Dragan Simić

50 years of fuzzy set theory and models for supplier assessment and selection: A literature review

Abstract Supplier assessment and selection mapping as an essential component of supply chain management are usually multi-criteria decision-making problems. Decision making is the thought process of selecting a logical choice from the available options. This is generally made under fuzzy environment. Fuzzy decision-making is a decision process using the sets whose boundaries are not sharply defined. The aim of this paper is to show how fuzzy set theory, fuzzy decision-making and hybrid solutions based on fuzzy can be used in the various models for supplier assessment and selection in a 50 year period.

A Survey of Hybrid Artificial Intelligence Algorithms for Dynamic Vehicle Routing Problem

In a Dynamic Vehicle Routing Problem (DVRP) new customer orders and changes of existing orders continually arrive and thus disrupt the optimal routing plan. This paper presents a survey of some of the recent hybrid artificial intelligence algorithms suitable for efficient optimization and re-optimization of different DVRPs. An artificial ant colony 2-OPT hybrid algorithm, a hybrid neighborhood search algorithm, and a hybrid heuristic algorithm are explained in detail. Particular interest is focused towards local improvement heuristic algorithms, such as 2-OPT algorithm and OR’s algorithm, which are regularly used in hybrid approaches for intra-route and inter-route improvements.

Hybrid patient classification system in nursing logistics activities

The history of patient classification in nursing dates back to the period of Florence Nightingale. The first and the foremost condition for providing quality nursing care, which is measured by care standards, and determined by number of hours of actual care, is the appropriate number of nurses. Patient classification criteria are discussed in this paper. Hybrid classification model based on learning vector quantization (LVQ) networks and self-organising maps (SOM) are purposed. It is possible to discus three types of experimental results. First result could be assessment of Braden scale and Mors scale by LVQ. Second result, the time for nursing logistics activities. The third is possibility to predict appropriate number of nurses for providing quality nursing care. This research was conducted on patients from Institute of Neurology, Clinical Centre of Vojvodina.

The spatial pheromone signal for ant colony optimisation

The effect of the passive insecticide on the ant colony Monomorius pharaonis is localised with minor losses -- only one ant. The information on the insecticide location is transferred through the colony in all directions with great speed. After deserting the basic trail, a rapid consolidation of the new ant colony is probably established by the spatial pheromone signal. A simulation model for the time calculation and the number of ants necessary for the formation of the shortest way between the nest and the fictive food source was formed. The basic ant performances have a prevailing part in the shortest trail formation and those are: the range of the radius pheromone signal and the intensity of the pheromone trail evaporation.

An approach of soft computing applications in clinical neurology

This paper briefly introduces various soft computing techniques and presents miscellaneous applications in clinical neurology domain. The aim is to present the large possibilities of applying soft computing to neurology related problems. Recently published data about use of soft computing in neurology are observed from the literature, surveyed and reviewed. This study detects which methodology or methodologies of soft computing are frequently used together to solve the specific problems of medicine. Recent developments in medicine show that diagnostic expert systems can help physicians make a definitive diagnosis. Automated diagnostic systems are important applications of pattern recognition, aiming at assisting physicians in making diagnostics decisions. Soft computing models have been researched and implemented in neurology for a very long time. This paper presents applications of soft computing models of the cutting edge researches in neurology domain.