An improved genetic algorithm to solve a customer driven low carbon job scheduling problem in service-oriented manufacturing

The traditional job shop scheduling problem usually takes performance indicators, such as the completion time, cost, as the global optimization objectives. However, with the service-oriented manufacturing pattern applied, each custom-er with manufacturing tasks will pay more attention to its own requirements and profits. Meanwhile, in order to respond to the global climate change, the manufacturing further focus on energy conservation and emission reduction. With an eye to the requirements in this new environment, in this paper, a non-cooperative game model for flexible job-shop scheduling with the goal of carbon emissions under constraint of due-date is given. In the model, the player refers to manufacturing tasks submitted by related customers, the strategy of each job refers to the selectable machines related to the operations of the job, and the payoff of each job refers to the makespan and carbon emissions. To get the optimal scheduling results, an improved genetic algorithm is adopted to solve the Nash equilibrium (NE) point of the mod-el. Finally, a case is designed to test the applicability and feasibility of the proposed model and method