Browsing by Author "Tasoglu, G"

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    Simulated annealing based simulation optimization method for solving integrated berth allocation and quay crane scheduling problems
    Tasoglu, G; Yildiz, G
    This paper proposes a simulation optimization based solution approach for the integrated Berth Allocation and Quay Crane Scheduling Problems (BAP and QCSP) considering simultaneously, for the first time, multi-quay hybrid berth layout, dynamic arrivals of vessels, stochastic handling times and non-crossing constraints of quay cranes. Initially, a Conflict-Free Quay Crane Scheduling Algorithm is proposed considering stochastic handling times. Then, a general parametric simulation model which represents the seaside operations of a typical container terminal is built. Finally, a Simulated Annealing based optimization procedure is integrated with this parametric simulation model to minimize the latest vessel departure time (i.e., makespan). The proposed simulation optimization procedure is applied to a real world inspired case problem. The results revealed that the proposed simulation optimization procedure can be used to solve large-sized real-port BAPs and QCSPs for the objective of minimizing makespan. Furthermore, the output of this simulation optimization procedure gives the decision maker the estimated berthing/unberthing times, berthing locations and quay crane schedules for each vessel for the most probable scenario.
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    A simulation-based genetic algorithm approach for the simultaneous consideration of reverse logistics network design and disassembly line balancing with sequencing
    Tasoglu, G; Ilgin, MA
    Reverse logistics (RL) network design and disassembly line balancing (DLB) decisions are generally considered separately. In addition, the effect of disassembly sequencing on DLB is ignored. However, companies can take better-informed decisions by simultaneously considering RL, DLB and disassembly sequencing issues. This integrated approach results in more efficient processes which reduce cost and idle time, while maximizing throughput and customer satisfaction. In this study, we propose a simulation-based genetic algorithm approach for the joint optimization of RL network design and DLB with sequencing decisions. The use of simulation modeling allows for the consideration of stochastic aspects associated with RL and DLB such as transportation and disassembly times. A numerical example was provided to present the applicability of the proposed approach. Moreover, a sensitivity analysis was carried out to study the impact of various parameters. The results indicate the superior performance of the proposed approach with respect to total cost.
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    Part transformation-based spare parts inventory control model for the high-tech industries
    Güçdemir, H; Tasoglu, G
    Timely and cost-effective supply of spare parts is the main purpose of spare parts inventory management and substitution is an effective way to fulfill demand on time. However, direct substitution of spare parts is not suitable for the high-tech industries due to the ever-changing nature of the product structures. Hence, parts should be transformed to be used as substitutes. This paper provides a novel spare parts inventory control model for the high-tech industries. In the proposed model, part transformation-based substitution is considered and the near-optimal values of spare part inventory levels (s, S) that minimize total cost are determined by using a simulated annealing based simulation optimization approach. Computational analyses are performed for a hypothetical inventory system by considering transformation and no-transformation cases. The results reveal that transformation is very useful for the companies who endure long production lead times and high penalty costs associated with backorders.(c) 2024 by the authors; licensee Growing Science, Canada