Hari Prasetyo

Survey on applications of biased-random key genetic algorithms for solving optimization problems

This paper presents a survey on studies devising biased-random key genetic algorithms (BRKGAs), a novel variant of the ordinary genetic algorithms (GAs) introduced in 2000s, for solving numerous optimization problems up to 2015. The aim is to provide a comprehensive picture of the development of the algorithms and the areas of implementation in literature yet. From the survey, a number of findings include: (1) number of studies tends to increase over the last five years dealing with various combinatorial optimization problems, however limited research deals with continuous variables, (2) local search procedure is the typical hybridization method used to enhance the performance of the algorithms, hence others improvement methods are still potential avenues for future research.

An inventory model of production-inventory policy for food products considering quality loss in raw materials

The majority research on inventory model for deteriorating items in multi-echelon system assumes that a physical depletion of some inventories occurred over time. It means that the inventory is depleted not only due to the demand but also due to some deteriorated items. In food products, however, during a certain period, its quantity remains the same while its quality does degrade over time. Therefore, this study is aimed at developing a more representative inventory model for mimicking food deterioration characteristics. A mathematical model is established to represent the integrated inventory policy of procurement-production-delivery in which the total profit of the whole system is the objective function. The length of the production cycle, the frequency of raw material procurement and finished goods deliveries over the production cycle are the controlling variables. The numerical test indicates that the proposed model yields to a better profit compared to the benchmark model.