NDLI: Optimum production planning model under probabilistic market demand

Content Provider	IEEE Xplore Digital Library
Author	Tian-Syung Lan Chih-Yao Lo Jian-Lun Deng
Copyright Year	2007
Description	Author affiliation: Yu-Da Coll. of Bus., Miao-li (Tian-Syung Lan; Chih-Yao Lo; Jian-Lun Deng)
Abstract	An Optimum Production Planning (OPP ) Model for a robot-served machine in the make-to-order (MTO) industry is proposed to minimize the production cost under deterministic order quantity and deadline constraints. The operational cost of the machine and the part handling robot, as well as the fixed costs for both equipments and the product holding cost are considered simultaneously into the objective of the model. This study not only implements the Lagrange Method to resolve the production planning problem, but also provides a verified cost-related property of the Lagrange Multiplier for budget and/or cost forecastingunder the deterministic market. Through the forecasted future demand, the step-by-step algorithm to reach the optimal production plan for the probabilistic market is then constructed. In addition, the versatility and adaptability of this study are exemplified through numerical simulation. This paper surely contributes the applicable solution to control a robot-served machine under certain market, as well as to plan the productivity of the machine and the robot in the forecasted future..
Starting Page	1153
Ending Page	1157
File Size	448865
Page Count	5
File Format	PDF
ISBN	9781424415281
DOI	10.1109/IEEM.2007.4419373
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-02
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Productivity Robot control Demand forecasting Machinery production industries probabilistic demand Lagrangian functions production plan Service robots make-to-order Production planning Economic forecasting Cost function Numerical simulation Lagrange Method
Content Type	Text
Resource Type	Article

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