Solving a new bi-objective joint replenishment inventory model with modified RAND and genetic algorithms

Autor:	Ommolbanin Yousefi, Seyed Jafar Sadjadi
Rok vydání:	2014
Předmět:	Mathematical optimization General Computer Science Operations research Computer science Heuristic (computer science) Inventory investment Genetic algorithm Bi objective Holding cost Minification Electrical and Electronic Engineering Joint (audio engineering)
Zdroj:	TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES. 22:1338-1353
ISSN:	1303-6203 1300-0632
DOI:	10.3906/elk-1205-22
Popis:	There are many cases in real inventory systems where more than one objective must be optimized. The main purpose of this research is to develop a multiobjective joint replenishment problem (JRP), where one objective is the minimization of the total inventory investment and another is the minimization of the total inventory ordering and holding costs. To solve the suggested model, 3 algorithms are proposed. In the rst algorithm, the existing RAND method, called the best heuristic for solving the JRP, is modied and a new heuristic algorithm is developed to be applicable to the JRP with 2 objectives. The second algorithm is a multiobjective genetic algorithm that has shown good performance for solving the JRP. Finally, a third algorithm is developed, using a combination of the 2 previous ones. The performances of these algorithms are then compared. Running the programs shows good performance in solving the 9200 randomly produced problems.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8778bc8fb287820c8f597fa43107e4b6 https://doi.org/10.3906/elk-1205-22 Zobrazit plný text záznamu Plný text ve formátu PDF