218 research outputs found

    The Elderly Centre Location Problem

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    © The Operational Research Society 2020. This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the Operational Research Society on 12 Feb 2020, available online: https://doi.org/10.1080/01605682.2020.1718020.Increased human life expectancy combined with declining birth rates around the globe has led to ageing populations, particularly in the developed world. This phenomenon brings about increased dependency ratios and calls for setting new policies for the elderly citizens. This comprises the provision of a set of life-enhancing services in an accessible and equitable way. In this paper, we consider a multi-period problem of locating senior centres offering these services to the elderly population against budget constraints and capacity limitations. We assume that the attractiveness of facilities to elderlies is inversely proportional with the travel time to access these facilities. Both consistent and inconsistent versions of the problem are considered, aiming at identifying the set of facilities to operate in each region at each period, the service type(s) to be offered and the allocation of budget in each period to location and operation of facilities. A mixed integer mathematical programming model is presented, an efficient iterated local search procedure is proposed and managerial insights are provided.Peer reviewedFinal Accepted Versio

    A competitive solution for cooperative truckload delivery

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    This paper introduces a solution for gain sharing in consortia of logistic providers where joint planning of truckload deliveries enables the reduction of empty kilometers. The highly competitive nature of freight transport markets necessitates solutions that distinguish among the logistics providers based on their characteristics, even in situations with two players only. We introduce desirable properties in these situations and propose a solution that satisfies such properties. By comparing the existing solutions against the introduced properties we demonstrate the advantages of our proposed solution

    Spot Market versus Full Charter Fleet:Decisions Support for Full Truck Load Tenders

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    This paper presents an approach to help business decision-makers gain market share by providing competitive tender offers for Full-Truck-Load (FTL) services. In particular, we compare operating a fleet of Full-Charter-Trucks (FCT), using spot-market (SM) capacity and a mixture of both options against each other. A Pickup and Delivery Problem is modeled, and solved using an Adaptive Large Neighborhood Search heuristic. Computational results indicate strong service benefits combining FCT and SM usage. Numerical experiments are presented in detail to support the findings. Additionally, a real-life case study originating from DB Schenker is presented.Comment: 22 pages, 4 figures, 9 table

    Hybrid simulation and optimization approach for green intermodal transportation problem with travel time uncertainty

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    The increasing volumes of road transportation contribute to congestion on road, which leads to delays and other negative impacts on the reliability of transportation. Moreover, transportation is one of the main contributors to the growth of carbon dioxide equivalent emissions, where the impact of road transportation is significant. Therefore, governmental organizations and private commercial companies are looking for greener transportation solutions to eliminate the negative externalities of road transportation. In this paper, we present a novel solution framework to support the operational-level decisions for intermodal transportation networks using a combination of an optimization model and simulation. The simulation model includes stochastic elements in form of uncertain travel times, whereas the optimization model represents a deterministic and linear multi-commodity service network design formulation. The intermodal transportation plan can be optimized according to different objectives, including costs, time and CO2e emissions. The proposed approach is successfully implemented to real-life scenarios where differences in transportation plans for alternative objectives are presented. The solutions for transportation networks with up to 250 services and 20 orders show that the approach is capable of delivering reliable solutions and identifying possible disruptions and alternatives for adapting the unreliable transportation plans

    Exact Two-Step Benders Decomposition for Two-Stage Stochastic Mixed-Integer Programs

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    Many real-life optimization problems belong to the class of two-stage stochastic mixed-integer programming problems with continuous recourse. This paper introduces Two-Step Benders Decomposition with Scenario Clustering (TBDS) as a general exact solution methodology for solving such stochastic programs to optimality. The method combines and generalizes Benders dual decomposition, partial Benders decomposition, and Scenario Clustering techniques and does so within a novel two-step decomposition along the binary and continuous first-stage decisions. We use TBDS to provide the first exact solutions for the so-called Time Window Assignment Traveling Salesperson problem. This is a canonical optimization problem for service-oriented vehicle routing; it considers jointly assigning time windows to customers and routing a vehicle among them while travel times are stochastic. Extensive experiments show that TBDS is superior to state-of-the-art approaches in the literature. It solves instances with up to 25 customers to optimality. It provides better lower and upper bounds that lead to faster convergence than related methods. For example, Benders dual decomposition cannot solve instances of 10 customers to optimality. We use TBDS to analyze the structure of the optimal solutions. By increasing routing costs only slightly, customer service can be improved tremendously, driven by smartly alternating between high- and low-variance travel arcs to reduce the impact of delay propagation throughout the executed vehicle route

    The time-dependent capacitated profitable tour problem with time windows and precedence constraints

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    We introduce the time-dependent capacitated profitable tour problem with time windows and precedence constraints. This problem concerns determining a tour and its departure time at the depot that maximizes the collected profit minus the total travel cost (measured by total travel time). To deal with road congestion, travel times are considered to be time-dependent. We develop a tailored labeling algorithm to find the optimal tour. Furthermore, we introduce dominance criteria to discard unpromising labels. Our computational results demonstrate that the algorithm is capable of solving instances with up to 150 locations (75 pickup and delivery requests) to optimality. Additionally, we present a restricted dynamic programing heuristic to improve the computation time. This heuristic does not guarantee optimality, but is able to find the optimal solution for 32 instances out of the 34 instances

    Last mile logistics: Research trends and needs

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    Aspiring green agendas in conjunction with tremendous economic pressures are resulting in an increased attention to the environment and technological innovations for improving existing logistics systems. Last mile logistics, in particular, are becoming much more than a consumer convenience necessity and a transportation optimization exercise. Rather, this area presents a true opportunity to foster both financial and environmental sustainability. This paper investigates recent technological advancements and pending needs related to business and social innovations, emphasizing green logistics and city logistics concepts. We discuss various pertinent aspects, including drones, delivery robots, truck platooning, collection and pickup points, collaborative logistics, integrated transportation, decarbonization and advanced transport analytics. From a mathematical perspective, we focus on the basic features of the vehicle routing problem and some of its variants. We provide recommendations around strategies that may facilitate the adoption of new effective technologies and innovations

    Stochastic vehicle routing with random time dependent travel times subject to perturbations

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    Assigning and scheduling vehicle routes in a stochastic time dependent environment is a crucial management problem. The assumption that in a real-life environment everything goes according to an a priori determined static schedule is unrealistic, resulting in a planning gap (i.e. difference in performance between planned route and actual route). Our methodology introduces the traffic congestion component based on queueing theory, thereby introducing an analytical expression for the expected travel. In real life travel times are subject to uncertainty, we solve a time dependent vehicle routing problem to find robust solutions, that can potentially absorb such uncertainties. We model uncertainty as perturbations that are randomly inserted on the routes, we optimize the perturbed solutions via Tabu Search. We conduct experiments on a set of 32 cities, and found that the perturbed solutions generally cope better with the uncertainty than the non-perturbed solutions, with a small increase in expected travel times
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