Open AccessAnalysis of Aggregate Production Planning Problem with Goal Programming Model
Author(s) -
P. K. A. Tyas,
Toni Bakhtiar,
Bib Paruhum Silalahi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1863/1/012005
Subject(s) - aggregate planning , production planning , production (economics) , operations research , weighting , profit (economics) , computer science , aggregate (composite) , goal programming , constraint (computer aided design) , operations management , mathematical optimization , engineering , economics , mathematics , microeconomics , medicine , mechanical engineering , materials science , composite material , radiology
Aggregate production planning is medium range planning (3-18 months) that determines production quantities, production time management, and resources management. In the real world, companies that faced this problem usually want to optimize some objective functions that conflict each other. Therefore, the purposes of this study are to reconstruct goal programming model and to solve multiobjective aggregate production planning problem. Also, to obtain numerical solutions of production planning scenarios and weights of goal programming objective function. The model is followed by the constraint of raw material capacity, workforce level, storage space, and production capacity. The proposed planning period is six months with four objective functions to maximize profit, minimize inventory cost, minimize subcontract cost, and minimize workforce changes cost. A set of hypothetical data is used to test the validity of the proposed model with several different scenarios. The result for each scenario has been fulfilled the company’s requirement and objectives. It described through monthly quantity and mechanism of the production planning. For in-depth analysis, the effect of different weighting on target’s achievement is evaluated with 24 combinations of case.