Improved Tabu Search Algorithm Research Articles

ROBUST OPTIMIZATION MODEL OF CONTAINER LINER ROUTES IN FEEDER LINE NETWORK

The universal application of the hub-and-spoke maritime network makes feeder line network key to restricting the quality and efficiency of maritime transportation. However, container liner routes in feeder line network are susceptible to the changes in shipment demand and international fuel prices. Therefore, based on the hub-and-spoke maritime network, this paper constructs a robust optimization model of container liner routes in feeder line network. Under the capacity and time constraints, routes optimization and ship equipment under uncertain environment are analysed. An improved tabu search algorithm was designed based on the characteristics of the model. The example analysis proves that the model can still ensure the robustness of routes under uncertain environment, which is more applicable than the deterministic model.

Order Distribution and Routing Optimization for Takeout Delivery under Drone–Rider Joint Delivery Mode

Order distribution and routing optimization of takeout delivery is a challenging research topic in the field of e-commerce. In this paper, we propose a drone–rider joint delivery mode with multi-distribution center collaboration for the problems of limited-service range, unreasonable distribution, high delivery cost, and tight time windows in the takeout delivery process. The model is constructed with the minimum delivery cost and the overall maximum customer satisfaction as the objective function, and a two-stage heuristic algorithm is designed to solve the model. In the first stage, Euclidean distance is used to classify customers into the regions belonging to different distribution centers, and the affinity propagation (AP) clustering algorithm is applied to allocate orders from different distribution centers. The second stage uses an improved tabu search algorithm for route optimization based on specifying the number of rider and drone calls. This paper takes China’s Ele.me and Meituan takeout as the reference object and uses the Solomon data set for research. The experimental results show that compared with the traditional rider delivery mode, the drone–rider joint delivery mode with multiple distribution center collaboration can effectively reduce the number of riders used, lower the delivery cost, and improve the overall customer satisfaction.

Design and research of automated warehouse simulation platform based on virtual visualization framework

The automated storage and retrieval system (AS/RS) has been developed rapidly and has been widely used in various fields. To achieve the ultimate goal of improving the efficiency of warehouse operation, this article studies the problem of goods location- allocation on the premise of a comprehensive analysis of the efficiency of automated warehouses and the overall stability of shelves. The mathematical model is established. Traditional Tabu search algorithms depend on Tabu table construction to avoid multiple continuous cycles that may occur in the direct search process. In this article, the improved algorithm can directly search and store in the Tabu table to prevent the algorithm from infinite search. A complete solution in that Tabu list continues to be passed to act as a point solution to replace the iteratively update product until a set iteration interval of the product is completed. The machine learning system can get the optimal solution. The improved algorithm is applied to solve the model, and the running results and the performance of the algorithm itself are obtained. The improved Tabu Search algorithm has more advantages in the process of solving the model because it reduces the size of the whole problem. The algorithm also uses the penalty optimization function as a measure of each solution. The greater the penalty is, the worse the solution is, and the smaller the penalty is, the better the solution is. The operation process of the material in and out of the warehouse in one day is studied, and the validity of the optimization scheme of goods location allocation is verified by substituting specific data for the solution. Finally, the case of goods in and out of the warehouse is simulated by using simulation software, and the running state and efficiency of the stacker are displayed according to the data. The Tabu direct search table is combined with the structure and function of the principle of amnesty direct search to achieve the goal of direct search and decision-making. The equilibrium coefficient of its operation is calculated by using the data, which proves that the scheme is feasible and has practical significance and research value.

Caching Placement Optimization Strategy Based on Comprehensive Utility in Edge Computing

With the convergence of the Internet of Things, 5G, and artificial intelligence, limited network bandwidth and bursts of incoming service requests seem to be the most important factors affecting user experience. Therefore, caching technology was introduced. In this paper, a caching placement optimization strategy based on comprehensive utility (CPOSCU) in edge computing is proposed. Firstly, the strategy involves quantifying the placement factors of data blocks, which include the popularity of data blocks, the remaining validity ratio of data blocks, and the substitution rate of servers. By analyzing the characteristics of cache objects and servers, these placement factors are modeled to determine the cache value of data blocks. Then, the optimization problem for cache placement is quantified comprehensively based on the cache value of data blocks, data block retrieval costs, data block placement costs, and replacement costs. Finally, to break out of the partial optimal solution for cache placement, a penalty strategy is introduced, and an improved tabu search algorithm is used to find the best edge server placement for cached objects. Experimental results demonstrate that the proposed caching strategy enhances the cache service rate, reduces user request latency and system overhead, and enhances the user experience.

A new focused crawler using an improved tabu search algorithm incorporating ontology and host information

A new focused crawler using an improved tabu search algorithm incorporating ontology and host information

Multi-satellite Mission Planning Algorithm Based on Preemptive Priority Model

<p>Aiming at the problems of high time-consuming and insufficient guarantee of high-priority task completion in the large-scale multi-satellite multi-observation mission planning application, a multi-satellite multi-mission planning model based on preemptive priority model is proposed. Combined with the whole-neighborhood greedy search strategy, an improved tabu search algorithm is designed, taking the combination of satellite observation windows as the decision variables. The designed algorithm realizes an efficient solver to the proposed mission planning model. The simulation results show that the average solving time of the algorithm is 144s under the problem scale of 100 satellites and 3000 tasks. Compared with the existing algorithm, the calculation time is shortened by at least 75% under the premise that the total number of planning tasks is comparable, which is more in line with the high time efficiency requirements for satellite mission planning in engineering applications.</p> <p> </p>

Reliability Calculation Method of Shipborne Vehicles' Sortie Mission for Dynamic Network Structure.

To provide decision support to the commander, it is necessary to calculate shipborne vehicles' sortie mission reliability during the formulation of the layout plan. Therefore, this paper presents the sortie mission network model and reliability calculation method for shipborne vehicles. Firstly, the shipborne vehicle layout and sortie task characteristics are used to establish the sortie mission network model. The shipborne vehicles' sortie mission reliability problem is transformed into a two-terminal network reliability problem. Secondly, the minimal path set method is used to calculate the two-terminal network reliability. An improved tabu search algorithm based on a strategy of breaking up the whole into parts is proposed to search for the minimal path set that matches the length. Finally, the sum of disjoint products is used to process the minimal path set to obtain the shipborne vehicles' sortie mission reliability calculation formula. A numerical analysis of two simplified shipborne vehicles' layouts is given to illustrate the calculation process of the method. This study provides a new evaluation index and an effective quantitative basis for the evaluation system of shipborne vehicles' layout. It also provides theoretical support for the development of decision-making related to the sortie mission of shipborne vehicles.

An improved tabu search algorithm for solving heterogeneous fixed fleet open vehicle routing problem with time windows

The heterogeneous fixed fleet open vehicle routing problem with time windows is a very significant type of the vehicle routing problem (VRP) that aims to find the minimum fixed and variable cost of transportation for a heterogeneous fleet with a fixed number in which the capacity of every vehicle and usage of the vehicles should not be ignored. Also, in this problem, each customer has a special time window for servicing and each vehicle starts its route from the warehouse and ends up in one of the customers. We propose a mixed integer linear programming model of this problem. Since this problem, as well as open VRP and VRP with fixed heterogeneous fleet are hard NP problem, an improved tabu search algorithm is proposed to solve the problem. Our proposed algorithm uses a modified sweep algorithm to generate some initial solutions. Besides, a variable tabu list and some new mechanisms for intensification and diversification mechanisms are used. Numerical results are presented to show the correctness of our model and finally, the efficiency of the proposed algorithm is compared with an exact algorithm, classic tabu search and simulated annealing. The obtained results prove the efficiency of the proposed algorithm.

Intermodal hub-and-spoke logistic network design with differentiated services: The case of China Railway Express

Designing the hub-and-spoke logistic network, a powerful structure in resource integration and cost reduction, has aroused ever-growing interest from scholars. Considering that, for realistic scenarios, customers’ demands must take on greater diversity, in this paper, a hub-and-spoke network with differentiated services is developed, allowing clients to choose their preferred service levels, while considering multiple transportation modes, with environmental parameters and economies of scale incorporated into the modeling process. Further, to solve the derived optimization problem effectively, an improved tabu search algorithm is proposed, in which an appropriate memory function is included to strengthen its adaptability. The improved algorithm is found to have outstanding efficiency when compared to arithmetic software and the genetic algorithm, for both small and large size instances. Finally, the theoretical findings are applied to the case of China Railway Express, to redesign the logistic network in a road-rail intermodal transportation mode and support decision-making over hub location and allocation. Our findings offer important practical insights that can aid contributions to evidence-informed policy formulation and implementation.

Offloading Strategy in Satellite-Ground Network

Subject to other constraints such as economic costs, limited ground network coverage and insufficient network service capabilities in remote areas have resulted in some unloading requests being rejected. In this paper, the satellite network is used as the supplement of the terrestrial network, and the satellite-ground fusion multi-layer MEC network is constructed to study the offloading strategy and resource allocation for multi-type tasks. Firstly, the priority of task unloading is divided, and then the optimization function is constructed with the goal of minimizing the system overhead, and the improved tabu search algorithm is used to solve the problem. Finally, the feasibility and superiority of the algorithm are verified by experimental simulation.

Urban Logistics Delivery Route Planning Based on a Single Metro Line

This paper proposed a framework to integrate metro for urban logistics delivery in order to avert the conflicts with ground transportation, reduce delivery cost, transport distance and delivery time of vehicles. Integrating metro-based Underground Logistics System (ULS) with ground delivery vehicles (GDVs), an urban logistics delivery path optimization model based on a single metro line was built to improve delivery efficiency. In handling this model, total transport cost was taken as the objective function by considering the capacity limitations of GDVs and soft customer time window (CTW), vehicle routing problem with Time Windows (VRPTW) was also taken into account. By means of the saving algorithm (CW algorithm) and the improved Tabu Search algorithm (CWTS), our established model was solved to optimize the delivery routes under metro-based ULS. Through the case analysis of a Logistics Transport Enterprise (LTE) in Nanjing, it was shown that our proposed algorithm consistently outperformed Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) in taking less computation time and obtaining better results. Our proposed delivery mode also had a great advantage compared to the traditional truck delivery (TD) alone in terms of saving cost and guaranteeing the quality of service (QoS). The sensitivity analysis of the capacity offered an optimal GDV capacity setting to reduce total delivery cost.

An Optimal Tree-Structured Repair Scheme of Multiple Failure Nodes for Distributed Storage Systems

To reduce recovery cost of repairing multiple failed nodes, many repair schemes have been proposed for erasure codes based distributed storage systems. However, most of the existing researches ignore the network topology of storage devices. Motivated by such considerations, we combine delay repair schemes with network topology and propose a tree-structured model based on fountain codes with large value of (n, k, r) to improve the repair efficiency. More precisely, with the consideration of network topology, a new target named data recovery cost is defined to measure the efficiency of coded fragment download and source file reconstruction, and then the optimal recovery threshold is derived to minimize the average data recovery cost of general tree-structured model. Moreover, we analyze and compare the average data recovery cost of general tree-structure with different systematic parameters. To further improve the data transmission efficiency, an optimal tree-structured scheme based on improved tabu search algorithm (ITSAORT) is proposed. Compared with other algorithms, the ITSA-ORT scheme uses Prim algorithm to generate the initial solution and then uses special method to obtain the corresponding neighborhood structure. The experimental results show that the proposed scheme can find a globally optimal solution and obtain lower cost of data recovery. In addition, the ITSA-ORT scheme has lower computational complexity than the optimal tree-structured scheme based on particle swarm optimization algorithm (PSO-ORT) and the optimal tree-structured scheme based on firefly algorithm (FA-ORT).

Advanced Planning and Scheduling Based on the Constraint Theory and Improved Tabu Search Algorithm

Background: Planning and scheduling of manufacturing enterprises are required to be rapid and accurate, which make is easy for the traditional enterprise resource planning system to meet the needs of enterprises. Objective: This paper introduces the theory of constraints and explains the control methods for production planning and scheduling. Methods: The main characteristics of the advanced planning and scheduling system are analyzed. Then, the modeling method based on the theory of constraints is used to study the scheduling path. Results: An example is given to validate the scheduling model and realize the optimization of production tasks on the bottleneck resources. Conclusion: The feasibility of the advanced planning and scheduling methods based on the theory of constraints is proved.

Electric Vehicle Routing Problems with Stochastic Demands and Dynamic Remedial Measures

With the rapid development of e-commerce, logistic enterprises must better predict customer demand to improve distribution efficiency, so as to deliver goods in advance, which makes logistics stochastic and dynamic. In order to deal with this challenge and respond to the concept of “green logistics,” an electric vehicle routing problem with stochastic demands (EVRPSD) and proactive remedial measures is investigated, and an EVRPSD model with probability constraints is established. At the same time, a hybrid heuristic algorithm, combining a saving method and an improved Tabu search algorithm, is proposed to solve the model. Moreover, two insertion strategies with the greedy algorithm for charging stations and dynamic nodes are introduced. Finally, a large number of experimental data show that the heuristic algorithm proposed in this paper is feasible and effective.

Supply Chain Planning Problem Considering Customer Inventory Holding Cost Based on an Improved Tabu Search Algorithm

Abstract This paper studies Inventory location routing problem in supply chain distribution network planning under vendor inventory management while considering customer inventory holding cost. In order to minimize the total cost of supply chain, an optimization model is established and an improved tabu search algorithm is used to solve the problem. From the analysis, it shows that the total cost decreases as the total vehicles capacity increases, and the maximum utilization of alternative vehicles and the minimum cost of the system don’t occur at the same time in some cases.

Research on a Demand Response Interactive Scheduling Model of Home Load Groups

In order to solve the problem that the potential between electrical appliances and each power supply is not timely in the current scheduling model, which may leads to high energy consumption and poor interaction performance, the research of interactive scheduling model for demand response of home load group is proposed. Four types of household appliances are obtained from the home load group structure diagram: core electrical appliances, electrical appliances capable of providing storing energy, electrical appliances of any use time, and load aggregators. By selecting multiple indicators, a load group response potential index system is constructed to measure the household based on the impacts of electrical and power response potentials; and two load-aggregate demand response models are established by integrating demand response resources through load aggregation. Furthermore, By combining the above two models with home load aggregation, the improved tabu search algorithm is used to determine the objective function and constraints, such that an interactive scheduling model for the family load group demand response is constructed. The experimental results show that the energy consumption of this model can be low after dispatching household load group. It can reduce household electricity consumption, and have good application performance. The model can effectively complete the interactive scheduling of the demand response of the home load group. It solves the problem of electricity wasting so as to save energy. In the meantime, it improves the user's real-time and convenient experience.

An Improved Tabu Search Algorithm for the Stochastic Vehicle Routing Problem With Soft Time Windows

In real-world environments, vehicle travel and service time will be affected by unpredictable factors and present a random state. Because of this situation, this article proposes the vehicle routing problem with soft time windows and stochastic travel and service time (SVRP-STW). The probability distribution of vehicle travel and service time are introduced into the model, and a stochastic programming model with modification is established to minimize the distribution cost. An Improved Tabu Search algorithm (I-TS) based on greedy algorithm is proposed, in which adaptive tabu length and neighborhood structure are introduced; the greedy algorithm is used instead of the random methods to generate the initial solution. Experiments on different scale instances prove the effectiveness and superiority of the proposed algorithm.

Pattern of E-marketplace Customer Shopping Behavior using Tabu Search and FP-Growth Algorithm

Pattern of customer shopping behavior can be known by analyzing market cart. This analysis is performed using Association Rule Mining (ARM) method in order to improve cross-sale. The weakness of ARM is if processed data is big data, it takes more time to process the data. To optimize the ARM, we perform merging algorithm with Improved Tabu Search (TS). The application of Improved TS algorithm as optimization algorithm for preprocessing datasets, data filtering, and sorting data closely related products on sales data can optimize the ARM processing. The method of Association Rule Mining (FP-Growth) to determine frequent K-itemset, Support value and Confidence value of data which is already sorted on TS is based on patterns which often appear in the dataset so it generates rules as reference of decision making for company. To measure the level of power of rule which has been formed, the Lift Ratio value was calculated. Based on the calculation of 97 rules produced, the lift ratio produces values > 1 of 82.54% and based on processing time, it produces the fastest data search in 1.66 seconds. When compared with previous research that uses the hybrid method, for data retrieval based on processing time, it produces the fastest data search within 12.3406 seconds, 150 seconds and 50 seconds. Previous studies have only compared the processing time of data searching without regard to validation / accuracy of data search. The test results in this study obtained more optimal results than when compared with the results of previous studies, namely in time efficiency and data mining in real time and more accurate data validation. As a conclusion, the resulting rule can be used as a reference in understanding shopping behavior patterns customer on the E-Marketplace.

Pattern of E-marketplace Customer Shopping Behavior using Tabu Search and FP-Growth Algorithm

Pattern of customer shopping behavior can be known by analyzing market cart. This analysis is performed using Association Rule Mining (ARM) method in order to improve cross-sale. The weakness of ARM is if processed data is big data, it takes more time to process the data. To optimize the ARM, we perform merging algorithm with Improved Tabu Search (TS). The application of Improved TS algorithm as optimization algorithm for preprocessing datasets, data filtering, and sorting data closely related products on sales data can optimize the ARM processing. The method of Association Rule Mining (FP-Growth) to determine frequent K-itemset, Support value and Confidence value of data which is already sorted on TS is based on patterns which often appear in the dataset so it generates rules as reference of decision making for company. To measure the level of power of rule which has been formed, the Lift Ratio value was calculated. Based on the calculation of 97 rules produced, the lift ratio produces values > 1 of 82.54% and based on processing time, it produces the fastest data search in 1.66 seconds. When compared with previous research that uses the hybrid method, for data retrieval based on processing time, it produces the fastest data search within 12.3406 seconds, 150 seconds and 50 seconds. Previous studies have only compared the processing time of data searching without regard to validation / accuracy of data search. The test results in this study obtained more optimal results than when compared with the results of previous studies, namely in time efficiency and data mining in real time and more accurate data validation. As a conclusion, the resulting rule can be used as a reference in understanding shopping behavior patterns customer on the E-Marketplace.

Meta-heuristic algorithm for solving vehicle routing problems with time windows and synchronized visit constraints in prefabricated systems

Prefabricated construction has attracted research interest as it can significantly improve the energy, cost, and time efficiency of construction. However, dispatching the required prefabricated components to construction sites in a prefabricated system is challenging. To address this issue, we modeled the dispatching problem as a special type of vehicle routing problem with time windows (VRPTW) and solved it by using an improved artificial bee colony (IABC) algorithm. First, to efficiently solve the cross-synchronization problem that occurs in prefabricated systems, two problem-specific lemmas were derived. Then, a hybrid initialization strategy was developed to generate feasible and efficient solutions and a well-designed encoding repair strategy was utilized to make solutions feasible. Finally, a variable length local search strategy was embedded to enhance the exploitation ability. To verify the performance of the proposed IABC algorithm, 55 instances were generated and used for simulation tests. Three efficient algorithms, including the two-phase genetic algorithm (TPGA), improved tabu search algorithm (ITSA), and adaptive large neighborhood search (ALNS) heuristic, were selected for detailed comparisons. Our simulation results show that, considering the energy consumption metric, the proposed algorithm yields average deviations of about 0.099, 0.096, and 0.143 times the values obtained with the TPGA, ITSA, and ALNS heuristic, respectively. The simulation results confirmed that the proposed algorithm can solve the VRPTW in prefabricated systems with high efficiency.