Browsing by Author "Cakir, Burcin"

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A Hybrid Simulated Annealing For A Multi-Objective Stochastic Assembly Line Balancing Problem
(2008) Cakir, Burcin; Dengiz, Berna; Altiparmak, Fulya; Xia, GP; Deng, XQ; 0000-0003-1730-4214; AAF-7020-2021
Asssembly line balancing is the problem of assigning tasks to the workstations, while optimizing one or more objectives without violating restrictions imposed on the line. In practice, task times may be random due to the worker fatigue, low skill levels, job dissatisfaction, poorly maintained equipment, defects in raw material, etc. When stochastic task times are taken consideration in assembly lines, balancing procedure is more complex due to the probability of incompleteness of stations times in a given cycle time. In this study, a multi-objective simulated annealing algorithm (m_SAA) is proposed for single-model, stochastic assembly line balancing problem with the aim of minimizing of smoothness index and total design cost. To obtain Pareto-optimal solutions, m_SAA implements tabu list and a multinomial probability mass function approach. The effectiveness of the proposed m_SAA is comparatively investigated using another SA using weight-sum approach on the test problems. Computational results show that m_SAA with multinomial probability mass function approach is more effective than SA with weight-sum approach in terms of quality of Pareto-optimal solutions.
Multi-Objective Optimization of A Stochastic Assembly Line Balancing: A Hybrid Simulated Annealing Algorithm
(2011) Cakir, Burcin; Altiparmak, Fulya; Dengiz, Berna; 0000-0003-1730-4214; AAF-7020-2021
This paper deals with multi-objective optimization of a single-model stochastic assembly line balancing problem with parallel stations. The objectives are as follows: (1) minimization of the smoothness index and (2) minimization of the design cost. To obtain Pareto-optimal solutions for the problem, we propose a new solution algorithm, based on simulated annealing (SA), called m_SAA, m_SAA implements a multinomial probability mass function approach, tabu list, repair algorithms and a diversification strategy. The effectiveness of m_SAA is investigated comparing its results with those obtained by another SA (using a weight-sum approach) on a suite of 24 test problems. Computational results show that m_SAA with a multinomial probability mass function approach is more effective than SA with weight-sum approach in terms of the quality of Pareto-optimal solutions. Moreover, we investigate the effects of properties (i.e., the tabu list, repair algorithms and diversification strategy) on the performance of m_SAA. (C) 2010 Elsevier Ltd. All rights reserved.
A review of power system planning and operational models for flexibility assessment in high solar energy penetration scenarios
(2020) Emmanuel, Michael; Doubleday, Kate; Cakir, Burcin; Markovic, Marija; Hodge, Bri-Mathias
This article reviews power system flexibility assessment, which is necessary to ensure both instantaneous stability and long-term security of supply under high penetrations of variable, uncertain, and asynchronous renewable energy resources, such as solar photovoltaics. This article reviews the concept of flexibility and summarizes metrics for evaluating power system flexibility, which is not yet available in the literature. Power system planning and operational models applicable for flexibility assessment, including net load analysis, capacity expansion, production cost, and dynamic models, are reviewed in a comprehensive literature survey, with a focus on high solar and other variable renewable energy penetrations. Each of these models applies different methodological approaches and feasibility criteria appropriate at different timescales. Finally, this article presents a conceptual integrated framework to combine these models for a holistic assessment of power system flexibility requirements across all timescales, from multiyear to sub-cycle.