The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools.
In @cite_54 , the authors propose a heuristic two-phase solution procedure for the dial-a-ride problem with two objectives. The first phase consists of an iterated variable neighborhood search-based heuristic, generating approximate weighted sum solutions and the second phase is a path relinking module, computing additional efficient solutions.
@cite_54
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In 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. © 2009 Wiley Periodicals, Inc. NETWORKS, 2009 This paper introduces the probabilistic dial-a-ride problem, and describes an efficient request-relocation neighborhood evaluation procedure for the problem. The running time of the procedure is @math , compared to @math for a straightforward approach. For solving the problem we embed the suggested evaluation procedure in a pure local search heuristic and in a tabu search heuristic. The quality of the solutions obtained by the two heuristics have been compared experimentally. Computational results confirm that our neighborhood evaluation technique is much faster than the straightforward one, and for cases with 144 users and 4 vehicles it is demonstrated that the computation time can be reduced by a factor larger than 27. This paper studies a dynamic dial-a-ride problem bearing complex constraints on a time-dependent network. A flexible scheduling scheme is proposed to dynamically cope with different stochastic events, such as the travelling time fluctuation, new requests, absences of customers, vehicle breakdowns, cancellations of requests, traffic jams and so on. A fast heuristic is proposed to re-optimize the schedule when a new event occurs. This heuristic consists of a properly organized local search strategy and uses a secondary objective function to drive the search out of local optima. Intensive computational simulations were carried out to evaluate the performance of this scheduling scheme and the influence of different stochastic factors. The simulation results of different scenarios with different percentage of dynamic requests reveal that this scheduling scheme can generate high quality schedules and is capable of coping with various stochastic events. In the dial-a-ride problem, users formulate requests for transportation from a specific origin to a specific destination. Transportation is carried out by vehicles providing a shared service. The problem consists of designing a set of minimum-cost vehicle routes satisfying capacity, duration, time window, pairing, precedence, and ride-time constraints. This paper introduces a mixed-integer programming formulation of the problem and a branch-and-cut algorithm. The algorithm uses new valid inequalities for the dial-a-ride problem as well as known valid inequalities for the traveling salesman, the vehicle routing, and the pick-up and delivery problems. Computational experiments performed on randomly generated instances show that the proposed approach can be used to solve small to medium-size instances.