Title: A possibilistic programming approach for capacitated lot-sizing problem in mixed assembly shops

Authors: Navid Sahebjamnia; Yalda Yahiazadeh Andavari; Zahra Safaie Koleti; Hamed Gholami Orimi

Addresses: School of Industrial and Systems Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran ' Faculty of Management and Accounting, Allameh Tabataba'i University, P.O. Box 14348-63111, Tehran, Iran ' Department of Industrial Engineering, Azad University, Firoozkoh Branch, P.O. Box 39818-38381, Firoozkoh, Iran ' Department of Industrial Engineering, Azad University, Arak Branch, P.O. Box 567/38135, Arak, Iran

Abstract: This paper develops a fuzzy multi-objective linear programme (FMOLP) model for solving the bi-objective capacitated lot-sizing problem (CLSP). The proposed model attempts to simultaneously minimise total cost consist of total production variation cost, inventory cost, backlog cost and total setup cost while maximising the resource utilisation. According to the structure of the mixed assembly shops, a multi-product model with multi-item that should be produced during multi-level has been designed. Based on the vagueness and imprecision of the real case, the production, holding and backorder costs and demand of the products were considered as fuzzy number that have their membership function during planning horizon in each period. An interactive fuzzy solution method is developed by combining a number of efficient solution algorithms from the recent literature in order to solve the proposed possibilistic model. In order to demonstrate the significance and applicability of the proposed model as well as the usefulness of the proposed solution approach, numerical experiments are conducted and the results are provided.

Keywords: capacitated lot sizing problem; CLSP; possibilistic programming; mixed assembly shops; fuzzy linear programming; multi-objective linear programming; production variation costs; inventory costs; backlog costs; setup costs; resource utilisation; uncertainty; modelling.

DOI: 10.1504/IJADS.2013.056881

International Journal of Applied Decision Sciences, 2013 Vol.6 No.4, pp.388 - 405

Received: 11 Apr 2013
Accepted: 17 May 2013
Published online: 28 Nov 2013 *

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