Evolutionary approaches for scheduling a flexible manufacturing system with automated guided vehicles and robots

Anandaraman, Chandramouli; Vikram, A.; Sankar, Madurai; Natarajan, Ramaraj

doi:10.5267/j.ijiec.2012.03.004

Cited by 9 publications

(5 citation statements)

References 25 publications

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“…The algorithm is tested by using test problems proposed by various researchers and the makespan obtained by the algorithm is compared with that obtained by other researchers are analyzed. Anandaraman et al [5] presented a solution for the simultaneous scheduling problem by evolutionary approach in FMS with vehicles and robots with the objectives to minimize the makespan-mean flow time and mean tardiness. The scheduling optimization is carried out using metaheuristic algorithm.…”

Section: A Simultaneous Scheduling In Fmsmentioning

confidence: 99%

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

Rao¹,

Kumari²,

Manohar³

et al. 2020

IJEAT

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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.

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Section: A Simultaneous Scheduling In Fmsmentioning

confidence: 99%

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

Rao¹,

Kumari²,

Manohar³

et al. 2020

IJEAT

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show abstract

“…Firstly, SFHA is proposed and used by Nara, K., Takeyama, T., & Kim, H. [1]. In a research paper by Chandramouli Anandaraman, Arunvikram, Madurai Sankarand and Ramaraj Natarajan [2] one can get some basic idea about scheduling optimization using two different algorithms one among is SFHA, which has been concluded stating SFHA shows best results. By doing modification in the conventional SHFA, there are two referred articles published by G.…”

Section: Impact Factor (Jcc): 76197 Scopus Indexed Journal Naas Ratimentioning

confidence: 99%

Application of Efficient SFHA and TLBO Algorithms for Cell Formation Problems in Cellular Manufacturing Environment

Rajesh¹,

Tjprc²

2019

IJMPERD

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Cellular Manufacturing (CM) is a basic Group Technology (GT) application that has been utilized in a few true modern applications. The issue of cell arrangement is considered as the principal most critical criteria in the design of Cellular Manufacturing Systems (CMS), so as to limit lead time and setup time to amplify productivity and profitability. In this paper, an efficient Sheep Flock Heredity algorithm (SFHA) and another proficient optimization technique, called Teaching-Learning-Based Optimization (TLBO) are discussed for the advancement of cellular manufacturing issues. That simple genetic algorithm SFHA is referred as multi-level genetic operations, which can obtain good solutions. SFHA algorithm was generally based on the natural change of sheep in the flock. The procedure of Teaching Learning Based Optimization is partitioned into two sections: the initial segment comprises of 'Teacher Phase' and the second part comprises of the 'Learner Phase'. 'Teacher Phase' implies gaining knowledge from the teacher and 'Learner Phase' implies learning by the communication among learners. So in this paper, the main aim is applying SFHA & TLBO algorithms for Cell Formation problems in Cellular Manufacturing Environment. Graphical User Interface (GUI) is developed for both SFHA and TLBO algorithms to solve any size of Cell Formation problem within fraction of seconds to obtain cells with number of Exceptional Elements and Grouping Efficacy.

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“…Many AIS algorithms have been proposed to find solutions to a widespread class of complex problems. AIS has been applied to the areas such as clustering (Karaboga & Ozturk, 2011;Abdi et al, 2012;Duma et al, 2013), anomaly detection (Gonzalez & Dasgupta, 2003), network security (Forrest & Hoffmeyr, 2000), optimization (Gao & Fang, 2009), scheduling (Engin & Döyen, 2004;Anandaraman et al, 2012), etc. The immune system is a dramatic and complex set of cells, molecules and organs with the primary role of defending of the host organism by pathogens (called antigens, Ag), which brings out an immune response.…”

Section: Immune Algorithmmentioning

confidence: 99%

Integrating the sequence dependent setup time open shop problem and preventive maintenance policies

Naboureh¹,

Safari²

2016

10.5267/j.dsl

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Evolutionary approaches for scheduling a flexible manufacturing system with automated guided vehicles and robots

Cited by 9 publications

References 25 publications

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

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

Application of Efficient SFHA and TLBO Algorithms for Cell Formation Problems in Cellular Manufacturing Environment

Integrating the sequence dependent setup time open shop problem and preventive maintenance policies

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