Adaptive Search Algorithm Research Articles

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.

Adaptive mutation sparrow search algorithm-Elman-AdaBoost model for predicting the deformation of subway tunnels

A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm (AM-SSA), called AMSSA-Elman-AdaBoost, is proposed for predicting the existing metro tunnel deformation induced by adjacent deep excavations in soft ground. The novelty is that the modified SSA proposes adaptive adjustment strategy to create a balance between the capacity of exploitation and exploration. In AM-SSA, firstly, the population is initialized by cat mapping chaotic sequences to improve the ergodicity and randomness of the individual sparrow, enhancing the global search ability. Then the individuals are adjusted by Tent chaotic disturbance and Cauchy mutation to avoid the population being too concentrated or scattered, expanding the local search ability. Finally, the adaptive producer-scrounger number adjustment formula is introduced to balance the ability to seek the global and local optimal. In addition, it leads to the improved algorithm achieving a better accuracy level and convergence speed compared with the original SSA. To demonstrate the effectiveness and reliability of AM-SSA, 23 classical benchmark functions and 25 IEEE Congress on Evolutionary Computation benchmark test functions (CEC2005), are employed as the numerical examples and investigated in comparison with some well-known optimization algorithms. The statistical results indicate the promising performance of AM-SSA in a variety of optimization with constrained and unknown search spaces. By utilizing the AdaBoost algorithm, multiple sets of weak AMSSA-Elman predictor functions are restructured into one strong predictor by successive iterations for the tunnel deformation prediction output. Additionally, the on-site monitoring data acquired from a deep excavation project in Ningbo, China, were selected as the training and testing sample. Meanwhile, the predictive outcomes are compared with those of other different optimization and machine learning techniques. In the end, the obtained results in this real-world geotechnical engineering field reveal the feasibility of the proposed hybrid algorithm model, illustrating its power and superiority in terms of computational efficiency, accuracy, stability, and robustness. More critically, by observing data in real time on daily basis, the structural safety associated with metro tunnels could be supervised, which enables decision-makers to take concrete control and protection measures.

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.

An adaptive metaheuristic optimization approach for Tennessee Eastman process for an industrial fault tolerant control system.

The Tennessee Eastman Process (TEP) is widely recognized as a standard reference for assessing the effectiveness of fault detection and false alarm tracking methods in intricate industrial operations. This paper presents a novel methodology that employs the Adaptive Crow Search Algorithm (ACSA) to improve fault identification capabilities and mitigate the occurrence of false alarms in the TEP. The ACSA is an optimization approach that draws inspiration from the observed behavior of crows in their natural environment. This algorithm possesses the capability to adapt its search behavior in response to the changing dynamics of the optimization process. The primary objective of our research is to devise a monitoring strategy that is adaptable in nature, with the aim of efficiently identifying faults within the TEP while simultaneously minimizing the occurrence of false alarms. The ACSA is applied in order to enhance the optimization of monitoring variables, alarm thresholds, and decision criteria selection and configuration. When compared to traditional static approaches, the ACSA-based monitoring strategy is better at finding faults and reducing false alarms because it adapts well to changes in process dynamics and disturbances. In order to assess the efficacy of our suggested methodology, we have conducted comprehensive simulations on the TEP dataset. The findings suggest that the monitoring strategy based on ACSA demonstrates superior fault identification rates while concurrently mitigating the frequency of false alarms. In addition, the flexibility of ACSA allows it to efficiently manage process variations, disturbances, and uncertainties, thereby enhancing its robustness and reliability in practical scenarios. To validate the effectiveness of our proposed approach, extensive simulations were conducted on the TEP dataset. The results indicate that the ACSA-based monitoring strategy achieves higher fault detection rates while simultaneously reducing the occurrence of false alarms. Moreover, the adaptability of ACSA enables it to effectively handle process variations, disturbances, and uncertainties, making it robust and reliable for real-world applications. The contributions of this research extend beyond the TEP, as the adaptive monitoring strategy utilizing ACSA can be applied to other complex industrial processes. The findings of this study provide valuable insights into the development of advanced fault detection and false alarm monitoring techniques, offering significant benefits in terms of process safety, reliability, and operational efficiency.

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.

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.

Photovoltaic output power estimation model based on multi-meteorological variable dimension reduction and improved variational mode decomposition algorithm

ABSTRACT The instability and intermittence of photovoltaic (PV) power generation challenge the large-scale integration of PV power generation systems into the power grid and the development of energy storage systems. To accurately predict the output power of PV power generation, a power prediction model based on the optimized variational modal decomposition (OVMD)-adaptive t-distribution sparrow search algorithm (tSSA)-least squares support vector machine (LSSVM) algorithm was built. The performance of the model was then verified by three-year meteorological data and real-time PV output power data of two different climatic regions in China. The mean absolute percentage error (MAPE) and root mean square error (RMSE) of this model were less than 3.1% and 0.40, respectively, and the determination coefficient (R2) of this model was greater than 95%. Moreover, the prediction accuracy was greatly improved compared to models based on the support vector machine (SVM), LSSVM, variational modal decomposition (VMD)-LSSVM, and VMD-SSA-LSSVM algorithms. Finally, by performing dimension reduction of meteorological variables related to PV output power, the performance of the proposed OVMD-tSSA-LSSVM model was further improved.

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.

A metaheuristic‐based method for analysis of tensegrity structures

SummaryTensegrity systems are construction system that offers solutions for lighter designs compared with a standard truss system consisting of tension and compression elements. Due to their structure, they are recommended in the design of space structures due to the use of lighter and easy‐to‐assemble structural elements such as cables, ropes, and similar to provide the desired durability. In this study, energy minimization was carried out to solve the structural element displacements. It is expected to reduce the total potential energy of the system with minimization. By minimizing the total potential energy of the tensegrity system models, the displacement of each building element is found for the equilibrium condition. Tensegrity models were analyzed by minimizing energy via the adaptive harmony search (AHS) algorithm. In this research, two distinct tensegrity structure specimens were employed. One comprised a cantilever beam, while the other adopted a cyclic model, forming eight equal octagons from eight nodes at both the base and the top. Additionally, an examination was conducted on a two‐layer iteration of the cyclic model. The method is robust for both space and planar tensegrity structures, allowing the determination of deformed shapes under various loads without design assumptions. The TPO/MA method demonstrates superiority in handling nonlinear and barely stable systems, as evidenced by examples illustrating its efficacy in maintaining structural form under increasing loads and challenging conditions.

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.

A Novel Bilevel Electromechanical Compound Braking Coordinated Control Strategy for Electric Vehicles

Due to the difference of response time and braking type between the motor and the pneumatic braking system, it is still difficult to coordinate the motor braking and the pneumatic braking to ensure the vehicle stability and maximal energy regeneration. To address this challenge, a bilevel electromechanical compound braking coordinated control strategy for electric vehicles is proposed considering general and emergency braking state. First, in general braking state, considering the delay characteristics of the pneumatic braking system, a Lagrange quadratic interpolation prediction algorithm is designed to start the pneumatic braking system in advance. Second, in emergency braking state, a model predictive control method is proposed to optimize the braking torque distribution while controlling the wheel slip ratio in a stable range. In order to obtain the optimal control effect, a modified adaptive cuckoo search algorithm is put forward, in which three adaptive impact factors are added. Finally, the proposed control strategy is verified under three road conditions and compared with the conventional control strategy. The results demonstrate significant improvements under gravel road condition, including a 7% increase in energy recovery efficiency, a 92.1% enhancement in the following effect of pneumatic braking torque, and a 43.5% reduction in wheel fluctuation.

Integrated Inbound and Outbound Scheduling for Coal Port: Constraint Programming and Adaptive Local Search

The effective production scheduling of dry bulk ports is a challenging task that demands meticulous planning, task allocation based on customer requirements, as well as strategic route and timing scheduling. Dry bulk ports dedicated to handling commodities like coal and iron ore frequently engage in blending operations as a strategic imperative to gain market competitiveness. The process of blending coal and ore entails the timely arrival of the requisite raw materials at predetermined locations. Simultaneously, it necessitates the coordination of the sequencing of goods entering and departing the port to align with the operational demands associated with material stockpiles. This paper describes and analyzes an operational scheduling problem encountered by one of the largest coal blending sea ports in China. Specifically, a rich constraint programming model is presented to define operation sequences integrating daily inbound and outbound services provided by the port, minimizing the overall operation time. In order to enhance the practicality of the method, a CP-based adaptive simulated annealing local search algorithm has been designed and developed for the optimization problem. The empirical validation of the proposed method is conducted using both real production data and generated experimental data adhering to specific rules. The results conclusively demonstrate the efficacy and feasibility of the proposed method. This also substantiates its practicality and effectiveness in real-world applications, facilitating efficient production and energy-saving operations for the coal port.

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.

Blind CFO estimation based on weighted subspace fitting criterion with fuzzy adaptive gravitational search algorithm

AbstractThis paper deals with the blind carrier frequency offset (CFO) estimation based on weighted subspace fitting (WSF) criterion with fuzzy adaptive gravitational search algorithm (GSA) for the interleaved orthogonal frequency-division multiplexing access (OFDMA) uplink system. For the CFO estimation problem, it is well known that the WSF has superior statistical characteristics and better estimation performance. However, the type of CFO estimation must pass through the high-dimensional space problem. Optimizing complex nonlinear multimodal functions requires a large computational load, which is difficult and not easy to maximize or minimize nonlinear cost functions in large parameter spaces. This paper firstly presents swarm intelligence (SI) optimization algorithms such as GSA, particle swarm optimization (PSO), and hybrid PSO and GSA (PSOGSA) to improve estimation accuracy and reduce the computational load of search. At the same time, this paper also integrates a fuzzy inference system to WSF-GSA to dynamically adjust the gravitational constant, which can not only reduce the searching computational load, but also improve the performance of GSA in the global optimization and solution accuracy. Finally, several simulation results are provided for illustrating the effectiveness of the proposed estimator.

Adaptive bald eagle search algorithm with elite swarm guiding and population memory crossover

Adaptive bald eagle search algorithm with elite swarm guiding and population memory crossover