Large Neighborhood Research Articles

Parcel delivery network optimization problem considering multiple hubs and consolidation of small-sized parcels

With the growth in delivery demand owing to the expansion of e-commerce and COVID-19, parcel delivery network optimization has become a significant issue for courier companies. This study introduces a real-world parcel delivery network problem in which parcels are transported from origin to destination spokes via one or two hubs. The parcels are sorted in hubs for distribution to spokes. Regular- and small-sized parcels are considered in the optimization problem. Moreover, small-sized parcels can be consolidated into gunnysacks based on their destination for efficient handling. A mixed-integer programming (MIP) model, large neighborhood search (LNS), and matheuristic algorithm combining the two aforementioned approaches were developed. Additionally, 90 benchmark instances reflecting real-world problems were generated. The MIP, matheuristic algorithm, and LNS performed well in small-, medium-, and large-sized instances, respectively. The benchmark instances and source codes are accessible online.

生鲜农产品冷链集货车辆路径与货物配载联合优化研究

As a bridge connecting agricultural production and consumption, the circulation of agricultural products has the function of connecting supply and demand, guiding production and promoting consumption. However, the development of rural logistics in China is slow, and most logistics centers still rely on experience to plan the pick-up vehicle routings, resulting in long transport time and high cost. In order to improve the efficiency of pick-up and reduce transportation costs, a joint optimization model of cold-chain pick-up vehicle routing and cargo allocation for fresh agricultural products was proposed in this study. Soft time window constraint and three-dimensional loading constraints were considered, and the lowest pick-up cost was used as optimization goals in this model. In addition, adaptive large neighborhood search algorithm (ALNS) and heuristic depth-first search algorithm (HDFS) were combined to solve the model. A case study of Kunming International Flower Auction Center was conducted to compare the schemes of pick-up vehicle routing before and after optimization. Results demonstrate that the pick-up cost after optimization decreases by 9.6 %, the number of vehicles decreases by one, the total volume utilization rate of vehicles increases by 23 %, and the total load utilization rate of vehicles increases by 15 %. This study provides a model reference and solution method for enterprise operators to formulate schemes of pick-up vehicle routing quickly and reasonably.

An ALNS-based approach for the traffic-police-routine-patrol-vehicle assignment problem in resource allocation analysis of traffic crashes

Objectives Imbalances between limited police resource allocations and the timely handling of road traffic crashes are prevalent. To optimize resource allocations and route choices for traffic police routine patrol vehicle (RPV) assignments, a dynamic crash handling response model was developed. Methods This approach was characterized by two objective functions: the minimum waiting time and the minimum number of RPVs. In particular, an adaptive large neighborhood search (ALNS) was designed to solve the model. Then, the proposed ALNS-based approach was examined using comprehensive traffic and crash data from Ningbo, China. Results Finally, a sensitivity analysis was conducted to evaluate the bi-objective of the proposed model and simultaneously demonstrate the efficiency of the obtained solutions. Two resolution methods, the global static resolution mode, and real-time dynamic resolution mode, were applied to explore the optimal solution. Conclusions The results show that the optimal allocation scheme for traffic police is 13 RPVs based on the global static resolution mode. Specifically, the average waiting time for traffic crash handling can be reduced to 5.5 min, with 53.8% less than 5.0 min and 90.0% less than 10.0 min.

Changes in microflora at high concentrations of pets in urban environments: assessment of risk to the environment and human health

Microbiological studies on soil samples from gardens for walking people and pets in two large neighbourhoods of the city of Sofia were carried out. The quantities of microorganisms from main groups, including pathogenic species, were determined with a view to evaluating the epizootological safety of these areas. Especially high were the values of the isolated Gram-negative bacteria (4.33x104 ±1.88 and 2.70x105 ±2.54), of Pseudomonas aeruginosa (1.75x104 ±3.03 and 3.00x104 ±4.58), of staphylococci, including pathogenic mannitol-positive species (3.48x106 ±2.99 and 4.95 x106 ±3.96), as well as of the fungi. Oval fungi (5.50x104 ±3.77 and 1.55x105 ±1.12) and the causative agents of dermatomycosis tinea or ringworm (2.75x104 ±1.92 and 7.60x104 ±3.72) were present in significant quantities. However, the important sanitary indicative bacteria E. coli and C. perfringens were below the detectable minimum. It was found that in the area with greater movement of people and animals, the amounts of microorganisms from all the studied groups were higher. The results obtained show that urban areas with high traffic from pets and their owners can be a source of infection for animals and humans.

Integrated rescheduling optimization of berth allocation and quay crane allocation with shifting strategies

Container terminal operations are a core aspect of vessel operations in ports and necessitate an intelligent rescheduling approach to manage complex uncertainty scenarios. This paper investigates the problem of berth allocation and quay crane (QC) assignment with shifting strategies. A variable-length rolling mechanism (VLRM) is designed to identify uncertainty scenarios and determine the optimal rescheduling time point. Notably, we introduce a novel cross-wharf shifting movements strategy (CWSM) and develop a rescheduling model that accounts for constraints related to tidal time windows. The proposed Adaptive Large Neighborhood Search algorithm (ALNS) is tailored to solve this problem effectively. Computational results from various instance sizes validate the efficacy of both the model and the ALNS algorithm. These results demonstrate that the ALNS algorithm exhibits distinct advantages in solution quality and computational time for large-scale instances. Furthermore, the inclusion of tidal time window constraints significantly amplifies the complexity of uncertainty scenarios, emphasizing their importance and the impossibility of neglecting them. The cross-wharf shifting movements strategy, presented herein, effectively mitigates the impact of uncertainty scenarios within a defined scope while enhancing adjustment flexibility.

Sustainable impact analysis of freight pooling strategies on city crowdsourcing logistics platform

This study investigates the effects of freight pooling strategies on urban crowdsourcing logistics, focusing on economic, social, and environmental outcomes. Utilising a mixed-integer linear programming model with an adaptive large neighbourhood search algorithm, our goal is to optimise the cost-efficiency of the freight pooling system. Real-world delivery and driver trajectory data from a major Chinese crowdsourcing logistics platform, along with high-resolution vehicle telematics data, validate our model in five scenarios, each defined by distinct cost coefficients reflecting diverse stakeholder priorities. Results show potential for up to a 21.3% reduction in carbon emissions, a 28.3% decrease in truck activity spatial coverage, and a 7.6% increase in available drivers. However, deadheading trips, due to order consolidation into fewer vehicles without an increase in overall demand, could offset maximum carbon reduction benefits by 16.4%. Other effects on customers’ and drivers’ welfare are explored for a comprehensive quantitative assessment of freight pooling strategies’ sustainability benefits.

Improving Distinguishability of Class for Graph Neural Networks

Graph Neural Networks (GNNs) have received widespread attention and applications due to their excellent performance in graph representation learning. Most existing GNNs can only aggregate 1-hop neighbors in a GNN layer, so they usually stack multiple GNN layers to obtain more information from larger neighborhoods. However, many studies have shown that model performance experiences a significant degradation with the increase of GNN layers. In this paper, we first introduce the concept of distinguishability of class to indirectly evaluate the learned node representations, and verify the positive correlation between distinguishability of class and model performance. Then, we propose a Graph Neural Network guided by Distinguishability of class (Disc-GNN) to monitor the representation learning, so as to learn better node representations and improve model performance. Specifically, we first perform inter-layer filtering and initial compensation based on Local Distinguishability of Class (LDC) in each layer, so that the learned node representations have the ability to distinguish different classes. Furthermore, we add a regularization term based on Global Distinguishability of Class (GDC) to achieve global optimization of model performance. Extensive experiments on six real-world datasets have shown that the competitive performance of Disc-GNN to the state-of-the-art methods on node classification and node clustering tasks.

Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search

Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in large-scale multi-agent systems. State-of-the-art anytime MAPF is based on Large Neighborhood Search (LNS), where a fast initial solution is iteratively optimized by destroying and repairing a fixed number of parts, i.e., the neighborhood of the solution, using randomized destroy heuristics and prioritized planning. Despite their recent success in various MAPF instances, current LNS-based approaches lack exploration and flexibility due to greedy optimization with a fixed neighborhood size which can lead to low-quality solutions in general. So far, these limitations have been addressed with extensive prior effort in tuning or offline machine learning beyond actual planning. In this paper, we focus on online learning in LNS and propose Bandit-based Adaptive LArge Neighborhood search Combined with Exploration (BALANCE). BALANCE uses a bi-level multi-armed bandit scheme to adapt the selection of destroy heuristics and neighborhood sizes on the fly during search. We evaluate BALANCE on multiple maps from the MAPF benchmark set and empirically demonstrate performance improvements of at least 50% compared to state-of-the-art anytime MAPF in large-scale scenarios. We find that Thompson Sampling performs particularly well compared to alternative multi-armed bandit algorithms.

A Bi-Objective Home Health Care Routing and Scheduling Problem under Uncertainty.

Home health care companies provide health care services to patients in their homes. Due to increasing demand, the provision of home health care services requires effective management of operational costs while satisfying both patients and caregivers. In practice, uncertain service times might lead to considerable delays that adversely affect service quality. To this end, this paper proposes a new bi-objective optimization problem to model the routing and scheduling problems under uncertainty in home health care, considering the qualification and workload of caregivers. A mixed-integer linear programming formulation is developed. Motivated by the challenge of computational time, we propose the Adaptive Large Neighborhood Search embedded in an Enhanced Multi-Directional Local Search framework (ALNS-EMDLS). A stochastic ALNS-EMDLS is introduced to handle uncertain service times for patients. Three kinds of metrics for evaluating the Pareto fronts highlight the efficiency of our proposed method. The sensitivity analysis validates the robustness of the proposed model and method. Finally, we apply the method to a real-life case and provide managerial recommendations.

The flexible airport bus and last-mile ride-sharing problem: Math-heuristic and metaheuristic approaches

Airport buses play a crucial role in addressing the last-mile problem of air travel, especially in cities and countries lacking inner-city rail transit systems. Nevertheless, airport buses are currently witnessing a decline in ridership due to drawbacks such as long departure intervals, inflexible stops, and considerable distances between stops. Consequently, delivering high-quality airport bus services has become a pressing concern for public transport operators. Motivated by new flexible buses and ride-sharing services, this paper explores a flexible airport bus service that integrates ride-sharing services for passengers traveling from bus stops to their destinations. This problem entails integrated decisions involving bus stop selection, passenger assignment to drop-off bus stops, as well as bus and ride-sharing routing. Accordingly, this problem presents more challenges in decision-making than traditional flexible bus or ride-sharing routing problems. We first develop an arc-based mixed-integer linear programming model. Subsequently, we design a double decomposition math-heuristic algorithm that builds upon logic-based Benders decomposition and column generation algorithms to obtain a near-optimal solution within practical computation time limits for practical-scale instances. Additionally, we implement an adaptive large neighborhood search algorithm to evaluate the solution quality of this math-heuristic algorithm and to solve large-scale instances. To validate the effectiveness of both the model and the algorithms, we conduct numerical experiments using instances derived from Shenzhen airport bus lines. The experimental results demonstrate that the flexible service mode offers significant advantages in reducing both passenger ride time and vehicle mileage over traditional airport bus or taxi modes.

Solution of the family traveling salesman problem using a hyper-heuristic approach

This article is concerned with a recently introduced variant of the generalized traveling salesman problem (GTSP) called the family traveling salesman problem (FTSP). Given a set of nodes partitioned into multiple clusters termed as families, the FTSP consists in finding a tour visiting a pre-specified number of nodes from each of these families in such a manner that the total distance traveled is minimized. FTSP finds application in order picking in modern warehouses, where similar items can be stored at different places as the latest technologies like radio frequency identification (RFID) facilitate item localization. To solve this problem in a manner that is both effective and efficient, a hyper-heuristic approach is presented. This approach makes use of three large neighborhood search methods as low level heuristics. The solution obtained through the hyper-heuristic approach is improved further by using a local search phase. To assess the performance of the proposed approach, computational experiments are performed on the 86 standard benchmark instances of the problem. The proposed approach obtained good-quality solutions in comparison to the state-of-the-art approaches on these instances. Moreover, our approach is several times faster on most of the large instances. We have also reported the performance of our approach on a set of 60 new benchmark instances.

The electric vehicle routing problem of a new mobile charging service

Abstract A novel mobile charging service that utilizes vehicle-to-vehicle (V2V) charging technology has recently been proposed as a supplement to fixed charging infrastructure (CI), enabling electric vehicles (EVs) to exchange electricity. This study formulates a vehicle routing problem (VRP) of vehicle-to-vehicle (V2V) charging, optimizing the routing of DVs to service RVs while taking into account their willingness to join the V2V charging platform. A mixed integer linear programming (MILP) model is established to optimize the VRP-V2V (i.e. the VRP of V2V charging), which is known to be NP-hard. To solve large-scale instances for real-world applications, we propose an adaptive large neighborhood search (ALNS) algorithm, which, when combined with the structure of the VRP-V2V problem, utilizes four local search procedures to enhance solution quality following destroy and repair operators. Results indicate that the proposed ALNS algorithm outperforms the optimization solver CPLEX in small-scale instances, and can solve large-scale instances that are infeasible using CPLEX solver. In a numerical analysis of Changsha's large-scale network, we demonstrate that the V2V platform can save an average of 33.1% on the charging cost of recharging vehicles, hence raising customer satisfaction with charging services and reducing range anxiety. The platform's profitability is also increased by using V2V charging in areas lacking fixed charging infrastructure.

A cooperative vehicle routing problem with delivery options for simultaneous pickup and delivery services in rural areas

The booming e-commerce is challenging express companies by requiring higher logistics service efficiency in rural China. This study investigates a cooperative vehicle routing problem for simultaneous pickup and delivery requests, in which the delivery orders can be fulfilled either by home delivery or customer pickup. The delivery option for each order is complex due to the optional cooperative relationships among express companies, the inter-dependence between self-pickup site selection and the overall online vehicle routing scheme, and the distance-dependent compensation to self-pickup customers. The problem is formulated as a mixed integer linear programming model and solved by a tailored adaptive large neighborhood search algorithm. A cost-saving allocation mechanism based on the core guaranteed Shapley value is applied to carrier collaboration. The numerical results show that the newly introduced cooperative distribution system can significantly reduce operational costs. Such improvements are robust even if the total demand changes or under different rural logistics network topologies.

An adaptive large neighborhood search for the multi-vehicle profitable tour problem with flexible compartments and mandatory customers

The home-refill delivery system is a business model that addresses the concerns of plastic waste and its impact on the environment. It allows customers to pick up their household goods at their doorsteps and refill them into their own containers. However, the difficulty in accessing customers’ locations and product consolidations are undeniable challenges. To overcome these issues, we introduce a new variant of the Profitable Tour Problem, named the multi-vehicle profitable tour problem with flexible compartments and mandatory customers (MVPTPFC-MC). The objective is to maximize the difference between the total collected profit and the traveling cost. We model the proposed problem as Mixed Integer Linear Programming and present an Adaptive Large Neighborhood Search (ALNS) algorithm to solve it. Our ALNS outperforms the commercial solver, Gurobi, and Large Neighborhood Search (LNS), as proven by giving better solutions within reasonable computational times. Both ALNS and LNS can obtain optimal solutions for all small instances and three better solutions than Gurobi for medium problems. Furthermore, ALNS is also robust and effective in solving large MVPTPFC-MC, as proven by resulting in better solutions within less CPU time than LNS. Finally, more analyses are conducted to justify the utilization of flexible compartment sizes by comparing it with fixed compartment sizes and to evaluate the robustness of MVPTPFC-MC. The results show that utilizing flexible compartment sizes can yield more benefits than fixed compartment sizes, particularly when the fleet size is limited, and there are fewer mandatory customers to serve.

Delivery routing for a mixed fleet of conventional and electric vehicles with road restrictions

The enforcement of stringent regulations capping carbon emissions has prompted city logistics enterprises to substitute electric vehicles (EVs) for conventional vehicles (CVs). For city logistics enterprises with a mixed fleet of CVs and EVs, this study investigates the delivery routing problem with road restrictions (DRPRR) for accessible time windows and bearing weight. We formulate the DRPRR as a mixed integer programming model to minimise the total operations cost. The model consists of the set-up cost of CVs and EVs, the diesel cost of CVs, the electricity cost of EVs, the carbon tax cost, and the penalty cost of vehicles waiting for roads to be accessible. To effectively solve the model, an adaptive large neighbourhood search (ALNS) algorithm is developed that consists of tailored destroy and repair operators with two alternative solution acceptance criteria. Numerical experiments are conducted to validate the effectiveness of the proposed model and ALNS algorithm. In small-scale instances, the ALNS with the Metropolis criterion finds the best solutions for 41 of 48 instances while maintaining a deviation of less than 2.5% from CPLEX in the remaining 7 instances, and its running time is significantly shorter than CPLEX. In large-scale instances, the ALNS with the Metropolis criterion has stronger solving ability and better stability than benchmark algorithms (i.e. GA-LS, LNS, and ALNS with a threshold acceptance criterion). We also address a real-world case and conduct a sensitivity analysis to provide useful managerial insights. Specifically, implementing road restrictions on accessible time windows and the carbon tax policy simultaneously is more appropriate from the comprehensive perspective of market activity and carbon emissions.

Two-echelon time-dependent vehicle routing problem with simultaneous pickup and delivery and satellite synchronization

The multi-echelon distribution strategy has been widely applied in city logistics systems. This study develops a mixed-integer linear programming model for a new variant of vehicle routing problem, namely, a two-echelon time-dependent vehicle routing problem with simultaneous pickup and delivery and satellite synchronization, in which customers are divided into multiple customer regions according to different geographical characteristics. The pickup and delivery activities with hard time windows are performed simultaneously by the same vehicles from depots to satellites in the first echelon and from satellites to customers in different customer regions in the second echelon. A vehicle speed function is developed for each echelon based on traffic conditions. The function depends on commuter traffic and urbanization level. Satellites with storage buffer capacities for split and consolidation are allowed. Thus, demand unloading, storage, and reloading are synchronized. Costs of transportation, loading/unloading, inventory, and environment during one complete distribution process are considered. A memetic algorithm (MA) is proposed including a split algorithm for chromosome decoding and a three-phase construction heuristic for initial population generation. Self-adaptive operators of crossover, mutation, and local search are designed, and a neighborhood route improvement strategy is devised that includes distance-related destroy, route-based destroy, distance-based greedy repair, and time-distance cluster repair operators. Results of different scales of numerical experiments and sensitivity analysis show the effectiveness of the model and MA by comparing it with an exact algorithm, CPLEX, and adaptive large neighborhood search.

Fleet scheduling for electric towing of aircraft under limited airport energy capacity

Taxiing aircraft using electric vehicles is seen as an effective solution to meet aviation targets of climate neutrality. However, making the transition to electric taxiing operations is expected to significantly increase the electricity demand at airports. In this paper we propose a mixed-integer linear program to schedule electric vehicles for aircraft towing and battery charging, while considering a limit for the supply of energy. The objective of the schedule is to maximize emissions savings. For computational tractability, we develop an Adaptive Large Neighbourhood Search which makes use of multiple local search heuristics to identify scheduling solutions. For daily scheduling with a small fleet size, the developed heuristic achieves solutions with an average 4% gap to the best linear programming solution. The results show that charging the vehicles during daytime is essential to maximize saved emissions: removing charging opportunities for a few hours during the day reduces the performance by an average of 6.4%. In addition, it is found that fast charging leads to low vehicle downtime, unless the battery size exceeds 750kWh, when charging rates over 150kW become unnecessary. Overall, our model provides support for infrastructure planning of airports during the transition to aircraft electric taxiing.

Locating charging stations and routing drones for efficient automated stocktaking

Drones have received growing attention in logistics recently. One possible application is deploying drones for auditing inventory in warehouses. With the use of drones, warehouses are able to increase inventory record accuracy and decrease labor costs. In this research, we introduce the stocktaking drone routing problem (STDRP), which consists of routing a fleet of drones through a warehouse for stocktaking purposes as well as deciding on the location of charging stations on the warehouse floor, which is necessary due to the limited battery capacity of the drones. Subsequently, we develop an adaptive large neighborhood search-based heuristic (ALNS) with novel solution encoding and decoding approaches to solve the STDRP. In a numerical study, we show that ALNS can solve realistic instances in reasonable time. We also derive recommendations regarding the ideal size of the drone fleet, the charging infrastructure, and battery capacity. Finally, we investigate the interplay between the storage assignment policy (such as the popular ABC rule) and stocktaking efficiency using drones.

Vehicle routing problem for fresh products distribution considering customer satisfaction through adaptive large neighborhood search

To deal with the fresh products distribution problem in city logistics, this paper focuses on the customer psychological behavior considering both delivery time and the fresh products quality from the perspective of customer satisfaction. Thereby, we propose a mixed-integer nonlinear programming model, among which a time satisfaction function depending on customers’ time windows and a quality satisfaction function considering the freshness of the products are introduced as constraints. Then, an improved Adaptive Large Neighborhood Search algorithm is designed with a new strategy to jump out of the local optimal solutions and with new operators considering satisfaction functions. Numerical experiments on benchmark instances of standard Solomon instances demonstrate the effectiveness and the efficiency of the proposed method. The calculation results showed that the proposed algorithm is superior to previous approaches by obtaining better feasible solutions with less calculation time. Furthermore, managemental insights are provided for enterprises on making wise investments while ensuring customer experience through optimizing distribution strategies.

Bus Rescheduling for Long-Term Benefits: An Integrated Model Focusing on Service Capability and Regularity

Unplanned disruptions, such as vehicle breakdowns, in a public transportation system can lead to severe delays and even service interruptions, preventing the successful implementation of subsequent plans and the overall stability of transit services. A common solution to address such issues is implementing a bus bridging service using an experience-based response strategy, involving the deployment of spare buses to continue affected services. However, with this approach, it becomes impractical and challenging to generate a feasible and rational rescheduling scheme for the remaining transit services when spare buses are insufficient or widespread disruptions occur. In response to this challenge, we propose an innovative model that integrates service capability and regularity, aiming to minimize rescheduling costs through timetable adjustments and scheduling reassignments. We apply dynamic programming to comprehensively consider the hysteresis effects of disruptions and achieve a long-term optimal rescheduling scheme. To efficiently solve the proposed model, the large neighborhood search algorithm is improved by incorporating operational rules. Finally, several experiments are conducted under an actual transit operation scenario in Shenzhen. The results demonstrate that our method significantly reduces trip cancellations and, simultaneously, diminishes the increase in the departure interval resulting from the adjusted schedule by 23.27%.