Exact and meta-heuristic approach for a general heterogeneous dial-a-ride problem with multiple depots

An adaptive large neighborhood search heuristic for the Pickup and Delivery Problem with Time Windows and Scheduled Lines

“…It is noteworthy that in Line 5 of the insertion algorithm, we do not consider every possible insertion. In order to avoid redundant computations in repairTransfers(X, r, T r ) and feasibleSchedule(X)) due to time windows, a preliminary time window check is applied as described in Braekers et al [4]. Due to the fact that PD-vehicle capacity constraints can be fully verified only after applying repairTransfers(X, r, T r ) procedure, capacity violations for the routes with no transferable requests can be easily detected.…”

The fleet size and mix dial-a-ride problem with reconfigurable vehicle capacity

“…The objective is to minimize the sum of route costs, satisfying constraints on vehicle capacity, time windows at origins and destinations of requests, and limits on the ride time of each passenger. Due to the combinatorial nature of the problem, exact methods (see for instance Ropke et al (2007); Ropke and Cordeau (2009) ;Braekers et al (2014)) are still limited in their ability to solve large size instances. Accordingly, several metaheuristics have been proposed to solve the DARP and its variants.…”

“…In particular, Cordeau and Laporte (2003) developed a Tabu Search algorithm and proposed a set of instances that are still used to benchmark new algorithms. The most successful heuristics that have been applied to these instances in the last ten years are the Variable Neighborhood Search (VNS) of Parragh et al (2010), the Hybrid LNS (HLNS) of Parragh and Schmid (2013), the Adaptive LNS (ALNS) of Masson et al (2014), the deterministic annealing algorithm of Braekers et al (2014), the evolutionary local search of Chassaing et al (2016) and the ALNS of Gschwind and Drexl (2016).…”

Circle Line Optimization of Shuttle Bus in Central Business District without Transit Hub

“…Such a system may offer higher quality of transport to clients with disability without increasing significantly the public transport expenditure. Braekers et al [10] presented a meta-heuristic algorithm which is an excellent and efficient tool for solving larger problems comparing to branch-and-cut algorithm. Lai et al [11], Saka [12], Chien and Qin [13] focused on the optimization of bus stop location and spacing.…”

“…Therefore, the proposed NSGA-II can improve the optimizing quality, and the algorithm has a good performance. 10 …”