A memetic algorithm for multi-objective distributed production scheduling: minimizing the makespan and total energy consumption

The classical distributed production scheduling problem (DPSP) assumes that factories are identical, and each factory is composed of just some machines. Inspired by the fact that manufacturers these days typically work across different factories, and each of these factories normally has some workshops, we study an important extension of the DPSP with different factories and workshops (DPFW), where jobs can be processed and transferred between the factories, workshops and machines. To the best of our knowledge, this is the very first time distributed production scheduling with different factories and workshops is studied. We propose a novel memetic algorithm (MA) to solve this DPFW, aiming to minimize the makespan and total energy consumption. The proposed MA is incorporated with a well-designed chromosome encoding method and a balance-transfer initialization method to generate a good initial population. An effective local search operator is also presented to improve the MA's convergence speed and fully exploit its solution space. A total of 50 DPFW benchmark instances are used to evaluate the performance of our MA. Computational experiments carried out confirm that the MA is able to easily obtain better solutions for the majority of the tested problem instances compared to three other well-known algorithms, demonstrating its superior performance over these algorithms in terms of solution quality. Our proposed method and the results presented here may be helpful for production managers who work with distributed manufacturing systems in scheduling their production activities by considering different factories and workshops. With this DPFW, imbalanced resource loads and unexpected bottlenecks, which regularly arise in traditional DPSP models, can be easily avoided.