Pelin Alcan

A genetic algorithm application using fuzzy processing times in non-identical parallel machine scheduling problem

There are many scheduling problems which are NP-hard in the literature. Several heuristics and dispatching rules are proposed to solve such hard combinatorial optimization problems. Genetic algorithms (GA) have shown great advantages in solving the combinatorial optimization problems in view of its characteristic that has high efficiency and that is fit for practical application [1]. Two different scale numerical examples demonstrate the genetic algorithm proposed is efficient and fit for larger scale identical parallel machine scheduling problem for minimizing the makespan. But, even though it is a common problem in the industry, only a small number of studies deal with non-identical parallel machines. In this article, a kind of genetic algorithm based on machine code for minimizing the processing times in non-identical machine scheduling problem is presented. Also triangular fuzzy processing times are used in order to adapt the GA to non-identical parallel machine scheduling problem in the paper. Fuzzy systems are excellent tools for representing heuristic, commonsense rules. That is why we try to use fuzzy systems in this study.

A distribution network optimization problem for third party logistics service providers

Third party logistics service providers (3PLs) have an important role in supply chain management. Increasing cooperation with 3PLs is expanding in todays business environment. Hence, 3PLs need to have an efficient distribution network to meet customer demands. Nevertheless, few researches have tried to propose a solution for distribution network problems of 3PLs. The optimization problem which is discussing in our study is solved in two stages. At the first stage, the assignment problem which includes assigning the order of the vehicles is solved with mixed integer programming by using GAMS 21.6/CPLEX. The output of the first stage is used as an input in the second stage. In this stage routes are determined for vehicles by developing a genetic algorithm by using C#.

Just-In Time Production System Using Fuzzy Logic Approach and a Simulation Application

While firms are operating in a global competitive environment, they are subjected to changes because of the increased competitiveness and developed technologies. Therefore, this transformation process forces to produce with just-in-time production and low cost products or services and leads to customer satisfaction. Until today, competitive conditions, efficiency, productivity and quality of production, forced the firms to put more emphasis on production systems. Therefore, the firms are more interested in scientific analysis, planning and controlling of their production systems. As a result, one of the newest approaches is Just in Time (JIT) production system which emerged after WWII in Japan and aims to decrease the inventory cost and maximize the quality. The philosophy of this approach is to produce the necessary amount of production, when and where needed at the required quality. But JIT production system is weak in unclear species. For this purpose, the general and necessary solution is using fuzzy logic. In this paper we discussed about the simulation of an assembly line with 3 steps; firstly using Kanban production method, secondly non-using Kanban production method, and lastly using Kanban production method with fuzzy times. And also the comparisons of these steps will also be studied in this paper.