Economies of scale in recoverable robust maintenance location routing for rolling stock

The stochastic maintenance location routing allocation problem for rolling stock

“…Economies of scale in production (Romeijn et al, 2010;Sharkey et al, 2011) or economies in scale for transportation (Lin et al, 2006;Wu et al, 2015) are studied in the literature. However, economies of scale in facility size are only briefly mentioned in Melo et al (2006) and to our best knowledge only studied in depth by Tönissen and Arts (2018).…”

“…Maintenance routing for rolling stock is studied by Anderegg et al (2003), Kroon (2005, 2007) and for aviation by Gopalan and Talluri (1998), Sarac et al (2006), Liang et al (2015) and many others. The combination of maintenance routing and facility location for aviation is studied by Feo and Bard (1989) and Gopalan (2014), for locomotives by Xie et al (2016), and for rolling stock by Tönissen et al (2019) and Tönissen and Arts (2018). The paper of Tönissen et al (2019), includes the maintenance routing for rolling stock in an aggregate way into a facility location model.…”

“…They model the maintenance location routing problem as two-stage robust optimization and stochastic programming problems and provide a Benders decomposition and a scenario addition algorithm to solve the models to optimality. This problem was extended by Tönissen and Arts (2018) to include unplanned maintenance, economies of scale in facility size and recoveries of the facility location decisions. Our paper extends these papers further by including allocation restrictions of the rolling stock types to the maintenance facilities.…”

“…The maintenance location routing problem for rolling stock was introduced by Tönissen et al (2019), and extended by Tönissen and Arts (2018). Tönissen and Arts (2018) show that the best strategy is to reduce transportation cost by locating many small facilities instead of opening a few large facilities to profit from economies of scale. However, their paper does not include allocation restrictions of the rolling stock types to the maintenance facilities.…”

High-Speed Railway EMUs’ Circulation Plan Optimization: A Two-Stage Optimization Algorithm Based on Column Generation

“…When EMUs circulation schemes are being planned, the maintenance constraint influences the EMUs operating procedures as the EMUs traveled mileage and time cannot be exceeded; this constraint cannot be directly and linearly expressed by common mathematical programming models. A heuristic method or an intelligent optimization algorithm is an effective method to address this constraint [1]- [7]. Zhou et al (2017) proposed an optimization model to optimize train connection times and maintenance costs and designed an efficient multi-group genetic algorithm to solve it [1].…”