A heuristic two‐phase solution approach for the multi‐objective dial‐a‐ride problem

Abstract

International audienceIn this article, we develop a heuristic two-phase solution procedure for the dial-a-ride problem with two objectives. Besides the minimum cost objective a client centered objective has been defined. Phase one consists of an iterated variable neighborhood search-based heuristic, generating approximate weighted sum solutions; phase two is a path relinking module, computing additional efficient solutions. Results for two sets of benchmark instances are reported. For the smaller instances, exact efficient sets are generated by means of the e-constraint method. Comparison shows that the proposed two-phase method is able to generate high-quality approximations of the true Pareto frontier

Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search

“…Parragh et al [56] designed an hybrid algorithm to solve the multi-objective dial-a-ride problem. A variable-neighborhood search algorithm is used to tackle weighted sum scalarizations defined by a regular sequence of weight vectors.…”

Models and algorithms for the heterogeneous dial-a-ride problem with driver-related constraints

“…This gives rise to the issue of service quality which can be ensured either through additional constraints or with extra terms in the objective function. The latter approach is followed, e.g., by Parragh et al (2009), while we adopt the first approach in this paper.…”

“…This VNS is based on the ones developed by Parragh et al (2009);Parragh et al (2010) and Parragh (2009) for the standard DARP and the multi-objective DARP: it also uses a simulated annealing type mechanism (Kirkpatrick et al 1983) in order to decide whether or not an ascending move shall be carried out. In addition, the employed iterative improvement phase is largely based on the one employed by Parragh et al (2009);Parragh et al (2010).…”

“…In addition, the employed iterative improvement phase is largely based on the one employed by Parragh et al (2009);Parragh et al (2010). However, in the current implementation, different neighborhood operators are employed.…”

GRASP with Hybrid Path Relinking for Bi-Objective Winner Determination in Combinatorial Transportation Auctions

“…There exists a small, but increasing number of successful Pareto-based restart-algorithms for other multi-objective problems. In particular, the Pareto-based GRASP approaches of AdensoDíaz, García-Carbajal, and Gupta (2008), Higgins, Hajkowicz, and Bui (2008), Ribeiro and Urrutia (2007), Boudia andPrins (2009), Arroyo, Vieira, andVianna (2008), Ishida, de Carvalho, Pozo, Goldbarg, and Goldbarg (2008), Reynolds andde la Iglesia (2009), andParragh, Doerner, Hartl, and have to be mentioned. None of these deals with multi-objective set covering problems.…”