Using mixed goal programming to optimize performance of extrusion process for multiple responses of irrigation pipes

The performance of the polyethylene extrusion process used in the plastic industry was optimized using mixed goal programming. Four responses, the roll weight, production cycle time, distance between emitters, and thickness, are crucial. After a combination of initial factor settings was determined, individual and moving range control charts were established for each response. Two-phase optimization was implemented using L18 and L9 arrays for conducting experiments. A two-phase fuzzy goal programming model was formulated and employed to determine the combination of optimal factor settings, which was used to perform experiments. The results showed a significant reduction in the average production time from 13.0 to 11.316 min. Moreover, the average respective relative percentages of variability reduction for the roll weight, production cycle time, distance between emitters, and thickness were 48.547%, 49.048%, 47.174%, and 63.704%, respectively, and the corresponding estimated process capability indices for the combination of initial (optimal) factor settings were 0.202 (1.440), 0.330 (0.914), and 0.460 (1.456). Such improvement results in significant reduction in quality costs.

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Optimizing performance of oil filling processes using fuzzy goal programming

Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering ◽

10.1177/0954408917698847 ◽

2017 ◽

Vol 232 (3) ◽

pp. 380-390 ◽

Cited By ~ 2

Author(s):

Abbas Al-Refaie ◽

Yaser Abu Ghazaleh ◽

Ming-Hsien Li

Keyword(s):

Production Rate ◽

Goal Programming ◽

Control Charts ◽

Programming Model ◽

Fuzzy Goal Programming ◽

Statistical Control ◽

Quality Costs ◽

Goal Programming Model ◽

Fuzzy Goal ◽

Filling Line

This research aims at improving the performance of the filling line machine using fuzzy goal programming. Two main quality responses including the number of defective products and the production rate of the filling machine are of main interest. Initially, the control charts for number of nonconforming and production rate, np and I-MR, respectively, are established and indicate that the process is in statistical control. However, the process is found incapable. The fuzzy goal programming model is adopted to identify the combination of optimal factor settings utilizing the Taguchi’s L16 array. The optimal factor settings are 5.6 s, 5.6 s, 6.4 s, 6.0 s, 75°, 75°, 1.9 cm, 2.5 cm, 1.0 s, 0.9 s, 5.8 s, and 0.11 s for timing nozzle # 1, timing nozzle # 2, timing nozzle # 3, timing nozzle # 4, weighing valve # 1, weighing valve # 2, crimping head # 1 height, crimping head # 2 height, crimping time # 1, crimping time # 2, crimping delay, and conveyer, respectively. Confirmation experiments are conducted at optimal factor settings. Results showed reduction in the number of defective cans and significant enhancement of the oil filling process capability. In conclusions, the fuzzy goal programming model is found to be an efficient technique in supporting the process engineers of oil filling line for obtaining significant yearly savings in quality costs and considerable productivity gains.

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Optimizing the Performance of Plastic Injection Molding Using Weighted Additive Model in Goal Programming

Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization ◽

10.4018/978-1-4666-1870-1.ch015 ◽

2013 ◽

pp. 218-229

Author(s):

Abbas Al-Refaie ◽

Ming-Hsien Li

Keyword(s):

Injection Molding ◽

Goal Programming ◽

Cycle Time ◽

Programming Model ◽

Additive Model ◽

Injection Molding Process ◽

Molding Process ◽

Goal Programming Model ◽

Process Factors ◽

Short Product

Injection molding process is increasingly more significant in today’s plastic production industries because it provides high-quality product, short product cycles, and light weight. This research optimizes the performance of this process with three main quality responses: defect count, cycle time, and spoon weight, using the weighted additive goal programming model. The three quality responses and process factors are described by appropriate membership functions. The Taguchi’s orthogonal array is then utilized to provide experimental layout. A linear optimization based on the weighted additive model in goal programming model is built to minimize the deviations of the product/process targets from their corresponding imprecise fuzzy values specified by the process engineer’s preferences. The results show that the average defect count is reduced from an average of 0.75 to 0.16. Moreover, the average cycle time becomes 13.06 seconds, which is significantly smaller than that obtained at initial factor settings (= 15.10 seconds). Finally, the average spoon weight is exactly on its target value of 2.0 gm.

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Optimizing the Performance of Plastic Injection Molding Using Weighted Additive Model in Goal Programming

International Journal of Fuzzy System Applications ◽

10.4018/ijfsa.2011040104 ◽

2011 ◽

Vol 1 (2) ◽

pp. 43-54 ◽

Cited By ~ 8

Author(s):

Abbas Al-Refaie ◽

Ming-Hsien Li

Keyword(s):

Injection Molding ◽

Goal Programming ◽

Cycle Time ◽

Programming Model ◽

Additive Model ◽

Injection Molding Process ◽

Molding Process ◽

Product Cycles ◽

Goal Programming Model ◽

Process Factors

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A Goal Programming Model for Capital Budgeting Decisions

Artha Vijnana Journal of The Gokhale Institute of Politics and Economics ◽

10.21648/arthavij/1981/v23/i3-4/116609 ◽

1981 ◽

Vol 23 (3-4) ◽

pp. 299

Author(s):

Jaidev Agarwal ◽

L. S. Porwal ◽

Kanti Swarup

Keyword(s):

Goal Programming ◽

Programming Model ◽

Capital Budgeting ◽

Goal Programming Model

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A Multicriteria Goal Programming Model for Ranking Universities

Mathematics ◽

10.3390/math9050459 ◽

2021 ◽

Vol 9 (5) ◽

pp. 459

Author(s):

Fernando García ◽

Francisco Guijarro ◽

Javier Oliver

Keyword(s):

Goal Programming ◽

Programming Model ◽

Decision Makers ◽

University Rankings ◽

Central Tendency ◽

Performance Variables ◽

World University Rankings ◽

Goal Programming Model ◽

Objective Quantification

This paper proposes the use of a goal programming model for the objective ranking of universities. This methodology has been successfully used in other areas to analyze the performance of firms by focusing on two opposite approaches: (a) one favouring those performance variables that are aligned with the central tendency of the majority of the variables used in the measurement of the performance, and (b) an alternative one that favours those different, singular, or independent performance variables. Our results are compared with the ranking proposed by two popular World University Rankings, and some insightful differences are outlined. We show how some top-performing universities occupy the best positions regardless of the approach followed by the goal programming model, hence confirming their leadership. In addition, our proposal allows for an objective quantification of the importance of each variable in the performance of universities, which could be of great interest to decision-makers.

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