Application of multi-objective genetic algorithm to aggregate production planning in a possibilistic environment

Shahed Mahmud, Md. Sanowar Hossain and Md. Mosharraf Hossain

International Journal of Industrial and Systems Engineering, 2018, vol. 30, issue 1, 40-59

Abstract: The present study is to develop an interactive possibilistic environment-based genetic algorithm for multi-product and multi-period aggregate production planning (APP). APP is the prerequisite in order to make material requirement planning and/or capacity requirement planning accurately. The present study attempts to minimise the production cost and the rate of changing in labour level where production costs, backordering cost, labour level changing cost, demand are considered as imprecise parameters. It is noted that all these imprecise parameters are defined by the triangular possibility distribution. As the overtime production capacity is the fraction of available regular time production capacity, it is defined separately to make the result more acceptable. The proposed methodology is finally applied to demonstrate an industrial case in order to justify the feasibility. The solution obtained by the proposed methodology is compared with other solutions in context with computation efficiency and solution practicability.

Keywords: aggregate production planning; APP; imprecise parameters; possibilistic environment; practicability; multi-objective genetic algorithm; MOGA. (search for similar items in EconPapers)
Date: 2018
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