A TOPSIS-based improved weighting approach with evolutionary computation

Abstract

Abstract Although optimization of weighted objectives is ubiquitous in production scheduling, the literature concerning the determination of the weights used in these objectives is scarce. Authors usually suppose that weights are given in advance, and focus on the solution methods for the specific problem at hand. However, weights directly affect optimality, and are of utmost importance in any practical scheduling problem. In this study, we propose a new TOPSIS-based weighting approach for single machine scheduling problem. First, factor weights to be used in customer evaluation are determined by fuzzy covariance matrix adaptation evolutionary strategy (FCMAES). Next, customers (jobs) are sorted using the technique for order of preference by similarity to ideal solution (TOPSIS), by means of which job weights are obtained. Finally, taking these weights as an input, a total weighted tardiness minimization problem is solved by using mixed-integer linear programming to find a suitable job sequence. Real data collected from a textile company of Turkey was employed. Results was compared with Fuzzy Genetic Algorithm (FGENETIC). FCMAES objective function value as 822.69 was obtained better than FGENETIC value as 817.24. This combined methodology may help companies make robust schedules not based purely on subjective judgment, find the best compromise between customer satisfaction and business needs, and thereby ensure profitability in the long run.