1,113 research outputs found

    Adapting the large neighborhood search to effectively solve pickup and delivery problems with time windows

    Get PDF
    Pickup and delivery problems with time windows (PDP-TW) are challenging combinatorial optimization problems widely occurring in modern distribution and transportation industry. A previous proposal successfully adapted and combined the well-known push forward insertion heuristic (PFIH) with a new repair-based swap operator as a computationally reduced version of the Large Neighborhood Search (LNS) to efficiently solve the PDP-TWs. In this paper, we focus on a more systematic scheme to adapt the LNS in order to effectively solve PDP-TWs. Up to our knowledge, this work represents the first attempt to systematically adapt LNS for solving PDP-TWs. Besides, the empirical results obtained by our search proposals affirmed the attractive capability of the LNS approach to effectively reduce the total distance traveled in solving PDP-TWs, with similar results also achieved by the original LNS method in tackling the conventional vehicle routing problems with time windows. Lastly, our proposal of adapting LNS to solve hard combinatorial problems opens up many possible directions for future investigations. © 2004 IEEE.published_or_final_versio

    Industrial and Tramp Ship Routing Problems: Closing the Gap for Real-Scale Instances

    Full text link
    Recent studies in maritime logistics have introduced a general ship routing problem and a benchmark suite based on real shipping segments, considering pickups and deliveries, cargo selection, ship-dependent starting locations, travel times and costs, time windows, and incompatibility constraints, among other features. Together, these characteristics pose considerable challenges for exact and heuristic methods, and some cases with as few as 18 cargoes remain unsolved. To face this challenge, we propose an exact branch-and-price (B&P) algorithm and a hybrid metaheuristic. Our exact method generates elementary routes, but exploits decremental state-space relaxation to speed up column generation, heuristic strong branching, as well as advanced preprocessing and route enumeration techniques. Our metaheuristic is a sophisticated extension of the unified hybrid genetic search. It exploits a set-partitioning phase and uses problem-tailored variation operators to efficiently handle all the problem characteristics. As shown in our experimental analyses, the B&P optimally solves 239/240 existing instances within one hour. Scalability experiments on even larger problems demonstrate that it can optimally solve problems with around 60 ships and 200 cargoes (i.e., 400 pickup and delivery services) and find optimality gaps below 1.04% on the largest cases with up to 260 cargoes. The hybrid metaheuristic outperforms all previous heuristics and produces near-optimal solutions within minutes. These results are noteworthy, since these instances are comparable in size with the largest problems routinely solved by shipping companies

    Heuristics for dynamic and stochastic routing in industrial shipping

    Get PDF
    Maritime transportation plays a central role in international trade, being responsible for the majority of long-distance shipments in terms of volume. One of the key aspects in the planning of maritime transportation systems is the routing of ships. While static and deterministic vehicle routing problems have been extensively studied in the last decades and can now be solved effectively with metaheuristics, many industrial applications are both dynamic and stochastic. In this spirit, this paper addresses a dynamic and stochastic maritime transportation problem arising in industrial shipping. Three heuristics adapted to this problem are considered and their performance in minimizing transportation costs is assessed. Extensive computational experiments show that the use of stochastic information within the proposed solution methods yields average cost savings of 2.5% on a set of realistic test instances

    Thirty years of heterogeneous vehicle routing

    No full text
    It has been around thirty years since the heterogeneous vehicle routing problem was introduced, and significant progress has since been made on this problem and its variants. The aim of this survey paper is to classify and review the literature on heterogeneous vehicle routing problems. The paper also presents a comparative analysis of the metaheuristic algorithms that have been proposed for these problems

    An Adaptive Tabu Search Heuristic for the Location Routing Pickup and Delivery Problem with Time Windows with a Theater Distribution Application

    Get PDF
    The time constrained pickup and delivery problem (PDPTW) is a problem of finding a set of routes for a fleet of vehicles in order to satisfy a set of transportation requests. Each request represents a user-specified pickup and delivery location. The PDPTW may be used to model many problems in logistics and public transportation. The location routing problem (LRP) is an extension of the vehicle routing problem where the solution identifies the optimal location of the depots and provides the vehicle schedules and distribution routes. This dissertation seeks to blend the PDPTW and LRP areas of research and formulate a location scheduling pickup and delivery problem with time windows (LPDPTW) in order to model the theater distribution problem and find excellent solutions. This research utilizes advanced tabu search techniques, including reactive tabu search and group theory applications, to develop a heuristic procedure for solving the LPDPTW. Tabu search is a metaheuristic that performs an intelligent search of the solution space. Group theory provides the structural foundation that supports the efficient search of the neighborhoods and movement through the solution space

    A general deep reinforcement learning hyperheuristic framework for solving combinatorial optimization problems

    Get PDF
    Many problem-specific heuristic frameworks have been developed to solve combinatorial optimization problems, but these frameworks do not generalize well to other problem domains. Metaheuristic frameworks aim to be more generalizable compared to traditional heuristics, however their performances suffer from poor selection of low-level heuristics (operators) during the search process. An example of heuristic selection in a metaheuristic framework is the adaptive layer of the popular framework of Adaptive Large Neighborhood Search (ALNS). Here, we propose a selection hyperheuristic framework that uses Deep Reinforcement Learning (Deep RL) as an alternative to the adaptive layer of ALNS. Unlike the adaptive layer which only considers heuristics’ past performance for future selection, a Deep RL agent is able to take into account additional information from the search process, e.g., the difference in objective value between iterations, to make better decisions. This is due to the representation power of Deep Learning methods and the decision making capability of the Deep RL agent which can learn to adapt to different problems and instance characteristics. In this paper, by integrating the Deep RL agent into the ALNS framework, we introduce Deep Reinforcement Learning Hyperheuristic (DRLH), a general framework for solving a wide variety of combinatorial optimization problems and show that our framework is better at selecting low-level heuristics at each step of the search process compared to ALNS and a Uniform Random Selection (URS). Our experiments also show that while ALNS can not properly handle a large pool of heuristics, DRLH is not negatively affected by increasing the number of heuristics.publishedVersio

    New variants of the time-dependent vehicle routing problem with time windows

    Get PDF

    New variants of the time-dependent vehicle routing problem with time windows

    Get PDF
    corecore