Design of virtual manufacturing cells: a mathematical programming approach

2005 ◽  
Vol 21 (3) ◽  
pp. 273-288 ◽  
Author(s):  
Jannes Slomp ◽  
Boppana V. Chowdary ◽  
Nallan C. Suresh
Author(s):  
Kamran Forghani ◽  
S. M. T. Fatemi Ghomi ◽  
Reza Kia

Cell formation, scheduling, and facility layout are three main decisions in designing manufacturing cells. In this paper, we address the integration of these decisions in virtual manufacturing cells considering assembly aspects and process routing. We develop a mathematical model to determine the machine cells, the layout of machines and workstations on the shop floor, the processing route of parts, and the production sequence of operations on the machines. In this mathematical model, material handling costs and cycle time are minimized. To the best of our knowledge, this is the first paper that concurrently addresses the scheduling and layout of virtual manufacturing cells with assembly aspects and so-called criteria. To effectively solve the problem, a Population-based Simulated Annealing (PSA) combined with linear programming is proposed. The practical usability of the developed model is demonstrated in a case study. Finally, instances from the literature are solved to evaluate the performance of the PSA. The comparison results showed the superior performance of the PSA in comparison with CPLEX solver and standard simulated annealing.


2008 ◽  
Vol 75 (1) ◽  
pp. 69-89 ◽  
Author(s):  
Hiroto Saigo ◽  
Sebastian Nowozin ◽  
Tadashi Kadowaki ◽  
Taku Kudo ◽  
Koji Tsuda

2021 ◽  
Author(s):  
Sheng-Hsing Nien ◽  
Liang-Hsuan Chen

Abstract This study develops a mathematical programming approach to establish intuitionistic fuzzy regression models (IFRMs) by considering the randomness and fuzziness of intuitionistic fuzzy observations. In contrast to existing approaches, the IFRMs are established in terms of five ordinary regression models representing the components of the estimated triangular intuitionistic fuzzy response variable. The optimal parameters of the five ordinary regression models are determined by solving the proposed mathematical programming problem, which is linearized to make the resolution process efficient. Based on the concepts of randomness and fuzziness in the formulation processes, the proposed approach can improve on existing approaches’ weaknesses with establishing IFRMs, such as the limitation of symmetrical triangular membership (or non-membership) functions, the determination of parameter signs in the model, and the wide spread of the estimated responses. In addition, some numerical explanatory variables included in the intuitionistic fuzzy observations are also allowed in the proposed approach, even though it was developed for intuitionistic fuzzy observations. In contrast to existing approaches, the proposed approach is general and flexible in applications. Comparisons show that the proposed approach outperforms existing approaches in terms of similarity and distance measures.


2014 ◽  
Vol 63 ◽  
pp. 674-680 ◽  
Author(s):  
S.D.O. Turner ◽  
D.A. Romero ◽  
P.Y. Zhang ◽  
C.H. Amon ◽  
T.C.Y. Chan

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