Browsing by Author "Korkmaz, Aslihan Gizem"

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Citation - WoS: 8
Citation - Scopus: 5
Operational Variable Job Scheduling With Eligibility Constraints: a Randomized Constraint-Graph Approach
(Vilnius Gediminas Tech Univ, 2009) Eliiyi Türsel, Deniz; Korkmaz, Aslihan Gizem; Cicek, Abdullah Ercuement
In this study, we consider the problem of Operational Variable Job Scheduling, also referred to as parallel machine scheduling with time windows. The problem is a more general version of the Fixed Job Scheduling problem, involving a Lime window for each job larger than its processing time. The objective is to find the optimal subset of the jobs that can be processed. An interesting application area ties in Optimal Berth Allocation, which involves the assignment of vessels arriving at the port to appropriate berths within their time windows, while maximizing the total profit from the served vessels. Eligibility constraints are also taken into consideration. We develop an integer programming model for the problem. We show that the problem is NP-hard, and develop a constraint-graph-based construction algorithm for generating near-optimal solutions. We use genetic algorithm and other improvement algorithms to enhance the solution. Computational experimentation reveals that our algorithm generates very high quality solutions in very small computation times.
Optimal Berth Allocation With Variable Job Scheduling
(Vilnius Gediminas Technical Univ Press, Technika, 2008) Eliiyi Türsel, Deniz; Korkmaz, Aslihan Gizem; Cicek, Abdullah Ercuement
In this study, the problem of Optimal Berth Allocation, which involves the assignment of ships arriving at the port to the appropriate berths within their time windows, while maximizing the total number of ships served is discussed. The problem is treated as a Variable Job Scheduling Problem and an integer programming model is developed. To contribute to the practical use of the study, the eligibility constraint is taken into consideration. Proving that the model is NP-hard, genetic algorithm is used to approximate the optimal solution. The formulated model is implemented and a dataset is generated. Using this dataset an experiment is conducted by changing the parameters of the genetic algorithm model. The initial run results of the tests are provided.