A bi-objective multi-item capacitated lot-sizing model: Two Pareto-based meta-heuristic algorithms. Issue 4 (1st October 2016)

• Record Type: Journal Article
• Authors: Mehdizadeh, Esmaeil; Hajipour, Vahid; Mohammadizadeh, Mohammad Reza
• Is Part Of: International journal of management science and engineering management. Volume 11:Issue 4(2016)
• Page Start: 279
• Page End: 293
• Publication Date: 2016-10-01
• Subjects: Lot-sizing problem -- multi-objective -- MOVDO -- NRGA -- NSGA-II -- RSM; C44 -- C52 -- C60 -- C61 -- C63; Management science -- Periodicals; Engineering -- Management -- Periodicals; Engineering -- Management; Management science; Periodicals; 658.005
• DOI: 10.1080/17509653.2015.1086965
• Languages: English
• ISSNs: 1750-9661

Abstract

Abstract: Lot-sizing problems (LSP) form a class of production planning problems in which available quantities are always considered as decision variables in the production plan. The goal of this paper is to present a multi-item capacitated lot-sizing problem (MICLSP) with setup times, safety stock deficit costs, demand shortage costs – both backorder and lost sale states – and different manners of production. Although a considerable amount of research has concentrated on model development and solution procedures in the terms of single-objective problems in the past decade, to make the model more realistic this paper develops a bi-objective mathematical programming model with two conflicting objectives including: (1) minimizing the total cost considered by the production plans including production costs with different manners of production, inventory costs, safety stock deficit costs, shortage costs and setup costs; (2) minimizing the required storage space. Considering that the proposed model is NP-hard, we propose two novel Pareto-based multi-objective meta-heuristic algorithms called multi-objective vibration damping optimization (MOVDO) and the non-dominated ranking genetic algorithm (NRGA) for the literature on LSP. In order to validate the performance of the proposed MOVDO and NRGA, a non-dominated sorting genetic algorithm (NSGA-II), one of the most common multi-objective meta-heuristic algorithms, is applied. The optimal solutions are also reported to justify the results. Finally, we calibrate both algorithms by robust response surface methodology (RSM); then, the results are analysed on some test problems, both graphically and statistically.