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.     

Computation of constrained optimum quantities and reorder points for time-weighted backorder penalties

The purpose of this paper and the accompanying tables is to facilitate the calculation of constrained optimum order quantities and reorder points for an inventory control system where the criterion of optimality is the minimization of expected inventory holding, ordering, and time-weighted backorder costs. The tables provided in the paper allow the identification of the optimal solution when order quantities and/or reorder points are restricted to a set of values which do not include the unconstrained optimal solution.     

Slow failure: Understanding America’s quagmire in Afghanistan

ABSTRACT

The United States government has no organised way of thinking about war termination other than seeking decisive military victory. This implicit assumption is inducing three major errors. First, the United States tends to select military-centric strategies that have low probabilities of success. Second, the United States is slow to modify losing or ineffective strategies due to cognitive obstacles, internal frictions, and patron-client challenges with the host nation government. Finally, as the U.S. government tires of the war and elects to withdraw, bargaining asymmetries prevent successful transitions (building the host nation to win on its own) or negotiations.     

The single-machine absolute-deviation early-tardy problem with random completion times

We consider sequencing n jobs on a single machine subject to job completion times arising from either machine breakdowns or other causes. The objective is to minimize an expected weighted combination of due dates, completion times, earliness, and tardiness penalties. The determination of optimal distinct due dates or optimal common due dates for a given schedule is investigated. The scheduling problem for a fixed common due date is considered when random completion times arise from machine breakdowns. The optimality of a V-shaped about (a point) T sequence is established when the number of machine breakdowns follows either a Poisson or a geometric distribution and the duration of a breakdown has an exponential distribution. © 1996 John Wiley & Sons, Inc.     

An exact algorithm for the maximal covering problem

This article develops a robust, exact algorithm for the maximal covering problem (MCP) using dual-based solution methods and greedy heuristics in branch and bound. Based on tests using randomly generated problems with problem parameters similar to those in the existing literature, the hybrid approach developed in this work appears to be effective over a wide range of MCP model parameters. The method is further validated on problems constructed from three real-world data sets. The extensive computational study compares the new method with other existing exact methods using problems that are as big, or larger than, those used in previous work on MCP. The results show that the proposed method is effective in most instances of MCP. In particular, it is shown that bounding schemes using Lagrangian relaxation are effective on MCP as a method of obtaining both exact and heuristic solutions. © 1996 John Wiley & Sons, Inc.     

On estimating survival for reliability models based on age-related stochastic comparisons

A new class of nonparametric reliability models is introduced and studied. A distribution is said to be better at age s than at age t (sBt) if the residual lifetime at age s is stochastically greater than or equal to the residual lifetime at age t. Applications to various forms of replacement policies, including the cannibalization of failed systems, are noted. For fixed s < t, the problem of estimating a survival curve assumed to belong to the sBt class is addressed using recursive methods. An sBt estimator is derived in closed form, and its uniform strong consistency at an optimal rate of convergence is demonstrated. A simulation study strongly supports the claim that the sBt estimator tends to outperform the empirical survivor function in small- and moderate-size samples. © 1993 John Wiley & Sons, Inc.     

Using grasp to solve the component grouping problem

Component grouping problems, a type of set-partitioning problem, arise in a number of different manufacturing and material logistics application areas. For example, in circuit board assembly, robotic work cells can be used to insert components onto a number of different types of circuit boards. Each type of circuit board requires particular components, with some components appearing on more than one type. The problem is to decide which components should be assigned to each work cell in order to minimize the number of visits by circuit boards to work cells. We describe two new heuristics for this problem, based on so-called greedy random adaptive search procedures (GRASP). With GRASP, a local search technique is replicated many times with different starting points. The starting points are determined by a greedy procedure with a probabilistic aspect. The best result is then kept as the solution. Computational experiments on problems based on data from actual manufacturing processes indicate that these GRASP methods outperform, both in speed and in solution quality, an earlier, network-flow-based heuristic. We also describe techniques for generating lower bounds for the component grouping problem, based on the combinatorial structure of a problem instance. The lower bounds for our real-world test problems averaged within 7%-8% of the heuristic solutions. Similar results are obtained for larger, randomly generated problems. © 1994 John Wiley & Sons. Inc.     

Optimal resource extraction with uncertain reserves

We consider the problem of finding a plan that maximizes the expected discounted return when extracting a nonrenewable resource having uncertain reserves. An extraction plan specifies the rate at which the resource is extracted as a function of time until the resource is exhausted or the time horizon is reached. The return per unit of resource extracted may depend on the rate of extraction, time, and the amount of resource previously extracted. We apply a new method called the generalized search optimization technique to find qualitative features of optimal plans and to devise algorithms for the numerical calculation of optimal plans.     

Viability analysis: A formulation aid for all classes of network models

Nonviable network models have edges which are forced to zero flow simply by the pattern of interconnection of the nodes. The original nonviability diagnosis algorithm [4] is extended here to cover all classes of network models, including pure, generalized, pure processing, nonconserving processing, and generalized processing. The extended algorithm relies on the conversion of all network forms to a pure processing form. Efficiency improvements to the original algorithm are also presented.