Measuring hard power: China’s economic growth and military capacity

China’s rapid economic growth is facilitating massive increases in its military spending and causing increased security concerns in Asia and the Western Pacific. But there is uncertainty over how large China’s military spending is relative to other countries, or how fast it is growing in real terms. We address this issue by deriving a relative military cost price index based on the relative unit costs of inputs. We find that China’s real military spending is much larger than suggested by exchange rate comparisons, and even larger than standard purchasing power parity comparisons. We also find, however, that the real growth of China’s military spending has been smaller than conventionally thought. This is due to rapidly growing wages in China and the large share of personnel in China’s military budget.     

A bicriterion approach to common flow allowances due window assignment and scheduling with controllable processing times

We investigate a single‐machine scheduling problem for which both the job processing times and due windows are decision variables to be determined by the decision maker. The job processing times are controllable as a linear or convex function of the amount of a common continuously divisible resource allocated to the jobs, where the resource allocated to the jobs can be used in discrete or continuous quantities. We use the common flow allowances due window assignment method to assign due windows to the jobs. We consider two performance criteria: (i) the total weighted number of early and tardy jobs plus the weighted due window assignment cost, and (ii) the resource consumption cost. For each resource consumption function, the objective is to minimize the first criterion, while keeping the value of the second criterion no greater than a given limit. We analyze the computational complexity, devise pseudo‐polynomial dynamic programming solution algorithms, and provide fully polynomial‐time approximation schemes and an enhanced volume algorithm to find high‐quality solutions quickly for the considered problems. We conduct extensive numerical studies to assess the performance of the algorithms. The computational results show that the proposed algorithms are very efficient in finding optimal or near‐optimal solutions. © 2017 Wiley Periodicals, Inc. Naval Research Logistics, 64: 41–63, 2017     

Global strategic studies: a manifesto

This contribution reflects on the state of strategic studies today and the criticism it has received in recent years, as being outdated and irrelevant. The authors formulate some premises for reinvigorating this field of inquiry by widening its scope and research agenda to do more justice to the wide variety of actors, perspectives and practices observable in the enterprise of strategy in our contemporary globalised world.     

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     

A model to design recreational boat mooring fields

This article develops a mathematical model and heuristic algorithm to design recreational boating mooring fields. The boating industry is important to the Florida economy, and boat storage is becoming a concern among those in the industry. The mooring field design problem is formulated to maximize the total number of boat feet moored in the mooring field. In the model, we allow two adjacent moorings to overlap, which introduces a risk that under certain conditions the boats on these moorings could contact each other. We identify the conditions when contact is possible and quantify the probability of contact. The mooring field design problem is formulated as a nonlinear mixed‐integer programming problem. To solve the problem, we decompose it into two separate models, a mooring radii assignment model and a mooring layout model, which are solved sequentially. The first is solved via exhaustive enumeration and the second via a depth‐first search algorithm. Two actual mooring fields are evaluated, and in both cases our model leads to better layouts than ones experts developed manually. The mooring field design model rationalizes the mooring field design and shows that in one case by increasing the risk from 0 to 1%, the mooring efficiency increases from 74.8% to 96.2%. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

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.