Heterogeneous discrete expenditure for diminishing returns

A probabilistic model is developed that applies to military bombardment, advertising for a mass audience, and other kinds of situations in which striking a target means that less of it is left to strike. The model provides the basis for decision analysis based on marginal gain in such circumstances. Heterogneous resources are considered as well as composite targets. All expenditures are quantized. The model has been developed as part of a computer-based military expert system, to replace a large complex set of expert opinions. In that application it sharply improves efficiency, yet conforms to major tenets of tactical doctrine.     

Assessment of hazardous-inspection policies

Inspection procedures may at times pose a hazard to the system being monitored. In this article, a simple hazardous-inspection model is considered. The character of the optimal ongoing inspection policy (for inspections subsequent to the first) is reviewed, and the possible forms of the optimal initiation policy are established. Efficient computational procedures are developed for calculating optimal policies. Some discussion is included of the counterintuitive nature of some of the results.     

A learning tabu search for a truck allocation problem with linear and nonlinear cost components

The two‐level problem studied in this article consists of optimizing the refueling costs of a fleet of locomotives over a railway network. The goal consists of determining: (1) the number of refueling trucks contracted for each yard (truck assignment problem denoted TAP) and (2) the refueling plan of each locomotive (fuel distribution problem denoted FDP). As the FDP can be solved efficiently with existing methods, the focus is put on the TAP only. In a first version of the problem (denoted (P1)), various linear costs (e.g., fuel, fixed cost associated with each refueling, weekly operating costs of trucks) have to be minimized while satisfying a set of constraints (e.g., limited capacities of the locomotives and the trucks). In contrast with the existing literature on this problem, two types of nonlinear cost components will also be considered, based on the following ideas: (1) if several trucks from the same fuel supplier are contracted for the same yard, the supplier is likely to propose discounted prices for that yard (Problem (P2)); (2) if a train stops too often on its route, a penalty is incurred, which represents the dissatisfaction of the clients (Problem (P3)). Even if exact methods based on a mixed integer linear program formulation are available for (P1), they are not appropriate anymore to tackle (P2) and (P3). Various methods are proposed for the TAP: a descent local search, a tabu search, and a learning tabu search (LTS). The latter is a new type of local search algorithm. It involves a learning process relying on a trail system, and it can be applied to any combinatorial optimization problem. Results are reported and discussed for a large set of instances (for (P1), (P2), and (P3)), and show the good performance of LTS. © 2014 Wiley Periodicals, Inc. 62:32–45, 2015     

Involving the elephant: technical isolation and the role of India in a possible solution to the Iranian nuclear crisis

Apart from North Korea, no state's nuclear program in the twenty-first century has raised more concern to international security than Iran's. While Iran insists that its nuclear program is strictly for civilian purpose in line with Article IV of non-proliferation treaty, the USA and its allies insist that Iran has military intentions and called for sanctions. The failure of sanctions to deter Iran from its nuclear agenda had made many scholars and policy-makers call for a preemptive attack on Iranian nuclear facilities. Situated within this debate, this paper positions itself as an antagonist to the preemptive airstrike option and argues that involving India in a possible nuclear “iron curtain” against Iran – a move known as technical isolation – remains the best option to the current nuclear crises.     

Resequencing with parallel queues to minimize the maximum number of items in the overflow area

This article treats an elementary optimization problem, where an inbound stream of successive items is to be resequenced with the help of multiple parallel queues in order to restore an intended target sequence. Whenever early items block the one item to be currently released into the target sequence, they are withdrawn from their queue and intermediately stored in an overflow area until their actual release is reached. We aim to minimize the maximum number of items simultaneously stored in the overflow area during the complete resequencing process. We met this problem in industry practice at a large German automobile producer, who has to resequence containers with car seats prior to the assembly process. We formalize the resulting resequencing problem and provide suited exact and heuristic solution algorithms. In our computational study, we also address managerial aspects such as how to properly avoid the negative effects of sequence alterations. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 401–415, 2016