A note on productivity gains in flexible robotic cells

Geismar, H. Neil; Sethi, Suresh P.; Sidney, Jeffrey B.; Sriskandarajah, Chelliah

doi:10.1007/s10696-005-5991-7

Cited by 15 publications

(12 citation statements)

References 26 publications

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“…In these cells the operations can be performed in any order, and each machine can be configured to perform any of the operations. Geismar et al (2004c) show that for m = 3 and m = 4, the largest productivity gain that can be realized by changing the assignment of operations to machines is 14 2 7 %. It is unknown whether this upper bound holds for m ≥ 5.…”

Section: Open Problems: Recommendations For Future Studymentioning

confidence: 99%

“…There are also additive travel-time cells in which the machines are arranged in a circle so that I and O are adjacent or in the same location (Drobouchevitch, Sethi, and Sriskandarajah, to appear;Geismar et al, 2004c;Sriskandarajah et al, 2004;Sethi, Sidney, and Sriskandarajah, 2001). In these cells, the robot may travel in either direction to move from one machine to another, e.g., to move from M 1 to M m−1 , it may be faster to go via I, O, and M m , than to go via M 2 , M 3 , .…”

Section: Travel-time Metricmentioning

confidence: 99%

See 1 more Smart Citation

Sequencing and Scheduling in Robotic Cells: Recent Developments

Dawande

Geismar

Sethi

et al. 2005

J Sched

Self Cite

190

122

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A great deal of work has been done to analyze the problem of robot move sequencing and part scheduling in robotic flowshop cells. We examine the recent developments in this literature. A robotic flowshop cell consists of a number of processing stages served by one or more robots. Each stage has one or more machines that perform that stage's processing. Types of robotic cells are differentiated from one another by certain characteristics, including robot type, robot travel-time, number of robots, types of parts processed, and use of parallel machines within stages. We focus on cyclic production of parts. A cycle is specified by a repeatable sequence of robot moves designed to transfer a set of parts between the machines for their processing.We start by providing a classification scheme for robotic cell scheduling problems that is based on three characteristics: machine environment, processing restrictions, and objective function, and discuss the influence of these characteristics on the methods of analysis employed. In addition to reporting recent results on classical robotic cell scheduling problems, we include results on robotic cells with advanced features such as dual gripper robots, parallel machines, and multiple robots. Next, we examine implementation issues that have been addressed in the practice-oriented literature and detail the optimal policies to use under various combinations of conditions. We conclude by describing some important open problems in the field.

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Section: Open Problems: Recommendations For Future Studymentioning

confidence: 99%

Section: Travel-time Metricmentioning

confidence: 99%

Sequencing and Scheduling in Robotic Cells: Recent Developments

Dawande

Geismar

Sethi

et al. 2005

J Sched

Self Cite

190

122

View full text Add to dashboard Cite

show abstract

“…Considering a case study in metal cutting industries, [11] established a unified notational and modelling structure to optimize two-and three-machine flexible SFRCs. They defined a flexible SFRC as the combination of a flexible manufacturing system (FMS) with a flow shop.…”

Section: Related Researchmentioning

confidence: 99%

“…Hence, this study is restricted to one-unit permutations. It is also assumed that the empty and occupied machines of each permutation are specified in advance since this permutation must meet the steady state cyclic requirement following from [11].…”

Section: Problem Notation and Definitionsmentioning

confidence: 99%

Notes on Feasibility and Optimality Conditions of Small-Scale Multifunction Robotic Cell Scheduling Problems With Pickup Restrictions

Foumani

Gunawan

Smith‐Miles

et al. 2015

IEEE Trans. Ind. Inf.

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 Abstract-Optimization of robotic workcells is a growing concern in automated manufacturing systems. This study develops a methodology to maximize the production rate of a multi-function robot (MFR) operating within a rotationally arranged robotic cell. A MFR is able to perform additional special operations while in transit between transferring parts from adjacent processing stages. Considering the free-pick up scenario, the cycle time formulas are initially developed for small-scale cells where a MFR interacts with either two or three machines. A methodology for finding the optimality regions of all possible permutations is presented. The results are then extended to the no-wait pick up scenario in which all parts must be processed from the input hopper to the output hopper, without any interruption either on or between machines. This analysis enables insightful evaluation of the productivity improvements of MFRs in real-life robotized workcells.

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“…Dawande et al [8] studied throughput maximization in robotic cells with constant travel-time. Also Geismar et al [9] considered productivity gains in flexible robotic cells. Deineko et al [10] investigated a special mode of the two-machine flexible cell; in this study they assumed that the first machine does one activity and the second one performs k activities step by step.…”

Section: Introductionmentioning

confidence: 99%

Applying Metaheuristic Algorithms for Output Rate Analysis in Two-Machine Robotic Manufacturing Cells

Fathian

Kamalabadi

Heydari

et al. 2013

International Journal of Advanced Robotic Systems

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This article analyses the output rate in twomachine flexible robotic manufacturing cells. The flexible CNC machines in this manufacturing cell can process different operations. The manufactured parts in the cell are identical and it is assumed that different operations are required to manufacture each part. Moreover, loading/unloading time of a part by the robot ( )  , robot movement time between the machines and input and output areas ( )  , and processing time of the j th part on the machines j (t ) are considered to be fixed. The main objective of this article is to minimize cycle time in order to increase the output rate of the manufacturing cell. To achieve this goal, it is important to optimally assign operations required for manufacturing a part to each machine and to determine the optimal robot moves sequence. Accordingly, existing feasible movement policies in the cell and their cycle times have been reviewed, and then these policies have been considered in a new machine layout and their cycle times have been calculated based on the new robot moves sequence. Afterwards, a mathematical model has been presented to select optimal cycle time in the manufacturing cell and this model has been solved by a branch and bound exact algorithm; since the mathematical model is non-linear and the optimal solution cannot be obtained, two metaheuristic algorithms-genetic and simulated annealing algorithms-have also been proposed to solve the model and their results have been compared.

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A note on productivity gains in flexible robotic cells

Cited by 15 publications

References 26 publications

Sequencing and Scheduling in Robotic Cells: Recent Developments

Sequencing and Scheduling in Robotic Cells: Recent Developments

Notes on Feasibility and Optimality Conditions of Small-Scale Multifunction Robotic Cell Scheduling Problems With Pickup Restrictions

Applying Metaheuristic Algorithms for Output Rate Analysis in Two-Machine Robotic Manufacturing Cells

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