Minimum cost VRP with time-dependent speed data and congestion charge

A heuristic algorithm, called LANCOST, is introduced for vehicle routing and scheduling problems to minimize the total travel cost, where the total travel cost includes fuel cost, driver cost and congestion charge. The fuel cost required is influenced by the speed. The speed for a vehicle to travel along any road in the network varies according to the time of travel. The variation in speed is caused by congestion which is greatest during morning and evening rush hours. If a vehicle enters the congestion charge zone at any time, a fixed charge is applied. A benchmark dataset is designed to test the algorithm. The algorithm is also used to schedule a fleet of delivery vehicles operating in the London area

Variable-depth adaptive large meighbourhood search algorithm for Open Periodic Vehicle Routing Problem with time windows

The Open Periodic Vehicle Routing Problem with Time Windows (OPVRPTW) is a practical transportation routing and scheduling problem arising from real-world scenarios. It shares some common features with some classic VRP variants. The problem has a tightly constrained large-scale solution space and requires well balanced diversification and intensification in search. In Variable Depth Neighbourhood Search, large neighbourhood depth prevents the search from trapping into local optima prematurely, while small depth provides thorough exploitation in local areas. Considering the multi-dimensional solution structure and tight constraints in OPVRPTW, a Variable-Depth Adaptive Large Neighbourhood Search (VD-ALNS) algorithm is proposed in this paper. Contributions of four tailored destroy operators and three repair operators at variable depths are investigated. Comparing to existing methods, VD-ALNS makes a good trade-off between exploration and exploitation, and produces promising results on both small and large size benchmark instances

An adaptive large neighbourhood search for asset protection during escaped wildfires

The asset protection problem is encountered where an uncontrollable fire is sweeping across a landscape comprising important infrastructure assets. Protective activities by teams of firefighters can reduce the risk of losing a particular asset. These activities must be performed during a time-window for each asset determined by the progression of the fire. The nature of some assets is such that they require the simultaneous presence of more than one fire vehicle and its capabilities must meet the requirements of each asset visited. The objective is then to maximise the value of the assets protected subject to constraints on the number and type of fire trucks available. The solution times to this problem using commercial solvers preclude their use for operational purposes. In this work we develop an Adaptive Large Neighbourhood Search algorithm (ALNS) based on problem-specific attributes. Several removal and insertion heuristics, including some new algorithms, are applied. A new benchmark set is generated by considering the problem attributes. In tests with small instances the ALNS is shown to achieve optimal, or near optimal, results in a fraction of the time required by CPLEX. In a second set of experiments comprising larger instances the ALNS was able to produce solutions in times suitable for operational purposes. These solutions mean that significantly more assets can be protected than would be the case otherwise

Multitrip vehicle routing with delivery options: a data-driven application to the parcel industry

To make the last mile of parcel delivery more efficient, service providers offer an increasing number of modes of delivery as alternatives to the traditional and often cost-intensive home delivery service. Parcel lockers and pickup stations can be utilized to reduce the number of stops and avoid costly detours. To design smart delivery networks, service providers must evaluate different business models. In this context, a multitrip vehicle routing problem with delivery options and location-dependent costs arises. We present a data-driven framework to evaluate alternative delivery strategies, formulate a corresponding model and solve the problem heuristically using adaptive large neighborhood search. By examining large, real-life instances from a major European parcel service, we determine the potential and benefits of different delivery options. Specifically, we show that delivery costs can be mitigated by consolidating orders in pickup stations and illustrate how pricing can be applied to steer customer demand toward profitable, eco-friendly products

Adaptive large neighborhood search for the commodity constrained split delivery VRP

International audienceThis paper addresses the commodity constrained split delivery vehicle routing problem (C-SDVRP) where customers require multiple commodities. This problem arises when customers accept to be delivered separately. All commodities can be mixed in a vehicle as long as the vehicle capacity is satisfied. Multiple visits to a customer are allowed, but a given commodity must be delivered in one delivery. In this paper, we propose a heuristic based on the adaptive large neighborhood search (ALNS) to solve the C-SDVRP, with the objective of efficiently tackling medium and large sized instances. We take into account the distinctive features of the C-SDVRP and adapt several local search moves to improve a solution. Moreover, a mathematical programming based operator (MPO) that reassigns commodities to routes is used to improve a new global best solution. Computational experiments have been performed on benchmark instances from the literature. The results assess the efficiency of the algorithm, which can provide a large number of new best-known solutions in short computational times

Joint optimization of product service system configuration and delivery with learning-based valid cut selection and a tailored heuristic

Most previous work on product service system configuration aims to meet the functionality need or ensure a cost-effective delivery separately, overlooking the mutual impact between the configuration and delivery procedures. In contrast to that, we jointly optimize the configuration scheme and the delivery plan to increase the customer satisfaction through a two-stage decision framework. However, this integration significantly heightens the model's complexity due to the interdependence of the two stages. To address this challenge, we introduce an exact algorithm for finding globally optimal solutions, as well as an efficient two-stage heuristic aiming at enhancing the efficiency. The exact algorithm is built upon the branch-and-bound algorithm which, however, becomes less efficient as the problem size increases. To counteract this, we devise a series of valid cuts to boost the convergence. Additionally, recognizing that the optimal bundle of valid cuts may vary depending on the specific case, we adopt artificial intelligence techniques to adaptively select valid cuts. This can lessen unnecessary search efforts when tackling new cases and further enhance the computational performance. Despite this, efficiently handling large-scale cases in real-world applications remains a challenge. To mitigate this, we customize an efficient two-stage heuristic to assure a practical applicability. In the first stage, an effective local search is used to identify an appropriate configuration scheme, which then serves as a hyperparameter for the second stage, inspired by the machine learning. The second stage focuses on optimizing the delivery plan. To obtain this plan, we dedicate a modified adaptive large neighborhood search algorithm, equipped with tailored operators and selection methods to enrich search capabilities. Furthermore, a feasibility protection procedure is specialized to rectify the infeasible solutions and secure the diversity of the solution pool simultaneously. Our numerical experiments underscore the importance of the two-stage optimization framework, demonstrate the effectiveness of adaptive valid cut selection, and highlight the superiority of our heuristic in handling complex optimization tasks

Проектирование маршрутных сетей городского пассажирского транспорта на основе эвристических алгоритмов

Работа посвящена созданию программного приложения для проектирования маршрутных сетей городского пассажирского транспорта (ГПТ). Приложение позволит строить маршруты, учитывающие интересы всех участников ГПТ.The work is about developing of program application for urban passenger transport route network design. The application will allow to design routes that pay attention to interests of all participants of urban passenger transport route network