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11
Three generic methods for evaluating stochastic multiechelon inventory systems. Working Paper
, 2007
"... Globalization, product proliferation and fast product innovation have significantly increased the complexities of supply chains in many industries. One of the most important advancements of supply chain management in recent years is the development of models and methodologies for controlling invento ..."
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Cited by 5 (3 self)
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Globalization, product proliferation and fast product innovation have significantly increased the complexities of supply chains in many industries. One of the most important advancements of supply chain management in recent years is the development of models and methodologies for controlling inventory in general supply networks under uncertainty, and their widespread applications to industry. These developments are based on three generic methods: the queueinginventory method, the leadtime demand method and the flowunit method. In this paper, we compare and contrast these methods by discussing their differences and connections, and showing how to apply them systematically to characterize and evaluate various network topologies with different supply processes, inventory policies and demand processes. Our objective is to forge links among research strands on various network topologies and to develop unified methodologies.
Simulation Optimization for the Stochastic Economic Lot Scheduling Problem with SequenceDependent Setup Times
"... We consider the stochastic economic lot scheduling problem (SELSP) with lost sales and random demand, where switching between products is subject to sequencedependent setup times. We propose a solution based on simulation optimization using an iterative twostep procedure which combines global poli ..."
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Cited by 1 (0 self)
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We consider the stochastic economic lot scheduling problem (SELSP) with lost sales and random demand, where switching between products is subject to sequencedependent setup times. We propose a solution based on simulation optimization using an iterative twostep procedure which combines global policy search with local search heuristics for the traveling salesman sequencing subproblem. To optimize the production cycle, we compare two criteria: minimizing total setup times and evenly distributing setups to obtain a more regular production cycle. Based on a numerical study, we find that a policy with a balanced production cycle outperforms other policies with unbalanced cycles.
1 Project Description
"... This project will examine the problem of controlling the inventory of an product with partially observed, nonstationary, random demand. The probability distribution for the demand process is not known with certainty at any point in time, and this distribution may randomly change from one period to ..."
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This project will examine the problem of controlling the inventory of an product with partially observed, nonstationary, random demand. The probability distribution for the demand process is not known with certainty at any point in time, and this distribution may randomly change from one period to the next. The underlying demand process is partially observed through the previous demand observations which are themselves random. Because the control decisions are made with only partial information about the demand process, the level of uncertainty and the cost of suboptimal decisions is much higher than for most problems considered in the research literature. The nonstationary aspect of the demand process further increases the uncertainty because older observations of demand are less valuable in identifying the current state than more recent observations. This problem is an accurate representation of the inventory control problems faced by many organizations. However, it has not been directly addressed in the inventory literature or by existing decision support systems � therefore, inventory managers are forced to make potentially costly simplifying assumptions when addressing this challenging problem. The primary objectives of this project are to 1. Develop a modeling framework that adequately captures the important aspects of the problem,
unknown title
, 2008
"... Comparison of heuristics for an economic lot scheduling problem … 437 ..."
Lead Time Minimization of MultiProduct, SingleProcessor System: A Comparison of Cyclic Policies
, 2005
"... Lead time minimization of a multiproduct, singleprocessor system: A comparison of cyclic policies ..."
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Lead time minimization of a multiproduct, singleprocessor system: A comparison of cyclic policies
STOCHASTIC MODELS OF MANUFACTURING AND SERVICE OPERATIONS SMMSO 2009 The Stochastic Economic Lot Sizing Problem for Continuous MultiGrade Production
"... We study a variant of the Stochastic Economic Lot Scheduling Problem (SELSP) in which a single production facility must produce several grades to meet random stationary demand for each grade from a common finishedgoods (FG) inventory buffer with limited storage capacity. Demand that can not be sati ..."
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We study a variant of the Stochastic Economic Lot Scheduling Problem (SELSP) in which a single production facility must produce several grades to meet random stationary demand for each grade from a common finishedgoods (FG) inventory buffer with limited storage capacity. Demand that can not be satisfied directly from inventory is lost. Raw material is always available, and the production facility produces at a constant rate. When the facility is set up to produce a particular grade, the only allowable changeovers are from that grade to next lower or higher grade. All changeover times are deterministic and equal to each other. There is a changeover cost per changeover occasion, a spillover cost per unit of product in excess, whenever there is not enough space in the FG buffer to store the produced grade, and a lostsales cost per unit short, whenever there is not enough FG inventory to satisfy demand. We model the SELSP as a discretetimeMarkov Decision Process (MDP), where in each time period we must decide whether to initiate a changeover to a neighboring grade or keep the setup of the production facility unchanged, based on the current state of the system, which is determined by the current setup of the facility and the FG inventory levels of all the grades. The goal is to minimize the infinitehorizon expected average cost. For 2grade and 3grade problems we can numerically solve the exact MDP problem using successive approximation. For problems with more than 3 grades, we develop a heuristic solution which is based on approximating the original multigrade problem into many 3grade subproblems and numerically solving each subproblem using successive approximation. We present and discuss numerical results for problem incidences with 2, 4 and 5 grades, using both the exact and the heuristic procedure.
©2005 INFORMS Recovering Cyclic Schedules Using Dynamic ProduceUpTo Policies
"... We consider facilities that follow a cyclic schedule to replenish the inventory of a set of items through production by a shared resource. We introduce a dynamic produceupto policy that recovers the target cyclic schedule after a single disruption, and is also shown to be effective when disruption ..."
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We consider facilities that follow a cyclic schedule to replenish the inventory of a set of items through production by a shared resource. We introduce a dynamic produceupto policy that recovers the target cyclic schedule after a single disruption, and is also shown to be effective when disruptions are more frequent. Our policy is more flexible than traditional recovery policies in that our policy is able to adjust the amount of idle time observed during recovery in response to disruptions, and yet reestablish the target idle time as the schedule recovers. This results in a policy that not only saves cost and time, but provides better schedule stability than other recovery procedures. Furthermore, unlike simple produceupto policies, our policy is anticipatory—replenishments will be speeded up or delayed, whichever is necessary, to help avoid congestion at the shared resource. In addition, recovery is controlled by a single “knob ” or parameter that can tune recovery to be fast and aggressive (frequent setups and small batches) or slow and methodical (few setups and larger batches). Finally, our policy is easy to implement, augmenting a traditional produceupto policy with a simple set of counters that control replenishment decisions. Subject classifications: production/scheduling: recovering cyclic schedules, bucket brigades; mathematics: discrete dynamical systems.
unknown title
, 2006
"... A statedependent polling model with klimited service ..."
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The Stochastic Economic Lot Scheduling Problem: A Survey
, 2005
"... We consider the production of multiple standardized products on a single machine with limited capacity and setup times under random demands and random production times, i.e., the socalled stochastic economic lot scheduling problem (SELSP). The main task for the production manager in this setting i ..."
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We consider the production of multiple standardized products on a single machine with limited capacity and setup times under random demands and random production times, i.e., the socalled stochastic economic lot scheduling problem (SELSP). The main task for the production manager in this setting is the construction of a production plan for the machine that minimizes the total costs, i.e., the sum of holding, backlogging and setup costs. Based on the critical elements of such a production plan, we give a classification and extensive overview of the research on the SELSP together with an indication of open research areas.
Part grouping in ELSP
"... Scheduling the production of several products with deterministic and constant demand on a single facility, with the objective of reducing the sum of holding costs and setup costs, has been studied in the literature under the name of ELSP Economic Lot Scheduling Problem. The economic lot scheduling ..."
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Scheduling the production of several products with deterministic and constant demand on a single facility, with the objective of reducing the sum of holding costs and setup costs, has been studied in the literature under the name of ELSP Economic Lot Scheduling Problem. The economic lot scheduling problem has been investigated extensively in the literature over