdae KimAbstract:Uncertainties inherent in Customer Demands make it difficult for supply chains to achieve just-in-time inventory replenishment, resulting in loosing sales opportunities or keeping excessive chain-wide inventories. In this paper, we propose two adaptive inventory-control models for a supply chain consisting of one supplier and multiple retailers. The one is a centralized model and the other is a decentralized model. The objective of the two models is to satisfy a target service level predefined for each retailer. The inventory-control parameters of the supplier and retailers are safety lead time and safety stocks, respectively. Unlike most extant inventory-control approaches, modelling the uncertainty of Customer Demand as a statistical distribution is not a prerequisite in the two models. Instead, using a reinforcement learning technique called action-value method, the control parameters are designed to adaptively change as Customer-Demand patterns changes. A simulation-based experiment was performed to compare the performance of the two inventory-control models.
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adaptive inventory control models in a supply chain with nonstationary Customer Demand
Journal of Korean Institute of Industrial Engineers, 2005Co-Authors: Jungeol Baek, Chang Ouk Kim, Jin JunAbstract:Uncertainties inherent in Customer Demand patterns make it difficult for supply chains to achieve just-in-time inventory replenishment, resulting in loosing sales opportunity or keeping excessive chain wide inventories. In this paper, we propose two intelligent adaptive inventory control models for a supply chain consisting of one supplier and multiple retailers, with the assumption of information sharing. The inventory control parameters of the supplier and retailers are order placement time to an outside source and reorder points in terms of inventory position, respectively. Unlike most extant inventory control approaches, modeling the uncertainty of Customer Demand as a stationary statistical distribution is not necessary in these models. Instead, using a reinforcement learning technique, the control parameters are designed to adaptively change as Customer Demand patterns change. A simulation based experiment was performed to compare the performance of the inventory control models.
Dobrila Petrovic - One of the best experts on this subject based on the ideXlab platform.
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Coordinated control of distribution supply chains in the presence of fuzzy Customer Demand
European Journal of Operational Research, 2008Co-Authors: Dobrila Petrovic, Keith Burnham, Ying Xie, Radivoj PetrovićAbstract:This paper considers a single product inventory control in a Distribution Supply Chain (DSC). The DSC operates in the presence of uncertainty in Customer Demands. The Demands are described by imprecise linguistic expressions that are modelled by discrete fuzzy sets. Inventories at each facility within the DSC are replenished by applying periodic review policies with optimal order up-to-quantities. Fuzzy Customer Demands imply fuzziness in inventory positions at the end of review intervals and in incurred relevant costs per unit time interval. The determination of the minimum of defuzzified total cost of the DSC is a complex problem which is solved by applying decomposition; the original problem is decomposed into a number of simpler independent optimisation subproblems, where each retailer and the warehouse determine their optimum periodic reviews and order up-to-quantities. An iterative coordination mechanism is proposed for changing the review periods and order up-to-quantities for each retailer and the warehouse in such a way that all parties within the DSC are satisfied with respect to total incurred costs per unit time interval. Coordination is performed by introducing fuzzy constraints on review periods and fuzzy tolerances on retailers and warehouse costs in local optimisation subproblems. © 2007.
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a heuristic procedure for the two level control of serial supply chains under fuzzy Customer Demand
International Journal of Production Economics, 2006Co-Authors: Ying Xie, Dobrila Petrovic, Keith J BurnhamAbstract:Abstract This paper presents a new hierarchical, two-level approach to inventory management and control in supply chains (SCs). A SC is viewed as a large-scale system that consists of production and inventory units, organised in a serial structure. It is supposed that the SC operates under uncertainty in Customer Demand, which is described by imprecise terms and modelled by fuzzy sets. Overall SC inventories control is achieved at two levels. First, a SC problem is decomposed into a number of subproblems related to its constituent parts, which form a followers level. Each follower is optimised independently according to its local objective. In order to improve overall SC performance, a leader level coordinates SC inventories control by modifying the optimisation subproblems at the followers level. This process is repeated iteratively until a satisfactory overall SC performance is achieved.
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simulation of supply chain behaviour and performance in an uncertain environment
International Journal of Production Economics, 2001Co-Authors: Dobrila PetrovicAbstract:Abstract This paper describes a special purpose simulation tool, SCSIM, developed for analysing supply chain (SC) behaviour and performance in the presence of uncertainty. SCSIM treats a SC which includes a raw material inventory, a number of in-process inventories, an end-product inventory and production facilities between them, linked in a series. Main sources of uncertainty inherent in the serial SC and its environment are identified, including Customer Demand, external supply of raw material and lead times to the facilities. Uncertainties perceived in these SC data are described by imprecise natural language expressions and they are modelled in SCSIM by fuzzy sets. Two types of models are combined in SCSIM: (1) SC fuzzy analytical models to determine the optimal order-up-to levels for all inventories in a fuzzy environment and (2) a SC simulation model to evaluate SC performance achieved over time by applying the order-up-to levels recommended by the fuzzy models. SCSIM can be used for various SCs analyses to gain a better understanding of SC behaviour and performance in the presence of uncertainty and to enhance decision making on operational SC control parameters. The application of SCSIM in analysing and quantifying the effects of changing uncertainty in Customer Demand is discussed and illustrated by a numerical example.
Robert Aboolian - One of the best experts on this subject based on the ideXlab platform.
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profit maximizing distributed service system design with congestion and elastic Demand
Transportation Science, 2012Co-Authors: Robert Aboolian, Oded Berman, Dmitry KrassAbstract:In this paper we develop a service network design model that explicitly takes into account the elasticity of Customer Demand with respect to travel distance and congestion delays. The model incorporates a feedback loop between Customer Demand and congestion at the facilities. The problem is to determine the number of facilities, their locations, their service capacity, and the assignment of Customers to facilities so as to maximize the overall profit of the system. Two versions of the problem are presented. In one, each facility is modeled as an M/M/1 queuing system where the service rate is a decision variable; in the other one, the facility is modeled as an M/M/k queuing model where the service rate is given, but the number k is a decision variable. An exact algorithm and heuristics are developed and tested via computational experiments. Although our model is of the “directed choice” type where the assignment of Customers to facilities is controlled by the decision maker, computational results show that in the vast majority of cases the Customers are assigned to the utility-maximizing facility, indicating that there is no conflict between the Customers' and decision makers' goals. A case study of locating preventive medicine clinics in Toronto, Ontario, illustrates the model.
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competitive facility location and design problem
European Journal of Operational Research, 2007Co-Authors: Robert Aboolian, Oded Berman, Dmitry KrassAbstract:Abstract We develop a spatial interaction model that seeks to simultaneously optimize location and design decisions for a set of new facilities. The facilities compete for Customer Demand with pre-existing competitive facilities and with each other. The Customer Demand is assumed to be elastic, expanding as the utility of the service offered by the facilities increases. Increases in the utility can be achieved by increasing the number of facilities, design improvements, or locating facilities closer to the Customer. We show that our model is able to capture some of the principal trade-offs involved in facility location and design decisions, including Demand cannibalization, market expansion, and design/location trade-offs. Managerial insights are obtained through sensitivity analysis of the model and through several illustrative examples. An efficient near-optimal solution approach, with adjustable error bound, is developed for the special case where only a finite number of design alternatives are available. Several heuristic approaches capable of handling large instances are also presented.
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competitive facility location model with concave Demand
European Journal of Operational Research, 2007Co-Authors: Robert Aboolian, Oded Berman, Dmitry KrassAbstract:Abstract We consider a spatial interaction model for locating a set of new facilities that compete for Customer Demand with each other, as well as with some pre-existing facilities to capture the “market expansion” and the “market cannibalization” effects. Customer Demand is assumed to be a concave non-decreasing function of the total utility derived by each Customer from the service offered by the facilities. The problem is formulated as a non-linear Knapsack problem, for which we develop a novel solution approach based on constructing an efficient piecewise linear approximation scheme for the objective function. This allows us to develop exact and α-optimal solution approaches capable of dealing with relatively large-scale instances of the model. We also develop a fast Heuristic Algorithm for which a tight worst-case error bound is established.
