Integrated stochastic disassembly line balancing and planning problem with machine specificity

The disassembly is a fundamental basis in converting End-of-Life (EOL) products into useful components. Related research becomes popular recently due to the increasing awareness of environmental protection and energy conservation. Yet, there are many opening questions needed to be investigated, especially the efficient coordination of different-level decisions under uncertainty is a big challenge. In this paper, a novel integrated stochastic disassembly line balancing and planning problem is studied to minimise the system cost, where component yield ratios and demands are assumed to be uncertain. In this work, machine specificities are considered for task processing, such as price, ability, and capacity. For the problem, a two-stage non-linear stochastic programming model is first constructed. Then, it is further transformed into a linear formulation. Based on problem property analysis, a valid inequality is proposed to reduce the search space of optimal solutions. Finally, a sample average approximation (SAA) and an L-shaped algorithm are adopted to solve the problem. Numerical experiments on randomly generated instances demonstrate that the valid inequality can save around 11% of average computation time, and the L-shaped algorithm can save around 64% of average computation time compared with the SAA algorithm without a big sacrifice of the solution quality.

Automated summary

Disassembly is essential for converting EOL products into useful components, with increasing popularity in research due to environmental protection concerns. The study focuses on integrating stochastic disassembly line balancing and planning to minimize system cost, considering uncertain component yield ratios and demands. Various machine specificities are taken into account for task processing, and a valid inequality is proposed to reduce the search space for optimal solutions, with the adoption of SAA and L-shaped algorithm for problem solution.