Integrated Decision Making in Production Department inside Internal Supply Chain

Watheq H. Laith^1, , Sawsan S.Abed Ali² and Mahmoud A. Mahmoud²

¹Department of statistical, College of Administration and Economic, University of Sumer, Al-Refaee, Thi-qar, Iraq

²Branch of Industrial Engineering, Department of Production Engineering and Metallurgy, University of Technology, Baghdad, Iraq

Cite this paper:
Watheq H. Laith, Sawsan S.Abed Ali and Mahmoud A. Mahmoud. Integrated Decision Making in Production Department inside Internal Supply Chain. American Journal of Industrial Engineering. 2016; 4(1):21-28. doi: 10.12691/ajie-4-1-4

Abstract

The most important drawback of existing methods used to solve the sequencing problems is the sequence must have a few products and dependent setup time for single demand. The main advantage of this new methodology, it using two methods to determine optimum products sequences with many products and muti-demands and also applied in Wasit company, that has production line produce multi-products. First, modified assignment method (MAM) depends on fundamental of TSP and using it decision maker to determine optimum products sequences step by step or demand by demand to short planning. The second method genetic algorithm (GA) depend on fundamental of (TSPPCA) and using it decision maker to determine optimum products sequences as global optimal or optimal entirely or long planning.

Keywords:
products sequencing modified assignment method genetic algorithm

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Figures

References:

[1]	Riddalls C.E., Bennett S., (2001). The Optimal Control of Batched Production and its Effect on Demand Amplification, International Journal of Production Economics Vol (72).
[2]	Cheng, T., Liu, Z. and Yu, W. (2001). Scheduling jobs with release dates and deadlines on a batch processing machine. IIE Transactions v(33).
[3]	Pinedo, M. (2002). Scheduling: Theory, Algorithms and Systems, 2nd ed, Prentice Hall, Englewood Cliffs, NJ.
[4]	Kreipl S. and Pinedo M (2004). Planning and Scheduling in Supply Chains:An Overview of Issues in Practice, Production and Operations Management Society, Spring Vol. (13).
[5]	Zhu, X. and Wilhelm, W.E. (2006). Scheduling and lot sizing with sequence-dependent setup: A literature review, IIE Transactions V(38), pp (987-1007).
[6]	Charnprasitphon A. (2007). Modeling and Analysis of the Batch Production Scheduling Problem for Perishable Products with Setup Times, PhD thesis, School of Industrial and System Engineering, Georgia Institute of Technology.
[7]	Jingxu H. (2008). Heuristic Procedures to Solve Sequencing and Scheduling Problems in Automobile Industry, Doctor thesis in Industrial Engineering./ The University of Tennessee, Knoxville.
[8]	Clark A., Morabito, R. and Toso, E. (2010). Production setup sequencing and lot-sizing at an animal nutrition plant through ATSP subtour elimination and patching. Journal of Scheduling, Vol. (13).
[9]	Salmasi, N., Logendran, R., Skandari, M. (2011). Makespan minimization of a flowshop sequence dependent group scheduling problem. Int. J. Adv. Manuf. Technol. Vol. (56).
[10]	Costa A., Fichera S. and Cappadonna, F. (2013). A Genetic Algorithm for Scheduling Both Job Families and Skilled Workforce. Int. J. Oper. Quant. Manag. V. (19).
[11]	Watheq H. L, Swsan S. A.and Mahmoud A. M. (2015). Determine The Optimal Sequence - dependent Setup Cost and / or Setup Time for Single Demand with Multiple Products Using Modified Assignment Method, Industrial Engineering Letters, Vol.5, No.8.
[12]	Watheq H. L, Swsan S. A. and Mahmoud A. M. (2015). Determine the Optimal Sequence-Dependent Completion Times for Multiple Demand with Multi-Products Using Genetic Algorithm Vol.5, No.8.