International Conference Integrated Models for Multi-level Planning and Scheduling in Supply Chain
2015.07.31 14:13
| author | Haejoong Kim |
|---|---|
| 2nd author | Jinwoo Park Hanil Jeong |
| presenter | |
| info | This conference paper has been presented by Haejoong Kim on the International workshop on Institutional View of SCM. The conference has been held in Tokyo, Japan, 2006/11/16 ~ 2006/11/18 . |
| year | 2006 |
| category | IC |
| start / end date | |
| city / nation | |
| 학회 | ISCM |
| keywords | production planning and scheduling, supply chain management, lot sizing and scheduling, genetic algorithm |
| abstract | Production planning and scheduling (PPS) is one of the core functions in the management of manufacturing industries and its importance is further emphasized by the growing attention focused on supply chain management. Most of the many studies on PPS have adopted a multi-phased, hierarchical approach in which the production plan is generated at high level, and then the schedule is generated at detailed level. One of the major problems of this approach is that it does not guarantee a feasible schedule with a given production plan. To resolve these limitations, many researchers have tried to integrate the planning and scheduling problems. However, these studies have also suffered 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 PPS based on the lot sizing approach, which we term the Job-based Multi-level lot sizing and Scheduling Model (JMSM). Unlike previous lot sizing approaches, detailed scheduling constraints and practical planning criteria are incorporated into our model. We address a mathematical formulation, propose heuristic solution procedures, and demonstrate the performance of our model by comparing the experimental results with those of other traditional and integrated approaches. Keyword: production planning and scheduling, supply chain management, lot sizing and scheduling, genetic algorithm |
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