Hybrid Evolutionary Algorithm for Integrated Supply Chain Network Design With Assembly Line Balancing

This paper addresses a supply chain network design problem simultaneously considering facility capacity selection and assembly line balancing. The studied supply chain is composed of manufacturers, assemblers and customers, and the objective of the problem is to minimize the total cost including shipping cost, facility fixed cost and station fixed cost. To solve the problem, we propose a hybrid evolutionary algorithm (HEA), which fuses genetic algorithm (GA) and model-based approaches. The genetic algorithm is developed as the search framework to determine which facilities to be established. Model-based approaches are employed in the decoding stage to determine the shipping amount between facilities and the task allocation in each assembler. Moreover, a knowledge-based heuristic is designed to generate a good initial population, and a problem-specific local intensification is designed to enhance the algorithm's exploitation ability. The proposed HEA is tested on randomly generated instances from small to large scales. The computational results demonstrate the effectiveness of the HEA and the self-designed heuristics.