On the one-to-one pickup-and-delivery problem with time windows and trailers

<p style='text-indent:20px;'>The Vehicle Routing Problem with Multiple Time Windows (VRPMTW) is a generalization of problems in real life logistics distribution, which has a wide range of applications and research values. Several neighborhood search based methods have been used to solve this kind of problem, but it still has drawbacks of generating numbers of infeasible solutions and falling into local optimum easily. In order to solve the problem of arbitrary selection for neighborhoods, a series of neighborhoods are designed and an adaptive strategy is used to select the neighborhood, which constitute the Adaptive Large Neighborhood Search(ALNS) algorithm framework. For escaping from the local optimum effectively in the search process, a local search based on destroy and repair operators is applied to shake the solution by adjusting the number of customers. The proposed method allows infeasible solutions to participate in the iterative process to expand the search space. At the same time, an archive is set to save the high-quality feasible solutions during the search process, and the infeasible solutions are periodically replaced. Computational experimental results on VRPMTW benchmark instances show that the proposed algorithm is effective and has obtained better solutions.</p>

The rural postman problem with time windows is the problem of serving some required edges with one vehicle; the vehicle must visit these edges during established time windows. This article presents a competitive adaptive large neighborhood search algorithm to solve the problem. Computational experiments are performed on a large set of instances with up to 104 required edges. The results show that this approach is efficient, significantly reducing the computational time on large instances and achieving good solutions: the algorithm is able to solve to optimality 224 of 232 instances. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 70(1), 44–59 2017

Mine planning involves the systematic design and coordination of mineral extraction from the earth’s crust, integrating exploration, production, and various engineering considerations. With increasing emphasis on environmental responsibility, the mining industry is under pressure to incorporate environmental considerations into mine planning. This paper addresses the precedence-constrained production scheduling problem (PCPSP) within the context of green long-term mining planning, aiming to optimize extraction processes while restricting carbon emission. Given the NP-hard nature of the PCPSP, this study introduces an adaptive large neighborhood search (ALNS) algorithm tailored specifically for long-term mine planning. A range of computational experiments have been carried out, including parameter tuning for the ALNS algorithm, comparisons against an exact solver, and analysis of our destroy-repair operators to determine their key elements. The efficacy of developed ALNS, evaluated using various benchmarks, reveals optimality gaps around 0.08. These results highlight the effectiveness of the proposed approach in addressing the PCPSP within mine planning and production scheduling, providing insights into improving mining operations while considering environmental concerns.

Freight bus is a new public transportation means for city logistics, and each freight bus can deliver and pick up goods at each customer/supplier location it passes. In this paper, we study the route planning problem of freight buses in an urban distribution system. Since each freight bus makes a tour visiting a set of pickup/delivery locations once at every given time interval in each day following a fixed route, the route planning problem can be considered a new variant of periodic vehicle routing problem with pickup and delivery. In order to solve the problem, a Mixed-Integer Linear Programming (MILP) model is formulated and an Adaptive Large Neighborhood Search (ALNS) algorithm is developed. The development of our algorithm takes into consideration specific characteristics of this problem, such as fixed route for each freight bus, possibly serving a demand in a later period but with a late service penalty, etc. The relevance of the mathematical model and the effectiveness of the proposed ALNS algorithm are proved by numerical experiments.

Additive manufacturing (AM) is progressively transforming supply chains with its advantages of design flexibility, high material utilization, and short production cycles. To fully realize the benefits of AM in supply chains, it is essential to comprehensively consider production and delivery strategies. This paper addresses the synchronization of production and delivery in a localized supply chain using AM. This new operational model integrates production and logistics activities to facilitate immediate delivery upon product completion. We first develop a mixed-integer programming model to describe the problem of minimizing the total time required to serve all customers with the constraints of production schedules and delivery routes. To solve this problem, we then design an enhanced adaptive large neighbourhood search (ALNS) algorithm that incorporates reinforcement learning (RL). The RL component optimizes the operator selection strategy during the search, with the proximal policy optimization algorithm enhancing the stability and speed of the optimization process. Finally, we conduct extensive numerical experiments to validate the effectiveness of the proposed algorithm by comparing it with commercial solvers and other heuristics. In addition, we explore various strategies for operating AM-enabled supply chains and provide insights to help companies better coordinate their operations in this new model.

In recent years, with the increasing adoption of electric vehicles (EVs) in waste collection systems, research on related vehicle routing optimization has garnered significant attention. However, existing studies predominantly focus on traditional fuel-powered vehicles, while constraints unique to EVs—such as limited driving range and charging requirements—further complicate the problem. This paper addresses the route optimization problem for electric vehicles in bulky waste collection. A route optimization model incorporating three-dimensional packing constraints is established. Feasible solutions are initially constructed using a Genetic Algorithm (GA), followed by refinement through an improved Adaptive Large Neighborhood Search (ALNS) algorithm. Enhanced destruction and repair operators are designed to simultaneously optimize charging station selection and the efficient spatial arrangement of bulky waste. Simulation experiments across multiple datasets validate the algorithm's feasibility. Results demonstrate that the proposed method effectively resolves charging demands during bulky waste collection and significantly reduces operational costs.

The construction industry is developing rapidly along with the acceleration of urbanization but accompanied by an increased amount of construction and demolition waste (CDW). From the perspective of sustainability, the existing research has mainly focused on CDW treatment or landfill disposal, but the challenge of reverse logistics of CDW recycling that provides overall CDW route planning for multiple participants and coordinates the transportation process between multiple participants is still unclear. This paper develops an optimization model for multi-depot vehicle routing problems with time windows (MDVRPTW) for CDW transportation that is capable of coordinating involved CDW participants and suggesting a cost-effective, environment-friendly, and resource-saving transportation plan. Firstly, economic cost, environmental pollution, and social impact are discussed to establish this optimization-oriented decision model for MDVRPTW. Then, a method combined with a large neighborhood search algorithm and a local search algorithm is developed to plan the transportation route for CDW reverse logistics process. With the numerical experiments, the computational results illustrate the better performance of this proposed method than those traditional methods such as adaptive large neighborhood search algorithm or adaptive genetic algorithm. Finally, a sensitivity analysis considering time window, vehicle capacity, and carbon tax rate is conducted respectively, which provides management implications to support the decision-making of resource utilization maximization for enterprises and carbon emission management for the government.

The mixed fleet heterogeneous dial-a-ride problem (MF-HDARP) consists of designing vehicle routes for a set of users by using a mixed fleet including both heterogeneous conventional and alternative fuel vehicles. In addition, a vehicle is allowed to refuel from a fuel station to eliminate the risk of running out of fuel during its service. We propose an efficient hybrid adaptive large neighborhood search (hybrid ALNS) algorithm for the MF-HDARP. The computational experiments show that the algorithm produces high quality solutions on our generated instances and on HDARP benchmarks instances. Computational experiments also highlight that the newest components added to the standard ALNS algorithm enhance intensification and diversification during the search process.

This paper introduces the Fleet Size and Mix Vehicle Routing Problem with Synchronized Visits (FSM-VRPS), an extension of the Vehicle Routing Problem with Synchronization (VRPS), where a mixed fleet composed of electric and conventional bikes, and passenger cars having different acquisition costs are considered. The problem consists of planning a set of different vehicle routes to serve a set of clients who may require more than one visit by different healthcare specialists, and some of these visits should be synchronized. Moreover, each client must be visited within a specified time window. In addition, the problem uses bikes to reduce Carbon Dioxide (CO2) emission for environmentally cleaner routing operations. This problem has many real-life applications, such as the scheduling of visits for homecare givers in the healthcare sector. We present a mixed integer linear-programming formulation and develop a Multi-Start Adaptive Large Neighborhood Search with Threshold Accepting algorithm. The results showed that our algorithm is highly effective on the FSM-VRPS, as well as on the heterogeneous VRPS. We also demonstrate the advantage of adopting different types of vehicles in terms of reducing the number of vehicles and costs. The analysis of the results also indicated that the new components added to the standard Adaptive Large Neighborhood Search algorithm enhanced intensification and diversification mechanisms during the search process.

Motivated by a situation faced by infrastructure service providers operating in urban areas with accessibility restrictions, we study the truck and trailer routing problem with time windows (TTRPTW). In this problem the vehicle fleet consists of trucks and trailers which may be decoupled. A set of customers has to be served and some of the customers can only be accessed by the truck without the trailer. This gives rise to the planning of truck-and-trailer routes containing truck-only subroutes, in addition to truck-only routes and truck-and-trailer routes without subroutes. We propose a branch-and-price algorithm for the TTRPTW, using problem specific enhancements in the pricing scheme and alternative lower bound computations. We also tailor an adaptive large neighborhood search algorithm to the TTRPTW in order to obtain good initial columns. When compared to existing metaheuristic algorithms we obtain highly competitive results. Some instances with up to 100 customers are solved to optimality with the proposed branch-and-price algorithm.

Simultaneous product and service delivery vehicle routing problem with time windows and order release dates

Rapid urban growth, the increasing importance of e-commerce and high consumer service expectations have given rise to new and innovative models for freight delivery within urban environments. Crowdsourced solutions – where drivers are not employed by a carrier but occasionally offer their services through on-line platforms and are contracted as required by carriers – are receiving growing attention from industry. We consider a crowdsourced system where drivers express their availability to perform delivery tasks for a given period of time and the platform communicates a schedule with requests to serve. We investigate the potential benefits of introducing transfers to support driver activities. At transfer locations, drivers can drop off packages for pick up by other drivers at a later time. We frame the problem as a Multi-Depot Pickup and Delivery Problem with Time Windows and Transfers, and propose an Adaptive Large Neighborhood Search algorithm that effectively identifies beneficial transfer opportunities and synchronizes driver operations. Computational experiments indicate that introducing transfer options can significantly reduce system-wide travel distance as well as the number of drivers required to serve a given set of requests, especially when drivers have short availability and requests have high service requirements.

Capacitated Vehicle Routing Problem (CVRP) is a representative type of Vehicle Routing Problem (VRP) and it is NP-hard. With the increase of the scale of the problem, the existing method is easy to fall into a local optimal solution, and the solution time is too long. To overcome these problems, in this paper, we propose an Enhanced Adaptive Large Neighborhood Search algorithm (EALNS). The EALNS adds a new type of linear removal strategy and selects several adjacent nodes on a route to be removed so that the vehicle can serve more customers. In the ALNS decision-making stage, an adaptive mechanism that weighs the time factor is added, so that each strategy combination can adjust the weight according to the solved time. Experiments are performed through three internationally published benchmarks. Experimental results show that the EALNS is competitive and can obtain satisfactory results in most instances. We compare with the optimal results from the collective best results reported in the literature, EALNS improves 2.30% average accuracy and significantly reduces the average solution time.

Knowledge-assisted adaptive large neighbourhood search algorithm for the satellite–ground link scheduling problem

Due to environmental friendliness, electric vehicles have become more and more popular nowadays in the transportation system. For many express companies, it is more and more important to meet the predetermined time window of customers. The uncertainty in travel times often causes uncertain energy consumption and uncertain recharging time, thus electric vehicles may miss the time windows of customers. Therefore, this paper addresses the electric vehicle routing problem with time windows under travel time uncertainty, which aims to determine the optimal delivery strategy under travel time uncertainty. To solve this problem, a robust optimization model is built based on the route-dependent uncertainty sets. However, considering the complexity of the problem, the robust model can only solve few instances including the small number of customers. Thus, a hybrid metaheuristic consisting of the adaptive large neighborhood search algorithm and the local search algorithm is proposed. The results show that the algorithm can obtain the optimal solution for the small-sized instances and the large-sized instances.