Optimal lot sizing in a multi-product MRP framework with stochastic demand and quality considerations

Date of Publication

1997

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Industrial Engineering

Subject Categories

Engineering

College

Gokongwei College of Engineering

Department/Unit

Industrial and Systems Engineering

Abstract/Summary

This paper tackles Materials Requirements Planning (MRP), a tool which is fast gaining acceptance in the manufacturing industry. Much of the costs and variables used in MRP are deterministic, which should be known beforehand. This is not always the case in the real world. This is the main reason why variables and costs should not be considered as constant. Thus the proponents have considered stochastic demand as input to the MRP model. Quality is also a part of the study as it is important to take into account the defects that are inherent in a manufacturing environment. Aside from this, a multi-product MRP structure will be considered. The inclusion of the three factors aforementioned will reflect a more realistic approach. All these would be formulated into a mathematical model to determine the optimal size orders that would minimize the total cost of manufacturing and ordering. Different literatures within the scope of the study were reviewed. Among the numerous literatures, Acebedo (1994), Porteus (1986), and Steinber, Napier (1980) were the three main literatures used as the backbone of the study. An integer linear programming approach was used to solve for the optimal values of the decision variables. The MILP88 linear programming software was utilized to obtain the results. A hypothetical company was used to test the validity of the model. The validity of the model was proven by the use of comparative analysis between the said model and an ordinary MRP model.

Abstract Format

html

Language

English

Format

Print

Accession Number

TU07804

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

63 numb. leaves ; Computer print-out.

Keywords

Stochastic processes; System analysis; Production planning; Mathematical models; Industrial engineering

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