Title

A multi-level multi-item lot-sizing model in a material requirements planning framework under stochastic demand

Date of Publication

1993

Document Type

Dissertation

Degree Name

Doctor of Business Administration

Subject Categories

Mathematics

College

Ramon V. Del Rosario College of Business

Department/Unit

Management and Organization Department

Thesis Adviser

Antonio Concepcion

Defense Panel Chair

Errol Perez

Defense Panel Member

Purba Rao
Osman Luis Ranit
Dolores De Leon

Abstract/Summary

Inventory management poses as one of the critical decision areas in a manufacturing environment. The importance of inventory in determining the firm's capability to service its market emphasizes this point. This research focuses on the determination of a lot-size order which will minimize total relevant inventory costs. It presents a mathematical model for determining optimal lot-sizes in a multi-level multi-item Material Requirements Planning (MRP) system subject to stochastic demand over a finite planning horizon.A local semiconductor firm provided demand and cost data related to one of their product lines to test the performance of the derived model. The data were simulated using two computer programs. The MRP Table Generator was written in Turbo Pascal and was used to generate standard MRP outputs. These outputs were inputted into the MILP88 software to compute for the optimal lot-size order using a deterministic integer linear programming framework. The simulation results indicated a substantial decrease in relevant inventory cost. Thus, it is concluded that the derived model provides better inventory ordering policy and presents an opportunity for beneficially employing it in actual manufacturing setting.

Abstract Format

html

Language

English

Format

Print

Accession Number

TG02103

Shelf Location

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

Physical Description

480 leaves, 28 cm. ; Computer print-out

Keywords

Stochastic processes; Materials management; Mathematical models; Mathematical optimization

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