Heuristic Computation of Periodic-Review Base Stock Inventory Policies

Heuristic Computation of Periodic-Review Base Stock Inventory Policie

Stock Optimization in Emergency Resupply Networks under Stuttering Poisson Demand

We consider a network in which field stocking locations (FSLs) manage multiple parts according to an (S-1,S) policy. Demand processes for the parts are assumed to be independent stuttering Poisson processes. Regular replenishments to an FSL occur from a regional stocking location (RSL) that has an unlimited supply of each part type. Demand in excess of supply at an FSL is routed to an emergency stocking location (ESL), which also employs an (S-1,S) policy to manage its inventory. Demand in excess of supply at the ESL is backordered. Lead time from the ESL to each FSL is assumed to be negligible compared to the RSL-ESL resupply time. In companion papers we have shown how to approximate the joint probability distributions of units on hand, units in regular resupply, and units in emergency resupply. In this paper, we focus on the problem of determining the stock levels at the FSLs and ESL across all part numbers that minimize backorder, and emergency resupply costs subject to an inventory investment budget constraint. The problem is shown to be a nonconvex integer programming problem, and we explore a collection of heuristics for solving the optimization problem

Optimizing Inventory in a Multi-Echelon Multi-Item Supply Chain with Time-Based Customer Service Level Agreements

Optimizing Inventory in a Multi-Echelon Multi-Item Supply Chain with Time-Based Customer Service Level Agreement

COVID-19 Models for Hospital Surge Capacity Planning: A Systematic Review

Objective: Health system preparedness for coronavirus disease (COVID-19) includes projecting the number and timing of cases requiring various types of treatment. Several tools were developed to assist in this planning process. This review highlights models that project both caseload and hospital capacity requirements over time. Methods: We systematically reviewed the medical and engineering literature according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We completed searches using PubMed, EMBASE, ISI Web of Science, Google Scholar, and the Google search engine. Results: The search strategy identified 690 articles. For a detailed review, we selected 6 models that met our predefined criteria. Half of the models did not include age-stratified parameters, and only 1 included the option to represent a second wave. Hospital patient flow was simplified in all models; however, some considered more complex patient pathways. One model included fatality ratios with length of stay (LOS) adjustments for survivors versus those who die, and accommodated different LOS for critical care patients with or without a ventilator. Conclusion: The results of our study provide information to physicians, hospital administrators, emergency response personnel, and governmental agencies on available models for preparing scenario-based plans for responding to the COVID-19 or similar type of outbreak

Approximation algorithms and hardness results for the joint replenishment Problepm with constant demands

19th Annual European Symposium, Saarbrücken, Germany, September 5-9, 2011. ProceedingsIn the Joint Replenishment Problem (JRP), the goal is to coordinate the replenishments of a collection of goods over time so that continuous demands are satisfied with minimum overall ordering and holding costs. We consider the case when demand rates are constant. Our main contribution is the first hardness result for any variant of JRP with constant demands. When replenishments per commodity are required to be periodic and the time horizon is infinite (which corresponds to the so-called general integer model with correction factor), we show that finding an optimal replenishment policy is at least as hard as integer factorization. This result provides the first theoretical evidence that the JRP with constant demands may have no polynomial-time algorithm and that relaxations and heuristics are called for. We then show that a simple modification of an algorithm by Wildeman et al. (1997) for the JRP gives a fully polynomial-time approximation scheme for the general integer model (without correction factor). We also extend their algorithm to the finite horizon case, achieving an approximation guarantee asymptotically equal to √9/8

Testing an AHP model for aircraft spare parts

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. This research aims to develop and test a practical and precise multi-criteria classification of aircraft spare part inventory management, using the Analytical Hierarchy Process (AHP), in order to maintain aircraft operational availability and to reduce unnecessary downtime. In this study, a multi-criteria model is developed within a large-scale aircraft maintenance and repair firm in Indonesia. Spare parts data were extracted from the inventory system and analysed to triangulate the model outcome, demonstrate its validity and compare the results against the mathematical results generated by the system’s algorithm. The findings suggest that AHP can lead to a transparent, rapid and systematic classification model, this is highly accurate and precise with an outcome comparable to traditional methods. The conclusion proposes that the AHP classification model is more effective and transparent than the currently available mathematical methods. This research contributes to the development of the multi-criteria inventory classification literature in the context of the aviation industry

Exact and Approximate Analysis of Multi-Echelon, Multi-Indenture Spare Parts Systems with Commonality

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Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject