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Growing Science » Uncertain Supply Chain Management » A rolling horizon-based heuristic to solve a multi-level general lot sizing and scheduling problem with multiple machines (MLGLSP_MM) in job shop manufacturing system

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Uncertain Supply Chain Management

ISSN 2291-6830 (Online) - ISSN 2291-6822 (Print)
Quarterly Publication
Volume 2 Issue 3 pp. 167-178 , 2014

A rolling horizon-based heuristic to solve a multi-level general lot sizing and scheduling problem with multiple machines (MLGLSP_MM) in job shop manufacturing system Pages 167-178 Right click to download the paper Download PDF

Authors: Mohammad Mohammadi, Omid Poursabzi

Keywords: Capacitated lot sizing and scheduling, Heuristic, Job shop, Rolling horizon approach, Sequence-dependent setup

Abstract: This article addresses multi-level lot sizing and scheduling problem in capacitated, dynamic and deterministic cases of a job shop manufacturing system with sequence-dependent setup times and costs assumptions. A new mixed-integer programing (MIP) model with big bucket time approach is provided to the problem formulation. It is well known that the capacitated lot sizing and scheduling problem (CLSP) is NP-hard. The problem of this paper that it is an extent of the CLSP is even more complicated; consequently, it necessitates the use of approximated methods to solve this problem. Hence, two new mixed integer programming-based approaches with rolling horizon framework have been used to solve this model. To evaluate the performance of the proposed model and algorithms, some numerical experiments are conducted. The comparative results indicate the superiority of the second heuristic.

How to cite this paper
Mohammadi, M & Poursabzi, O. (2014). A rolling horizon-based heuristic to solve a multi-level general lot sizing and scheduling problem with multiple machines (MLGLSP_MM) in job shop manufacturing system.Uncertain Supply Chain Management, 2(3), 167-178.

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Journal: Uncertain Supply Chain Management | Year: 2014 | Volume: 2 | Issue: 3 | Views: 2999 | Reviews: 0

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