A TOPSIS-Based Improved Weighting Approach with Evolutionary Computation

Transactions on Fuzzy Sets and Systems (TFSS), vol.3, no.1, pp.171-183, 2024 (Peer-Reviewed Journal)

Although optimization of weighted objectives is ubiquitous in production scheduling, the literature

concerning the determination of 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

settle the class of optimal solutions, and are of utmost importance in any practical scheduling problem. In this

study, we propose a new weighting approach for single machine scheduling problems. First, factor weights to be

used in customer evaluation are found by solving a nonlinear optimization problem using the covariance matrix

adaptation evolutionary strategy (CMAES) under fuzzy environment that takes a pairwise comparison matrix

as input. Next, customers 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 the best job sequence.

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.