Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints

• Published in: Robotics and computer-integrated manufacturing Vol. 59; pp. 143 - 157
• Main Authors: Dai, Min, Tang, Dunbing, Giret, Adriana, Salido, Miguel A.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2019; Elsevier BV
• Subjects: Algorithms; Energy conservation; Energy consumption; Energy management; Environmental impact; Flexible job shop scheduling problems; Flexible manufacturing systems; Genetic algorithm; Genetic algorithms; Job shop scheduling; Job shops; Multi-objective optimization; Multiple objective analysis; Nonlinear programming; Optimization; Production scheduling; Resource management; Transportation; Transportation time; Energy consumption; Multi-objective optimization; Transportation time; Flexible job shop scheduling problems; Genetic algorithm
• ISSN: 0736-5845; 1879-2537
• DOI: 10.1016/j.rcim.2019.04.006

•We model a multi-objective energy-efficient scheduling with transportation times.•We develop an enhanced genetic algorithm to get Pareto solutions.•The proposed algorithm is effective in solving the EFJSP and is capable of obtaining better Pareto solutions than the multi-objective genetic algorithm.•An energy-saving scheduling strategy could be considered by reducing the transportation time. Manufacturing enterprises nowadays face huge environmental challenges because of energy consumption and associated environmental impacts. One of the effective strategies to reduce energy consumption is by employing intelligent scheduling techniques. Production scheduling can have significant impact on energy saving in manufacturing system from the operation management point of view. Resource flexibility and complex constraints in flexible manufacturing system make production scheduling a complicated nonlinear programming problem. To this end, a multi-objective optimization model with the objective of minimizing energy consumption and makespan is formulated for a flexible job shop scheduling problem with transportation constraints. Then, an enhanced genetic algorithm is developed to solve the problem. Finally, comprehensive experiments are carried out to evaluate the performance of the proposed model and algorithm. The experimental results revealed that the proposed model and algorithm can solve the problem effectively and efficiently. This may provide a basis for the decision makers to consider energy-efficient scheduling in flexible manufacturing system.