An analysis of single item inventory systems with returns

Inventory systems with returns are systems in which there are units returned in a repairable state, as well as demands for units in a serviceable state, where the return and demand processes are independent. We begin by examining the control of a single item at a single location in which the stationary return rate is less than the stationary demand rate. This necessitates an occasional procurement of units from an outside source. We present a cost model of this system, which we assume is managed under a continuous review procurement policy, and develop a solution method for finding the policy parameter values. The key to the analysis is the use of a normally distributed random variable to approximate the steady-state distribution of net inventory. Next, we study a single item, two echelon system in which a warehouse (the upper echelon) supports N(N ? 1) retailers (the lower echelon). In this case, customers return units in a repairable state as well as demand units in a serviceable state at the retailer level only. We assume the constant system return rate is less than the constant system demand rate so that a procurement is required at certain times from an outside supplier. We develop a cost model of this two echelon system assuming that each location follows a continuous review procurement policy. We also present an algorithm for finding the policy parameter values at each location that is based on the method used to solve the single location problem.     

Scheduling of stochastic tasks on two parallel processors

We consider the problem of scheduling n tasks on two identical parallel processors. We show both in the case when the processing times for the n tasks are independent exponential random variables, and when they are independent hyperexponentials which are mixtures of two fixed exponentials, that the policy of performing tasks with longest expected processing time (LEPT) first minimizes the expected makespan, and that in the hyperexponential case the policy of performing tasks with shortest expected processing time (SEPT) first minimizes the expected flow time. The approach is simpler than the dynamic programming approach recently employed by Bruno and Downey.     

Benders' cuts guided large neighborhood search for the traveling umpire problem

This article introduces the use of Benders' cuts to guide a large neighborhood search to solve the traveling umpire problem, a sports scheduling problem inspired by the real‐life needs of the officials of a sports league. At each time slot, a greedy matching heuristic is used to construct a schedule. When an infeasibility is recognized first a single step backtracking is tried to resolve the infeasibility. If unsuccessful, Benders' cuts are generated to guide a large neighborhood search to ensure feasibility and to improve the solution. Realizing the inherent symmetry present in the problem, a large family of cuts are generated and their effectiveness is tested. The resulting approach is able to find better solutions to many instances of this problem. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Economic Losses From a Fire in a Dense-Packed U.S. Spent Fuel Pool

In 2013, the staff of the U.S. Nuclear Regulatory Commission estimated the reduction of the off-site economic losses from a fire in a drained U.S. spent fuel pool if fuel that had cooled for more than five years were transferred to dry cask storage—an option it called “expedited transfer.” In this article, it is shown that the savings would be much higher than the NRC estimated. Savings increase to about $2 trillion if: losses beyond 50 miles are included; the land-contamination threshold for long-term population relocation is changed to that used for the Chernobyl and Fukushima accidents and recommended by the U.S. Environmental Protection Agency; and, based on the experience of Japan, decontamination of land areas to levels acceptable for population return is assumed to take at least four years. If expedited transfer were implemented, the off-site economic losses would be reduced by about 98%.     

Parallel machine scheduling with eligibility constraints: A composite dispatching rule to minimize total weighted tardiness

We study a parallel machine scheduling problem, where a job j can only be processed on a specific subset of machines M_j, and the M_j subsets of the n jobs are nested. We develop a two‐phase heuristic for minimizing the total weighted tardiness subject to the machine eligibility constraints. In the first phase, we compute the factors and statistics that characterize a problem instance. In the second phase, we propose a new composite dispatching rule, the Apparent Tardiness Cost with Flexibility considerations (ATCF) rule, which is governed by several scaling parameters of which the values are determined by the factors obtained in the first phase. The ATCF rule is a generalization of the well‐known ATC rule which is very widely used in practice. We further discuss how to improve the dispatching rule using some simple but powerful properties without requiring additional computation time, and the improvement is quite satisfactory. We apply the Sequential Uniform Design Method to design our experiments and conduct an extensive computational study, and we perform tests on the performance of the ATCF rule using a real data set from a large hospital in China. We further compare its performance with that of the classical ATC rule. We also compare the schedules improved by the ATCF rule with what we believe are Near Optimal schedules generated by a general search procedure. The computational results show that especially with a low due date tightness, the ATCF rule performs significantly better than the well‐known ATC rule generating much improved schedules that are close to the Near Optimal schedules. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 249–267, 2017     

Layers of Experiments with Adaptive Combined Design

In the field of nanofabrication, engineers often face unique challenges in resource‐limited experimental budgets, the sensitive nature of process behavior with respect to controllable variables, and highly demanding tolerance requirements. To effectively overcome these challenges, this article proposes a methodology for a sequential design of experiments through batches of experimental runs, aptly named Layers of Experiments with Adaptive Combined Design (LoE/ACD). In higher layers, where process behavior is less understood, experimental regions cover more design space and data points are more spread out. In lower layers, experimental regions are more focused to improve understanding of process sensitivities in a local, data‐rich environment. The experimental design is a combination of a space‐filling and an optimal design with a tuning parameter that is dependent on the amount of information accumulated over the various layers. The proposed LoE/ACD method is applied to optimize a carbon dioxide (epet‐CO2) assisted deposition process for fabricating silver nanoparticles with pressure and temperature variables. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 127–142, 2015     

Effects of lethality in naval combat models

In the context of both discrete time salvo models and continuous time Lanchester models we examine the effect on naval combat of lethality: that is, the relative balance between the offensive and defensive attributes of the units involved. We define three distinct levels of lethality and describe the distinguishing features of combat for each level. We discuss the implications of these characteristics for naval decision‐makers; in particular, we show that the usefulness of the intuitive concept “more is better” varies greatly depending on the lethality level. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

Higher‐order Markov chain models for categorical data sequences*

In this paper we study higher‐order Markov chain models for analyzing categorical data sequences. We propose an efficient estimation method for the model parameters. Data sequences such as DNA and sales demand are used to illustrate the predicting power of our proposed models. In particular, we apply the developed higher‐order Markov chain model to the server logs data. The objective here is to model the users' behavior in accessing information and to predict their behavior in the future. Our tests are based on a realistic web log and our model shows an improvement in prediction. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

Generalized programming by linear approximation of the dual gradient: Equality constraints

A modified generalized programming procedure is presented for solving concave programming problems with equality constraints. The procedure constructs convenient linear approximations of the gradient of the dual and finds points where the approximating functions vanish. In the quadratic programming case, the procedure is finitely convergent. Global convergence is established for the non-quadratic case. Illustrative numerical examples are included.