This thesis investigates the multi-objective optimization of job shop scheduling in a distributed layout context, focusing on minimizing makespan, travel distance, and energy consumption. It develops an efficient Genetic Algorithm (GA) using the Weighted Sum Approach (WSA) and extends the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve the Distributed Job Shop Scheduling Problem. The distributed layout is explored as an alternative to traditional functional layouts, offering improved adaptability and responsiveness to dynamic production demands.

The study compares WSA’s aggregated solutions with the Pareto-optimal results of NSGA-II, analyzing trade-offs between objectives. Results demonstrate that, compared to functional layouts, distributed layouts consistently yield lower costs, shorter travel distances, and reduced completion times. Additionally, the thesis addresses challenges such as maintaining diversity in NSGA-II solutions and proposes parallel computing to enhance simulation efficiency.