A periodic re-planning approach for demand-driven wood remanufacturing industry: a real-scale application

Abstract

This article develops an experimental platform to select production planning policy in demand-driven wood remanufacturing industry. This industry is characterised by divergent co-production, alternative processes, a make-to-order philosophy and short order cycle times. Under such complex characteristics, the selection of an efficient production plan is a complex task. Previous work has failed to address all the industrial characteristics encountered in wood remanufacturing mills. After defining key performance indicators (KPIs) to measure the production plan efficiency, our methodology uses a periodic re-planning strategy based on a rolling horizon. Then, mixed-integer programming models are formulated leading to different planning approaches. Finally, the resulting decision framework is experimented to prescribe the best planning policy based on the selected KPI. Each production planning is characterised by its planning approach and factors related to the re-planning interval and the planning horizon length. Simulations are conducted using multiple best subset selections combined with an experimental design approach. Using industrial data from a wood remanufacturing mill in Eastern Canada, results indicate that the manufacturing mill should use a planning approach that minimises cost, while utilising the full system capacity. Results also quantify the benefit of using lower re-planning intervals and higher planning horizons.

Keywords:

• re-planning
• uncertain demand
• co-production
• wood remanufacturing industry
• mixed-integer programming
• simulation

Funding

This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Strategic network on Value Chain Optimization (VCO).

Notes

1. For readers unfamiliar with the methods used in simulation to compare many alternative system configurations, Banks et al. (2010) is recommended as a reference book (in particular Chapter 12).