An Artificial Bee Colony Based Hyper-heuristic for the Single Machine Order Acceptance and Scheduling Problem

Abstract

This paper presents an artificial bee colony based hyper-heuristic for solving the order acceptance and scheduling (OAS) problem in a single machine environment. The OAS problem gives the flexibility to accept or reject an order where the systems have limited production capacity and on-time delivery constraints. The OAS problem, which is a typical $\mathcal {NP}$-hard problem, becomes more complex when a sequence-dependent setup time is incurred between two consecutive orders. Solving an $\mathcal {NP}$-hard problem through exact approaches is computationally expensive and they fail to solve large-size instances. Therefore, we proposed hyper-heuristic in which artificial bee colony (ABC) algorithm is employed as a search methodology for the OAS problem. Hyper-heuristic works on the search space of heuristics, whereas ABC algorithm works on the solution space of the problem. A guided heuristic, which works on search space of heuristics, is developed to search the best heuristic from a set of heuristics residing at the lower level of hyper-heuristic. The proposed approach is compared with the state-of-the-art approaches. The computational results show that the integration of ABC algorithm into hyper-heuristic outperformed the other approaches in terms of average and minimum deviation from the upper bound.