Integrated model for production planning and scheduling in a supply chain using benchmarked genetic algorithm

Production planning and scheduling is one of the core functions in manufacturing systems. Furthermore, this task is drawing even more attention in supply chain environments as problems become harder and more complicated. Most of the traditional approaches to production planning and scheduling have a...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2008-12, Vol.39 (11-12), p.1207-1226
Main Authors: Kim, Haejoong, Jeong, Han-Il, Park, Jinwoo
Format: Article
Language:English
Subjects:
CAE) and Design
Computer-Aided Engineering (CAD
Engineering
Genetic algorithms
Industrial and Production Engineering
Lot sizing
Mechanical Engineering
Media Management
Original Article
Production planning
Production scheduling
Schedules
Structural hierarchy
Supply chains
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Summary:Production planning and scheduling is one of the core functions in manufacturing systems. Furthermore, this task is drawing even more attention in supply chain environments as problems become harder and more complicated. Most of the traditional approaches to production planning and scheduling have adopted a multi-phased, hierarchical and decompositional approach. This traditional approach does not guarantee a feasible production schedule. And even when capacity constraints are satisfied, it may generate an expensive schedule. In order to overcome the limitations of the traditional approach, several previous studies tried to integrate the production planning and scheduling problems. However, these studies also have some limitations, due to their intrinsic characteristics and the method for incorporating the hierarchical product structure into the scheduling model. In this paper we present a new integrated model for production planning and scheduling for multi-item and multi-level production. Unlike previous lot sizing approaches, detailed scheduling constraints and practical planning criteria are incorporated into our model. We present a mathematical formulation, propose a heuristic solution procedure, and demonstrate the performance of our model by comparing the experimental results with those of a traditional approach and optimal solution.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-007-1298-z