Britain and Hiroshima

Most historical accounts of the atomic bombings of Japan show little interest in Britain's explicit authorization for the attacks. Meanwhile, the few historians who have attempted to explain it rely on a unitary, rational actor model of the British state that is misleading. This article demonstrates that high-ranking British officials became anxious early on about the strategic consequences of a peremptory use of the new weapon. Therefore, especially over the course of 1944 they sought to engage Washington on the linked questions of the bomb's wartime use and its postwar control. However, these officials' initiatives were rebuffed by Prime Minister Winston Churchill, who paved the way to the bombings based on a fervent desire for Anglo-American integration, and on a dim understanding of the bomb's revolutionary potential.     

Optimization in simulation: Current issues and the future outlook

Simulation is commonly used to find the best values of decision variables for problems which defy analytical solutions. This objective is similar to that of optimization problems and thus, mathematical programming techniques may be applied to simulation. However, the application of mathematical programming techniques, e.g., the gradient methods, to simulation is compounded by the random nature of simulation responses and by the complexity of the statistical issues involved. The literature relevant to optimization in simulation is scattered, and no comprehensive and up-to-date treatment of the subject is presently available. To that end, this article brings together numerous concepts related to t he problem of optimization in simulation. Specifically, it discusses the application of mathematical programming techniques to optimization in simulation, response surface methodology and designs, perturbation analysis, and frequency domain simulation experiments. The article provides a user with an overview of the available optimization techniues and identifies future research possibilities.     

Postoptimality analysis in integer programming by implicit enumeration: The mixed integer case

This paper develops a method for doing postoptimality analysis on the mixed integer programming problem. The proposed procedures form a natural adjunct to enumerative I.P. algorithms that are linear programming based, and they are designed, in effect, to capitalize on insights generated as the problem is initially solved to do subsequent analysis upon it. In particular, limited ranging analysis is possible on selected parameters, as is the efficient resolving of the problem following parameter changes.     

Bounds for circular error probabilities

Bounds for P(X + X ⩽ k²σ) are given where X₁ and X₂ are independent normal variables having zero means and variances σ, σ, respectively. This is generalized when X₁ and X₂ are dependent variables with known covariance matrix.     

An exact ceiling point algorithm for general integer linear programming

We present an algorithm called the exact ceiling point algorithm (XCPA) for solving the pure, general integer linear programming problem (P). A recent report by the authors demonstrates that, if the set of feasible integer solutions for (P) is nonempty and bounded, all optimal solutions for (P) are “feasible 1-ceiling points,” roughly, feasible integer solutions lying on or near the boundary of the feasible region for the LP-relaxation associated with (P). Consequently, the XCPA solves (P) by implicitly enumerating only feasible 1-ceiling points, making use of conditional bounds and “double backtracking.” We discuss the results of computational testing on a set of 48 problems taken from the literature.     

Minimizing flow time on a single machine with job classes and setup times

We examine the static sequencing problem of ordering the processing of jobs on a single machine so as to minimize the average weighted flow time. It is assumed that all jobs have zero ready times, and that the jobs are grouped into classes with the property that setup tasks are only required when processing switches from jobs of one class to jobs of another class. The time required for each setup task is given by the sum of a setdown time from the previous class and a setup time for the new class. We show that an algorithm presented in the literature for solving a special case of this problem gives suboptimal solutions. A number of properties of the optimal solution are derived, and their use in algorithms is evaluated. Computational results are presented for both a branch-and-bound procedure and a simpler depth-first search.     

A branch-and-bound algorithm for flow-path design of automated guided vehicle systems

An algorithm for determining the optimal, unidirectional flow path for an automated guided vehicle system with a given facility layout is presented. The problem is formulated as an integer program. The objective is to minimize the total distance traveled by vehicles subject to the constraint that the resulting network consists of a single strongly connected component. A specialized branch-and-bound solution procedure is discussed in detail.