Adaptive Large Neighborhood Search Algorithm Research Articles

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.

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 modeling 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.

Many-to-many locomotive routing problem for the steel industry

The locomotive routing problem (LRP) considers the transportation of non-motorised containers called torpedo ladle cars (TPCs) within a steelworks. Locomotives are responsible for picking up and delivering TPCs to fulfil requests for various facilities, including steelmaking, ironmaking, heating, and repair facilities. This study introduces the many-to-many locomotive routing problem (M2MLRP), specifically tailored for the steel industry. M2MLRP was formulated using pattern- and routing-based models, and a logic-based Benders-decomposition-based matheuristic algorithm was developed. The pattern-based model successfully solved 20 small randomly generated instances, while the proposed matheuristic algorithm demonstrated the rapid generation of near-optimal solutions. Notably, unlike the pattern- and routing-based models, the proposed algorithm extended its capability to generate good feasible solutions for medium- and large-sized instances. Across all instances, the matheuristic algorithm outperformed both the routing-based model and an adaptive large neighbourhood search algorithm, showcasing its superior solution quality. This study provides a significant contribution to the field by being the first to address M2MLRP in the context of the steel industry.

An Adaptive Large Neighborhood Search Algorithm for Equipment Scheduling in the Railway Yard of an Automated Container Terminal

In container sea–rail combined transport, the railway yard in an automated container terminal (RYACT) is the link in the whole logistics transportation process, and its operation and scheduling efficiency directly affect the efficiency of logistics. To improve the equipment scheduling efficiency of an RYACT, this study examines the “RYACT–train” cooperative optimization problem in the mode of “unloading before loading” for train containers. A mixed-integer programming model with the objective of minimizing the maximum completion time of automated rail-mounted gantry crane (ARMG) tasks is established. An adaptive large neighborhood search (ALNS) algorithm and random search algorithm (RSA) are designed to solve the abovementioned problem, and the feasibility of the model and algorithm is verified by experiments. At the same time, the target value and calculation time of the model and algorithms are compared. The experimental results show that the model and the proposed algorithms are feasible and can effectively solve the “RYACT–train” cooperative optimization problem. The model only obtains the optimal solution of the “RYACT–train” cooperative scheduling problem with no more than 50 tasks within a limited time, and the ALNS algorithm can solve examples of various scales within a reasonable amount of time. The target value of the ALNS solution is smaller than that of the RSA solution.

A parallel deep adaptive large neighbourhood search algorithm for distributed heterogeneous hybrid flow shops with mixed-model assembly scheduling

ABSTRACT Nowadays, manufacturing enterprises must have fast response and flexible production capabilities to meet personalized and diversified market demands. Mixed-model production and distributed production have become the preferred production methods for enterprises. This article studies a distributed heterogeneous hybrid flow shop scheduling problem with a mixed-model assembly line (DHHFSP-MMAL), which consists of manufacturing and assembly stages. The DHHFSP-MMAL is modelled by a mixed integer linear programming (MILP) model. Three constructive heuristics and a parallel deep adaptive large neighbourhood search (PDALNS) problem are presented. A constructive heuristic with a group strategy is employed to obtain an initial solution. Several deep destroy-and-repair operators are proposed where problem-specific greedy local search methods are applied. The PDALNS assigns weights to destroy-and-repair operators to guide the selection of operators. The parallel computing technique is introduced to increase the efficiency of training. The experiments demonstrate that the PDALNS algorithm is an efficient and effective algorithm for solving the DHHFSP-MMAL problem.

A hybrid multi-objective solution approach for a reliable truck-drone routing problem integrated with pickup and delivery services

ABSTRACT Humanitarian logistics aims to reduce the arrival time of relief goods and optimize services in disaster-affected regions. This paper introduces a multi-objective hybrid truck-drone routing problem with pickup and delivery services, considering reliability (MHTDRP-PD-R) to enhance relief operations within humanitarian logistics. The model is formulated as a mixed-integer linear programming problem with the simultaneous goals of minimizing transportation time and maximizing reliability through routing and inventory decisions made by multiple trucks and drones. An adaptive large neighborhood search (ALNS) algorithm, coupled with heuristic technique, is developed to tackle the problem. The ALNS algorithm refines initial solutions through destroy and repair operators, integrated with multi-objective optimization methods to create a unified objective function. Small and large-scale test problems are then employed to evaluate solution quality using multi-objective optimization. The calculation results show the superiority of the developed ALNS integrated with the ε-constraint method regarding the objective value and solution time.

Heterogeneous vehicle scheduling with precedence constraints

The problem of heterogeneous vehicle scheduling with precedence constraints is inspired by the transportation service in tourism areas. Tourists must take the shuttle vehicles provided by the areas because of the long distances between the scenic spots. The scheduling of vehicles in tourism areas is complicated because the transportation requests of tourists are precedence-constrained temporally and spatially. The problem optimises both the cost of using vehicles and the waiting time of tourists. A mixed-integer linear programming model is formulated according to the description of a graph. An adaptive large neighbourhood search algorithm with several specialised operators is designed to solve the problem. Experiments based on randomly generated instances validate the mathematical model and the algorithm. A real-size instance based on Qiandao Lake in China is also analysed. The results indicate that the algorithm outperforms the model. The sensitivities of key parameters are analysed with managerial insights presented.

An efficient multi-objective adaptive large neighborhood search algorithm for solving a disassembly line balancing model considering idle rate, smoothness, labor cost, and energy consumption

The concept of green manufacturing emphasizes the importance of product disassembly in achieving energy-efficient recycling and remanufacturing operations. Disassembly line balancing (DLB) is a critical component of the product disassembly process, wherein a set of tasks must be allocated to workstations for disassembly. This study proposes a multi-objective DLB model that aims to minimize multiple conflicting objectives simultaneously including idle rate, smoothness, labor cost, and energy consumption. A key innovation of this study involves the creation of a tailored adaptive large neighborhood search (ALNS) algorithm which is one of the first studies in the literature on product disassembly algorithms. The developed ALNS employs efficient construction and destruction heuristics to solve the proposed multi-objective DLB problem. The ALNS algorithm aims to destroy and repair solutions effectively, while a local search procedure helps it escape from local optimum solutions. The provided DLB model is effectively solved by applying the proposed ALNS algorithm to the disassembly process of a turbine reducer. The obtained results from this application serve as a compelling demonstration of the efficiency and effectiveness of the proposed approach. Furthermore, comparisons conducted with various state-of-the-art algorithms using small and large instance sets consistently highlight the superiority of the proposed ALNS approach.

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.

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.

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.

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.

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.

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.

Integrated location and inventory planning in service parts logistics with last-mile delivery outsourcing

Outsourcing last-mile delivery to express companies provides an innovative strategy for capital goods manufacturers to improve service parts logistics. Given delivery dilemma due to geographically dispersed customers and low-frequency uncertain demands, we develop an integrated location and inventory problem with last-mile delivery outsourcing in a two-echelon service parts logistics. The proposed non-linear mixed integer programming model captures important decisions involving (1) location of local warehouses, (2) customer allocation to opened local warehouses, (3) inventory levels at these local warehouses, (4) outsourcing or self-operating last-mile delivery. We then replace service level constraints that cause nonlinearity with two new sets of constraints, and construct a linear mixed integer formulation that could be solved by GUROBI solver for some small-scale problems. It is still a challenging problem for large scale case, for which we propose an adaptive large neighbourhood search (ALNS) algorithm. Our extensive computational experiments verify the effectiveness of our model and algorithm. Computational experiments’ results indicate that outsourcing last-mile delivery amounting to around 12 % total cost savings on average, compared to self-operating in a real-world large instance from our research partner. Furthermore, we test some key parameters with computational experiments, and provide some managerial implications.

Heuristic Algorithms for Heterogeneous and Multi-Trip Electric Vehicle Routing Problem with Pickup and Delivery

In light of the widespread use of electric vehicles for urban distribution, this paper delves into the electric vehicle routing problem (EVRP): specifically addressing multiple trips per vehicle, diverse vehicle types, and simultaneous pickup and delivery. The primary objective is to minimize the overall cost, which encompasses travel expenses, waiting times, recharging costs, and fixed vehicle costs. The focal problem is formulated as a heterogeneous and multi-trip electric vehicle routing problem with pickup and delivery (H-MT-EVRP-PD). Additionally, we introduce two heuristic algorithms to efficiently approximate solutions within a reasonable computational time. The variable neighborhood search (VNS) algorithm and the adaptive large neighborhood search (ALNS) algorithm are presented and compared based on our computational experiences with both. Through solving a series of large-scale real-world instances for the H-MT-EVRP-PD and smaller instances using an exact method, we demonstrate the efficacy of the proposed approaches.

Automatic Guided Vehicle Scheduling in Automated Container Terminals Based on a Hybrid Mode of Battery Swapping and Charging

Automatic guided vehicles (AGVs) in the horizontal area play a crucial role in determining the operational efficiency of automated container terminals (ACTs). To improve the operational efficiency of an ACT, it is essential to decrease the impact of battery capacity limitations on AGV scheduling. To address this problem, this paper introduces battery swapping and opportunity charging modes into the AGV system and proposes a new AGV scheduling problem considering the hybrid mode. Firstly, this study describes the AGV scheduling problem of the automated container terminals considering both loading and unloading tasks under the hybrid mode of battery swapping and charging. Thereafter, a mixed-integer programming model is established to minimize the sum of energy costs and delay costs. Secondly, an effective adaptive large neighborhood search algorithm is proposed to solve the problem, in which the initial solution construction, destroy operators, and repair operators are designed according to the hybrid mode. Finally, numerical experiments are conducted to analyze the effectiveness of the model and the optimization performance of the algorithm. The results demonstrate that the hybrid mode of battery swapping and charging can effectively reduce the number of battery swapping times and scheduling costs compared to the existing mode.