AUTHOR COPY Increasing the total net revenue for single machine order acceptance and scheduling problems using an artificial bee colony algorithm S-W Lin 1 and K-C Ying 2 1 Department of Information Management, Chang Gung University, Taoyuan, Taiwan, R.O.C; and 2 Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan, R.O.C The order acceptance and scheduling (OAS) problem is an important topic for make-to-order production systems with limited production capacity and tight delivery requirements. This paper proposes a new algorithm based on Artificial Bee Colony (ABC) for solving the single machine OAS problem with release dates and sequence-dependent setup times. The performance of the proposed ABC-based algorithm was validated by a benchmark problem set of test instances with up to 100 orders. Experimental results showed that the proposed ABC-based algorithm outperformed three state-of-art metaheuristic-based algorithms from the literature. It is believed that this study successfully demonstrates a high-performance algorithm that can serve as a new benchmark approach for future research on the OAS problem addressed in this study. Journal of the Operational Research Society (2013) 64, 293–311. doi:10.1057/jors.2012.47; published online 9 May 2012 Keywords: order acceptance and scheduling problem; single machine; artificial bee colony algorithm; sequence-dependent setup times 1. Introduction Since the pioneering work of Guerrero and Kern (1988), the order acceptance and scheduling (OAS) problem has been recognized as an important topic and continuously attracted attention from researchers and practitioners. The OAS problem mainly focuses on two joint decisions: one is to decide which orders should be accepted for processing, and the other is to decide the processing sequence of the accepted orders (Slotnick, 2011). Therefore, a trade-off between the revenues and associated costs brought in by the accepting orders should be considered for making decisions (Guerrero and Kern, 1988). In practice, this is necessary for many make-to-order production systems (eg the printing and laminating company), with limited production capacity and tight order delivery deadline requirements (Herbots et al, 2007; Og˘uz et al, 2010). Over the past two decades, a diversity of OAS problems of different problem characteristics and objectives has been studied. For detailed discussion and taxonomy of the application models and available algorithms for various OAS problems, the reader is referred to the excellent literature surveys of Keskinocak and Tayur (2004) and Slotnick (2011). In this study, we focus on the OAS problem in a single machine environment. A number of exact methods have been applied to this problem, including (but not limited to) integer programming (Stern and Avivi, 1990), mixed-integer programming (Charnsirisakskul et al, 2004; Charnsirisakskul et al, 2006), mixed-integer linear programming (Yang and Geunes, 2003; Og˘ uz et al, 2010; Nobibon and Leus, 2011), dynamic programming (Lewis and Slotnick, 2002; Engels et al, 2003; Gordon and Strusevich, 2009) and branch-and-bound (Slotnick and Morton, 2007; Nobibon and Leus, 2011) algorithms. While relatively easy to conceptualize, the typical single machine OAS problem under most problem characteristics and performance criteria is NP-hard (Ghosh, 1997; Slotnick and Morton, 2007; Og˘ uz et al, 2010). For a problem of such complexity, the computational requirements for obtaining optimal solutions by exact methods are severe even for very moderately sized problems. Consequently, in practice, decision makers often seek approximation algo- rithms that generate near-optimum solutions for problems with a large number of orders, as is usual in industry, in a reasonable computational time. Currently available approximation algorithms for sol- ving single machine OAS problems can be classified into two categories: constructive heuristics (CHs) and improvement heuristics (IHs). For CHs such as those Journal of the Operational Research Society (2013) 64, 293–311 © 2013 Operational Research Society Ltd. All rights reserved. 0160-5682/13 www.palgrave-journals.com/jors/  Correspondence: K-C Ying, Department of Industrial Engineering and Management, National Taipei University of Technology, No. 1, Sec. 3, Chung-Hsiao E. Road, Taipei 10608, Taiwan, R.O.C.