A column generation approach for a school bus routing problem with resource constraints

Intelligent Safety Transport Framework for Schools: A Review of Route Planning and Tracking Systems

“…ISTSs were developed due to recent advancements in route planning systems [6], [12], [23], [29]- [33], [35]- [37], school vehicle-student tracking and monitoring systems [8], [18], [38]- [43]. Figure 2 shows the functions of a typical ISTS.…”

“…Bus stop selection can be done through heuristic approaches [29], [36], [37] or by exact methods based on mathematical programming [12], [21], [22], [23], [37]. The heuristic approach follows either the Location-Allocation-Routing (LAR) [15], [24], [25], [26] or the Allocation-Routing-Location (ARL) [27], [28], [29] strategies.…”

“…Figure 2 shows the functions of a typical ISTS. Each ISTS function applies its own methods to find the best solutions [6], [7], [8], [12], [17], [21] for the challenges in each function. For instance, GPS [39] and RFID [18] tags are used for vehicle and student tracking.…”

“…As a result, proper safety route planning and monitoring strategies for school vehicles and children are in high demand [4], [5]. Efforts to reduce school transport injuries have begun through constraint-based school bus routing [12] and peer to peer children monitoring [13] while they are on route. Recent innovations [6]- [8] in STS, gave birth to Intelligent School Transportation Systems (ISTSs), which addressed some of the issues [9], [10], [11] in STS such as optimal routing of school vehicles, continuous monitoring of school buses and children.…”

A Relation of Dominance for the Bicriterion Bus Routing Problem

“…Thus, determining the set of visited bus stops is a part of the problem. The following methods of resolving the SBRPBSS are proposed: a genetic algorithm (Díaz-Parra et al, 2012;Kang et al, 2015), a column-generation-based algorithm (Kinable et al, 2014;Riera-Ledesma and Salazar-González, 2013), a GRASP + VND (variable neighborhood descent) matheuristic (Schittekat et al, 2013), an artificial ant colony with a variable neighborhood local search algorithm (Euchi and Mraihi, 2012), continuous approximation (Ellegood et al, 2015). In the work of Chalkia et al (2014) the SBRPBSS is modified and the safety of the bus stop (the size and location of the waiting area, the quality of the ground in the waiting area, and the visibility of the stop for approaching drivers, pedestrian crossing, etc.)…”