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dc.contributor.advisorHuy, Nguyen Hoang
dc.contributor.authorTan, Hoang Don Xuan
dc.date.accessioned2019-01-26T06:59:30Z
dc.date.available2019-01-26T06:59:30Z
dc.date.issued2017
dc.identifier.other022003963
dc.identifier.urihttp://keep.hcmiu.edu.vn:8080/handle/123456789/3078
dc.description.abstractForecasting is one of the most important steps in planning for production. With the precise forecasted demands as the inputs, the production plan can be more accurate. Furthermore, the performance of the production system depends completely on the plan for production. For that reason, battery manufacturer as GS Vietnam is not an exception. For GS Vietnam, the production type is mostly lot size production, which means that the production has to be based on the demands or order from customer so the more accurate the plan, the more efficency the result. From that requirements, the objective of this study is proposing a framework to conduct the most precise forecasting and propose a production plan which is able to satisfy those demands with the lowest cost. Time-series analysis is applied in order to perfom the forecasting. By applying this method, the trend of the market could be identified, which could support the company to determine the future demands. With the forecasting. This study also applies the fuzzy model to demonstrate the actual production plant in three different expectation values (Pessimistic, Mean, Optimistic). Software C-plex is used to solve the Probabilistic Linear Programming. The results of this study are the forecasted demands calculated by the time series analysis, which are the inputs of the fuzzy linear programming, and the aggregate planning from the result of the fuzzy linear programming model. Keywords: Regression Analysis, Demand Forecasting, Possibility Linear Programming, Aggregate Production Planning,en_US
dc.language.isoen_USen_US
dc.publisherInternational University - HCMCen_US
dc.subjectAggregate production planning; Regression analysis; Possibility Linear programmingen_US
dc.titleApplying time series analysis for demand forecasting and probabilistic linear programming to aggregate production planning for GS battery Vietnamen_US
dc.typeThesisen_US


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