Stochastic service network design with rerouting Ruibin Bai a,⇑ , Stein W. Wallace b , Jingpeng Li d , Alain Yee-Loong Chong c a Division of Computer Science, University of Nottingham Ningbo China, Ningbo 315100, China b Department of Business and Management Science, Norwegian School of Economics, NO-5045 Bergen, Norway c Nottingham University Business School China, University of Nottingham Ningbo China, Ningbo 315100, China d Department of Computer Science and Mathematics, University of Stirling, Stirling FK9 4LA, UK article info Article history: Received 7 May 2013 Received in revised form 5 November 2013 Accepted 6 November 2013 Keywords: Service network design Stochastic programming Transportation logistics Rerouting abstract Service network design under uncertainty is fundamentally crucial for all freight transpor- tation companies. The main challenge is to strike a balance between two conflicting objec- tives: low network setup costs and low expected operational costs. Together these have a significant impact on the quality of freight services. Increasing redundancy at crucial net- work links is a common way to improve network flexibility. However, in a highly uncertain environment, a single predefined network is unlikely to suit all possible future scenarios, unless it is prohibitively costly. Hence, rescheduling is often an effective alternative. In this paper, we proposed a new stochastic freight service network design model with vehicle rerouting options. The proposed model explicitly introduces a set of integer variables for vehicle rerouting in the second stage of the stochastic program. Although computationally more expensive, the resultant model provides more options (i.e. rerouting) and flexibility for planners to deal with uncertainties more effectively. The new model was tested on a set of instances adapted from the literature and its performance and characteristics are studied through both comparative studies and detailed analyses at the solution structure level. Implications for practical applications are discussed and further research directions are also provided. Ó 2013 Elsevier Ltd. All rights reserved. 1. Background and motivation Service network design is one of the fundamental problems faced by the freight transportation industry. It is normally viewed as a tactical planning problem in which the company has to decide which terminals will have direct transportation services and at what frequency. In some cases, it also determines the best combination of transportation modes, and peri- odic vehicular schedules to ensure the continuity of services. Although closely related to classic network flow problems (Ahuja et al., 1993), which can be solved very efficiently, the service network design problem has proven to be one of the most difficult combinatorial optimisation problems around (Crainic and Kim, 2007). Solving real-life problem in- stances to optimality is generally not possible. Opportunities to develop practical decision support systems for this problem have been strengthened by the latest advances in high performance computing and hybrid optimisation tech- niques. This has led to increased research attention in service network design in the past decade. Detailed reviews of such research efforts can be found in Christiansen et al. (2007) for maritime transportation, Crainic (2003) for long-haul transportation and Crainic and Kim (2007) for intermodal transportation. Most research cited in these reviews is concerned with models and solution methods for deterministic cases. However, freight services are subject to various 0191-2615/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.trb.2013.11.001 ⇑ Corresponding author. Tel.: +86 574 88180278. E-mail addresses: ruibin.bai@nottingham.edu.cn (R. Bai), stein.wallace@nhh.no (S.W. Wallace), jli@cs.stir.ac.uk (J. Li), alain.chong@nottingham.edu.cn (A.Y.-L. Chong). Transportation Research Part B 60 (2014) 50–65 Contents lists available at ScienceDirect Transportation Research Part B journal homepage: www.elsevier.com/locate/trb