The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform
Ioannis Ch. Paschalidis - One of the best experts on this subject based on the ideXlab platform.
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Inventory Control for Supply Chains with Service Level Constraints: A Synergy between Large Deviations and Perturbation Analysis
Annals of Operations Research, 2004Co-Authors: Ioannis Ch. Paschalidis, Yong Liu, Christos G. Cassandras, Christos PanayiotouAbstract:We consider a model of a supply chain consisting of n production facilities in tandem and producing a single product class. External Demand is met from the finished goods inventory maintained in front of the most downstream facility (stage 1); Unsatisfied Demand is backlogged. We adopt a base-stock production policy at each stage of the supply chain, according to which the facility at stage i produces if inventory falls below a certain level w _ i and idles otherwise. We seek to optimize the hedging vector w =( w _1,..., w _ n ) to minimize expected inventory costs at all stages subject to maintaining the stockout probability at stage 1 below a prescribed level (service level constraint). We make rather general modeling assumptions on Demand and production processes that include autocorrelated stochastic processes. We solve this stochastic optimization problem by combining analytical (large deviations) and sample path-based (perturbation analysis) techniques. We demonstrate that there is a natural synergy between these two approaches.
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Large Deviations-Based Asymptotics for Inventory Control in Supply Chains
Operations Research, 2003Co-Authors: Ioannis Ch. Paschalidis, Yong LiuAbstract:We consider a model of a capacitated single-class supply chain consisting of production facilities (stages) in tandem. External Demand is met from the available finished goods inventory maintained in front of the most downstream facility; Unsatisfied Demand is backlogged. Every stage orders from its upstream facility, thus production is constrained by the local production capacity and the availability of upstream inventory. We propose production policies in two separate cases: (1) when each facility has information about its local inventory only, and (2) when each facility has knowledge of the total downstream inventory. In case (1) the proposed policy guarantees that stockout probabilities at each stage stay bounded below given constants (service level constraints). In case (2) the proposed policy minimizes total expected inventory cost subject to desirable service-level constraints. In both cases the parameters of the proposed policies are obtained analytically based on large deviations asymptotics, which leads to drastic computational savings compared to simulation. An important feature of our model is that it accommodates autocorrelated Demand and service processes, both critical features of modern failure-prone manufacturing systems. We demonstrate that detailed distributional information on Demand and service processes, which is incorporated into large deviations asymptotics, is critical in inventory control decisions. We discuss extensions to a multiclass setting and to a model where Unsatisfied Demand is lost instead of backordered.
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Probabilistic Service Level Guarantees in Make-to-Stock Manufacturing Systems
Operations Research, 2001Co-Authors: Dimitris Bertsimas, Ioannis Ch. PaschalidisAbstract:We consider a model of a multiclass make-to-stock manufacturing system. External Demand for each product class is met from the available finished goods inventory; Unsatisfied Demand is backlogged. The objective is to devise a production policy that minimizes inventory costs subject to guaranteeing stockout probabilities to stay bounded above by given constants e j , for each product classj( service level guarantees). Such a policy determines whether the facility should be producing ( idling decisions), and if it should, which product class ( sequencing decisions). Approximating the original system, we analyze a correspondingfluid model to make sequencing decisions and employlarge deviations techniques to make idling ones. We consider both linear and quadratic inventory cost structures to obtain apriority-based and ageneralized longest queue first-based production policy, respectively. An important feature of our model is that it accommodates autocorrelated Demand and service processes, both critical features of modern failure-prone manufacturing systems.
Michael Tortorella - One of the best experts on this subject based on the ideXlab platform.
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New insights on multi-state component criticality and importance
Reliability Engineering & System Safety, 2006Co-Authors: Jose Emmanuel Ramirez-marquez, Claudio M. Rocco, Bethel A. Gebre, David W. Coit, Michael TortorellaAbstract:In this paper, new importance measures for multi-state systems with multi-state components are introduced and evaluated. These new measures complement and enhance current work done in the area of multi-state reliability. In general, importance measures are used to evaluate and rank the criticality of component or component states with respect to system reliability. The focus of the study is to provide intuitive and clear importance measures that can be used to enhance system reliability from two perspectives: (1) how a specific component affects multi-state system reliability and (2) how a particular component state or set of states affects multi-state system reliability. The first measure Unsatisfied Demand index, provides insight regarding a component or component state contribution to Unsatisfied Demand. The second measure multi-state failure frequency index, elaborates on an approach that quantifies the contribution of a particular component or component state to system failure. Finally, the multi-state redundancy importance identifies where to allocate component redundancy as to improve system reliability. The findings of this study indicate that both perspectives can be used to complement each other and as an effective tool to assess component criticality. Examples illustrate and compare the proposed measures with previous multi-state importance measures.
Yong Liu - One of the best experts on this subject based on the ideXlab platform.
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Inventory Control for Supply Chains with Service Level Constraints: A Synergy between Large Deviations and Perturbation Analysis
Annals of Operations Research, 2004Co-Authors: Ioannis Ch. Paschalidis, Yong Liu, Christos G. Cassandras, Christos PanayiotouAbstract:We consider a model of a supply chain consisting of n production facilities in tandem and producing a single product class. External Demand is met from the finished goods inventory maintained in front of the most downstream facility (stage 1); Unsatisfied Demand is backlogged. We adopt a base-stock production policy at each stage of the supply chain, according to which the facility at stage i produces if inventory falls below a certain level w _ i and idles otherwise. We seek to optimize the hedging vector w =( w _1,..., w _ n ) to minimize expected inventory costs at all stages subject to maintaining the stockout probability at stage 1 below a prescribed level (service level constraint). We make rather general modeling assumptions on Demand and production processes that include autocorrelated stochastic processes. We solve this stochastic optimization problem by combining analytical (large deviations) and sample path-based (perturbation analysis) techniques. We demonstrate that there is a natural synergy between these two approaches.
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Large Deviations-Based Asymptotics for Inventory Control in Supply Chains
Operations Research, 2003Co-Authors: Ioannis Ch. Paschalidis, Yong LiuAbstract:We consider a model of a capacitated single-class supply chain consisting of production facilities (stages) in tandem. External Demand is met from the available finished goods inventory maintained in front of the most downstream facility; Unsatisfied Demand is backlogged. Every stage orders from its upstream facility, thus production is constrained by the local production capacity and the availability of upstream inventory. We propose production policies in two separate cases: (1) when each facility has information about its local inventory only, and (2) when each facility has knowledge of the total downstream inventory. In case (1) the proposed policy guarantees that stockout probabilities at each stage stay bounded below given constants (service level constraints). In case (2) the proposed policy minimizes total expected inventory cost subject to desirable service-level constraints. In both cases the parameters of the proposed policies are obtained analytically based on large deviations asymptotics, which leads to drastic computational savings compared to simulation. An important feature of our model is that it accommodates autocorrelated Demand and service processes, both critical features of modern failure-prone manufacturing systems. We demonstrate that detailed distributional information on Demand and service processes, which is incorporated into large deviations asymptotics, is critical in inventory control decisions. We discuss extensions to a multiclass setting and to a model where Unsatisfied Demand is lost instead of backordered.
Michael C Fu - One of the best experts on this subject based on the ideXlab platform.
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optimization of s s inventory systems with random lead times and a service level constraint
Management Science, 1998Co-Authors: Sridhar Bashyam, Michael C FuAbstract:A major assumption in the analysis of (s, S) inventory systems with stochastic lead times is that orders are received in the same sequence as they are placed. Even under this assumption, much of the work to date has focused on the unconstrained optimization of the system, in which a penalty cost for Unsatisfied Demand is assigned. The literature on constrained optimization, wherein a service level requirement needs to be met, is more sparse. In this paper, we consider the constrained optimization problem, where orders are allowed to cross in time. We propose a feasible directions procedure that is simulation based, and present computational results for a large number of test cases. In the vast majority of cases, we come within 5% of estimated optimality.
Michael Todinov - One of the best experts on this subject based on the ideXlab platform.
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Reliability and Risk Controlled by the Simultaneous Presence of Random Events on a Time Interval
ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, 2017Co-Authors: Michael TodinovAbstract:The paper treats the important problem related to risk controlled by the simultaneous presence of critical events, randomly appearing on a time interval and shows that the expected time fraction of simultaneously present events does not depend on the distribution of events durations. In addition, the paper shows that the probability of simultaneous presence of critical events is practically insensitive to the distribution of the events durations. These counter-intuitive results provide the powerful opportunity to evaluate the risk of overlapping of random events through the mean duration times of the events only, without requiring the distributions of the events durations or their variance. A closed-form expression for the expected fraction of Unsatisfied Demand for random Demands following a homogeneous Poisson process in a time interval is introduced for the first time. In addition, a closed-form expression related to the expected time fraction of Unsatisfied Demand, for a fixed number of consumers initiating random Demands with a specified probability, is also introduced for the first time. The concepts stochastic separation of random events based on the probability of overlapping and the average overlapped fraction are also introduced. Methods for providing stochastic separation and optimal stochastic separation achieving balance between risk and cost of risk reduction are presented.
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Evaluating the risk of Unsatisfied random Demand on a time interval
Artificial Intelligence Research, 2015Co-Authors: Michael TodinovAbstract:This paper focuses on an important and very common problem and presents a theoretical framework for solving it: “determining the risk of Unsatisfied request from users placing random Demands on a time interval”. For the common case of a single source servicing a number of consumers, a closed-form solution has been derived for the risk of collision of random Demands. Based on the closed-form solution, an efficient optimisation method has been developed for determining the optimal number of consumers that can be serviced by a single source, such that the probability of Unsatisfied Demand remains below a maximal tolerable level. A central part of the proposed theoretical framework is a general equation evaluating the risk of Unsatisfied Demand by the expected fraction of time of Unsatisfied Demand. The derived equation covers multiple sources servicing multiple consumers. Finally, the conducted parametric studies revealed an unexpected finding: the risk of collision of random Demands on a time interval is practically insensitive to the standard deviations of the durations of Demands. This surprising result provides the valuable opportunity to work with random Demand times characterised by their means only, without supplying their probability distributions or variances.
