An Enhanced Genetic Algorithm for the Generalized Traveling Salesman Problem

Jafarzadeh, Hassan; Moradinasab, Nazanin; Elyasi, Maryam

doi:10.48084/etasr.1570

Cited by 15 publications

(8 citation statements)

References 22 publications

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“…Objective function: (19) and (20) represent the minimized configuration of AGVs and picking stations; (21) represents the minimization of equipment operation cost in the unmanned warehouse; (22) indicates that the total completion time of the actual task needs to meet the demand for retrieval efficiency; (23) and (24) respectively represent the resource constraints of AGVs and picking stations; (25) is the value range of the variable.…”

Section: Equipment Optimal Configuration and Layout Modelmentioning

confidence: 99%

“…Ronghua Chen [20] proposed a self-learning genetic algorithm based on GA is proposed to solve the jobshop scheduling problem. Jafarzadeh et al [21] introduced a genetic algorithm combined with nearest-neighbor domain search, which can greatly improve the solving quality and solving time of generalized travel agent problem; Carlos E. Andrade [22] present the Multi-Parent Biased Random-Key Genetic Algorithm with Implicit Path Relinking (BRKGA-MP-IPR) with its real-world applications.…”

Section: Introductionmentioning

confidence: 99%

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Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

et al. 2021

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To improve the throughput performance of the autonomous guide vehicle (AGV) unmanned storage system, a two-stage mathematical model was established. The model also considers the equipment configuration of the AGV unmanned storage system and the AGV-picking stations dual resource coordination scheduling problem. In the equipment-task scheduling phase, the model aims at the shortest order completion time, while the equipment configuration and layout model aims at the minimum equipment configuration and operating cost. To solve the two-stage model, a two-layer genetic algorithm was designed. The inner layer algorithm was used to optimize the task scheduling order of AGVs and picking stations. The results of the inner layer algorithm are fed back to the outer model to optimize configuration of the equipment and the picking station's layout. The inner and outer loops are combined to obtain the optimal equipment configuration scheme. Through the simulation study of an enterprise AGV unmanned storage case, the optimal equipment configuration combination and picking stations layout scheme are obtained. Compared with the equipment configuration scheme based on the principle the task scheduling in operation is another key link that affects the picking efficiency of an unmanned warehouse of random task scheduling and the principle of shortest job time first; The model can improve the efficiency of warehouse retrieval and minimize the number of equipment configurations. Finally, the improved genetic algorithm is used to solve the model, and the performance is compared with that of LINGO to verify the effectiveness of the improved algorithm.

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Section: Equipment Optimal Configuration and Layout Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

et al. 2021

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“…They are based on the principles of genetics and natural selection, with the central idea being the selection of the fittest individuals in a population, who then recombine with others or mutate into new forms to generate new groups [41]. GAs are particularly applied to complex optimization problems, which are challenges that have different parameters or characteristics that need to be combined in search of the best solution and, at the same time, cannot be represented mathematically [42,43]. Figure 1 illustrates the flowchart of the GA algorithm, from the initial population to when a suitable result is obtained.…”

Section: A Genetic Algorithmsmentioning

confidence: 99%

A Review of Optimization Algorithms for University Timetable Scheduling

Alghamdi

Alsubait

Alhakami

et al. 2020

Eng. Technol. Appl. Sci. Res.

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The university course timetabling problem looks for the best schedule, to satisfy given criteria as a set of given resources, which may contain lecturers, groups of students, classrooms, or laboratories. Developing a timetable is a fundamental requirement for the healthy functioning of all educational and administrative parts of an academic institution. However, factors such as the availability of hours, the number of subjects, and the allocation of teachers make the timetable problem very complex. This study intends to review several optimization algorithms that could be applied as possible solutions for the university student course timetable problem. The reviewed algorithms take into account the demands of institutional constraints for course timetable management.

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“…A new genetic firefly hybrid algorithm based on the neural network was designed in [21] for finding the best position in the data cube. A new algorithm was designed in [22] for finding the best path in all the clusters based on the generalized travelling salesman problem. This algorithm was based on the minimum cost tour within a clustered set of cities.…”

Section: Optimization Using Genetic Algorithmmentioning

confidence: 99%

Genetic Algorithm based Optimization of Uniform Circular Array

Kumar

Dhull

2020

Eng. Technol. Appl. Sci. Res.

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Signal estimation at the antenna is a major challenge of the antenna array structure because the received signals have different directions. Therefore, in this paper, a Genetic Algorithm (GA) is applied to the uniform circular array for the optimization of array structure in regard to its geometry. On the optimized array structure, four different algorithms (Estimation of Signal Parameter via Rotational Invariance Technique – ESPRIT, First Order Forward Prediction - FOFP, Beamscan, and Multiple Signal Classification - MUSIC) have been implemented in order to estimate the signal direction accurately with quick estimation time. The accuracy has been calculated with Root Mean Square Error (RMSE) indices. From the experimental analysis, it has been found that the performance of the ESPRIT algorithm is better than the others in terms of accuracy and estimation time.

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An Enhanced Genetic Algorithm for the Generalized Traveling Salesman Problem

Cited by 15 publications

References 22 publications

Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

A Review of Optimization Algorithms for University Timetable Scheduling

Genetic Algorithm based Optimization of Uniform Circular Array

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