Continuous accumulation games on discrete locations

In an accumulation game, a HIDER attempts to accumulate a certain number of objects or a certain quantity of material before a certain time, and a SEEKER attempts to prevent this. In a continuous accumulation game the HIDER can pile material either at locations $1, 2, …, n, or over a region in space. The HIDER will win (payoff 1) it if accumulates N units of material before a given time, and the goal of the SEEKER will win (payoff 0) otherwise. We assume the HIDER can place continuous material such as fuel at discrete locations i = 1, 2, …, n, and the game is played in discrete time. At each time k > 0 the HIDER acquires h units of material and can distribute it among all of the locations. At the same time, k, the SEEKER can search a certain number s < n of the locations, and will confiscate (or destroy) all material found. After explicitly describing what we mean by a continuous accumulation game on discrete locations, we prove a theorem that gives a condition under which the HIDER can always win by using a uniform distribution at each stage of the game. When this condition does not hold, special cases and examples show that the resulting game becomes complicated even when played only for a single stage. We reduce the single stage game to an optimization problem, and also obtain some partial results on its solution. We also consider accumulation games where the locations are arranged in either a circle or in a line segment and the SEEKER must search a series of adjacent locations. © 2002 John Wiley & Sons, Inc. Naval Research Logistics, 49: 60–77, 2002; DOI 10.1002/nav.1048     

Exploiting self‐canceling demand point aggregation error for some planar rectilinear median location problems

When solving location problems in practice it is quite common to aggregate demand points into centroids. Solving a location problem with aggregated demand data is computationally easier, but the aggregation process introduces error. We develop theory and algorithms for certain types of centroid aggregations for rectilinear 1‐median problems. The objective is to construct an aggregation that minimizes the maximum aggregation error. We focus on row‐column aggregations, and make use of aggregation results for 1‐median problems on the line to do aggregation for 1‐median problems in the plane. The aggregations developed for the 1‐median problem are then used to construct approximate n‐median problems. We test the theory computationally on n‐median problems (n ≥ 1) using both randomly generated, as well as real, data. Every error measure we consider can be well approximated by some power function in the number of aggregate demand points. Each such function exhibits decreasing returns to scale. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 614–637, 2003.     

Partitioning of biweighted trees

A districting problem is formulated as a network partitioning model where each link has one weight to denote travel time and another weight to denote workload. The objective of the problem is to minimize the maximum diameter of the districts while equalizing the workload among the districts. The case of tree networks is addressed and efficient algorithms are developed when the network is to be partitioned into two or three districts. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 143–158, 2002; DOI 10.1002/nav.10003     

Technical note: A computationally efficient algorithm for undiscounted Markov decision processes with restricted observations

We present a computationally efficient procedure to determine control policies for an infinite horizon Markov Decision process with restricted observations. The optimal policy for the system with restricted observations is a function of the observation process and not the unobservable states of the system. Thus, the policy is stationary with respect to the partitioned state space. The algorithm we propose addresses the undiscounted average cost case. The algorithm combines a local search with a modified version of Howard's (Dynamic programming and Markov processes, MIT Press, Cambridge, MA, 1960) policy iteration method. We demonstrate empirically that the algorithm finds the optimal deterministic policy for over 96% of the problem instances generated. For large scale problem instances, we demonstrate that the average cost associated with the local optimal policy is lower than the average cost associated with an integer rounded policy produced by the algorithm of Serin and Kulkarni Math Methods Oper Res 61 (2005) 311–328. © 2008 Wiley Periodicals, Inc. Naval Research Logistics 2009     

The determination of optimal base-stock inventory policy when the costs of under- and oversupply are uncertain

This article considers the determination of the optimal base-stock inventory policy for the newsboy inventory model when there is uncertainty about either or both of its basic cost inputs: either C_u, the marginal cost of an undersupply mistake, or C_o, the marginal cost of an oversupply mistake. Such uncertainties often arise in implementing the newsboy model, especially with respect to C_u, whose value depends mostly on the often-imponderable economic consequences of a lost sale or backorder. Given this uncertainty, we use decision theory to propose and analyze two measures of policy “goodness” and two base-stock selection criteria, which in combination provide four alternative “optimal” base-stock policies. Formulas and/or conditions defining each alternative policy are provided. Our empirical study indicates that the recommended policy can be quite sensitive to the measure/criterion chosen, and that the consequences of the wrong choice can be quite considerable.     

A multifaceted heuristic for the orienteering problem

The orienteering problem involves the selection of a path between an origin and a destination which maximizes total score subject to a time restriction. In previous work we presented an effective heuristic for this NP-hard problem that outperformed other heuristics from the literature. In this article we describe and test a significantly improved procedure. The new procedure is based on four concepts—center of gravity, randomness, subgravity, and learning. These concepts combine to yield a procedure which is much faster and which results in more nearly optimal solutions than previous procedures.     

Determination of efficient points in multiple-objective location problems

This article is devoted to an MCDM problem connected with locational analysis. The MCDM problem can be formulated so as to minimize the distance between a facility and a given set of points. The efficient points of this problem are candidates for optimal solutions to many location problems. We propose an algorithm to find all efficient points when distance is measured by any polyhedral norm.     

Analysis of project scheduling strategies in a client-contractor environment

In this article we consider a cost-minimization model to investigate scheduling strategies for multistaged projects in a client-contractor environment. This type of environment is symptomatic of temporal changes in project definition and scope. At prespecified epochs the client conducts an external evaluation of the project and either accepts or rejects the contractor's current work. The resulting uncertainty from the client's review is modeled via monotonically varying acceptance probabilities. The model is designed primarily to address the interaction between earliest-, intermediate-, and latest-start options and project-crashing stragies for a broad range of penalty costs. Theoretical results are introduced, while numerical examples for both exponentially and polynomially based acceptance probabilities are discussed.