A genetic algorithm approach to generate an integrated multiplant aggregate production-distribution plan

P. Ashoka Varthanan, N. Murugan and G. Mohan Kumar

International Journal of Services and Operations Management, 2010, vol. 7, issue 1, 76-96

Abstract: Industries adopting multisite manufacturing allocate their forecasted demand from various customers/demand centres to the respective plants based on gross production, inventory holding and distribution costs. But demand allocation (by considering the gross production cost) will not be appropriate, as production in each plant can be carried out through regular, overtime and outsourcing means. Also, this will lead to the allocation of the lion's share of demand to a plant whose regular production cost is cheaper, but its overtime/outsourced production costs may be costlier than the regular production cost of other plants. In this paper, an aggregate production-distribution plan considering all the above-mentioned costs is developed for a renowned bearing manufacturing industry in India. The proposed Integer Nonlinear Programming (INLP) model is solved using a genetic algorithm and the results are compared with LINGO 8.0, a popular operations research software. The performance of the genetic algorithm is found to be superior to that of the LINGO 8.0 results.

Keywords: demand forecasting; aggregate production-distribution planning; genetic algorithms; integer nonlinear programming; INLP; operations research; multisite manufacturing; bearing manufacture; bearings; demand allocation. (search for similar items in EconPapers)
Date: 2010
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