Browsing by Author "Tan, Hao"

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A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems
(2018-01-01)
Lu, Po-Hsiang
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Wu, Muh-Cherng
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Tan, Hao
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Peng, Yong-Han
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Chen, Chen-Fu
This paper proposes a genetic algorithm for solving distributed and flexible job-shop scheduling (DFJS) problems. A DFJS problem involves three scheduling decisions: (1) job-to-cell assignment, (2) operation-sequencing, and (3) operation-to-machine assignment. Therefore, solving a DFJS problem is essentially a 3-dimensional solution space search problem; each dimension represents a type of decision. The algorithm is developed by proposing a new and concise chromosome representation , which models a 3-dimensional scheduling solution by a 1-dimensional scheme (i.e., a sequence of all jobs to be scheduled). That is, the chromosome space is 1-dimensional (1D) and the solution space is 3-dimensional (3D). In , we develop a 1D-to-3D decoding method to convert a 1D chromosome into a 3D solution. In addition, given a 3D solution, we use a refinement method to improve the scheduling performance and subsequently use a 3D-to-1D encoding method to convert the refined 3D solution into a 1D chromosome. The 1D-to-3D decoding method is designed to obtain a "good" 3D solution which tends to be load-balanced. In contrast, the refinement and 3D-to-1D encoding methods of a 3D solution provides a novel way (rather than by genetic operators) to generate new chromosomes, which are herein called shadow chromosomes. Numerical experiments indicate that outperforms the IGA developed by De Giovanni and Pezzella (Eur J Oper Res 200:395-408, 2010), which is the up-to-date best-performing genetic algorithm in solving DFJS problems.
The determinants of photovoltaic system costs: an evaluation using a hierarchical learning curve model
(2016-01-20)
Trappey, Amy J. C.
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Trappey, Charles V.
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Tan, Hao
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Liu, Penny H. Y.
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Li, Shin-Je
The uptake of solar power globally as an important alternative energy source to fossil fuels, together with a rapid fall in the cost of photovoltaic (PV) systems, has been phenomenal during the past decade. This trend is widely anticipated to continue for the years to come. The decline in PV installation costs, like many other new technologies through history, has been largely driven by the learning curve effect. However, it is suggested that other factors, such as costs of key production inputs and prices of competing technologies, also impact the costs of PV systems. In this study, we construct a hierarchical learning curve model to quantify the effects that various factors have on installation costs of PV systems based on empirical data from Taiwan. The results show that, in addition to the learning curve effect as underpinned by an increase of cumulative PV capacity, reductions to silicon price have significantly contributed to the decline of the final installation costs of PV systems in Taiwan. By quantifying the effects of critical cost factors, the learning curve effects on PV installation costs in Taiwan are defined which enable a more accurate projection of PV installation costs for governments, PV producers, operators and users. (C) 2015 Published by Elsevier Ltd.