Abstract:

A new method of scheduling fields (Inverse scheduling problem, ISP) is discussed under the theories and methods of inverse optimization. The definition of an inverse scheduling problem is that the exact values of parameters (e.g. processing times, due dates) are controllable and a feasible job sequence is given but not optimal and pre-specified job sequence(s) become optimal through adjusting processing parameters for a target. The model of multi-objective ISP which considers scheduling efficiency and system stability is build. The adjustment of processing parameters, the changing of system and the weighted completion time are considered in this model. A hybrid multi-objective genetic algorithm is proposed to solve this problem. In order to improve the performance of algorithm, the multiple strategies are mixed, including rapid non-dominated sorting method, two kinds of diversity strategy, hybrid elitism strategy and efficient local search strategy. Finally, public problem instances and ANOVA analysis are provided for the proposed algorithm. The results demonstrate the effectiveness of the algorithm.

Key words: inverse scheduling, local search method, multi-objective evolutionary algorithm, shop scheduling, Atherosclerosis, Multivariate Curve Resolution, Photoacoustic Spectroscopy, Quantitative Analysis, Spectroscopic imaging