摘要 :
This paper examines the impact of customer order sizes on a make-to-stock system with multiple demand classes. We first characterize the manufacturer's optimal production and rationing policies when the demand is nonunitary and lo...
展开
This paper examines the impact of customer order sizes on a make-to-stock system with multiple demand classes. We first characterize the manufacturer's optimal production and rationing policies when the demand is nonunitary and lost if unsatisfied. We also investigate the optimal policies of a backorder system with two demand classes and fixed order sizes. Through a numerical study, we show the effects of batch orders on the manufacturer's inventory cost as well as on the benefit of optimal stock rationing. It is shown that batch ordering may reduce the manufacturer's overall cost if carefully introduced in a first-come-first-served (FCFS) system. With the same effective demand rates, the customers' order sizes also have a strong impact on the benefit of optimal stock rationing.
收起
摘要 :
We consider the notoriously difficult discrete-time inventory model with stochastic demands, a constant lead time, and lost sales. We show that the effective state space is a relatively manageable compact set. Then, we test variou...
展开
We consider the notoriously difficult discrete-time inventory model with stochastic demands, a constant lead time, and lost sales. We show that the effective state space is a relatively manageable compact set. Then, we test various plausible heuristics. We find that several perform reasonably well, although none is perfect. However, the standard base-stock policy (a direct analogue of the optimal policy for a backlog system) performs badly. We also show that the optimal cost is increasing in the lead time.
收起
摘要 :
Matching blood supply and demand in an efficient manner is the key to minimize the shortage and waste in blood supply chain management. Blood heterogeneous demand further increases the difficulty and brings much challenges to bloo...
展开
Matching blood supply and demand in an efficient manner is the key to minimize the shortage and waste in blood supply chain management. Blood heterogeneous demand further increases the difficulty and brings much challenges to blood supply chain management. To alleviate above problems, we build blood ordering and collection models in blood supply chain under two emergency replenishment strategies by considering blood heterogeneous demand. The blood heterogeneous demand is modelled as two demand classes, i.e., high-priority stochastic emergency demand and low-priority regular deterministic demand. We examine the optimal ordering policy of the hospital and optimal collection policy of the blood center. The effect of different emergency replenishment strategies and high-priority stochastic emergency demand fluctuation are analysed. Through numerical analysis, we find that facing a low demand fluctuation, the hospital prefers to order enough blood instead of replenishing during the period, while the blood center prefers to provide emergency replenishment for both demands. In the situation of high fluctuation, emergency replenishment for both demands is the best for the hospital while the blood center would not like to provide replenishment and hope the hospital to order for once. However, emergency replenishment for both demands is always better off for the entire blood supply chain. The hospital and blood center can be coordinated to agree on the emergency replenishment for both demands and a Pareto improvement can be achieved.
收起
摘要 :
This paper is motivated by two phenomena observed in many queueing systems in practice. The first is the partitioning of server capacity among different customers based on their service time requirements. The second is rush hour d...
展开
This paper is motivated by two phenomena observed in many queueing systems in practice. The first is the partitioning of server capacity among different customers based on their service time requirements. The second is rush hour demand where a large number of customers arrive over a short period of time followed by few or no arrivals for an extended period thereafter. We study a system with multiple parallel servers and multiple customer classes. The servers can be partitioned into server groups, each dedicated to a single customer class. The system operates under a rush hour regime with a large number of customers arriving at the beginning of the rush hour period. We show that this allows us to reduce the problem to one that is deterministic and for which closed-form solutions can be obtained. We compare the performance of the system with and without server partitioning during rush hour and address three basic questions. (1) Is partitioning beneficial to the system? (2) Is it equally beneficial to all customer classes? (3) If it is implemented, what is an optimal partition? We evaluate the applicability of our results to systems where customers arrive over time using (1) deterministic fluid models and (2) simulation models for systems with stochastic interarrival times.
收起
摘要 :
We consider a manufacturer serving two customer classes where one wants the item immediately and the second receives a discount to accept a delay. We show that an (S, R, B) base-stock policy is optimal under differentiation and no...
展开
We consider a manufacturer serving two customer classes where one wants the item immediately and the second receives a discount to accept a delay. We show that an (S, R, B) base-stock policy is optimal under differentiation and non-differentiation where S, R, and B are the order-up-to, reserve-up-to, and backlog-up-to amounts. (C) 2007 Elsevier B.V. All rights reserved.
收起
摘要 :
This article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unles...
展开
This article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unless otherwise noted. For more information, contact permissions@informs.org. The Publisher does not warrant or guarantee the article's accuracy, completeness, merchantability, fitness for a particular purpose, or non-infringement. Descriptions of, or references to, products or publications, or inclusion of an advertisement in this article, neither constitutes nor implies a guarantee, endorsement, or
收起
摘要 :
In retail stores, there is an increasing need for predicting item demand using accumulated purchase history data to cope with the fluctuating consumer demands. These fluctuations in item demand are influenced by e10.14742/ajet.ter...
展开
In retail stores, there is an increasing need for predicting item demand using accumulated purchase history data to cope with the fluctuating consumer demands. These fluctuations in item demand are influenced by e10.14742/ajet.ternal factors and consumer preferences. Among these, store characteristics and weather conditions, which are closely related to consumer behavior, have strong effects on item demand. For this reason, it is very important to quantitatively grasp demand fluctuations of items that are influenced by changes in weather conditions for each store by using an integrated analysis of the purchase history data of many stores and weather conditions. In this research, we focus on the temperature difference, which is the average temperature difference from the previous day, as a weather condition affecting item sales. Because consumer feeling about a temperature is dependent on the temperature difference from the previous day, it is meaningful to construct a prediction model using this information. In this research, we propose a latent class model to e10.14742/ajet.press the relationship between weather conditions, store characteristics, and item demand fluctuation. Also, through an analysis e10.14742/ajet.periment using an actual data set, we show the usefulness of the proposed model by e10.14742/ajet.tracting items that are influenced by weather conditions.
收起
摘要 :
Stock rationing is an inventory policy that allows differential treatment of customer classes without using separate inventories. In this paper, we propose a dynamic rationing policy for continuous-review inventory systems, which ...
展开
Stock rationing is an inventory policy that allows differential treatment of customer classes without using separate inventories. In this paper, we propose a dynamic rationing policy for continuous-review inventory systems, which utilizes the information on the status of the outstanding replenishment orders. For both backordering and lost sales environments, we conduct simulation studies to compare the performance of the dynamic policy with the static critical level and the common stock policies and quantify the gain obtained. We propose two new bounds on the optimum dynamic rationing policy that enables us to tell how much of the potential gain the proposed dynamic policy realizes. We discuss the conditions under which stock rationing - both dynamic and static - is beneficial and assess the value of the dynamic policy.
收起
摘要 :
We study inventory rationing in a system with multiple demand classes and lost sales. It is assumed to have at most one outstanding order, resulting in two periods in an order cycle separated by the time of order release. We revie...
展开
We study inventory rationing in a system with multiple demand classes and lost sales. It is assumed to have at most one outstanding order, resulting in two periods in an order cycle separated by the time of order release. We review the most related work by Melchiors (2001,2003) (Ph.D. thesis, University of Aarhus, Int. J. Prod. Econ. 81-82, (11), 461-468), and find that the existing approximated and optimal policies are not easy to obtain due to computational complexity. Also as the rationing issue before order release is not well addressed in literature, in this paper we prove the static rationing being optimal. Furthermore in such a system with two distinct periods, the optimal rationing policy is a combination of a dynamic policy during the replenishment lead time and a static policy before order release. In order to make the rationing policies to be readily used in practice, we introduce two approximated methods for calculating the rationing levels in two periods, respectively. The results, in particular the combination of static and dynamic rationing, outperform the existing approximations in literature. In addition, the computation is obviously simplified due to the efficient algorithm of dynamic rationing and the explicit expressions of static rationing. (C) 2015 Elsevier B.V. All rights reserved.
收起
摘要 :
In this paper, we consider an inventory problem with two demand classes having different priorities. The appropriate policy of rationing the available stock, i.e. reserving some stock for meeting prospective future demand of prefe...
展开
In this paper, we consider an inventory problem with two demand classes having different priorities. The appropriate policy of rationing the available stock, i.e. reserving some stock for meeting prospective future demand of preferred customers at the expense of deliberately losing some of the currently materialized demand of lower demand class(es), relies on the estimation of the future demand. Utilizing current signals on future demand, which we refer to as imperfect advance demand information (ADI), decreases uncertainty on future demand and may help to make better decisions on when to start rejecting lower class demand. We develop a model that incorporates imperfect ADI with inventory ordering (replenishment) decision and rationing available stock. In a two-period setting, we show some structural properties, solve the rationing problem, and propose solution methods based on Monte Carlo simulation for the ordering problem. We conduct numerical tests to measure the impact of system parameters on the expected value of imperfect ADI, and provide useful managerial insights.
收起
原文获取