Lot-Sizing in a Foundry Using Genetic Algorithm and Repair Functions

2005 ◽  
pp. 101-111 ◽  
Author(s):  
Jerzy Duda
Keyword(s):  
Genetic Algorithm ◽  
Lot Sizing

scholarly journals Population Based Metaheuristic Algorithm Approach for Analysis of Multi-Item Multi-Period Procurement Lot Sizing Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Prasanna Kumar ◽  
Mervin Herbert ◽  
Srikanth Rao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Inventory Management ◽  
Lot Sizing ◽  
Population Based ◽  
Optimization Approach ◽  
Time Data ◽  
Significant Difference ◽  
Lot Sizing Problem ◽  
Order Quantities

This research study focuses on the optimization of multi-item multi-period procurement lot sizing problem for inventory management. Mathematical model is developed which considers different practical constraints like storage space and budget. The aim is to find optimum order quantities of the product so that total cost of inventory is minimized. The NP-hard mathematical model is solved by adopting a novel ant colony optimization approach. Due to lack of benchmark method specified in the literature to assess the performance of the above approach, another metaheuristic based program of genetic algorithm is also employed to solve the problem. The parameters of genetic algorithm model are calibrated using Taguchi method of experiments. The performance of both algorithms is compared using ANOVA analysis with the real time data collected from a valve manufacturing company. It is verified that two methods have not shown any significant difference as far as objective function value is considered. But genetic algorithm is far better than the ACO method when compared on the basis of CPU execution time.

A Genetic Algorithm for Solving Single Level Lot–Sizing Problems

2012 ◽  
Author(s):  
Nasaruddin Zenon ◽  
Ab. Rahman Ahmad ◽  
Rosmah Ali
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Programming ◽  
Key Words ◽  
Local Minimum ◽  
Global Minimum ◽  
Lot Sizing ◽  
Initial Population ◽  
Single Level ◽  
Lot Sizing Problem ◽  
The Cost

Masalah pensaizan lot satu aras timbul apabila suatu syarikat pengeluar ingin menjanakan perancangan pengeluaran terperinci bagi produk berpandukan suatu perancangan agregat. Walaupun masalah ini telah dikaji dengan meluas, hanya pendekatan pengaturcaraan dinamik dapat menjamin penyelesaian yang minimum secara global. Maka heuristik-heuristik stokastik yang mampu melepasi minimum tempatan adalah diperlukan. Kajian ini mencadangkan kaedah algoritma genetik untuk menyelesaikan masalah-masalah pensaizan lot satu aras, serta membincangkan beberapa contoh aplikasi kaedah tersebut. Dalam pelaksanaan kaedah ini, heuristik penjanaan populasi pensaizan lot yang dapat menjanakan populasi awal digunakan untuk menyediakan kromosom. Kromosom ini digunakan sebagai input untuk algoritma genetik dengan operator-operator yang khusus bagi masalah pensaizan lot. Gabungan heuristik penjanaan populasi dengan algoritma genetik menghasilkan penumpuan yang lebih pantas dalam proses mendapatkan skim pensaizan lot yang optimum disebabkan oleh ketersauran populasi awal yang digunakan. Kata kunci: ALgorithm Genetik; Pensaizan lot The single level lot-sizing problem arises whenever a manufacturing company wishes to translate an aggregate plan for production of an end item into a detailed planning of its production. Although the cost driven problem is widely studied in the literature, only laborious dynamic programming approaches are known to guarantee global minimum. Thus, stochastically-based heuristics that have the mechanism to escape from local minimum are needed. In this paper a genetic algorithm for solving single level lot-sizing problems is proposed and the results of applying the algorithm to example problems are discussed. In our implementation, a lot-sizing population-generating heuristic is used to feed chromosomes to a genetic algorithm with operators specially designed for lot-sizing problems. The combination of the population-generating heuristic with genetic algorithm results in a faster convergence in finding the optimal lot-sizing scheme due to the guaranteed feasibility of the initial population. Key words: Genetic Algorithm; Lot-sizing

An Object-Oriented Framework for Rapid Genetic Algorithm Development

2007 ◽  
pp. 608-624 ◽  
Author(s):  
A. L. Medaglia
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Lot Sizing ◽  
Rapid Development ◽  
Object Oriented ◽  
Lot Sizing Problem ◽  
Fast Development ◽  
Complex Optimization

JGA, the acronym for Java Genetic Algorithm, is a computational object-oriented framework for rapid development of evolutionary algorithms for solving complex optimization problems. This chapter describes the JGA framework and illustrates its use on the dynamic inventory lot-sizing problem. Using this problem as benchmark, JGA is compared against three other tools, namely, GAlib, an open C++ implementation; GADS, a commercial MatlabÒ toolbox; and PROC GA, a commercial (yet experimental) SASÒ procedure. JGA has proved to be a flexible and extensible object-oriented framework for the fast development of single (and multi-objective) genetic algorithms by providing a collection of ready-to-use modules (Java classes) that comprise the nucleus of any genetic algorithm. Furthermore, JGA has also been designed to be embedded in larger applications that solve complex business problems.

Sign in / Sign up

Export Citation Format

Share Document