Large Neighborhood Research Articles

Delivery Planning of Electricity and Commodities by EVs Using Adaptive Large Neighborhood Search

Delivery Planning of Electricity and Commodities by EVs Using Adaptive Large Neighborhood Search

Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling

Dispatching vehicle fleets to serve flights is a key task in airport ground handling (AGH). Due to the notable growth of flights, it is challenging to simultaneously schedule multiple types of operations (services) for a large number of flights, where each type of operation is performed by one specific vehicle fleet. To tackle this issue, we first represent the operation scheduling as a complex vehicle routing problem and formulate it as a mixed integer linear programming (MILP) model. Then given the graph representation of the MILP model, we propose a learning assisted large neighborhood search (LNS) method using data generated based on real scenarios, where we integrate imitation learning and graph convolutional network (GCN) to learn a destroy operator to automatically select variables, and employ an off-the-shelf solver as the repair operator to reoptimize the selected variables. Experimental results based on a real airport show that the proposed method allows for handling up to 200 flights with 10 types of operations simultaneously, and outperforms state-of-the-art methods. Moreover, the learned method performs consistently accompanying different solvers, and generalizes well on larger instances, verifying the versatility and scalability of our method.

Integrated ground vehicle and drone routing with simultaneous surveillance coverage for evading intentional disruption

The purpose of this study is to examine the use of drones to provide transportation security during times of intentional disruption. Due to the nature of valuable goods, their transportation is always associated with certain risks, necessitating implementing security measures to ensure the transportation process’s security. This study aimed to address the security of valuable goods transportation by proposing a new routing problem using drones termed integrated routing and surveillance (IRS) for ground and aerial vehicles operating in dynamic environments. This approach addresses the problem of deliberate disruption in the planning and implementation phases. Risks are estimated during the planning phase using historical data, and routing is designed to minimize risk and travel costs. Drones conduct online surveillance of the solution routes generated during the planning phase throughout the implementation phase. Ground vehicles are only permitted to enter a link if drones assess the risks before their entry. Drones’ arrival times for this assessment of each ground link are determined by non-predefined time windows. Drones can visit multiple ground links if their time windows overlap in the proposed problem. If the drones detect a suspicious agent, they will perform dynamic replanning to avoid the risk source. Additionally, the proposed problem is optimized using new ALNS (adaptive large neighborhood search) and SA-based (simulated annealing) algorithms. The proposed algorithm’s efficiency and effectiveness are evaluated for small and large cases. The results demonstrate the proposed model’s applicability and superiority to ALNS.

Timetable synchronization of the last several trains at night in an urban rail transit network

To improve passenger accessibility and reduce the travel time cost at night, this paper aims to optimize the synchronization of the last several trains’ timetables on each line of an urban rail transit (URT) network. A space-time network is constructed to describe the train flow and passenger flow in the URT network, based on which an integer programming model is formulated. To effectively solve the proposed model, we decompose the problem into two levels and propose an iterative algorithm. In the upper level, an adaptive large neighbourhood search (ALNS) method is developed to generate new train timetables in the URT network, which are then evaluated by the passenger flow problem in the lower level, and the optimization-evaluation iteration continues until the termination conditions are met. The method and algorithm are applied to a large-scale instance using data for the Wuhan URT network at night, which involve 9 urban rail transit lines and over 119,000 passengers, and the results show that the number of inaccessible passengers is reduced by 27.53%, and the average travel time cost is reduced by 2.70%, compared with the original timetable. Then, the experimental results on a small-scale instance show that the proposed algorithm can find a near-optimal solution in a short time, which illustrates the effectiveness and efficiency of the ALNS method.

Mediating governance goals with patients and nurses satisfaction: a multi-actor multi-objective problem including fairness

ABSTRACT Balancing conflicting goals among different stakeholders is a challenging problem in various application domains. In this paper, we analyse it in the context of home healthcare. Fairness objective functions for nurses and patients are combined with system-level governance goals of the territorial centers in charge of the assistance service. From the solution of several multi-objective problems including one objective for each actor hierarchically ordered, the best goal for each stakeholder is identified and interesting managerial conclusions are drawn. To efficiently solve large-size instances, we introduce a parallel Adaptive Large Neighborhood Search algorithm with destroy and repair operators customised to solve multi-objective multi-actor problems. The algorithm proves to be highly efficient and effective when compared to a commercial Mixed Integer Programming solver, either in its plain form or enforced by a mild start and a primal heuristic. Additionally, we devise a metaheuristic method to generate the Pareto frontier of an instance.

Solving dynamic satellite image data downlink scheduling problem via an adaptive bi-objective optimization algorithm

The satellite image data downlink scheduling problem (SIDSP) plays a critical role in the mission planning operation of earth observation satellites. However, with recent developments in satellite technology, the traditional SIDSP is poorly effective for modern satellites. To offer additional modeling flexibility and renewed capabilities, a dynamic SIDSP (DSIDSP), which combines two interlinked operations of image data segmentation and image data downlink dynamically, was introduced. We have formulated the DSIDSP as a bi-objective problem of optimizing the image data transmission rate and the service-balance degree. Harnessing the power of an adaptive large neighborhood search (ALNS) algorithm with a nondominated sorting genetic algorithm II (NSGA-II), an adaptive bi-objective memetic algorithm, NSGA2ALNS, is developed to solve DSIDSP. Results of extensive computational experiments carried out using benchmark instances are also presented. Our experimental results reveal that the NSGA2ALNS algorithm is an effective and efficient method of solving DSIDSP based on various performance metrics. In addition, new benchmark instances are also provided for DSIDSP that could be used in future research.

Two-echelon collaborative routing problem with heterogeneous crowd-shippers

In this study, we consider a setting in which a retail store uses occasional couriers (OCs), which are not regular couriers but work as couriers on occasion, and in-store customers, which make a single delivery on the way back to their origin locations from the retail store, to perform the last-mile delivery along with its own fleet of couriers. To represent this setting, we design a two-echelon hetero-collaborative routing problem (2E-HCRP) that facilitates parcel transfers between couriers and OCs at customer nodes, in addition to in-store customer deliveries for optimizing last-mile services. We develop a mixed-integer linear programming (MILP) model and an adaptive large neighborhood search (ALNS) heuristic algorithm to devise cost-effective delivery plans that incorporate couriers, OCs, and in-store customers simultaneously. Furthermore, we introduce novel neighborhood search algorithms specifically tailored for parcel transfers and a scheduling algorithm designed for enhancing OC utilization and timely delivery to customers. We evaluate the performance of the proposed algorithms with the genetic algorithm, Monte Carlo method, and neighborhood search. The results indicate that our proposed algorithms outperform the others in minimizing travel costs and time window violations. Moreover, we provide valuable managerial implications through sensitivity analysis.

Two-echelon vehicle routing problem with direct deliveries and access time windows

Nowadays, heavy-duty trucks are usually restricted from entering urban areas due to emission and congestion problems in most cities. So, a two-echelon distribution system becomes vastly applicable, in which satellites transfer cargo and avoid heavy-duty trucks entering urban areas. However, with the development of new energy vehicles and access policies and regulations, some environmentally friendly trucks are accessible to urban areas within specific time windows of daytime directly. In light of this, we introduce a two-echelon vehicle routing problem with direct deliveries and access time windows (2E-VRPDDATW) arising in city logistics. In this problem, customers are served by the two-echelon distribution systems within their required time windows and are allowed to be directly delivered by the first echelon vehicles within access time windows, which provides more flexible routing schemes. A novel mixed integer linear programming (MILP) model for the above problem is presented, and the corresponding adaptive large neighborhood algorithm is developed. The computational experiments are provided to verify the validity of the proposed model and the effectiveness of the algorithm. Finally, the sensitivity analysis shows some interesting findings about the number of two-echelon vehicles and the assignment of customers. Moreover, we conclude that direct deliveries are advantageous in transportation costs when the proportion of the end time of access time window to the last end time of customers is greater than 0.25.

The Electric Vehicle Routing Problem with Time Windows, Partial Recharges, and Parcel Lockers

This paper presents an extension of the Electric Vehicle Routing Problem with Time Windows and Partial Recharges (EVRPTW-PR), which incorporates the use of parcel lockers as a delivery method (i.e., self-pickup method). This variant, named the electric vehicle routing problem with time windows, partial recharges, and parcel lockers (EVRPTW-PR-PL), focuses on minimizing delivery costs by employing a homogeneous fleet of electric vehicles (EVs) and providing two delivery methods for serving customers: home delivery and self-pickup methods. We derive a mathematical formulation and propose an adaptive large neighborhood search (ALNS) algorithm to address EVRPTW-PR-PL. Moreover, in ALNS, the solution representation is constructed to handle the assignment of delivery methods. The performance of our proposed ALNS algorithm is evaluated by solving EVRPTW-PR benchmark instances. Finally, the results of EVRPTW-PR-PL obtained by using the GUROBI solver and our ALNS algorithm are provided, accompanied by managerial insights on the implementation of parcel lockers.

Scheduling aerial resource operations for the extinction of large-scale wildfires

The significant increase in large-scale wildfire events in recent decades, caused primarily by climate change, has resulted in a growing number of aerial resources being used in suppression efforts. Present-day management lacks efficient and scalable algorithms for complex aerial resource allocation and scheduling for the extinction of such fires, which is crucial to ensuring safety while maximizing the efficiency of operations. In this work, we present a Mixed Integer Linear Programming (MILP) optimization model tailored to large-scale wildfires for the daily scheduling of aerial operations. The main objective is to achieve a prioritized target water flow over all areas of operation and all time periods. Minimal target completion across individual areas and time periods and total water output are also maximized as secondary and ternary objectives, respectively. An efficient and scalable multi-start heuristic, combining a randomized greedy approach with simulated annealing employing large neighborhood search techniques, is proposed for larger instances. A diverse set of problem instances is generated with varying sizes and extinction strategies to test the approaches. Results indicate that the heuristic can achieve (near)-optimal solutions for smaller instances solvable by the MILP, and gives solutions approaching target water flows for larger problem sizes. The algorithm is parallelizable and has been shown to give promising results in a small number of iterations, making it applicable for both night-before planning and, more time-sensitive, early-morning scheduling.

Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study

Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study

Learning-Based personal models for joint optimization of thermal comfort and energy consumption in flexible workplaces

Due to distinct preferences across individuals, a large proportion of people are not satisfied with the thermal environments of their workplaces. Although recent studies have investigated flexible workplaces to meet the different preferences of individuals, the feasibility of adopting them to real-world environments is limited by the room capacity and potential for extra energy consumption. To address the knowledge gaps, this paper proposes a framework to jointly optimize thermal comfort and energy consumption of buildings through flexible workplaces by considering personal thermal preferences. A joint optimization algorithm that integrates building energy and personal thermal comfort models is developed based on the concept of the Large Neighborhood Search (LNS) algorithm. Analytical energy prediction models are obtained through Nonlinear Polynomial Regression (NPR) and personal thermal comfort models are established using Support Vector Machine Models (SVM). To verify the algorithm, a case study considering two scenarios (with and without existing personal thermal comfort data) is presented. The results indicate the proposed optimization algorithm can significantly improve the thermal comfort of the occupants while saving 22% and 14 % of energy consumption in the two scenarios, respectively. The proposed optimization method is valuable for building managers while adopting the concept of flexible workplaces.

Multi-Trip Vehicle Routing Problem with Time Windows and Resource Synchronization on Heterogeneous Facilities

Inspired by long-distance road transport in industrial logistics in China, this paper studies a simultaneous loading scheduling and vehicle routing problem over a multi-workday planning horizon. Industrial cargo often requires specialized facilities, and these facilities vary in performance and quantity and are subject to available time constraints. Consequently, achieving coordinated optimization of vehicle routing and loading scheduling becomes a significant challenge in practice. We describe the studied problem as a multi-trip vehicle routing problem with time windows and resource synchronization on heterogeneous facilities. First, we develop a mixed integer programming model in a multi-workday setting to minimize the total travel distance and the number of vehicles. Moreover, a three-phase heuristic approach is developed. An initial solution is constructed using a sequential strategy in the first phase, and then an adaptive large neighbourhood search and a post-optimization procedure based on ejection chains are, respectively, designed to optimize the two hierarchical objective functions. Finally, extensive computational experiments are conducted to demonstrate the effectiveness of the proposed method. Specifically, the research results indicate that in long-distance road transport in industrial scenarios, expanding the planning horizon from a single workday to a multi-workday period could significantly reduce logistics operational costs and improve service quality.

Newton’s Method for Global Free Flight Trajectory Optimization

Globally optimal free flight trajectory optimization can be achieved with a combination of discrete and continuous optimization. A key requirement is that Newton’s method for continuous optimization converges in a sufficiently large neighborhood around a minimizer. We show in this paper that, under certain assumptions, this is the case.

An Adaptive Large Neighborhood Search Heuristic for the Electric Vehicle Routing Problems with Time Windows and Recharging Strategies

This study addresses a new electric vehicle routing problem with time windows and recharging strategies (EVRPTW-RS), where two recharging policies (i.e., full or partial recharging) and three recharging technologies (i.e., normal, rapid, and ultra-rapid) are considered. For this problem, we first develop a mixed-integer linear programming model defined in a series of vertices including a depot, a series of recharging stations, and a set of customers. Due to the strong NP-hardness of EVRPTW-RS, a tailored adaptive large neighborhood search heuristic (ALNS) which contains a number of advanced efficient procedures tailored to handle the proposed problem is developed. Numerical experiments for benchmark instances generated based on the Greater Toronto Area and Ontario in Canada are conducted to evaluate the performance of the proposed model and ALNS. Computational results demonstrate that the ALNS is highly effective in solving EVRPTW-RS and outperforms commercial solver CPLEX. Moreover, the advantages of the proposed recharging strategies are illustrated and some recommendations are provided for stakeholders when using electric vehicles for delivery.

A tailored adaptive large neighborhood search algorithm for the air cargo partitioning problem with a piecewise linear cost function

A tailored adaptive large neighborhood search algorithm for the air cargo partitioning problem with a piecewise linear cost function

Content-Aware Warping for View Synthesis.

Existing image-based rendering methods usually adopt depth-based image warping operation to synthesize novel views. In this paper, we reason the essential limitations of the traditional warping operation to be the limited neighborhood and only distance-based interpolation weights. To this end, we propose content-aware warping, which adaptively learns the interpolation weights for pixels of a relatively large neighborhood from their contextual information via a lightweight neural network. Based on this learnable warping module, we propose a new end-to-end learning-based framework for novel view synthesis from a set of input source views, in which two additional modules, namely confidence-based blending and feature-assistant spatial refinement, are naturally proposed to handle the occlusion issue and capture the spatial correlation among pixels of the synthesized view, respectively. Besides, we also propose a weight-smoothness loss term to regularize the network. Experimental results on light field datasets with wide baselines and multi-view datasets show that the proposed method significantly outperforms state-of-the-art methods both quantitatively and visually. The source code is publicly available at https://github.com/MantangGuo/CW4VS.

A branch-and-regret algorithm for the same-day delivery problem

We study a dynamic vehicle routing problem where stochastic customers request urgent deliveries characterized by restricted time windows. The aim is to use a fleet of vehicles to maximize the number of served requests and minimize the traveled distance. The problem is known in the literature as the same-day delivery problem, and it is of high importance because it models a number of real-world applications, including the delivery of online purchases. We solve the same-day delivery problem by proposing a novel branch-and-regret algorithm in which sampled scenarios are used to anticipate future events and an adaptive large neighborhood search is iteratively invoked to optimize routing plans. The branch-and-regret is equipped with four innovation elements: a new way to model the subproblem, a new policy to generate scenarios, new consensus functions, and a new branching scheme Extensive computational experiments on a large variety of instances prove the outstanding performance of the branch-and-regret, also in comparison with recent literature, in terms of served requests, traveled distance, and computational effort.

Research on Green Vehicle Path Planning of AGVs with Simultaneous Pickup and Delivery in Intelligent Workshop

In this study, we present and discuss a variant of the classic vehicle routing problem (VRP), the green automated guided vehicle (AGV) routing problem, which involves simultaneous pickup and delivery with time windows (GVRPSPDTW) in an intelligent workshop. The research object is AGV energy consumption. First, we conduct a comprehensive analysis of the mechanical forces present during AGV transportation and evaluate the overall operational efficiency of the workshop. Then, we construct a green vehicle path planning model to minimize the energy consumption during AGV transportation and the standby period. Hence, the problems considered in this study are modeled based on asymmetry, making the problem solving more complex. We also design a hybrid differential evolution algorithm based on large neighborhood search (DE-LNS) to increase the local search ability of the algorithm. To enhance the optimal quality of solutions, we design an adaptive scaling factor and use the squirrel migration operator to optimize the population. Last, extensive computational experiments, which are generated from the VRPSPDTW instances set and a real case of an intelligent workshop, are designed to evaluate and demonstrate the efficiency and effectiveness of the proposed model and algorithm. The experimental results show that DE-LNS yields competitive results, compared to advanced heuristic algorithms. The effectiveness and applicability of the proposed algorithm are verified. Additionally, the proposed model demonstrates significant energy-saving potential in workshop logistics. It can optimize energy consumption by 15.3% compared with the traditional VRPSPDTW model. Consequently, the model proposed in this research carries substantial implications for minimizing total energy consumption costs and exhibits promising prospects for real-world application in intelligent workshops.

A genetic algorithm with trip-adjustment strategy for multi-depot electric bus scheduling problems

Bus scheduling problem is vital to ensure service quality and save operational cost. Electric bus scheduling problem is difficult to solve because of vehicles' limited driving range and charging requirements. This article investigates the multi-depot electric bus scheduling problem (MD-EBSP). A genetic algorithm with trip-adjustment strategy (GA-TAS) is proposed for the MD-EBSP. Firstly, a genetic algorithm (GA) with three crossover operators and seven mutation operators is devised to find a set of solutions. In each generation, a randomly selected crossover (mutation) operator is used to update the individuals. The solutions found by the GA are further improved by a trip-adjustment strategy (TAS) to obtain the final solution. The GA-TAS is compared with an adaptive large neighbourhood search method, a heuristic procedure, and experience-based scheduling schemes on seven problem instances. Experiments show that the GA-TAS outperforms comparative approaches on a number of vehicle and balancing vehicle driving tasks.