Nilsen Kundakci

Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem

We developed efficient hybrid genetic algorithms for dynamic job shop scheduling.A new KK heuristic is proposed and it is combined with genetic algorithm.The problem includes new job arrival, machine breakdown and changes in processing time.In conclusion, proposed methodologies generate outstanding solutions. Job shop scheduling has been the focus of a substantial amount of research over the last decade and most of these approaches are formulated and designed to address the static job shop scheduling problem. Dynamic events such as random job arrivals, machine breakdowns and changes in processing time, which are inevitable occurrences in production environment, are ignored in static job shop scheduling problem. As dynamic job shop scheduling problem is known NP-hard combinatorial optimization, this paper introduces efficient hybrid Genetic Algorithm (GA) methodologies for minimizing makespan in this kind of problem. Various benchmark problems including the number of jobs, the number of machines, and different dynamic events are generated and detailed numerical experiments are carried out to evaluate the performance of proposed methodologies. The numerical results indicate that the proposed methods produce superior solutions for well-known benchmark problems compared to those reported in the literature.

Combination of DEMATEL and ANP for the cargo shipping company selection problem

Cargo shipping is a vital part of logistic functions but selecting the right cargo shipping company is a difficult task for companies. This selection can be handled with the help of multi criteria decision making (MCDM) methods. This paper attempts to solve the cargo shipping company selection problem by adopting MCDM model that combines Decision Making Trial and Evaluation Laboratory (DEMATEL) and analytic network process (ANP). DEMATEL is employed to compute the effects between selection criteria, and ANP is used to determine the best cargo shipping company considering all alternatives.

Selection of vineyard location for a winery with fuzzy AHP and consistent fuzzy linguistic preference relations

Nowadays making decisions quickly and selecting the best alternative are very important for companies to gain competitive advantage in a complex environment. In this manner vineyard location selection problem of a winery is considered in this paper. Because this selection affects the quality of wine as the grape quality affects the wine quality directly. Selection of vineyard location is a multi-criteria decision problem and the conventional methods for selection problems are inadequate for dealing with the imprecise or vague nature of linguistic assessments. To overcome this difficulty, fuzzy multi-criteria decision making (MCDM) methods are proposed. This paper attempts to solve the vineyard location selection problem by adopting two methods: fuzzy analytic hierarchy process (fuzzy AHP) and consistent fuzzy linguistic preference relations. The proposed methods have been applied to a vineyard location selection problem of a winery in Denizli, Turkey. After determining the criteria and alternatives that affect the vineyard location decisions, these methods are used to solve the problem and results are presented. The similarities and differences of two methods are also discussed.