scholarly journals Simultaneous Scheduling of Machines and AGVs in FMS Through Ant Colony Optimization Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 1392-1397
Keyword(s):  
Ant Colony Optimization ◽  
Manufacturing Systems ◽  
Material Handling ◽  
Manufacturing System ◽  
Response Times ◽  
Life Cycles ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Ant Colony Optimization Algorithm ◽  
Simultaneous Scheduling

High amount of flexibility and quick response times have become essential features of modern manufacturing systems where customers are demanding a variety of products with reduced product life cycles. Flexible manufacturing system (FMS) is the right choice to achieve these challenging tasks. The performance of FMS is dependent on the selection of scheduling policy of the manufacturing system. In Traditional scheduling problems machines are as considered alone. But material handling equipment’s are also valuable resources in FMS. The scheduling of AGVs is needed to be optimized and harmonized with machine operations. Scheduling in FMS is a well-known NP-hard problem due to considerations of material handling and machine scheduling. Many researchers addressed machine and AGVs individually. In this work an attempt is made to schedule both the machines and AGVs simultaneously. For solving these problems-a new metaheuristic Ant Colony Optimization (ACO) algorithm is proposed.

scholarly journals Simultaneous Scheduling of Machines and AGVs in FMS Through Hybrid JAYA Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 1982-1988
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Material Handling ◽  
Manufacturing System ◽  
Response Times ◽  
Life Cycles ◽  
Jaya Algorithm ◽  
Scheduling Problems ◽  
The Right ◽  
Simultaneous Scheduling

High amount of flexibility and quick response times have become essential features of modern manufacturing systems where customers are demanding a variety of products with reduced product life cycles. Flexible manufacturing system (FMS) is the right choice to achieve these challenging tasks. The performance of FMS is dependent on the selection of scheduling policy of the manufacturing system. In Traditional scheduling problems machines are as considered alone. But material handling equipment’s are also valuable resources in FMS. The scheduling of AGVs is needed to be optimized and harmonized with machine operations. Scheduling in FMS is a well-known NP-hard problem due to considerations of material handling and machine scheduling. Many researchers addressed machine and AGVs individually. In this work an attempt is made to schedule both the machines and AGVs simultaneously. For solving these problems- a new hybrid metaheuristic JAYA algorithm (HJAYA) is proposed.

scholarly journals Simultaneous Scheduling of Machines and AGVs in FMS through Simulated Annealing Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 2235-2240
Keyword(s):  
Simulated Annealing ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Material Handling ◽  
Manufacturing System ◽  
Simulated Annealing Algorithm ◽  
Response Times ◽  
Life Cycles ◽  
Scheduling Problems ◽  
Simultaneous Scheduling

High amount of flexibility and quick response times have become essential features of modern manufacturing systems where customers are demanding a variety of products with reduced product life cycles. Flexible manufacturing system (FMS) is the right choice to achieve these challenging tasks. The performance of FMS is dependent on the selection of scheduling policy of the manufacturing system. In Traditional scheduling problems machines are as considered alone. But material handling equipment’s are also valuable resources in FMS. The scheduling of AGVs is needed to be optimized and harmonized with machine operations. Scheduling in FMS is a well-known NP-hard problem due to considerations of material handling and machine scheduling. Many researchers addressed machine and AGVs individually. In this work an attempt is made to schedule both the machines and AGVs simultaneously. For solving these problems- a new metaheuristic Simulated Annealing (SA) algorithm is proposed.

Penerapan Algoritma ACO untuk Penjadwalan Kuliah Pengganti pada Perguruan Tinggi (Studi Kasus: Program Studi Informatika, Universitas Multimedia Nusantara)

2019 ◽  
Vol 9 (2) ◽  
pp. 79-85
Author(s):  
Indah Noviasari ◽  
Andre Rusli ◽  
Seng Hansun
Keyword(s):  
Ant Colony Optimization ◽  
Educational Institution ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Perceived Ease Of Use ◽  
Ant Colony Optimization Algorithm ◽  
Scheduling System ◽  
Course Scheduling

Students and scheduling are both essential parts in a higher educational institution. However, after schedules are arranged and students has agreed to them, there are some occasions that can occur beyond the control of the university or lecturer which require the courses to be cancelled and arranged for replacement course schedules. At Universitas Multimedia Nusantara, an agreement between lecturers and students manually every time to establish a replacement course. The agreement consists of a replacement date and time that will be registered to the division of BAAK UMN which then enter the new schedule to the system. In this study, Ant Colony Optimization algorithm is implemented for scheduling replacement courses to make it easier and less time consuming. The Ant Colony Optimization (ACO) algorithm is chosen because it is proven to be effective when implemented to many scheduling problems. Result shows that ACO could enhance the scheduling system in Universitas Multimedia Nusantara, which specifically tested on the Department of Informatics replacement course scheduling system. Furthermore, the newly built system has also been tested by several lecturers of Informatics UMN with a good level of perceived usefulness and perceived ease of use. Keywords—scheduling system, replacement course, Universitas Multimedia Nusantara, Ant Colony Optimization

Job Shop-Scheduling Problems- Single Process

2010 ◽  
Vol 44-47 ◽  
pp. 330-334
Author(s):  
Ramezan Ali Mahdavinejad
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Ant Colony ◽  
Dispatching Rules ◽  
Scheduling Problems ◽  
Ant Colony Optimization Algorithm ◽  
Jobshop Scheduling ◽  
Single Process

In this paper, single-processors jobshop scheduling problems are solved by a heuristic algorithm based on the hybrid of priority dispatching rules according to an ant colony optimization algorithm. The objective function is to minimize the makespan, i.e. total completion time, in which a simultanous presence of various kinds of ferons is allowed. The process of finding the best solution will be improved by using the suitable hybrid of priority dispatching rules. Ant colony optimization algorithm, not only promote the ability of this proposed algorithm, but also decreases the total working time because of decreasing in setup times and modifying the working production line. By solving some problems as samples (i.e. Fisher's & Thomson's problems), this algorithm is compared with the others. The results show that when the size of the problem becomes lorger, the deviation from lower limit increases, but its rate decreases with the size of the problems, so that it reaches to its limit.

scholarly journals Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

2016 ◽  
Vol 3 (2) ◽  
pp. 149-158 ◽  
Author(s):  
Imam Ahmad Ashari ◽  
Much Aziz Muslim ◽  
Alamsyah Alamsyah
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Ant Colony Optimization Algorithm ◽  
Course Scheduling ◽  
The University

Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization algorithm in solving the case of course scheduling.

An integrated ant colony optimization algorithm to solve job allocating and tool scheduling problem

2016 ◽  
Vol 232 (1) ◽  
pp. 172-182 ◽  
Author(s):  
Xu Zhang ◽  
Shilong Wang ◽  
Lili Yi ◽  
Hong Xue ◽  
Songsong Yang ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Ant Colony ◽  
Scheduling Problem ◽  
Ant System ◽  
Ant Colony Optimization Algorithm ◽  
Application Range

In this article, max–min ant colony optimization algorithm is proposed to determine how to allocate jobs and schedule tools with the objective of minimizing the makespan of processing plans in flexible manufacturing system. To expand the application range of max–min ant colony optimization algorithm, tool movement policy is selected as the running mode of flexible manufacturing system, which assumes that tools are shared among work centers and each operation is allowed to be machined by different kinds of tools. In the process of converting this scheduling problem into traveling salesman problem, disjunctive graph is modified to possess more than one path between each neighbor node. Besides providing practical methods of initializing pheromone, selecting node and calculating pheromone increment, max–min ant colony optimization algorithm employs the pheromone updating rule in max–min ant system to limit pheromone amount in a range, of which the upper and lower boundaries are updated after each iteration by formulations involving the current optimal makespan, the average number of optional tools and parameters. Finally, different sizes of processing plans are randomly generated, through which max–min ant colony optimization algorithm is proved effectively to tackle early stagnation and local convergence and thus obtains better solution than ant colony optimization algorithm and bidirectional convergence ant colony optimization algorithm.

Sign in / Sign up

Export Citation Format

Share Document