Job Shop Scheduling With Profit and Entropy as Performance Measures

To solve the unilateral problems of job-shop scheduling performance measures used in previous studies,by using an unified measure-profit integrated time,quality,cost,energy and environment along with entropy as evaluating the scheduling effectiveness,an approach for job-shop scheduling algorithm is proposed based on genetic algorithm. Wherein,the profit is regarded as the adaptive value of the chromosome in genetic algorithm,a set of suboptimal scheduling schemes gained via searches,and the best scheduling scheme is gained after decision-making for the suboptimal scheduling schemes with profit and entropy as objectives summed up by entropic weights. Examples and programs in program language C# are used to illustrate validation. It shows that the proposed approach have some advantage than that of traditional scheduling algorithms in all sidedness and practicability of performance measures.