A knowledge-guided Estimation of Distribution Algorithm for energy-efficient Joint Robotic Assembly Line Balancing and Feeding Problem

The assembly line serves as a fundamental system in discrete production. To address the challenges in balancing robotic assembly lines, timely part feeding, and the need for sustainable manufacturing, this paper studies an energy-efficient Joint Robotic Assembly Line Balancing and Feeding Problem (JRALB-FP) with the criteria of minimizing both cycle time and total fuel consumption cost. Considering the complexity of the multi-problem and multi-objective optimization, a knowledge-guided Estimation of Distribution Algorithm (KEDA) is proposed to solve energy-efficient JRALB-FP. First, a probability model of EDA for task-workstation allocation paired with a heuristic method-based sampling mechanism is created. Using this probability model, a specific encoding mechanism is designed for part-trailer allocation, and good initial solutions are produced. Second, several properties of the bi-objective problem are analyzed to guide the design of local search operators for both objectives optimization. Third, the updating mechanism of the probability model is designed to learn from the elite solutions. Fourth, two knowledge-guided local search operators are designed and implemented to exploit better non-dominated solutions sufficiently. A design of experiment is carried out to determine the parameters. Extensive computational tests and comparisons with the state-of-the-art multi-objective algorithms are carried out, which verify the effectiveness of the knowledge-guided local search operators, the problem-oriented heuristic-based sampling mechanism, and the special designs of the KEDA in solving the energy-efficient JRALB-FP.