This short article presents control designs of truck braking system. It describes design comparisons among the simple internal model control (SIMC), Ziegler-Nichol (ZN), Cohen-Coon (CC) methods, and the proposed bacterial-foraging-tabu-search (BFTS) metaheuristics. The proposed BF-TF algorithms are explained. The article also presents simulation results and discussions. Keywords—truck braking c...

Service network design involves determination of the most cost-effective transportation network and service characteristics subject to various constraints. Good progress has been made in developing metaheuristic approaches that can compete or even outperform some commercial software packages (Ghamlouche et al 2004; Pedersen et al 2009). However, since most of these metaheuristic methods involve...

Multicommodity distribution problem is one of the most interesting and useful models in mathematical programming due to its major role in distribution networks. The purpose of this paper is to describe and solve a special class of multicommodity distribution problems in which shipment of a commodity from a plant to a customer would go through different distribution centers. The problem is t...

This paper presents a hybrid tabu search strategy for lot sizing problems. This strategy allows the exploitation of the quality of the well-known relax-and-fix heuristic, inside a tabu search framework which enforces diversity. The computational results show an advantage of this strategy when compared to a version of the relax-and-fix heuristic and to time constrained branch-and-bound.

We study and compare asynchronous parallelization strategies for tabu search, and evaluate the impact on performance and solution quality of some important algorithmic design parameters: number of processors, handling of exchanged information, etc. Parallelization approaches are implemented and compared by using a tabu search algorithm for multicommodity location-allocation problems with balanc...

Article history: Received 21 January 2010 Accepted 27 October 2010 Available online 9 November 2010

An explicit exploration strategy is necessary in reinforcement learning (RL) to balance the need to reduce the uncertainty associated with the expected outcome of an action and the need to converge to a solution. This dependency is more acute in on-policy reinforcement learning where the exploration guides the search for an optimal solution. The need for a self-regulating exploration is manifes...

In the past several years, it has amply been demonstrated that metaheuristics, such as simulated annealing, tabu search, genetic algorithms and ant colony are simple, adaptable and computationally efficient to solve a large range of combinatorial problems including hard single objective industrial. In this paper, we propose a formalisation of the search process for finding compromise solutions ...