A Reinforcement Learning Approach for Disassembly Line Balancing Problem

The disassembly line balancing (DLB) problem is the process of allocating a set of disassembly tasks to an ordered sequence of workstations in such a way that optimizes some performance measures (e.g., number of stations, hazardous components number, cycle time and workload). However, it is difficult to find a solution for the DLB problem in an optimal manner due to the complexity of the problem. In this paper, a reinforcement learning approach is proposed to solve the disassembly line balancing problem. The objective is to minimize the number of workstations with a given cycle time. The proposed method is tested on benchmark problems from literature.