Large Neighborhood Research Articles

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.

A Quantum-Inspired Ant Colony Optimization Algorithm for Parking Lot Rental to Shared E-Scooter Services

Electric scooter sharing mobility services have recently spread in major cities all around the world. However, the bad parking behavior of users has become a major source of issues, provoking accidents and compromising urban decorum of public areas. Reducing wild parking habits can be pursued by setting reserved parking spaces. In this work, we consider the problem faced by a municipality that hosts e-scooter sharing services and must choose which locations in its territory may be rented as reserved parking lots to sharing companies, with the aim of maximizing a return on renting and while taking into account spatial consideration and parking needs of local residents. Since this problem may result difficult to solve even for a state-of-the-art optimization software, we propose a hybrid metaheuristic solution algorithm combining a quantum-inspired ant colony optimization algorithm with an exact large neighborhood search. Results of computational tests considering realistic instances referring to the Italian capital city of Rome show the superior performance of the proposed hybrid metaheuristic.

Solving the probabilistic drone routing problem: Searching for victims in the aftermath of disasters

AbstractSeveral major industrial disasters happen each year around the world. They usually involve limited accessibility, poor ground conditions, and toxic wastes. As a consequence, this reduces the efficiency of humanitarian operations. In such a context, flying drones may be a viable alternative: faster, no dependency on ground conditions, and larger areas scanned. They are also better suited for following the population and the crisis dynamic. For such a purpose, various issues have to be addressed such as defining and optimizing the drone's routes, their energy consumption, choosing the relay points for recharging equipment, among others. In this study, several additional features from existing works are considered: first, a probability of identifying individuals is defined. Thus, each node can be scanned several times in order to improve the observation. In addition, the nodes are prioritized according to a given heatmap. The probabilistic drone routing problem (PDRP) consists of finding a route, that is, a sequence of trips, for each drone such that the sum of the expected number of identified individuals on all routes is maximized. Constraints on energy consumption, collision avoidance and drone‐base assignment are considered. We propose a heuristic and metaheuristics based on the adaptive large neighborhood search for the PDRP. The methods are tested on theoretical instances, as well as on a case study of the Beirut Port explosion on August 4, 2020, in order to analyze the performance of the proposed methods.

Large neighborhood local search method with MIP techniques for large-scale machining scheduling with many constraints

This study addresses the problem of scheduling machining operations in a highly automated manufacturing environment while considering the work styles of workers. In actual manufacturing, many aspects of the operation must be considered, such as constraints related to the works to be machined in the machining schedule and the states of the workers. To derive good solutions for such a large-scale problem with many constraints within a realistic amount of computation time, we develop an optimization technique based on a mixed-integer programming (MIP)-based large neighborhood local search method for the machining scheduling problem. Then, computer experiments on a problem based on actual machining requirements are performed to verify the validity of the proposed method.

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.

A customized adaptive large neighborhood search algorithm for solving a multi-objective home health care problem in a pandemic environment

This paper addresses a home health care routing and scheduling problem (HHCRSP) specifically focusing on the context of a pandemic environment. The investigated HHCRSP involves assigning appropriate caregivers to patients and optimizing caregiver routes while minimizing total travel costs, workload differences among caregivers, and negative patient preferences. The problem accounts for synchronized visits, lunch breaks, time windows, and special pandemic constraints (e.g., maximum total contact times for each caregiver and multi-trip routes). To our knowledge, this is the first study to address these constraints and goals simultaneously in the HHCRSP. To solve the problem, we propose a customized algorithm, AMOALNS, which combines archived multi-objective simulated annealing (AMOSA) with adaptive large neighborhood search (ALNS). Our AMOALNS generates a new solution iteratively through problem-specific heuristics within ALNS, employing a multi-objective optimization framework to intelligently disrupt and repair feasible solutions. The adaptive updating of heuristic weights is determined by the domination relation between the newly generated solution and non-dominated solutions stored in the archive of AMOSA. To show the performance of the AMOALNS through extensive experiments, we compare it with the epsilon constraint method and other state-of-the-art multi-objective algorithms in the literature. Additionally, we use Pareto front analysis to aid decision-makers in comprehending the shape of the Pareto front and making informed choices based on their preferences. The trade-off analysis shows that minimizing total travel costs is the hardest to achieve, and optimizing workload balancing is worthwhile. Also, most of the non-dominated solutions can simultaneously achieve low workload balancing and high preference of patients. Finally, the sensitivity analyses reveal that the time windows classifications and instances sizes have significant impact on the compromising solutions.

A linearly decreasing deterministic annealing algorithm for the multi-vehicle dial-a-ride problem.

Dial a ride problem (DARP) is a complex version of the pick-up and delivery problem with many practical applications in the field of transportation. This study proposes an enhanced deterministic annealing algorithm for the solution of large-scale multi-vehicle DARPs. The proposed method always explores the feasible search space; therefore, a feasible solution is guaranteed at any point of termination. This method utilises advanced local search operators to accelerate the search for optimal solutions and it relies on a linearly decreasing deterministic annealing schedule to limit poor jumps during the course of search. This study puts forward a systematic series of experiments to compare the performance of solution methods from various angles. The proposed method is compared with the most efficient methods reported in the literature i.e., the Adaptive Large Neighbourhood Search (ALNS), Evolutionary Local Search (ELS), and Deterministic Annealing (DA) using standard benchmarks. The results suggest that the proposed algorithm is on average faster than the state-of-the-art algorithms in reaching competitive objective values across the range of benchmarks.

An adaptive large neighborhood search heuristic for the multi-port continuous berth allocation problem

In this paper, we study a problem that integrates the vessel scheduling problem with the berth allocation into a collaborative problem denoted as the multi-port continuous berth allocation problem (MCBAP). This problem optimizes the berth allocation of a set of ships simultaneously in multiple ports while also considering the sailing speed of ships between ports. Due to the highly combinatorial character of the problem, exact methods struggle to scale to large-size instances, which points to exploring heuristic methods. We present a mixed-integer problem formulation for the MCBAP and introduce an adaptive large neighborhood search (ALNS) algorithm enhanced with a local search procedure to solve it. The computational results highlight the method’s suitability for larger instances by providing high-quality solutions in short computational times. Practical insights indicate that the carriers’ and terminal operators’ operational costs are impacted in different ways by fuel prices, external ships at port, and the modeling of a continuous quay.

A Bayesian Optimization Approach for Tuning a Grouping Genetic Algorithm for Solving Practically Oriented Pickup and Delivery Problems

Background: The Multi Depot Pickup and Delivery Problem with Time Windows and Heterogeneous Vehicle Fleets (MDPDPTWHV) is a strongly practically oriented routing problem with many real-world constraints. Due to its complexity, solution approaches with sufficiently good quality ideally contain several operators with certain probabilities.Thus, automatically selecting the best parameter configurations enhances the overall solution quality. Methods: To solve the MDPDPTWHV, we present a Grouping Genetic Algorithm (GGA) framework with several operators and population management variants. A Bayesian Optimization (BO) approach is introduced to optimize the GGA’s parameter configuration. The parameter tuning is evaluated on five data sets which differ in several structural characteristics and contain 1200 problem instances. The outcomes of the parameter-tuned GGA are compared to both the initial GGA parameter configuration and a state-of-the-art Adaptive Large Neighborhood Search (ALNS). Results: The presented GGA framework achieves a better solution quality than the ALNS, even for the initial parameter configuration used. The mean value of the relative error is less than 0.9% and its standard deviation is less than 1.31% for every problem class. For the ALNS, these values are up to three times higher and the GGA is up to 38% faster than the ALNS. Conclusions: It is shown that the BO, as a parameter tuning approach, is a good choice in improving the performance of the considered meta-heuristic over all instances in each data set. In addition, the best parameter configuration per problem class with the same characteristics is able to improve both the frequency of finding the best solution, as well as the relative error to this solution, significantly.

Integrated Transit-Oriented Development (TOD) with suburban rail network design problem for maximizing profits

ABSTRACT Driven by the transit-oriented development (TOD) policy for rail stations, this study focuses on the network design of rapid suburban railways and the TOD zone design of corresponding suburban rail stations within metropolitan areas. A multi-level rail transit network was introduced, and the newly planned suburban rail network was considered as an expansion of the existing network to provide rapid services between urban and suburban areas. An integrated programming model was proposed to determine the physical and service routes of suburban rail network, the frequencies of each line, and the TOD plan along the selected rail stations. The integrated model is solved using the Adaptive Large Neighborhood Search (ALNS) algorithm. The effectiveness of the proposed model and methods is illustrated through numerical experiments, and several scenarios are designed for parameter analysis.

An adaptive large neighborhood search for the multi-point dynamic aggregation problem

An adaptive large neighborhood search for the multi-point dynamic aggregation problem

Multi-period time window assignment for attended home delivery

We study a multi-period stochastic variant of the Time Window Assignment Vehicle Routing Problem, where customers’ demands, locations, and service times are uncertain. Customers are partitioned into geographical zones, each of which has to be visited a predetermined number of times over a planning period of several days. Whenever a zone is visited, a time window is assigned. Time windows are decided before knowing customers and their demands. A fleet of homogeneous vehicles is available to serve customers each day. At a tactical level, the problem looks for a static time window assignment that minimizes the expected traveling costs plus the expected penalty costs for unserved customers. We propose a two-stage formulation and a solution approach, which relies on the Sample Average Approximation Method, while encompassing a perturbation method to assign time windows in the first stage and an Adaptive Large Neighborhood Search to optimize routes in the second stage. We experimentally evaluate three instance sets, including real ones from a Canadian company, comparing our results to lower bounds from the exact solution of a deterministic equivalent formulation over a finite number of scenarios. Our method outperforms the manual approach used by the company.

Two-Echelon Location–Routing Problem of Perishable Products Based on the Integrated Mode of In-Store Pick-Up And Delivery

The integrated service mode of in-store pick-up and delivery has become common in the post-epidemic period owing to the combined online and offline purchases of perishable products. This study investigates the diverse requirements of in-store pick-up and delivery customers. Then, it establishes a two-echelon location–routing model for a perishable food distribution network to minimize total cost as an objective. An adaptive large neighborhood search (ALNS) algorithm was also developed to solve the foregoing problem. To test the algorithm, instances from those of Solomon are derived. The proposed ALNS algorithm was found to achieve satisfactory performance with respect to speed and accuracy by comparing its results with those of the CPLEX software for a 12-node small-scale instance. The applicability and stability of the ALNS algorithm were further verified using different types of instances with more nodes. Different proportions of in-store pick-up and delivery customers were set, and the total cost of location–routing schemes under these proportions was compared. The results show that an integrated service type compared with the single delivery service mode and single in-store pick-up service mode can save 7.98% and 11.44% of the total cost, respectively.

An adaptive large neighborhood search algorithm for parallel assembly lines scheduling problem with complex fixture constraints

In recent years, the value chain facilitates the transition of competition among enterprises from a single link to a comprehensive one, thereby driving intelligent upgrades within manufacturing enterprises. The intelligent upgrading also imposes new constraints on the traditional assembly line production mode. Inspired by the real production practices of company L, a global intelligent terminal manufacturing enterprise headquartered in China, this study addresses the parallel heterogeneous assembly line scheduling problem with fixture constraints (HALSFC). To tackle this challenging problem, we propose a mixed integer linear programming (MILP) model that aims to maximize the number of completed work orders within a specified time. To our best knowledge, this study is among the first attempts to address the HALSFC problem with setups and related work orders. Due to the NP-hardness of the problem, we propose an improved adaptive large neighborhood search algorithm (IALNS) for solving HALSFC. We evaluate both model functionalities and algorithm effectiveness using instances generated based on the real production data of company L. Extensive experimental results demonstrate the effectiveness and efficiency of IALNS compared to MILP, Tabu search algorithm (TS) and genetic algorithm (GA), especially for medium- and large-scale instances. Additionally, the sensitivity analysis of the quality inspection time, the related work orders proportion and the minimum cooling time of fixtures is also conducted.

Blockchain-Enabled Infection Sample Collection System Using Two-Echelon Drone-Assisted Mechanism

The collection and transportation of samples are crucial steps in stopping the initial spread of infectious diseases. This process demands high levels of safety and timeliness. The rapid advancement of technologies such as the Internet of Things (IoT) and blockchain offers a viable solution to this challenge. To this end, we propose a Blockchain-enabled Infection Sample Collection system (BISC) consisting of a two-echelon drone-assisted mechanism. The system utilizes collector drones to gather samples from user points and transport them to designated transit points, while deliverer drones convey the packaged samples from transit points to testing centers. We formulate the described problem as a Two-Echelon Heterogeneous Drone Routing Problem with Transit point Synchronization (2E-HDRP-TS). To obtain near-optimal solutions to 2E-HDRP-TS, we introduce a multi-objective Adaptive Large Neighborhood Search algorithm for Drone Routing (ALNS-RD). The algorithm’s multi-objective functions are designed to minimize the total collection time of infection samples and the exposure index. In addition to traditional search operators, ALNS-RD incorporates two new search operators based on flight distance and exposure index to enhance solution efficiency and safety. Through a comparison with benchmark algorithms such as NSGA-II and MOLNS, the effectiveness and efficiency of the proposed ALNS-RD algorithm are validated, demonstrating its superior performance across all five instances with diverse complexity levels.

An adaptive variable neighbourhood search approach for the dynamic vehicle routing problem

In the traditional vehicle routing problem (VRP), a route plan is pre-determined and remains unchanged afterwards. However in practice, several unforeseen events could occur at any point, which cause traffic congestion and delay to the original planned routes. It is therefore important to re-optimise the routes by taking into consideration the real-time information, leading to the Dynamic VRP (DVRP). While most of the DVRP literature mainly focused on the customer requests as the dynamic aspect, this paper, however, concentrates on the dynamic traffic information based on the level of urgency of the accidents. Critical nodes are introduced into the network to provide a diversion opportunity for the en-route vehicle. This novel concept of ‘criticality’ is also more practical than the commonly adopted strategy that allows instantaneous diversion at the current vehicle location. We proposed an adaptive variable neighbourhood search (AVNS) algorithm to generate routes in the static environment which is then adapted accordingly for the dynamic setting. This is a two stage VNS approach with the first one acts as a learning stage whose information is then used in stage 2. Here, a smaller number of neighbourhoods and local searches are chosen at each iteration while adopting a pseudo-random selection procedure derived from stage 1. To provide solution diversity, a large neighbourhood search is also embedded into the search. The flexibility and adaptability of our AVNS approach are demonstrated by the high quality solutions obtained when tested on the commonly used VRP datasets, ranging in size from 50 to 1200 customers, which are modified accordingly. In addition, managerial insights related to the tightness of the routes are also presented and analysed.

An adaptive large neighbourhood search algorithm for blocking flowshop scheduling problem with sequence-dependent setup times

An adaptive large neighbourhood search algorithm for blocking flowshop scheduling problem with sequence-dependent setup times

An adaptive large neighborhood search for the order picking process: the case of a retail distribution company in Italy

Order picking is one of the most important activities in warehouse management. By optimising the picking process, warehouse operations can be managed efficiently in terms of both time and logistics costs. In this work we apply operations research techniques to support the order picking process of an Italian retail distribution company. The picking process poses several challenges from an optimisation point of view, and the related optimization problems are very complex. Therefore, we define an Adaptive Large Neighbourhood Search (ALNS) algorithm that heuristically solves the problem. The proposed metaheuristic is tested on a set of 101 real orders processed by the company within one day. The computational experiments show that the ALNS shows good performances in terms of effectiveness, compared to the optimal solution, as well as allows to implement a better organisation of the order picking process than the one currently adopted by the company.

A Deep Reinforcement Learning-Based Adaptive Large Neighborhood Search for CapacitatedElectric Vehicle Routing Problems

A Deep Reinforcement Learning-Based Adaptive Large Neighborhood Search for CapacitatedElectric Vehicle Routing Problems