An Adaptive Large Neighborhood Search for Single-Machine Batch Processing Scheduling With 2-D Rectangular Bin-Packing Constraints
Ju Zeng, Xiaopan Zhang
Abstract
Spatial resource allocation is common in batch scheduling problems. It is usually modeled as simple capacity constraints, which hurts the accuracy of a solution if a geometric layout is required in multidimensional space. In this article, a multiobjective mixed-integer linear programming model including total weighted waiting time and resource utilization is proposed for single batch processing machine scheduling, in which the two-dimensional (2-D) rectangular packing constraints are introduced to handle a 2-D layout more precisely. An adaptive large neighborhood search algorithm with three kinds of destroy operators and repair operators is developed to approximately solve the problem. Experiments based on lockage scheduling show that our algorithm found exact optimal solutions in most cases of no more than 30 items and outperformed typical genetic algorithm, simulated annealing, and two kinds of variable neighborhood descent algorithms in all cases of no more than 50 items.