In this work we investigate opportunities offered by telematics and analytics to enable better informed, and more integrated, collaborative management decisions on construction sites. We focus on efficient refuelling of assets across construction sites. More specifically, we develop decision support models that, by leveraging data supplied by different assets, schedule refuelling operations by minimising the distance travelled by the bowser truck as well as fuel shortages. Motivated by a practical case study elicited in the context of a project we recently conducted at the C610 Systemwide Crossrail site, we introduce the Dynamic Bowser Routing Problem. In this problem the decision maker aims to dynamically refuel, by dispatching a bowser truck, a set of assets which consume fuel and whose location changes over time; the goal is to ensure that assets do not run out of fuel and that the bowser covers the minimum possible distance. We investigate deterministic and stochastic variants of this problem. To tackle deterministic variants, we introduce and contrast a bilinear programming model and a mixed-integer linear programming model. To tackle stochastic variants, by employing a new general purpose software library for stochastic modeling, we introduce a complete stochastic dynamic programming model as well as a novel heuristic we named "sample waning." We consider two stochastic variants of the problem in which asset locations or asset fuel consumptions are uncertain. We demonstrate the effectiveness of our approaches in the context of an extensive computational study designed around information and data collected at C610 Systemwide as well as supplied by our project partners.
The origins of the Truck Dispatching Problem,'' a generalization of the Traveling Salesman Problem, date back to the seminal work by @cite_0 . Since the early days, a sizeable literature developed on the so-called Vehicle Routing Problem (VRP), whose aim is to dispatch a fleet of vehicles on a given network to serve a set of customers while meeting a number of constraints. A comprehensive discussion on the VRP can be found in .
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Vehicle Routing Problems have been extensively analyzed to reduce transportation costs. More particularly, the Vehicle Routing Problem with Time Windows (VRPTW) imposes the period of time of customer availability as a constraint, a common characteristic in real world situations. Using minimization of the total distance as the main objective to be fulfilled, this work implements an efficient algorithm which associates non-monotonic Simulated Annealing to Hill-Climbing and Random Restart. The algorithm is compared to the best results published in the literature for the 56 Solomon instances and it is shown how statistical methods can be used to boost the performance of the method. Publisher Summary This chapter discusses some of the most important vehicle routing problem types. The vehicle routing problem lies at the heart of distribution management. It is faced each day by thousands of companies and organizations engaged in the delivery and collection of goods or people. Because conditions vary from one setting to the next, the objectives and constraints encountered in practice are highly variable. Most algorithmic research and software development in this area focus on a limited number of prototype problems. By building enough flexibility in optimization systems, these can be adapted to various practical contexts. The classical vehicle routing problem (VRP) is one of the most popular problems in combinatorial optimization, and its study has given rise to several exact and heuristic solution techniques of general applicability. The chapter presents a comprehensive overview of the available exact and heuristic algorithms for the VRP, most of which have been adapted to solve other variants. Abstract The routing of a fleet of vehicles to service a set of customers is important in the field of distribution. Vehicle routing problems (VRP) arise in many real-life applications within transportation and logistics. In this paper, we study a variant of the general VRP, VRP with time windows and split deliveries (VRPTWSD). Time constrained routing is relevant for applications where a schedule has to be followed. The option of splitting a demand makes it possible to service a customer whose demand exceeds the vehicle capacity. Splitting also allows to decrease costs. Although VRPTWSD is a relaxation of the VRP with time windows, the problem does not become easier to solve, and is still NP-hard. In this work, we propose a solution method based on tabu search for solving the VRPTWSD without imposing any restrictions on the split delivery options. We also give an analysis of experimental results on problems with 100 customers. This paper is the second part of a comprehensive survey on routing problems involving pickups and deliveries. Basically, two problem classes can be distinguished. The first part dealt with the transportation of goods from the depot to linehaul customers and from backhaul customers to the depot. The second part now considers all those problems where goods are transported between pickup and delivery locations, denoted as Vehicle Routing Problems with Pickups and Deliveries (VRPPD). These are the Pickup and Delivery Vehicle Routing Problem (PDVRP – unpaired pickup and delivery points), the classical Pickup and Delivery Problem (PDP – paired pickup and delivery points), and the Dial-A-Ride Problem (DARP – passenger transportation between paired pickup and delivery points and user inconvenience taken into consideration). Single as well as multi vehicle mathematical problem formulations for all three VRPPD types are given, and the respective exact, heuristic, and metaheuristic solution methods are discussed.