scholarly journals A GENERAL GOAL PROGRAMMING MODEL FOR SOLVING AGGREGATE PRODUCTION PLANNING PROBLEMS WITH IMPRECISE GOALS

2008 ◽  
Vol 4 (4) ◽  
pp. 1-10
Author(s):  
MEZGHANI M. ◽  
LOUKIL T. ◽  
DAMMAK A.
Keyword(s):  
Production Planning ◽  
Goal Programming ◽  
Programming Model ◽  
Aggregate Production Planning ◽  
Aggregate Production ◽  
Goal Programming Model ◽  
General Goal ◽  
Planning Problems

scholarly journals Hybrid Uncertainty-Goal Programming Model with Scaled Index for Production Planning Assessment

FinTech ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 1-24
Author(s):  
Junzo Watada ◽  
Nureize Binti Arbaiy ◽  
Qiuhong Chen
Keyword(s):  
Production Planning ◽  
Goal Programming ◽  
Optimization Problems ◽  
Programming Model ◽  
Business Goals ◽  
Target Values ◽  
Goal Programming Model ◽  
Quantitative Results ◽  
Hybrid Uncertainty ◽  
Fuzzy Random

Goal programming (GP) can be thought of as an extension or generalization of linear programming to handle multiple, normally conflicting objective measures. Each of these measures is given a goal or target value to be achieved. Unwanted deviations from this set of target values are then minimized in an achievement function. Production planning is an important process that aims to leverage the resources available in industry to achieve one or more business goals. However, the production planning that typically uses mathematical models has its own challenges where parameter models are sometimes difficult to find easily and accurately. Data collected with various data collection methods and human experts’ judgments are often prone to uncertainties that can affect the information presented by quantitative results. This study focuses on resolving data uncertainties as well as multi-objective optimization using fuzzy random methods and GP in production planning problems. GP was enhanced with fuzzy random features. Scalable approaches and maximum minimum operators were then used to solve multi-object optimization problems. Scaled indices were also introduced to resolve fuzzy symbols containing unspecified relationships. The application results indicate that the proposed approach can mitigate the characteristics of uncertainty in the analysis and achieve a satisfactory optimized solution.

Sign in / Sign up

Export Citation Format

Share Document