Genetic Tabu Search Algorithm Research Articles

A fuzzy interval optimization approach for p-hub median problem under uncertain information.

Stochastic and robust optimization approaches often result in sub-optimal solutions for the uncertain p-hub median problem when continuous design parameters are discretized to form different environmental scenarios. To solve this problem, this paper proposes a triangular fuzzy number model for the Non-Strict Uncapacitated Multi-Allocation p-hub Median Problem. To enhance the quality and the speed of optimization, a novel optimization approach, combining the triangular fuzzy number evaluation index with the Genetic-Tabu Search algorithm, is proposed. During the iterations of the Genetic-Tabu Search algorithm for finding the optimal solution, the fitness of fuzzy hub schemes is calculated by considering the relative positional relationships of triangular fuzzy number membership functions. This approach directly addresses the triangular fuzzy number model and ensures the integrity of information in the p-hub problem as much as possible. It is verified by the classic Civil Aeronautics Board and several self-constructed data sets. The results indicate that, compared to the traditional Genetic Algorithm and Tabu Search algorithm, the Genetic-Tabu Search algorithm reduces average computation time by 49.05% and 40.93%, respectively. Compared to traditional random, robust, and real-number-based optimization approaches, the proposed optimization approach reduces the total cost in uncertain environments by 1.47%, 2.80%, and 8.85%, respectively.

Multi-echelon sustainable reverse logistics network design with incentive mechanism for eco-packages

With the pollution caused by express packaging waste, eco-packages are being commonly used to promote a green supply chain. However, extant research has yet to address the efficacious reverse logistics network design problem while considering both economic and environmental consumption in the recycling process. In this study, a multi-echelon reverse logistics network is designed to collect eco-packages from customers, instead of door-to-door recycling by couriers. This study aims to solve the capacity location-routing problem in a reverse logistics network integrated with the incentive mechanism for customers, which is regarded as NP-hard. To avoid the combinatorial explosion, a multi-objective hybrid genetic algorithm-tabu search (MOHGATS) is developed to reduce search space and improve search speed by changing search strategies. Computational experiments are conducted to verify the proposed model and algorithm based on several test instances and a real-world case study. The impacts of the quantity of eco-packages, the incentive costs for customers, and the quantity and capacity of recycling stations on the reverse logistics network are explored from both economic and environmental perspectives, providing a reference for practical operation and promotion of eco-packages on a large scale.

A hybrid genetic tabu search algorithm for distributed flexible job shop scheduling problems

The distributed flexible job-shop scheduling problem (DFJSP) is an extension of the flexible job shop scheduling problem, which is a famous NP-complete combinatorial optimization problem. DFJSP consists of three sub-problems, assigning jobs to appropriate factories, scheduling operations to suitable machines, and determining the operation sequence on machines. Due to the complexity of DFJSP, there is only a little research on the problem. There are several benchmark sets proposed to test the performance of methods for solving DFJSP. However, many instances remain unresolved after a long time. This paper proposes a hybrid genetic tabu search algorithm (HGTSA) for DFJSP. The proposed HGTSA combines the global search ability of the genetic algorithm (GA) and the local search ability of the tabu search (TS) well. Two genetic operators are designed based on the critical factory to discrete the population. A new neighborhood structure is introduced into TS to search for more space of neighborhood solutions. To evaluate the performance of HGTSA, this paper compares it with four state-of-the-art algorithms on 69 benchmark instances. The experimental results demonstrate that HGTSA outperforms these comparison algorithms in terms of solution quality and computation efficiency. In particular, HGTSA has found 13 newly upper bounds of these benchmark instances.

Retracted: Population Economic Transfer Function Model Based on Genetic Tabu Search Algorithm

Retracted: Population Economic Transfer Function Model Based on Genetic Tabu Search Algorithm

Single-machine scheduling with periodic maintenance and learning effect

This paper discusses a single-machine scheduling problem with periodic maintenance activities and position-based learning effect to minimize the makespan. To obtain exact solutions of small-scale problems, one new two-stage binary integer programming model is formulated. In addition, a branch and bound algorithm combining boundary method and pruning rules is also proposed. According to the property of the optimal solution, a special search neighborhood is constructed. A hybrid genetic-tabu search algorithm based on genetic mechanism with tabu technique as an operator is proposed to solve medium-scale and large-scale problems. Moreover, to improve the efficiency of genetic algorithm and hybrid genetic-tabu search algorithm, Taguchi method is used for parameter tuning. Furthermore, computational experiments are carried out to compare the efficiency and performance of these algorithms.

Population Economic Transfer Function Model Based on Genetic Tabu Search Algorithm

Aiming at the problem that the evaluation effect of the traditional analysis model of population economic transfer is not good, a population economic transfer function model based on the genetic tabu search algorithm is proposed. Firstly, the defects of the genetic algorithm are analyzed, and the tabu search algorithm is integrated into the genetic tabu search algorithm to obtain the genetic tabu search algorithm, which avoids the problems of premature convergence and low search efficiency. Secondly, the population migration algorithm is designed. On this basis, the design process of the population economic transfer function model is determined. Finally, the effectiveness of the model is verified by comparative experiments. The experimental results show that when the population area is 50 km2 and 500 km2, the evaluation accuracy of the proposed method is 99% and 97%, respectively, which can avoid falling into a local maximum advantage to a certain extent.

Integrated Production and Transportation Scheduling Method in Hybrid Flow Shop

The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, and both of those problems are summarized as NP-hard problems. However, only a few studies have considered them simultaneously. This paper solves the integrated production and transportation scheduling problem (IPTSP) in hybrid flow shops, which is an extension of the hybrid flow shop scheduling problem (HFSP). In addition to the production scheduling on machines, the transportation scheduling process on automated guided vehicles (AGVs) is considered as another optimization process. In this problem, the transfer tasks of jobs are performed by a certain number of AGVs. To solve it, we make some preparation (including the establishment of task pool, the new solution representation and the new solution evaluation), which can ensure that satisfactory solutions can be found efficiently while appropriately reducing the scale of search space. Then, an effective genetic tabu search algorithm is used to minimize the makespan. Finally, two groups of instances are designed and three types of experiments are conducted to evaluate the performance of the proposed method. The results show that the proposed method is effective to solve the integrated production and transportation scheduling problem.

A combined GA-TS algorithm for two-echelon dynamic vehicle routing with proactive satellite stations

The make-to-stock strategy can improve the operational efficiency of urban freight delivery, especially in a dynamic transportation system with idle storage resources. This study proposes a two-echelon dynamic vehicle routing problem with proactive satellite stations (2E-DVRP-PSSs), which converts customers with available idle storage to satellite stations and optimizes the operating cost and make-to-stock cost. In 2E-DVRP-PSSs, heavy trucks depart from the depot, serve original static customers and proactive satellite stations (PSSs), and then return to the depot in the first-echelon network. In the second-echelon network, light vehicles depart from the PSSs to serve the dynamic customer demands. To compute the delivery routes efficiently, a hybrid algorithm integrating the cutting plane and improved genetic algorithm-tabu search (GA-TS) algorithm is proposed and implemented. An exact method using Gurobi solver and an improved GA-TS algorithm with multiply optimization strategies are also incorporated. Furthermore, the effectiveness of the 2E-DVRP-PSS formulation and the applicability of the hybrid algorithm for various instances are experimentally evaluated. An empirical case study of a two-echelon dynamic network in Dalian, China indicates that the proposed make-to-stock strategy with the PSS network can reduce costs and improve the efficiency of operations in urban transportation networks.

Robust facility layout design for flexible manufacturing: a doe-based heuristic

Flexible manufacturing systems (FMS) should be able to respond to changing manufacturing requirements and environments. From the layout point of view, FMS need to be rearranged to fit the new requirements. However, rearranging the layout is often undesirable due to its unpredicted high costs and production disruption. This paper proposes a practical approach to mitigate the effects and repercussions of changing environments and avoid rearranging the layout. A robust layout approach is presented, where changes in product demand and mix are absorbed by altering product routes and not rearranging the layout. In this approach, the problem is decomposed into two sub-problems: sub-problem 1 (SP1) where a robust layout is constructed, and sub-problem 2 (SP2) to obtain the best routes of products. To solve SP1, design of experiments is used to find a critical period, which is the period most affected under demand changes. Then, the layout for the critical period is determined using a hybridized genetic-tabu search algorithm. Then SP2 is solved by a branch and cut algorithm to obtain the optimal routes of the products in each period. The performance of the proposed methodology is illustrated using a case study and is benchmarked against rival ones from the literature.

Flexible job-shop scheduling problem with unrelated parallel machines and resources-dependent processing times: a tabu search algorithm

ABSTRACT Flexible job shop scheduling problem is one of the most important topics in production management and is one of the most complex topics in combinatorial optimization. This problem is a generalization of job shop and parallel machines scheduling problem. Since the efficient allocation of resources can improve the performance of manufacturing, here, to reduce the processing time of jobs, additional resources are assigned to machines. In fact, in this paper, the effect of flexible resources in the flexible job shop scheduling problem with unrelated parallel machines and sequence-dependent setup time is investigated. Also, by presenting a mixed-integer linear programming model, an attempt has been made to minimize the costs of makespan, total weighted tardiness, delivery time and inventory. After solving this model by the GAMS, due to the NP-hardness of the problem, a tabu search (TS) algorithm is utilized for large-size instances. Finally, the obtained results are compared with the genetic algorithm (GA). To verify the statistical validity of the computational experiments and confirm which the best algorithm between the TS algorithm and GA is, a Kruskal–Wallis test is used. The results show that the TS algorithm is better than the GA.

A solution to the university course timetabling problem using a hybrid method based on genetic algorithms

In this study, we address the current issues that usually manifest during the programming of university courses, classified as University Course Timetabling Problem, which is considered as a NP-hard problem due to the high computational demand that it requires.To solve the problem, a Mixed Integer Linear Programming model is proposed, which serves as a reference when dimensioning the problem and the restrictions that must be considered. Next, a hybrid metaheuristic method is designed based on the HGATS algorithm, Hybrid Genetic Algorithm Tabu Search Approach, developed by [16], which combines the diversification capacity of the Genetic Algorithm with the strategy of intensification of the Tabu Search Algorithm. Finally, the validation of the proposed algorithm is performed using the data from the programming of the classes from the academic periods 2018-1 and 2018-2 for the academic program of Industrial Engineering at the Industrial University of Santander, obtaining interesting solutions in a reasonable computational time, being that the process of organizing the schedule by the coordinator can last from hours to days, depending on your ability.

Hybrid genetic–tabu search algorithm to optimize the route for capacitated vehicle routing problem with time window

Optimization of transportation and distribution costs is one of the important issues in the supply chain management area. It is caused by their large contribution to the logistics costs that can reach up to 40%. Thus, choosing the right route is one of the efforts that can be done to resolve the issue. This study aims to optimize the capacitated vehicle routing problem with time windows (CVRPTW) for mineral water company distributor with pick-up and delivery problem. To achieve the aim, this study used hybrid algorithm, Genetic Algorithm (GA) and Tabu Search Algorithm (TS). The selection of this hybrid algorithm is due to its capability in minimizing travel distance. The result of this study shows that not only the algorithm has successfully reduced the existing route but also predicted the optimum number of homogenous fleet. By running the algorithm, this study concludes that the number of the optimum routes for this study can be reduced for up to 15.99% than the existing route.

A hybrid genetic Tabu search algorithm for minimising total completion time in a flexible job-shop scheduling problem

The flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop problem in which an operation may be executed by any machine out of a set of candidate machines. This paper addresses the FJSP under partial and total flexibility with the objective of minimising the total completion time. The FJSP is one of the most complex problems. Thus, exact methods are not effective for solving this problem and heuristic approaches are generally used to find near optimal solutions within a reasonable computation time. We develop a hybrid approach combining genetic algorithms and the Tabu search metaheuristic. The resolution approach is based on a joint resolution of the inherent assignment and sequencing subproblems. To evaluate the performance of the proposed algorithms, several benchmark instances of FJSP are used. The experimental results prove the effectiveness and efficiency of the proposed hybridisation. [Received 28 May 2019; Revised 2 July 2019; Revised 2 November 2019; Accepted 11 January 2020]

Investigating the effect of carbon tax and carbon quota policy to achieve low carbon logistics operations

Developing a low-carbon economy and reducing carbon dioxide emission have become a consensus for both academics and practitioners. However, the existing literature did not pay enough attention in interrogating the impacts of Carbon Tax (CT) and Carbon Quota (CQ) policy on distribution costs and carbon dioxide emission in the field of vehicle routing problem. Moreover, the investigated subsidies factor is also incomplete. This research stands on the position of the company to study the impact of CT and CQ policy on aforementioned two aspects. A mathematical model is developed to achieve the best low carbon vehicle routing under the optimal policy. The optimization goal of this research is to minimize the total cost that includes vehicle-using, transportation, CT, CQ, and raw material subsidy costs. An improved optimization algorithm, namely Genetic Algorithm-Tabu Search (GA-TS), is proposed to solve a given business case. In the simulation experiments, GA-TS and a traditional GA are compared and the results show the advantage of GA-TS on reducing the total cost and carbon dioxide emission. Furthermore, the experiments also explore the total cost and carbon dioxide emission under three scenarios (Benchmark, CT and CQ), incorporating four policies: CT, Carbon Tax Subsidy (CTS), CQ, and Carbon Quota Subsidy (CQS). It is concluded that CQS is the ideal policy to minimize distribution cost and carbon dioxide emission. In addition, the impact of vehicles’ capacities on the total cost and carbon dioxide emission is also analyzed in this research. This research also aimed at assisting practitioners in better formulating delivery routes, as well as policy makers in developing carbon policies. Finally, the limitations and the future research directions of this research are also discussed.

Development of a responsive optimisation framework for decision-making in precision agriculture

Emerging digital technologies and data advances (e.g. smart machinery, remote sensing) not only enable Agriculture 4.0 to envisage interconnected agro-ecosystems and precision agriculture but also demand responsive decision-making. This study presents a mathematical optimisation model to bring real-time data and information to precision decision-support and to optimise short-term farming operation. To achieve responsive decision-support, we proposed two meta-heuristic algorithms i.e. a tailored genetic algorithm and a hybrid genetic-tabu search algorithm for solving the deterministic optimisation. The developed responsive optimisation framework has been applied to a hypothetical case study to optimise sugarcane harvesting in the KwaZulu Natal region in South Africa. In comparison with the optimal solutions derived from the exact algorithm, the proposed meta-heuristic methods lead to near optimal solutions (less than 5% from optimality) and significantly reduced computational time by over 95%. Our results suggest that the tailored genetic algorithm enables rapid solution searching but the solution quality on sugarcane harvesting cannot compete with the exact method. The hybrid genetic-tabu search algorithm achieved a good trade-off between computational time reduction and solution optimality, demonstrating the potential to enhance responsive decision making in precision sugarcane farming. Our research highlights the development of the responsive optimisation framework combining mixed integer linear programming and hybrid meta-heuristic search algorithms and its applications in real-time decision-making under Agriculture 4.0 vision.

Network on Chip Low-power Mapping Method based on Tabu Search Genetic Algorithm

In order to effectively map more and more complex application tasks to the network on chip processing unit to complete the related tasks with less energy consumption, a new low power mapping method with a combination of genetic algorithm and tabu search algorithm is proposed. The method makes full use of the powerful global search ability of the genetic algorithm, and combines the local search ability of the tabu search and the prominent mountain turning features to compensate for the weak local search ability and premature defects of the genetic algorithm, so as to achieve better low power consumption effect on NoC. The experimental results show that under the same experimental platform and power consumption model, the new algorithm has a great energy efficiency improvement compared with the early genetic algorithm, and has energy efficiency advantages compared with the later improved MGA and AGA algorithms.

Application of a Multi-Satellite Dynamic Mission Scheduling Model Based on Mission Priority in Emergency Response.

Emergency observations are missions executed by Earth observation satellites to support urgent ground operations. Emergency observations become more important for meeting the requirements of highly dynamic and highly time-sensitive observation missions, such as disaster monitoring and early warning. Considering the complex scheduling problem of Earth observation satellites under emergency conditions, a multi-satellite dynamic mission scheduling model based on mission priority is proposed in this paper. A calculation model of mission priority is designed for emergency missions based on seven impact factors. In the satellite mission scheduling, the resource constraints of scheduling are analyzed in detail, and the optimization objective function is built to maximize the observation mission priority and mission revenues, and minimize the waiting time for missions that require urgency for execution time. Then, the hybrid genetic tabu search algorithm is used to obtain the initial satellite scheduling plan. In case of the dynamic arrival of new emergency missions before scheduling plan releases, a dynamic scheduling algorithm based on mission priority is proposed to solve the scheduling problem caused by newly arrived missions and to obtain the scheduling plan of newly arrived missions. A simulation experiment was conducted for different numbers of initial missions and newly arrived missions, and the scheduling results were evaluated with a model performance evaluation function. The results show that the execution probability of high-priority missions increased because the mission priority was taken into account in the model. In the case of more satellite resources, when new missions dynamically arrived, the satellite resources can be reasonably allocated to these missions based on the mission priority. Overall, this approach reduces the complexity of the dynamic adjustment and maintains the stability of the initial scheduling plan.

Multi-Objective Optimization Control of Distributed Electric Drive Vehicles Based on Optimal Torque Distribution

To improve the total efficiency of the drive system and the driving safety of distributed electric drive vehicles, this paper proposes a multi-objective optimization method based on torque allocation optimization. First, in the vehicle nonlinear dynamics model, the response surface method is used to perform regression analysis on the test data of the drive motor to obtain the drive motor efficiency function. Second, based on the demand torque value of the distributed electric drive system, the objective functions that characterize the optimization of the drive system efficiency and the optimization of the vehicle driving safety are established. Moreover, the linear weighting method with adaptive weight coefficients is used to transform the solution of the above two objective functions into a multi-objective optimization problem under constraint conditions. Furthermore, the second-generation nondominated sorting genetic algorithm (NSGA-II) and the hybrid genetic Tabu search algorithm (HGTSA) are used to solve the above multi-objective optimization problem to obtain the optimal torque distribution of the distributed electric drive system. Finally, the NEDC operating conditions were selected to verify NSGA-II, the HGTSA and the commonly used average distribution method. The simulation test results show that NSGA-II and the HGTSA can improve the driving efficiency and vehicle driving safety of distributed electric drive systems relative to the average distribution method. In particular, the optimization effect of the HGTSA is more prominent, and stability is more quickly achieved.

Route optimization model in collaborative logistics network for mixed transportation problem considered cost discount based on GATS

A mixed transportation problem with centralized and decentralized transportation is proposed in collaborative logistics network. All tasks can be centralized to transport for once, twice or transported directly to destination node. A route optimization programming model to minimize the total costs is proposed in consideration of cost discount, road section capacity, overload penalty, advance delivery cost and hard time window. The hybrid genetic tabu search algorithm is presented to solve the model. To test the feasibility and validity of proposed model and hybrid algorithm, a simulation example is presented and solved. The optimal solution of experiment shows that the hybrid algorithm can obtain better solution, better convergence speed and high calculation efficiency.

Development, application, and comparison of hybrid meta-heuristics for urban land-use allocation optimization: Tabu search, genetic, GRASP, and simulated annealing algorithms

Land-use optimization problem (LUOP) that seeks to allocate different land types to land units involves various dimensions and deals with numerous conflicting objectives and a large set of data and variables. Single meta-heuristics are broadly developed and applied for solving LUOP. Despite the acceptable solutions derived from these algorithms, researchers in both academic and practical areas face the interesting question: can we develop an algorithm with better efficiency and solution quality? In the literature of operation research, hybridization, a combination of meta-heuristics, was introduced as a way of generating better algorithms. Therefore, this paper aims at developing novel algorithms through hybridizing Tabu search (TS), genetic algorithm (GA), GRASP, and simulated annealing (SA) and examining their quality and efficiency in practice. Accordingly, low-level teamwork GRASP–GA–TS (LLTGRGATS), high-level relay Greedy–GA–TS, and high-level teamwork SA were developed. Firstly, these algorithms were applied for solving small- and large-size single-row facility layout problem to evaluate their performance and functionality and to select the satisfactory algorithm in comparison with the other developed hybrids. Secondly, the selected algorithm, LLTGRGATS, and SVNS, a recent hybrid algorithm proposed for solving LUOP, were performed on a study area to solve a LUOP with two constraints and seven nonlinear objective functions. The outputs showed that the quality and efficiency of LLTGRGATS were slightly better than those of SVNS and it can be considered as a favorable tool for land-use planning process.