Machine loading in flexible manufacturing systems considering routeing flexibility

Rajamani, Divakar; Adil, Gajendra Kumar

doi:10.1007/bf01845696

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…have not been included in the proposed algorithm, which makes it non-realistic for practical purposes. Some other analytical approaches have also been formulated by researchers (Nasir and Elsayad 1990;Ghosh and Gaimon 1992;Sodhi et al 1994;Dolinska and Besant 1995;Jinyan et al 1995;Rajamani and Adil 1996;Das and Nagendra 1997;Guerrero et al 1999;Chan 1999;Saygin and Kilic 1999).…”

Section: Scheduling Techniques In Fmsmentioning

confidence: 96%

A review of some issues and identification of some barriers in the implementation of FMS

Raj

Shankar

Suhaib

2007

Int J Flex Manuf Syst

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Global competition, advancements in technology and ever changing customers' demand have made the manufacturing companies to realize the importance of flexible manufacturing systems (FMS). These organizations are looking at FMS as a viable alternative to enhance their competitive edge. But, implementation of this universally accepted and challenging technology is not an easy task. A large number of articles have been reviewed and it is found that the existing literature lacks in providing a clear picture about the implementation of FMS. In this paper, work of various researchers has been studied and it is found that it is really a very difficult task for any organization to transform into FMS on the basis of existing research results. A wide gap exists between the proposed approaches/algorithms for the design of different components of FMS and the real-life complexities. Besides describing the gap in various issues related to FMS, some barriers, which inhibit the adaptation and implementation of

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Section: Scheduling Techniques In Fmsmentioning

confidence: 96%

A review of some issues and identification of some barriers in the implementation of FMS

Raj

Shankar

Suhaib

2007

Int J Flex Manuf Syst

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“…Table 1 is review of literature on mathematical modelling of loading problem of FMS. Stecke (1983b) Grouping and loading Need to decrease computational time Ammons et al (1985) General loading problem for discrete optimization Heuristics improves computational efficiency & effectiveness Berrada & Stecke (1986) Minimize Stecke (1983a) Maximize throughput and machine utilizations Future need to develop efficient heuristic algorithms for more real life solution Stecke (1986) Optimal allocation ratios Developed queueing network model where information is suppressed Greene & Sadowski (1986) Minimize make span, flow time and lateness Identified variables and constraints necessary to solve real world program Sarin & Chen (1987) Minimize machining cost Lagrangian relaxation is proposed Ventura et al (1988) Minimize make-span Heuristic algorithms are proposed Henery et al (1990) Balancing of workload and maximize flexibility Mathematical solution was found impractical Rajamani & Adil (1996) Routeing flexibility Routing flexibility is required for rigid loading schedules Nayak & Acharya (1998) Minimize number of batches heuristic has been proposed Ozdamarl & Barbarosoglu (1999) Minimize the holding cost GA-SA hybrid heuristics were developed Lee & Kim (2000) Minimize maximum workload Better performance with partial grouping than total grouping, solved by heuristics Kumar & Shanker (2000) Genetic algorithms for constrained optimization GA shows near-optimum performance and need of modern heuristic techniques Kumar & Shanker (2001) Balancing of workloads Results are in agreement with previous findings Yang & Wu (2002) Balancing of workloads Tested for small size test problems only Gamila & Motavalli (2003) Minimize total processing time Used computer generated data for validation Tadeusz (2004) Minimize inter-station transfer time Very high computational effort is required for realistic problems Chan et al (2004) Minimize system unbalance and maximize throughput Validated only for small set of test problems Require further extension of research Chen & Ho (2005) Minimize flow time & tool cost and workload unbalancing multi-objective genetic algorithm (GA) is proposed Bilgin & Azizoglu (2006) Optimization of total processing time near-optimal solution in reasonable time Nagarjuna et al (2006) Minimize system u...…”

Section: The Literature Reviewmentioning

confidence: 99%

Meta-hierarchical-heuristic-mathematical- model of loading problems in flexible manufacturing system for development of an intelligent approach

Singh¹,

Khan²

2016

10.5267/j.ijiec

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Flexible manufacturing system (FMS) promises a wide range of manufacturing benefits in terms of flexibility and productivity. These benefits are targeted by efficient production planning. Part type selection, machine grouping, deciding production ratio, resource allocation and machine loading are five identified production planning problems. Machine loading is the most identified complex problem solved with aid of computers. System up gradation and newer technology adoption are the primary needs of efficient FMS generating new scopes of research in the field. The literature review is carried and the critical analysis is being executed in the present work. This paper presents the outcomes of the mathematical modelling techniques for loading of machines in FMS's. It was also analysed that the mathematical modelling is necessary for accurate and reliable analysis for practical applications. However, excessive computations need to be avoided and heuristics have to be used for real-world problems. This paper presents the heuristics-mathematical modelling of loading problem with machine processing time as primary input. The aim of the present work is to solve a real-world machine loading problem with an objective of balancing the workload of the FMS with decreased computational time. A Matlab code is developed for the solution and the results are found most accurate and reliable as presented in the paper.

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“…It has been used to solve a variety of combinatorial optimization problems, eg. the vehicle routing problem by (Bullnheimer et al 1999) the traveling salesman problem by (Dorigo et al 1997) and the industrial layout problems studied by (Hami et al 2007;Corry andKozan 2004 andRajamani andAdil 1996). Abbospour et al (2001) proposed the ACO-IM (Inverse Modeling) method for estimating soil hydraulic parameters.…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

Structural System Identification in the Time Domain using Evolutionary and Behaviorally Inspired Algorithms and their Hybrids

2009

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In this study, parametric identification of structural properties such as stiffness and damping is carried out using acceleration responses in the time domain. The process consists of minimizing the difference between the experimentally measured and theoretically predicted acceleration responses. The unknown parameters of certain numerical models, viz., a ten degree of freedom lumped mass system, a nine member truss and a non-uniform simply supported beam are thus identified. Evolutionary and behaviorally inspired optimization algorithms are used for minimization operations. The performance of their hybrid combinations is also investigated. Genetic Algorithm (GA) is a well known evolutionary algorithm used in system identification. Recently Particle Swarm Optimization (PSO), a behaviorally inspired algorithm, has emerged as a strong contender to GA in speed and accuracy. The discrete Ant Colony Optimization (ACO) method is yet another behaviorally inspired method studied here. The performance (speed and accuracy) of each algorithm alone and in their hybrid combinations such as GA with PSO, ACO with PSO and ACO with GA are extensively investigated using the numerical examples with effects of noise added for realism. The GA+PSO hybrid algorithm was found to give the best performance in speed and accuracy compared to all others. The next best in performance was pure PSO followed by pure GA. ACO performed poorly in all the cases.

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Machine loading in flexible manufacturing systems considering routeing flexibility

Cited by 6 publications

References 11 publications

A review of some issues and identification of some barriers in the implementation of FMS

A review of some issues and identification of some barriers in the implementation of FMS

Meta-hierarchical-heuristic-mathematical- model of loading problems in flexible manufacturing system for development of an intelligent approach

Structural System Identification in the Time Domain using Evolutionary and Behaviorally Inspired Algorithms and their Hybrids

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