Global Bacteria Optimization Meta-Heuristic Algorithm for Jobshop Scheduling

Global Bacteria Optimization Meta-Heuristic Algorithm for Jobshop Scheduling

Jairo R. Montoya-Torres (Universidad de La Sabana, Colombia), Libardo S. Gómez-Vizcaíno (Fundación Centro de Investigación en Modelación Empresarial del Caribe, Colombia), Elyn L. Solano-Charris (Universidad de La Sabana, Colombia) and Carlos D. Paternina-Arboleda (Universidad del Norte, Colombia)
DOI: 10.4018/joris.2010100103
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Abstract

This paper examines the problem of jobshop scheduling with either makespan minimization or total tardiness minimization, which are both known to be NP-hard. The authors propose the use of a meta-heuristic procedure inspired from bacterial phototaxis. This procedure, called Global Bacteria Optimization (GBO), emulates the reaction of some organisms (bacteria) to light stimulation. Computational experiments are performed using well-known instances from literature. Results show that the algorithm equals and even outperforms previous state-of-the-art procedures in terms of quality of solution and requires very short computational time.
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Problem Description And Overview Of Literature

As stated before, scheduling is a decision-making process that is used on a regular basis in many manufacturing and services industries. It deals with the allocation of resources (often simply called machines) to task (jobs) over given time periods and its goal is to optimize one or more objectives (Pinedo, 2008). Efficient production schedules can result in substantial improvements in productivity and cost reductions in manufacturing and service industries. Generating a feasible schedule that best meets management’s objectives is a difficult task that firms face every day (Ozgur & Brown, 1995). Among the various types of scheduling problems, jobshop scheduling is one of the most challenging.

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