Adaptive Large Neighborhood Search Algorithm Research Articles

Parallel adaptive large neighborhood search based on spark to solve VRPTW

Aiming at the multi-objective vehicle path planning problem with time windows (VRPTW), a Spark-based parallel Adaptive Large Neighborhood Search algorithm (Spark-ALNS) is proposed to solve it. The main design of the 4-point strategy: (1) Design a new simulated annealing algorithm cooling strategy to achieve a better jump out of the local optimal solution. (2) Adopt CW initialization to accelerate the convergence speed. (3) Use three destruction operators and three repair operators to implement local path optimization. (4) A new parallel strategy is proposed to improve the algorithm’s accuracy and reduce the running time. To illustrate the algorithm’s effectiveness, the arithmetic example in Solomon is used as an example. The experimental results show that the proposed Spark-ALNS can find better solutions, get the known optimal solutions for 41 out of 56 instances, and find new optimal solutions for 31 algorithms, which outperforms other evolutionary algorithms. The runtime is 3–5 times better than other parallel algorithms and is able to solve VRPTW effectively.

A two-phase adaptive large neighborhood search algorithm for the electric location routing problem with range anxiety

In response to environmental pollution and global warming, electric vehicles (EVs) have been widely applied in supply chain, and lots of researches have been focused on the electric location routing problem (ELRP) to optimize the location of battery-swapping stations (BSSs). However, few studies have simultaneously considered the EV’s collaborative behavior and range anxiety in ELRP. Therefore, this study focuses on formulating an ELRP with battery-swapping stations considering both collaborative behavior and range anxiety. The model takes into account the collaboration between the supply and demand sides to share supply chain resources effectively. The detour probability function is employed to handle the uncertainty caused by drivers’ range anxiety during the trip, which can be evaluated through a proposed range anxiety function. In addition, a new two-dimensional matrix-based solution representation is proposed to explore ELRP optimal solutions intuitively and efficiently. To solve the proposed model effectively, a two-phase adaptive large neighborhood search (TALNS) algorithm that integrates the adaptive large neighborhood search (ALNS) algorithm and the extended binary particle swarm optimization (EBPSO) algorithm is proposed. In the EBPSO algorithm, a new position update mechanism and local search strategy are used to strengthen the local search ability. In the ALNS algorithm, multiple destroy and repair operators are developed for the proposed model. Further, to validate the effectiveness and performance of the proposed algorithm, comparison experiments with the Gurobi optimizer and other baseline heuristic algorithms are conducted.

Improved Adaptive Large Neighborhood Search Algorithm Based on the Two-Stage Framework for Scheduling Multiple Super-Agile Satellites

Improved Adaptive Large Neighborhood Search Algorithm Based on the Two-Stage Framework for Scheduling Multiple Super-Agile Satellites

Meal Delivery Routing Problem with Stochastic Meal Preparation Times and Customer Locations

AbstractWe investigate the Meal Delivery Routing Problem (MDRP), managing courier assignments between restaurants and customers. Our proposed variant considers uncertainties in meal preparation times and future order numbers with their locations, mirroring real challenges meal delivery providers face. Employing a rolling-horizon framework integrating Sample Average Approximation (SAA) and the Adaptive Large Neighborhood Search (ALNS) algorithm, we analyze modified Grubhub MDRP instances. Considering route planning uncertainties, our approach identifies routes at least 25% more profitable than deterministic methods reliant on expected values. Our study underscores the pivotal role of efficient meal preparation time management, impacting order rejections, customer satisfaction, and operational efficiency.

Co-optimizing electric bus dispatching and charging considering limited resources and battery degradation

This paper aims to formulate a mathematical model for a multi-type electric bus scheduling problem to determine the optimal fleet composition, bus-to-trip assignment, and partial charging schedule, where the battery degradation, nonlinear charging, and the constraint of charging station capacity are considered. A time-expanded network is proposed to represent the bus-to-trip assignment and partial charging. An adaptive large neighborhood search algorithm is designed to solve the problem. Using a multi-line bus network in Nanjing as the case, empirical operational data is used to generate monthly timetable samples to simulate the uncertainty of trip travel time and energy consumption. The result shows that the charging station capacity can be reduced from 20 (real-world case) to 12, considering the cost-effectiveness and robustness of the bus system. The result of this study also provides suggestions on the charging duration choices and the starting state-of-charge for different periods of the day. In peak and off-peak hours, 20-30-minute charging is recommended for electric buses with state-of-charge lower than 30 %, and 10-minute charging is more recommended when the state-of-charge of the electric bus is between 30 % and 70 %.

Research on Train Loading and Unloading Mode and Scheduling Optimization in Automated Container Terminals

In some automated container terminals, railway lines have been implemented into the port, saving container transfer time. However, the equipment scheduling level of the railway yard needs to be improved for managers. In the equipment scheduling of loading and unloading containers for railway trains, the operation modes “full unloading and full loading” and “synchronous loading and unloading” are often adopted. Due to the long length of the railway yard and the line of one train, there are two ways to arrange loading and unloading tasks for automated rail-mounted gantry cranes (ARMGs): one is to pre-assign tasks for ARMGs, and the other is to not pre-assign tasks for ARMGs. To investigate the efficacy of these different operation modes and methods of assigning tasks, this study formulated three mixed-integer linear programming (MILP) models with the goal of minimizing the ARMG task completion time. An adaptive large neighborhood search algorithm was used to tackle the scheduling problem. The scheduling effects of different operation modes and methods for assignment tasks were compared in terms of their calculation time and the completion time of ARMG tasks. Notably, the findings reveal that, with an increase in the number of tasks, the “pre-assign” task arrangement had a limited effect on the completion time of the ARMG tasks, made the calculation time shorter, and reduced the complexity of the problem. From the perspective of the completion time of ARMG tasks, the time under the “synchronous loading and unloading” operation mode was less than that of the “full unloading and full loading” operation mode. Therefore, it is recommended that the managers of the railway yard in an automated container terminal adopt the “synchronous loading and unloading” operation mode but determine the task assignment method according to decision time requirements. In addition, when the number of tasks is large, to decrease the time to complete ARMG tasks, the manager can adopt the “non-pre-assign” task distribution method.

Sharing a Ride: A Dual-Service Model of People and Parcels Sharing Taxis with Loose Time Windows of Parcels

(1) Efficient resource utilization in urban transport necessitates the integration of passenger and freight transport systems. Current research focuses on dynamically responding to both passenger and parcel orders, typically by initially planning passenger routes and then dynamically inserting parcel requests. However, this approach overlooks the inherent flexibility in parcel delivery times compared to the stringent time constraints of passenger transport. (2) This study introduces a novel approach to enhance taxi resource utilization by proposing a shared model for people and parcel transport, designated as the SARP-LTW (Sharing a ride problem with loose time windows of parcels) model. Our model accommodates loose time windows for parcel deliveries and initially defines the parcel delivery routes for each taxi before each working day, which was prior to addressing passenger requests. Once the working day of each taxi commences, all taxis will prioritize serving the dynamic passenger travel requests, minimizing the delay for these requests, with the only requirement being to ensure that all pre-scheduled parcels can be delivered to their destinations. (3) This dual-service approach aims to optimize profits while balancing the time-sensitivity of passenger orders against the flexibility in parcel delivery. Furthermore, we improved the adaptive large neighborhood search algorithm by introducing an ant colony information update mechanism (AC-ALNS) to solve the SARP-LTW efficiently. (4) Numerical analysis of the well-known Solomon set of benchmark instances demonstrates that the SARP-LTW model outperforms the SARP model in profit rate, revenue, and revenue stability, with improvements of 48%, 46%, and 49%, respectively. Our proposed approach enables taxi companies to maximize vehicle utilization, reducing idle time and increasing revenue.

Collaborative truck–drone–motorcycle delivery of emergency supplies for mountain wildfire suppression

This paper studies the transportation of emergency supplies for mountain wildfire suppression by trucks, drones, and motorcycles. The primary objective is to minimize the time of emergency supplies delivery. By examining the structure of mountainous road networks, the involvement of motorcycles and unmanned aerial vehicles (drones) in collaborative delivery operations, the risk associated with fire spread, and the optimal location for supplies distribution, this problem has been formulated as a collaborative truck–drone–motorcycle location-routing robust optimization model. The aim of introducing a robust optimization model is to ensure the feasibility of the routes under uncertain demand conditions. A box uncertainty set is employed to describe the uncertainty of emergent demands. An improved adaptive large neighborhood search (IALNS) algorithm is developed to solve this problem. A delete operator, a collaborative truck–drone delivery strategy, and a location strategy are introduced to handle various constraints involved in the problem, search the optimal location of the supplies distribution point, and compute vehicle routings. Our method has been compared with several existing algorithms, such as genetic algorithm, simulated annealing algorithm, tabu search algorithm, and whale optimization algorithm. Their average gap with IALNS are respectively 50.64%, 21.20%, 32.59%, and 19.13%. The experimental results demonstrate the efficacy and efficiency of the proposed model and algorithm.

Two-echelon collaborative many-to-many pickup and delivery problem for agricultural wholesale markets with workload balance

In the context of cooperation distribution among multiple wholesale markets, each customer can place orders with multiple agricultural wholesale markets, and each agricultural wholesale market can supply different products. To improve their distribution efficiency and avoid traffic congestion, cargoes need to be transshipped among collaborative distribution of different agricultural wholesale markets owing to their product heterogeneity. Each agricultural wholesale market undertakes the cost of completing the task for the reassigned customers of finding a balance among them, respectively. Fairness of allocation is achieved through workload balance and individual rationality constraints instead of monetary transfer payments. Aimed at the collaboration problem, a two-echelon collaborative many-to-many pickup and delivery problem with workload balance (2E-MPDP-WB) is presented in this study. A mixed-integer programming model for 2E-MPDP-WB is established, and a two-stage iterative algorithm combining branch-and-bound algorithm and adaptive large neighborhood search algorithm is proposed according to the problem structure. Some valid inequalities are also proposed. Finally, computational experiments show the correctness of the model and effectiveness of the algorithm, and sensitivity analysis is performed from four aspects, namely, costs before and after collaboration, workload balance, market geographical distribution, and demand mixing degree. The findings provide management insights for the collaborative distribution of multiple wholesale markets.

The traveling purchaser problem for perishable foods

This paper addresses the well-known traveling purchaser problem (TPP) considering restrictions related to perishable food, which is called the traveling purchaser problem for perishable foods (TPP-PF). In addition to the main assumptions of the capacitated TPP, the TPP-PF takes into account the release times of perishable foods at markets. In this context, a product can only be purchased at a market after it becomes available for sale. The TPP-PF also considers that the quality of perishable products decreases due to waiting on the market shelves and during transportation, and it additionally takes the deterioration cost of the foods into account. The aim of the problem is to find the best procurement and route plan for the purchaser to minimize the total transportation cost of the temperature-controlled vehicle, the damage cost of perishable foods, and the purchasing cost. Since the deterioration cost of products based on the waiting time is determined through exponential functions, the problem is formulated as a mixed-integer non-linear programming model. To find efficient results for the problem, an adaptive large neighborhood search (ALNS) algorithm is introduced with new problem-specific destroy/repair operators. Furthermore, an additional mechanism is included in the ALNS to change the direction of the search when the algorithm cannot improve the best solution after a number of iterations. In order to analyze the performance of the proposed ALNS, a new benchmark problem set is generated using a well-known TPP benchmark set. The proposed ALNS is first performed for the TPP-PF instances and compared to GUROBI and a simulated annealing algorithm. Following the TPP-PF experiments, the ALNS is carried out for the TPP instances and compared to six state-of-the-art solution approaches. The computational results show that the proposed ALNS outperforms those approaches by finding better results for almost all instances.

Integrated optimization of pilot and pilot carrier routing in seaports

Pilotage is an important service for vessels to enter and leave seaports. When vessels entering or leaving their berths, pilots are assigned to provide assistance on board to maneuver the vessels. Pilot carriers, such as pilot boats and helicopters are utilized to transport pilots from the pilot station to the vessels at appointed ground. With the increasing number of calling vessels, the pilotage plan should be made properly considering the limited pilots and carriers resources in seaports to enhance timing reliability, which is a key port performance indicator. This paper studies the pilot and pilot carrier routing problem arising in seaports, where both the routes of pilots and carriers are determined jointly to achieve the lowest pilotage costs. The required service time intervals of vessels, the synchronization between pilots and carriers and the limited working duration for both pilots and carriers are considered. The problem is formulated as an arc-flow model, and a hybrid multi-start adaptive large neighborhood search and local search algorithm (mALNS-LS) is developed. Efficient departure time and cost computation methods are designed based on a set of forward and backward functions, and a model-based post optimization is applied. Computational experiments verify the performance of the proposed mALNS-LS in terms of solution quality and efficiency.

A study on multiple types of yard cranes rescheduling in ports considering carbon emission

ABSTRACT This paper investigates the rescheduling problem of multiple oil-driven and electric-driven yard cranes across multiple container yards in semi-automated container terminals under random faults. Efficient fault diagnosis and handling mechanisms are provided to mitigate the effects of faults on the performance of yard operations. At the same time, a rescheduling model is proposed, aiming to minimize weighted carbon emissions and completion time. By analyzing the features of the problem, a tailored adaptive large neighborhood search (ALNS) algorithm is designed to solve the model. The effectiveness of the ALNS is verified through problem instances generated from the operational data of the port of Dalian in China. The factors considered in this paper are relevant to actual port operations, thus offering reliable solutions for scheduling and greening of ports.

Adaptive large neighborhood search algorithm with reinforcement search strategy for solving extended cooperative multi task assignment problem of UAVs

In response to the increasing complexity of military operations, the use of multiple heterogeneous unmanned aerial vehicles (UAVs) is essential for efficiently executing complex missions. This paper introduces an extended cooperative multi-task assignment problem (ECMTAP), which involves deploying heterogeneous UAVs from different base stations to accomplish specific missions, with a focus on minimizing overall mission completion time. ECMTAP categorizes targets into various types, each associated with unique task sets including {reconnaissance}, {attack, evaluation}, and {reconnaissance, attack, evaluation}. ECMTAP requires that attack tasks follow reconnaissance tasks, and evaluation tasks follow attack tasks, adding complexity due to specific timing constraints on each task. To tackle this problem, we propose a novel algorithm, the reinforcement search strategy-based adaptive large neighborhood search (RSALNS). To enhance the search capability, RASLNS utilizes two key destroy-repair operations: the intra-target tasks adjustment strategy and the evaluation tasks adjustment strategy. The former operation dismantles and reconstructs task sequences within a target, potentially resulting in suboptimal assignment of evaluation tasks, while the latter operation reassigns these tasks based on the first operation's output. Extensive experiments validate the effectiveness of the RSALNS algorithm in solving the ECMTAP, demonstrating its capability to generate high-quality solutions efficiently.

The low-carbon vehicle routing problem with dynamic speed on steep roads

The low-carbon vehicle routing problem with dynamic speeds on steep roads (LCVRPDS-SR) considers the combined effects of dynamic speeds, steep roads, and loads on carbon emissions. Earlier low-carbon vehicle routing problems typically assumed that vehicles travel at a constant speed on flat roads. However, such models do not apply in urban or rural areas with steep roads. Although the subsequent studies further explored the effect of steep roads, their performance are still suboptimal since they fail to take into account the varying speeds on the terrain. This paper proposes an extended LCVRPDS-SR model that tackles dynamic speed decisions on steep roads for the low-carbon vehicle routing problem. The objective function is non-linear and considers only environmental factors. Then an improved adaptive large neighborhood search algorithm is presented, including a new speed optimization algorithm and several improved removal and insertion operators. Extensive experiments are conducted on the generated instances to verify the effectiveness of the model and algorithm and derive managerial insights. The significant reduction in greenhouse gas emissions is achieved when considering dynamic speeds.

An Energy-Efficient Logistic Drone Routing Method Considering Dynamic Drone Speed and Payload

Unmanned aerial vehicles (UAVs), or drones, are recognized for their potential to improve efficiency in last-mile delivery. Unlike the vehicle routing problem, drone route design is challenging due to several operational signatures, such as speed optimization, multi-trip operation, and energy consumption estimation. Drone energy consumption is a nonlinear function of both speed and payload. Moreover, the high speed of drones can significantly curtail the drone range, thereby limiting the efficiency of drone delivery systems. This paper addresses the trade-off between speed and flight range in a multi-trip drone routing problem with variable flight speeds (DRP–VFS). We propose a new model to specifically consider energy constraints using a nonlinear energy consumption model and treat drone speeds as decision variables. The DRP–VFS is initially formulated using mixed-integer linear programming (MILP) to minimize energy consumption. To solve large-scale instances, we propose a three-phase adaptive large neighborhood search (ALNS) algorithm and compare its performance with a commercial MIP solver. The experimental results demonstrate that the proposed method is capable of effectively identifying suboptimal solutions in practical scenarios. Furthermore, results indicate that operating drones at variable speeds leads to about 21% energy savings compared to fixed speeds, with advantages in cost savings and range extension.

Optimizing routing and scheduling of shared autonomous electric taxis considering capacity constrained parking facilities

This paper focuses on routing and scheduling of autonomous electric vehicles to provide reservation-based shared ride services, while a set of parking facilities with limited capacity are used for vehicle intermittent charging. A mixed-integer linear program model is formulated in the form of a vehicle routing problem with satellite facilities (VRPSF), subject to a series of additional time and capacity-related constraints. The objective of the model is to minimize the total operating costs of the system, including those related to vehicle miles traveled and the deployed vehicle fleet size. The number of vehicles inside each parking facility is tracked so as to ensure that the capacity is never exceeded throughout the service horizon. A customized solution method based on an adaptive large neighborhood search algorithm with an explicit treatment of parking facility choices is developed. A series of numerical experiments, consisting of both hypothetical examples and a real-world case study in Hangzhou, China, have been conducted to evaluate the effectiveness and applicability of the proposed model and algorithm. The results demonstrate that ride-sharing services and parking facilities have the potential to significantly reduce the total vehicle energy consumption and operating costs for a shared autonomous electric taxi (SAET) operator in practical scenarios.

Combinatorial optimization methods for yarn dyeing planning

AbstractManaging yarn dyeing processes is one of the most challenging problems in the textile industry due to its computational complexity. This process combines characteristics of multidimensional knapsack, bin packing, and unrelated parallel machine scheduling problems. Multiple customer orders need to be combined as batches and assigned to different shifts of a limited number of machines. However, several practical factors such as physical attributes of customer orders, dyeing machine eligibility conditions like flotte, color type, chemical recipe, and volume capacity of dye make this problem significantly unique. Furthermore, alongside its economic aspects, minimizing the waste of natural resources during the machine changeover and energy are sustainability concerns of the problem. The contradictory nature of these two makes the planning problem multi-objective, which adds another complexity for planners. Hence, in this paper, we first propose a novel mathematical model for this scientifically highly challenging yet very practical problem from the textile industry. Then we propose Adaptive Large Neighbourhood Search (ALNS) algorithms to solve industrial-size instances of the problem. Our computational results show that the proposed algorithm provides near-optimal solutions in very short computational times. This paper provides significant contributions to flexible manufacturing research, including a mixed-integer programming model for a novel industrial problem, providing an effective and efficient adaptive large neighborhood search algorithm for delivering high-quality solutions quickly, and addressing the inefficiencies of manual scheduling in textile companies; reducing a time-consuming planning task from hours to minutes.

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

Communications between satellites and the ground supported by satellite–ground links (SGLs) are indispensable to the daily management of large-scale low-orbit communication satellite constellations. The SGL scheduling problem (SGLSP) focuses on determining which nonconflicting SGLs should be activated to maximize the total linking time. To solve this problem, we build an integer programming (IP) model and simultaneously propose a knowledge-assisted adaptive large neighbourhood search algorithm (KA-ALNS) based on this model, unifying the strengths of IP and data mining within the ALNS framework. In this algorithm, IP, which is deeply embedded in the ALNS, is employed to further refine the solution improved by ALNS. Then, a data mining method named frequent pattern mining is employed for extracting knowledge of excellent solutions to construct new solutions. The KA-ALNS algorithm that combines data mining methods and IP modelling can effectively solve the SGLSP. A three-day experimental setting with 20 ground stations in the China region, 40 ground stations in the world region (each station is equipped with 2 antennas), and a constellation containing 168 satellites is conducted. Computational tests with 12 different scale instances indicate that KA-ALNS outperforms two state-of-the-art algorithms given both 10 and 60 min of scheduling time.

Waiting Strategy for the Dynamic Meal Delivery Routing Problem with Time-Sensitive Customers Using a Hybrid Adaptive Genetic Algorithm and Adaptive Large Neighborhood Search Algorithm

In this paper, we study the dynamic meal delivery routing problem (MDRP) with time-sensitive customers. The multi-objective MDRP optimization model is developed to maximize customer satisfaction and minimize delay penalty cost and riding cost. To solve the dynamic MDRP, a novel waiting strategy is proposed to divide the dynamic problem into a series of static subproblems. This waiting strategy utilizes the decision threshold to determine rerouting points based on the number of dynamic meal orders. Meanwhile, time-sensitive priority is introduced to accelerate assignment and routing decisions for orders from customers with high time sensitivity. For each static subproblem, a hybrid AGA–ALNS algorithm that incorporates the adaptive genetic algorithm and adaptive large neighborhood search is developed to improve both the global and local search capabilities of the genetic algorithm. We validate the performance of the proposed waiting strategy and the AGA–ALNS algorithm through numerical instances. In addition, managerial insights are obtained from sensitivity analysis experiments.

UAV-rider coordinated dispatching for the on-demand delivery service provider

Fast response is critical to the success of the on-demand delivery service providers. Recently, more and more on-demand delivery service providers have started trying to use UAVs/drones to cope with the increasing challenges due to delivery service-sensitive demand. In this study, we propose a novel UAV-rider coordinated on-demand delivery model from the perspective of the on-demand delivery service provider. The objective aims to provide cost-effective delivery solutions by introducing the UAV/drone fleets into the current rider-only on-demand delivery system. A mixed integer linear programming (MILP) model is formulated to solve the problem, which is verified by the solver Cplex. To further analyze the impacts of the integration of UAVs on the riders’ deployment in practice, we develop a modified adaptive large neighborhood search (ALNS) algorithm to solve real business large-scale instances. The mechanism of how to improve the operational efficiency of the delivery service provider by integrating UAVs and the best-coordinated mode between UAVs and riders is discussed by conducting real-data-based numerical experiments. The results show that, for the case with a small number of orders in a dispatch cycle, the pure UAV model is best for the on-demand delivery service provider. When there are a large number of orders received in one dispatch cycle, the UAV-rider coordinated model is the best one to achieve cost-effective solutions. Some useful managerial insights are obtained through extensive experiments.