Large Neighborhood Search Algorithm Research Articles

P-D pair based EV-sharing vehicle relocation problem with route familiarity

This paper focuses on the electric vehicle (EV) relocation problem with dispatcher-specific times (E-VRePDST) in one-way station-based EV-sharing systems. An innovative modeling method based on the p-d pairs is developed, and the dispatcher-specific travel time is adopted to model the dispatchers’ route familiarity. Thus, preconditions and time windows for every dispatcher to traverse every pair of pickup and delivery node (p-d pair) are solved out. Then, the p-d pair based model is constructed. The proposed modeling method efficiently reduces the complexity of the associated constraints for E-VRePDST, and the numerical experiments indicate that the p-d pair based model is more efficient in terms of the solution quality and run time than the existing models based on separately treated pickup and delivery nodes. Next, a large neighborhood search (LNS) algorithm is designed and proved to be efficient compared with the results by using CPLEX. Finally, a case study of the EV-sharing system is conducted, and it shows that each dispatcher is distributed to the route with relatively more requests located in the familiar stations.

An Integrated Territory Planning and Vehicle Routing Approach for a Multi-Objective Residential Waste Collection Problem

In this paper, we address a multi-objective residential waste collection problem with an integrated territory planning and vehicle routing approach. Dividing the problem into territories enables drivers to carry out the same route every week so they get familiar with it and residents put out their bins at the appropriate time. Another benefit is to reduce the computation time for large problems, since the complex characteristics of the involved vehicle routing problem make it otherwise difficult to solve. There are three characteristics that are important for good territory planning: minimum overlap, minimum travel time, and balanced workload. The purpose of this paper is to investigate the influence these three objectives have on each other, since they might be contradictory. Moreover, an Adaptive Large Neighborhood Search (ALNS) algorithm is developed for this specific problem which uses a K-means algorithm to generate the initial solution for territories. The results with the three objectives are shown to be useful for planners seeking to make informed decisions through the trade-off across different solutions with the Pareto frontiers provided. Moreover, the ALNS algorithm is shown to find good quality solutions in a reasonable computational time.

Determining optimal production plant location and vehicle route in upstream supply chain network for date sap processing industry

Bangladesh is blessed with various agro-based natural resources like Date sap, extracted from date trees. As this date sap is found in rural areas in large quantities annually but a very small fraction is converted into some value-added delicious foods at a domestic level while a large portion is left underutilized due to negligence, improper collection, and preservation system from the industry level. The processed delicious foods have conspicuous demand in the national market due to their nutritious value and the growth of the national economy. Despite its economic importance, very little researches have been conducted in this field for its industrial processing. So, this research implies to improve this straggled sector providing much attention for collecting raw sap from source and processing into value-added products from industrial level cost-effectively. The key objectives of this paper are to determine optimal facility location for processing date sap and set vehicle routes that can pick up date sap from source to processing plant simultaneously curtailing operational transportation costs. Initially, a Mixed Integer Linear Programming (MILP) model is introduced to determine optimal facility location. Besides, the Large Neighborhood Search (LNS) algorithm has been used to find the optimal set of vehicle routes. This paper outlines a summary of final results that Jessore (A south-western city in Bangladesh) is an optimal plant location and 10 vehicles are necessary for covering 15 areas which ultimately optimize the total supply time, respecting constraints concerning routing, timing, capacity, and supply as well transportation costs.

An adaptive large neighbourhood search algorithm for diameter bounded network design problems

An adaptive large neighbourhood search algorithm for diameter bounded network design problems

Drone routing and optimization for post-disaster inspection

Aerial drones have received increased attention by humanitarian organizations due to their potential to serve/monitor a disaster-affected region. In this study, we propose a mixed-integer linear programming model for a Heterogeneous Fixed Fleet Drone Routing problem (HFFDRP) that minimizes the post-disaster inspection cost of a disaster-affected area by accounting a number of drone trajectory-specific factors into consideration, such as battery recharging costs, servicing costs, drone hovering, turning, accelerating, cruising, and decelerating costs, and many others. Two heuristic algorithms are proposed, namely, Adaptive Large Neighborhood Search (ALNS) algorithm and Modified Backtracking Adaptive Threshold Accepting (MBATA) algorithm, to solve the largest instances of our proposed optimization model in a reasonable amount of time. Computational results indicate that the proposed MBATA algorithm is capable of producing high-quality solutions consistently within a reasonable amount of time. Finally, a real-life case study is used to visualize and validate the model results and to draw managerial insights that reveal how network design decisions vary with key drone design parameters.

Two-phase Matheuristic for the vehicle routing problem with reverse cross-docking

Cross-dockingis a useful concept used by many companies to control the product flow. It enables the transshipment process of products from suppliers to customers. This research thus extends the benefit of cross-docking with reverse logistics, since return process management has become an important field in various businesses. The vehicle routing problem in a distribution network is considered to be an integrated model, namely the vehicle routing problem with reverse cross-docking (VRP-RCD). This study develops a mathematical model to minimize the costs of moving products in a four-level supply chain network that involves suppliers, cross-dock, customers, and outlets. A matheuristic based on an adaptive large neighborhood search (ALNS) algorithm and a set partitioning formulation is introduced to solve benchmark instances. We compare the results against those obtained by optimization software, as well as other algorithms such as ALNS, a hybrid algorithm based on large neighborhood search and simulated annealing (LNS-SA), and ALNS-SA. Experimental results show the competitiveness of the matheuristic that is able to obtain all optimal solutions for small instances within shorter computational times. For larger instances, the matheuristic outperforms the other algorithms using the same computational times. Finally, we analyze the importance of the set partitioning formulation and the different operators.

Van-based robot hybrid pickup and delivery routing problem

We present a two-echelon van-based robot last-mile pickup and delivery system in urban settings. Robots can visit areas with van access restrictions, such as pedestrianized areas or university campuses. The van stops at parking nodes to drop off and/or pick up its robot, and to replenish its robot and/or swap its robot’s battery if needed. Five van/robot pickup and delivery cases are considered, according to the roles the van/robot plays in the process of pickup/delivery and whether the van transports its robot.To model the proposed problem, we introduce a mixed-integer program including time, freight, and energy. We further propose an adaptive large neighborhood search algorithm to solve larger instances and a capacity feasibility test approach for a single route. We then assess the influence of parking node density on model output. A case study based on a realistic city scenario is introduced. A sensitivity analysis is performed on the robot’s travel cost and maximum travel distances, and the effect of van no-go areas is studied. Two classical models (two-echelon vehicle routing with hybrid pickup and delivery and parallel van and robot scheduling with hybrid pickup and delivery) are compared with ours, and results show our model is competitive in appropriate scenario settings. We therefore advocate using the two-echelon van-based robot last-mile pickup and delivery system in urban areas.

The vehicle relocation problem with operation teams in one-way carsharing systems

The imbalance of available vehicles at different stations is an outstanding challenge in one-way carsharing systems. Dedicated to this issue, the operation teams in one company in Shanghai, China, each of which contains several workers, take operation vehicles to rebalance sharing vehicles among stations. A pertinent optimization problem, called the vehicle relocation problem with operation teams, is encountered in practice to find the relocation pairs of stations and the visiting routes of operation vehicles. In this paper, a mathematical programming model for minimizing the sum of relocation distance of sharing vehicles and travel distance of operation vehicles is constructed. An adaptive large neighbourhood search algorithm with several problem-specific algorithmic components is developed to efficiently solve the problem. Computational results validate the competitive performance of the proposed approach by comparing it with the commercial optimization software and a sequential approach.

Optimization of Rider Scheduling for a Food Delivery Service in O2O Business

Services such as Meituan and Uber Eats have revolutionized the way the customer can find and order from restaurants. Numerous independent restaurants are competing for orders placed by customers via online food ordering platforms. Ordering takeout food on smartphone apps has become more and more prevalent in recent years. There are some operational challenges that takeout food service providers have to deal with, e.g., customer demand fluctuates over time and region. In this sense, the service providers sometimes ignore the fact that some riders may be idle in several periods in regions, while, in contrast, there may be a shortage of riders in other situations. In order to address this problem, we introduce a two-stage model to optimize scheduling of riders for instant food deliveries. A service provider platform expectantly schedules the least quantity of riders to deliver within expected arrival time to satisfy customer demand in different regions and time periods. We introduce a two-stage model that adopts the method of mixed-integer programming (MIP), characterize relevant aspects of the scenario, and propose an optimization algorithm for scheduling riders. We also divide the delivery service region and time into smaller parts in terms of granularity. The large neighborhood search algorithm is validated through numerical experiments and is shown to meet the design objectives. Furthermore, this study reveals that the optimization of rider resource is beneficial to reduce overall cost of the delivery. Takeout food service platforms decide scheduling shifts (start time and duration) of the riders to achieve a service level target at minimum cost. Additional sensitivity analyses, such as the tightness of the order time windows associated with the orders and riders’ familiarity with delivery regions, are also discussed

A novel risk perspective on location-routing planning: An application in cash transportation

The critical role of supply chain security for businesses and government agencies has resulted in significant efforts over the last two decades to reduce vulnerability or disruption in supply chains. This paper addresses the routing problem of security carriers for high-value shipment transportation by developing a rich variant of the vehicle routing problem. To secure the route plans, the predictability of vehicle paths beside the travel costs is minimized by proposing a new integrated dynamic risk index. We present a mixed integer linear programming formulation for this problem, called the secure pickup and delivery problem with time windows (S-PDPTW). Moreover, a meta-heuristic solution method based on the adaptive large neighborhood search algorithm is developed to tackle the large-size instances. Extensive computational experiments for the target problem and the proposed algorithm demonstrate the efficiency of all developed procedures. Using the geographical information system, we provide some managerial insights based on a real case from the strategic and operational perspectives, whereby the applicability of the developed model is clearly shown in considerably reducing the risk value against a slight increase in classic objective value.

A large neighborhood search algorithm to optimize a demand-responsive feeder service

Feeder services are public transit services that transport people from a low demand, typically suburban, area to a high demand area, such as a transportation hub or a city. Here, passengers continue their journey using traditional forms of public transport. On the one hand, on-demand feeder services have been a topic of discussion in a number of recent studies, since these services can serve the demand efficiently. On the other hand, traditional feeder services with predetermined routes and timetables provide predictability and easier cost control. In this paper, a demand-responsive feeder service is considered, which combines positive characteristics of both traditional services as well as on-demand-only services. This feeder service has mandatory bus stops which are always serviced, as well as optional bus stops which are only serviced when there is demand for transportation nearby. To optimize the performance of this feeder service, a large neighborhood search heuristic is developed. Experimental results on 14 benchmark instances illustrate that the LNS algorithm obtains solutions with an average gap of 1% or less compared to the optimal solution, within 1s of runtime. Larger instances can also be solved, typically in less than 60s. The results also show that the demand-responsive feeder service generally outperforms a traditional service in terms of service quality, often by more than 60%.

Metaheuristics for the Minimum Gap Graph Partitioning Problem

The Minimum Gap Graph Partitioning Problem (MGGPP) consists in partitioning a vertex-weighted undirected graph into a given number of connected subgraphs with the minimum difference between the largest and the smallest weight in each subgraph. We propose a two-level Tabu Search algorithm and an Adaptive Large Neighborhood Search algorithm to solve the MGGPP in reasonable time on instances with up to about 23000 vertices. The quality of the heuristic solutions is assessed comparing them with the solutions of a polynomially solvable combinatorial relaxation.

Mixed electric bus fleet scheduling problem with partial mixed-route and partial recharging

Given the goal of reducing emissions and saving energy, an increasing number of transit agencies have proposed electrification plans for public transport buses. The two fundamental challenges to adopting electric vehicles in transit operations are the purchase of appropriate electric vehicles to establish the bus fleet and the creation of an efficient schedule and recharging plan. This paper examines the multi-depot and multi-vehicle type electric vehicle scheduling problem with partial mixed-route strategy and partial recharging policy. The partial mixed-route strategy proposed in this paper allows multiple transit routes to operate in a more cost-efficient way. Moreover, it takes into account the bus allocation problem of the transit network fleet to meet the parking restrictions of each depot. The problem is formulated in a mixed-integer programming model, and an adaptive large neighborhood search (ALNS) algorithm with new mechanisms specific to the problem is proposed to apply the model in a more efficient manner. The dataset of a real transit network in Nanjing is used for case study, and the performance of ALNS is tested by using randomly generated instances from the dataset. The results show that the proposed method is effective in finding high quality solutions and adopting partial recharging policy can reduce the fleet size and the total cost while providing advantages depending on the operational parameters of the schedule. In addition, comparison of two schedules using different partial mixed-route strategies shows that there may be two sides of adopting mixed-route scheduling.

Observation scheduling problem for AEOS with a comprehensive task clustering

Considering the flexible attitude maneuver and the narrow field of view of agile Earth observation satellite (AEOS) together, a comprehensive task clustering (CTC) is proposed to improve the observation scheduling problem for AEOS (OSP-FAS). Since the observation scheduling problem for AEOS with comprehensive task clustering (OSWCTC) is a dynamic combination optimization problem, two optimization objectives, the lossrate (LR) of the image quality and the energy consumption (EC), are proposed to format OSWCTC as a bi-objective optimization model. Harnessing the power of an adaptive large neighborhood search (ALNS) algorithm with a nondominated sorting genetic algorithm II (NSGA-II), a bi-objective optimization algorithm, ALNS+NSGA-II, is developed to solve OSWCTC. Based on the existing instances, the efficiency of ALNS+NSGA-II is analyzed from several aspects, meanwhile, results of extensive computational experiments are presented which disclose that OSPFAS considering CTC produces superior outcomes.

The fleet size and mix vehicle routing problem with synchronized visits

ABSTRACT 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.

Timetable coordination in a rail transit network with time-dependent passenger demand

With the expansion of urban rail networks and the increase of passengers demand, the coordination of strongly connected lines becomes more and more important, because passengers transfer several times during their trips and major transfer stations in the rail network often suffer from over-crowdedness, especially during peak-hours. In this paper, we study the optimization of coordinated train timetables for an urban rail network, which is a tactical timetabling problem and includes several operational constraints and time-dependent passenger-related data. We propose a mathematical formulation with the objective of minimizing the crowdedness of stations during peak hours to synchronously generate the optimal coordinated train timetables. By introducing several sets of passenger flow variables, the timetable coordination problem is formulated as a mixed-integer linear programming problem, that is possible to solve to optimum. To capture the train carrying capacity constraints, we explicitly incorporate the number of in-vehicle passengers in the modelling framework by considering the number of boarding and alighting passengers as passenger flow variables. To improve the computational efficiency of large-scale instances, we develop an Adaptive Large Neighborhood Search (ALNS) algorithm with a set of destroying and repairing operators and a decomposition-based ALNS algorithm. Real-world case studies based on the operational data of Beijing urban rail network are conducted to verify the effectiveness of timetable coordination. The computational results illustrate that the proposed approaches reduce the level of crowdedness of metro stations by around 8% in comparison with the current practical timetable of the investigated Beijing urban rail network.

Optimization on metro timetable considering train capacity and passenger demand from intercity railways

Purpose To evacuate passengers arriving at intercity railway stations efficiently, metros and intercity railways usually share the same station or have stations close to each other. When intercity trains arrive intensively, a great number of passengers will burst into the metro station connecting with the intercity railway station within a short period, while the number of passengers will decrease substantially when intercity trains arrive sparsely. The metro timetables with regular headway currently adopted in real-world operations cannot handle the injected passenger demand properly. Timetable optimization of metro lines connecting with intercity railway stations is essential to improve service quality. Design/methodology/approach Based on arrival times of intercity trains and the entire process for passengers transferring from railway to metro, this paper develops a mathematical model to characterize the time-varying demand of passengers arriving at the platform of a metro station connecting with an intercity railway station. Provided the time-varying passenger demand and capacity of metro trains, a timetable model to optimize train departure time of a bi-direction metro line where an intermediate station connects with an intercity railway station is proposed. The objective is to minimize waiting time of passengers at the connecting station. The proposed timetable model is solved by an adaptive large neighborhood search algorithm. Findings Real-world case studies show that the prediction accuracy of the proposed model on passenger demand at the connecting station is higher than 90%, and the timetable model can reduce waiting time of passengers at the connecting station by 28.47% which is increased by 5% approximately than the calculation results of the generic algorithm. Originality/value This paper puts forward a model to predict the number of passengers arriving at the platform of connection stations via analyzing the entire process for passengers transferring from intercity trains to metros. Also, a timetable optimization model aiming at minimizing passenger waiting time of a metro line where an intermediate station is connected to an intercity railway station is proposed.

The vehicle routing problem with relaxed priority rules

The Vehicle Routing Problem (VRP) is one of the most studied topics in Operations Research. Among the numerous variants of the VRP, this research addresses the VRP with relaxed priority rules (VRP-RPR) in which customers are assigned to several priority groups and customers with the highest priorities typically need to be served before lower priority ones. Additional rules are used to control the trade-off between priority and cost efficiency. We propose a Mixed Integer Linear Programming (MILP) model to formulate the problem and to solve small-sized instances. A metaheuristic based on the Adaptive Large Neighborhood Search (ALNS) algorithm with problem-tailored components is then designed to handle the problem at larger scales. The experimental results demonstrate the performance of our proposed algorithm. Remarkably, it outperforms a metaheuristic recently proposed to solve the Clustered Traveling Saleman Problem with d-relaxed priority rule (CTSP-d), a special case of VRP-RPR, in both solution quality and computational time.

Multi-modal transport of perishable products with demand uncertainty and empty repositioning: A scenario-based rolling horizon framework

We study the planning of a multi-modal transportation system with perishable products, demand uncertainty and repositioning of the empty Returnable Transport Items (RTIs). We propose a rolling horizon framework where we periodically re-optimize. As such, relevant responses and actions to new occurred demand are taken, and possible updates to the transportation and repositioning plans can be made. Our rolling horizon framework considers the uncertainty of customer demand, formulated as a Scenario-based Two-Stage Program (STSP) for which a set of scenarios is generated. An Adaptive Large Neighborhood Search (ALNS) algorithm is used to solve this scenario-based problem. Our proposed ALNS algorithm employs new operators and strategies to solve this complex and large problem. We give detailed computational analysis on the properties of our framework, evaluating the effects of stochastic demand, and we provide practical insights.

A Hybrid Large Neighborhood Search Algorithm for Solving the Multi Depot UAV Swarm Routing Problem

This paper focuses on a modified Multi-Depot Unmanned Aerial Vehicle Routing Problem (MMDUAVRP). Comparing to classic multi-depot vehicle routing problem, our studied problem has no constraints to restrict the depot where the Unmanned Aerial Vehicle (UAV) departs and returns. This work aims to minimize the number of UAVs and total distance traveled by all UAVs. This problem is mathematically formulated in this paper and a heuristic-assignment based hybrid large neighborhood search(HLNS) is proposed to solve it. Extensive computational experiments are conducted to verify the performance of HLNS. The HLNS algorithm was first tested on Multi Traveling Salesman Problem which is a simplified version of the MMDUAVRP, and high quality solutions have been obtained. Experimental results by compared with CPLEX and other well-known algorithms suggest that our proposed algorithm provides better solutions within a comparatively shorter period of time. In addition, we also conduct sensitivity analysis on the location of depots and task points that may affect the total cost of solution.