Scheduling methods applied to flowshop

production systems

José Fragozo

Dept. Of Industrial Engineering, Universidad Del Norte

Barranquilla, COLOMBIA

Jmfragozo@uninorte.edu.co

Abstract- Nowadays technology increases in an

exponential rate that is only overcome by our

ambition of keep growing, there are some

multinational companies that manage more money

than some little countries, geographic borders are

being replaced with economic borders, the whole

planet is globalized markets, to be in the

competition companies need to plan, control and

program the operations including obviously

production that is the vertebral column of the

supply chain, logical algorithms are good tools

when a scheduling is necessary but are not the

bests, there are too many ways to set up a

productive system and logical algorithms are

optimal for only specific setups.

I. INTRODUCTION

Flowshop scheduling is one of the most well-known

scheduling problems. Since Johnson´s work, that

create and algorithm that has his name and works to

minimize the makespan and is optimal for 2 machines

setup, various scheduling criteria have been

considered, the some of the most well-known are

makespan, maximum tardiness, maximum flowtime

and total flowtime. Some researchers extended single-

objective flowshop scheduling problems to

multiobjective problems. Logical algorithms only

take in account one objective, and in a lot of simple

situations works perfectly, however in industrial

world the majority of situations are not as simple as

that, in this paper we are going to review the paper “A

Weight-Based Multiobjective Genetic Algorithm for

Flowshop Scheduling” published by Zhimin Fang

where develop as the name says a Multiobjective

Genetic Algorithm for Flowshop Scheduling

(WBMOGA).

II. AWEIGHT-BASED MULTIOBJECTIVE

GENETIC ALGORITHM FOR

FLOWSHOP SCHEDULING

Big companies that manage thousands of references,

hundreds of clients and hundreds of vendors with

different features can deal with more than one

problem at the same time, multiobjetive algorithms,

this heuristic algorithm in particular start searching

for all the possible solutions, let’s review the example

that appear on the paper, there is flowshop problem

with 20 jobs and 10 machines, the information is on

the next table.

Table1.

The algorithm in this case will stop once evaluate

48600 possible solutions, algorithm start comparing

pairs of solutions, and start trying with the all possible

combinations, if a solution dominate the other is

better, and is saving the best options, once it finish

only the 10 better options are the potential solutions

and the it choose the best one.

III. CONCLUSIONS

Scheduling methods needs to be applied according to

the situation´s complexity, classic methods are good

backgrounds when a scheduling its required however

are not optimal in all scenarios, even can give us

wrong solutions in some cases, scheduling is a

powerful tool to ordinate works in a company in order

to raise the objective, or like in this case the

objectives with this multiobjetives algorithm,

heuristic algorithms require tools like powerful

computers that maybe will be a significant investment

but the results compensates the investment, that is

sure, a scheduled company is a competitive company

that has a perfect equilibrium with the vendors, the

clients and itself.

IV. ACKNOWLEDGMENTS

This paper was supported by “Universidad Del

Norte”, Ing. Daniel Romero and Ing. Carlos Paternina

that provides us with the knowledge in classic

scheduling methods and always emphasized us to

investigate in the new methods.

V. REFERENCES

[1] Zhimin Fang;, "A Weight-Based Multiobjective Genetic Algorithm for

Flowshop Scheduling," Artificial Intelligence and Computational

Intelligence, 2009. AICI '09. International Conference on , vol.1, no.,

pp.373-377, 7-8 Nov. 2009

doi: 10.1109/AICI.2009.130