A heuristic with tie breaking for certain 0–1 integer programming models

A heuristic for 0–1 integer programming is proposed that features a specific rule for breaking ties that occur when attempting to determine a variable to set to 1 during a given iteration. It is tested on a large number of small- to moderate-sized randomly generated generalized set-packing models. Solutions are compared to those obtained using an existing well-regarded heuristic and to solutions to the linear programming relaxations. Results indicate that the proposed heuristic outperforms the existing heuristic except for models in which the number of constraints is large relative to the number of variables. In this case, it performs on par with the existing heuristic. Results also indicate that use of a specific rule for tie breaking can be very effective, especially for low-density models in which the number of variables is large relative to the number of constraints.     

Scheduling stochastic jobs on a single machine subject to breakdowns

A single machine is available to process a collection of stochastic jobs. There may be technological constraints on the job set. The machine sometimes breaks down. Costs are incurred and rewards are earned during processing. We seek strategies for processing the jobs which maximize the total expected reward earned.     

Confidence intervals related to sequential test for the exponential distribution

One-sided sequential tests for the mean of an exponential distribution are proposed, and the related confidence intervals are computed. The tests behave like the classical sequential probability-ration test when the mean is small and like a fixed-time test when the mean is large and accurate estimation is important.     

Developing an optimal repair-replacement strategy for pallets

A Markovian model is presented for the development of an optimal repair-replacement policy for the situation requiring a decision only at failure. The problem is characterized by the presence of growth which is integrated into the formulation. The model is applied to an actual problem, with data analysis and results given. Substantial savings are indicated.     

Expedients for solving some specially structured mixed-integer programs

In this paper we consider dual angular and angular structured mixed integer programs which arise in some practical applications. For these problems we describe efficient methods for generating a desirable set of Benders' cuts with which one may initialize the partitioning scheme of Benders. Our research is motivated by the computational experience of McDaniel and Devine who have shown that the set of Benders' cuts which are binding at the optimum to the linear relaxation of the mixed integer program, play an important role in determining an optimal mixed integer solution. As incidental results in our development, we provide some useful remarks regarding Benders' and Dantzig-Wolfe's decomposition procedures. The computational experience reported seems to support the expedients recommended in this paper.     

The cyclic separation scheduling problem

This paper deals with the problem of scheduling items (tasks, employees, equipment, etc.) over a finite time horizon so as to minimize total cost expenditures while maintaining a predefined separation between certain items. The problem is cyclic, because the same schedule will be repeated over several consecutive time periods of equal length. Thus, requirements are present to maintain the separation of items not only within the individual time periods considered, but also between items in adjoining periods. A special purpose branch-and-bound algorithm is developed to solve this scheduling problem by taking advantage of its cyclic nature. Computational results are given.     

A branch‐and‐price approach for the stochastic generalized assignment problem

In this article, we address a stochastic generalized assignment machine scheduling problem in which the processing times of jobs are assumed to be random variables. We develop a branch‐and‐price (B&P) approach for solving this problem wherein the pricing problem is separable with respect to each machine, and has the structure of a multidimensional knapsack problem. In addition, we explore two other extensions of this method—one that utilizes a dual‐stabilization technique and another that incorporates an advanced‐start procedure to obtain an initial feasible solution. We compare the performance of these methods with that of the branch‐and‐cut (B&C) method within CPLEX. Our results show that all B&P‐based approaches perform better than the B&C method, with the best performance obtained for the B&P procedure that includes both the extensions aforementioned. We also utilize a Monte Carlo method within the B&P scheme, which affords the use of a small subset of scenarios at a time to estimate the “true” optimal objective function value. Our experimental investigation reveals that this approach readily yields solutions lying within 5% of optimality, while providing more than a 10‐fold savings in CPU times in comparison with the best of the other proposed B&P procedures. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 131–143, 2014     

The Political Economy of Legislation on Terrorism

^{1 1}This research was funded in part by a grant from the Lowe Institute of Political Economy. The corresponding author is S. Brock Blomerg. We thank Chris Blomberg and Brianna Loyosa for their excellent research assistance on the project. This paper presents an empirical analysis of what drives congressional legislation on terrorism during the period 1995 to 2010. We utilize and augment current methodology to compile and analyze data on sponsorship and cosponsorship of terrorism related data. Our results on the sources of legislation on terrorism are largely in line with past examinations of the importance of committee membership and leadership, party majority-ship, and other political factors. Further, we find that the most significant and robust drive for legislation on terrorism is the September 11^th attacks. And while the impact of 9/11 affected legislative productivity everywhere, we find that it most significantly affected states surrounding New York and Washington D.C. Our results indicate that the economy may be one factor motivating politicians to legislate on terrorism; however, these results are not robust.