Combining multicriteria analysis and tabu search for dial-a-ride problems

Abstract

In the Dial-a-Ride Problem (DARP) the aim is to design vehicle routes for a set of users who must be transported between given origin and destination pairs, subject to a variety of side constraints. The standard DARP objective is cost minimization. In addition to cost, the objectives considered in this paper include three terms related to quality of service. This gives rise to a multicriteria problem. The problem is solved by means of a flexible and simple metaheuristic which efficiently integrates the reference point method for multicriteria optimization within a tabu search mechanism. Extensive tests were performed on randomly generated data and on real-life data provided by a major transporter in the Montreal area. Results indicate that the algorithm can yield a rich set of non-dominated solutions. It can also be employed to determine good trade-offs between cost and quality of service.

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

“…Among these solution approaches, the TS algorithm developed by Glover [32] has been very successful in finding close to optimal solutions for engineering problems [33][34][35][36][37]. The TS algorithm starts searching with an initial solution, based on which a set of feasible solutions is constructed as the neighborhood.…”

A branch-and-cut algorithm for a realistic dial-a-ride problem

“…They can provide an initial solution (Toth and Vigo, 1997;Aldaihani and Dessouky, 2003) for the improvement heuristics, or applied in the instances when hundreds of requests are involved and a solution is needed in seconds (Jaw et al, 1986;Diana and Dessouky, 2004;Luo and Schonfeld, 2007). Improvement heuristics, on the other hand, usually attempt to improve the quality of a solution by a local search procedure involving relocation or exchange of vertices or clients (Toth and Vigo, 1997;Cordeau and Laporte, 2003b;Xiang et al, 2006;Kirchler and Wolfler Calvo, 2013;Paquette et al, 2013). To date, the deterministic annealing algorithm proposed by Braekers et al (2014) has been the best heuristic for the homogeneous and heterogeneous DARP, both in terms of solution quality and computation time.…”

Stochastic optimization approach for the car placement problem in ridesharing systems

“…Attanasio et al (2004) proposed a parallel tabu search for the case where passengers arrive in real time and thus are matched to existing routes as they arrive. Other metaheuristics that have also been proposed include the genetic algorithm that is presented in Jorgensen et al (2006), the variable neighborhood search algorithm of Parragh et al (2010), the granular tabu search algorithm of Kirchler and Calvo (2013), and the multicriteria tabu search of Paquette et al (2013) which considers different objectives that include cost minimization and quality of the service that relate to waiting and travel times.…”