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Now showing 1 - 10 of 68

An integrated algorithm for solving multi-customer joint replenishment problem with districting consideration
(2020-06-01)
Yao, Ming-Jong
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Lin, Jen-Yen
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Lin, Yu-Liang
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Fang, Shu-Cherng
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運輸與物流管理系註：原交通所+運管所
This paper studies a multi-customer joint replenishment problem with districting consideration (MJRPDC) which is of particular importance to a company that outsources its transportation and delivery operations to a third-party logistics (3PL) service provider. To solve the problem, we first propose an innovative search algorithm for solving the traditional multi-customer joint replenishment problem in a given zone. Then we design a GA-based framework to handle the corresponding districting problem based on the performance of each district evaluated by using the proposed search algorithm. The proposed methodologies are demonstrated by using an example of solving MJRPDC for a bank.
An evolving fuzzy planning mechanism for a ubiquitous manufacturing system
(2020-06-01)
Chen, Toly
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Wang, Yu-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
Existing ubiquitous manufacturing (UM) systems usually plan production and logistics simultaneously to enhance efficiency. A UM system can be further benefited by pursuing economical fabrication, improving product quality, and enhancing partner relationships, which involves possibly conflicting goals, but has rarely been investigated. To address this, an evolving fuzzy approach is proposed in this study by hybridizing diversifying fuzzy analytic hierarchy process (DFAHP), genetic algorithm (GA), and fuzzy technique for order preference by similarity to the ideal solution (FTOPSIS). The evolving fuzzy approach has been applied to a UM system. The superiority of the evolving fuzzy approach over existing methods in meeting various goals was successfully demonstrated by the experimental results.
A coordinated location-inventory problem with supply disruptions: A two-phase queuing theory-optimization model approach
(2020-04-01)
Liu, Yunhua
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Dehghani, Ehsan
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Jabalameli, Mohammad Saeed
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Diabat, Ali
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Lu, Chung-Cheng
Despite their prominent role in real-life, supply disruptions and inherent variability in parameters have been virtually overlooked in the area of the location-inventory problem. To push forward the relevant literature, addressing the aforementioned issues is of paramount importance. In this sense, this paper deals with a coordinated location-inventory problem in a stochastic supply chain system where facilities are subject to random supply disruptions. The demand and replenishment lead-times are also considered to be hemmed in by uncertainties. To tackle the location-inventory problem in the concerned supply chain, a two-phase approach based on the queuing theory and optimization model techniques is devised. Applying the queuing theory, the first phase tackles the uncertainty issues and gains some performance measures of the system. The attained results are later incorporated into the optimization model. In the second phase, the optimization model determines the strategic and tactical decisions across the supply chain in an integrated manner. Because the presented model is complicated to solve through exact methods, a tailored hybrid genetic algorithm embedded with direct search method is exploited, which is capable of finding quality solutions in an efficient way. Eventually, various sensitivity analyses are conducted from which interesting insights are derived.
Considering stockers in reentrant hybrid flow shop scheduling with limited buffer capacity
(2020-01-01)
Lin, Chun-Cheng
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Liu, Wan-Yu
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Chen, Yu-Hsiang
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工業工程與管理學系
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Department of Industrial Engineering and Management
Diversification of products has increased the involvement of reentrant manufacturing processes, in which a job returns multiple times to a machine at the preceding workflow stage to continue the manufacturing process. Reentrant flow shop manufacturing can substantially improve manufacturing efficiency when scheduled properly. In practice, advanced manufacturing companies (e.g., semiconductor foundries) have introduced automated material handling system (AMHS), including stockers that serve as centralized inventory buffer space for temporarily storing the inventories owing to limited buffer capacity of each machine. However, no previous studies on reentrant flow shop scheduling have considered the impact of limited buffer capacity or stockers on scheduling efficiency. Consequently, this study investigated the application of stockers in solving the reentrant hybrid flow shop scheduling problem with limited buffer capacity. With the objective of optimizing the makespan and mean flowtime of a schedule, this problem is NP-hard because it generalizes the flow shop problem. Therefore, this study developed a hybrid harmony search and genetic algorithm (HHSGA) for the problem, in which limited buffer capacity and stockers cause solution decoding to be non-trivial. Experimental comparison on scheduling problems with different numbers of jobs showed that the HHSGA performed better than conventional algorithms. Moreover, among three manufacturing conditions (i.e., with buffers and stockers, with buffers only, and with stockers only), the results indicated that the condition using inventory buffers and stockers was more beneficial.
Merits of using chromosome representations and shadow chromosomes in genetic algorithms for solving scheduling problems
(2019-08-01)
Lin, Chi-Shiuan
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Lee, I-Ling
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Wu, Muh-Cherng
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工業工程與管理學系
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Department of Industrial Engineering and Management
Two conjectures, the use of incomplete chromosome representations and shadow chromosomes may improve the performance of genetic algorithms (GAs), are examined in this study. The examination entails testing distributed flexible job shop scheduling (DFJS) problems subject to preventive maintenance (PM) that involve four scheduling decisions. Genetic algorithms based on a complete chromosome representation that explicitly models the four decisions have been developed previously. By contrast, herein, two incomplete chromosome representations are proposed, whereby the conjectured advantages are two-fold. First, an incomplete chromosome representation models two scheduling decisions, and the remaining two are decoded by heuristic rules designed to ensure the load balance of manufacturing resources. Therefore, scheduling solutions with load imbalance will not be generated, which will help prevent the execution of ineffective searches. Second, a novel method of generating new chromosomes is developed and employed, instead of using traditional genetic operations. These chromosomes, called shadow chromosomes, are generated from good quality scheduling solutions and they may improve performance. Based on these two conjectures, four GAs are proposed. Numerical experiments reveal that each proposed GA outperforms the prior GAs substantially and the two conjectures are thus well justified. These findings shed light on the application of the two conjectures for developing metaheuristic algorithms to solve other high-dimensional space search problems.
Discovery of prognostic biomarkers for predicting lung cancer metastasis using microarray and survival data
(2015-02-21)
Huang, Hui-Ling
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Wu, Yu-Chung
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Su, Li-Jen
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Huang, Yun-Ju
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Charoenkwan, Phasit
Background: Few studies have investigated prognostic biomarkers of distant metastases of lung cancer. One of the central difficulties in identifying biomarkers from microarray data is the availability of only a small number of samples, which results overtraining. Recently obtained evidence reveals that epithelial-mesenchymal transition (EMT) of tumor cells causes metastasis, which is detrimental to patients' survival. Results: This work proposes a novel optimization approach to discovering EMT-related prognostic biomarkers to predict the distant metastasis of lung cancer using both microarray and survival data. This weighted objective function maximizes both the accuracy of prediction of distant metastasis and the area between the disease-free survival curves of the non-distant and distant metastases. Seventy-eight patients with lung cancer and a follow-up time of 120 months are used to identify a set of gene markers and an independent cohort of 26 patients is used to evaluate the identified biomarkers. The medical records of the 78 patients show a significant difference between the disease-free survival times of the 37 non-distant-and the 41 distant-metastasis patients. The experimental results thus obtained are as follows. 1) The use of disease-free survival curves can compensate for the shortcoming of insufficient samples and greatly increase the test accuracy by 11.10%; and 2) the support vector machine with a set of 17 transcripts, such as CCL16 and CDKN2AIP, can yield a leave-one-out cross-validation accuracy of 93.59%, a test accuracy of 76.92%, a large disease-free survival area of 74.81%, and a mean survival prediction error of 3.99 months. The identified putative biomarkers are examined using related studies and signaling pathways to reveal the potential effectiveness of the biomarkers in prospective confirmatory studies. Conclusions: The proposed new optimization approach to identifying prognostic biomarkers by combining multiple sources of data (microarray and survival) can facilitate the accurate selection of biomarkers that are most relevant to the disease while solving the problem of insufficient samples.
Last-mile distribution planning for fruit-and-vegetable cold chains
(2018-01-01)
Hsiao, Yu-Hsiang
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Chen, Mu-Chen
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Lu, Kuan-Yu
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Chin, Cheng-Lin
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運輸與物流管理系註：原交通所+運管所
Purpose - The purpose of this paper is to formulate and solve a last-mile distribution plan problem with concern for the quality of fruits and vegetables in cold chains. Design/methodology/approach - The vehicle routing problem with time windows (VRPTW) is extended based on the characteristics of fruit-and-vegetable cold chains. The properties of multiple perishable foods, continuing decline in quality, various requirements for quality levels and optimal temperature settings during vehicle transportation are considered in the VRPTW. The product quality level is defined by the estimation of residual shelf life, which changes with temperature, and is characterized by a stepped decrease during the transportation process as time goes on. A genetic algorithm (GA) is adapted to solve the problem because of its convincing ability to solve VRPTW-related problems. For this purpose, solution encoding, a fitness function and evolution operators are designed to deal with the complicated problem herein. Findings - A distribution plan including required fleet size, vehicle routing sequence and what quality level should be shipped out to account for the quality degradation during vehicle transportation is generated. The results indicate that the fulfillment of various requirements of different customers for various fruits and vegetables and quality levels can be ensured with cost considerations. Originality/value - This study presents a problem for last-mile delivery of fresh fruits and vegetables which considers multiple practical scenarios not studied previously. A solution algorithm based on a GA is developed to address this problem. The proposed model is easily applied to other types of perishable products.
A novel methodology for stock investment using high utility episode mining and genetic algorithm
(2017-10-01)
Lin, Yu-Feng
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Huang, Chien-Feng
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Tseng, Vincent S.
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資訊工程學系
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Department of Computer Science
In this paper, we present a novel methodology for stock investment using the technique of high utility episode mining and genetic algorithms. Our objective is to devise a profitable episode-based investment model to reveal hidden events that are associated with high utility in the stock market. The time series data of stock price and the derived technical indicators, including moving average, moving average convergence and divergence, random index and bias index, are used for the construction of episode events. We then employ the genetic algorithm for the simultaneous optimization on parameters and selection of subsets of models. The empirical results show that our proposed method significantly outperforms the state-of-the-art methods in terms of annualized returns of investment and precision. We also provide a set of Z-tests to statistically validate the effectiveness of our proposed method. Based upon the promising results obtained, we expect this novel methodology can advance the research in data mining for computational finance and provide an alternative to stock investment in practice. (C) 2017 Elsevier B.V. All rights reserved.
Balancing latency and cost in software-defined vehicular networks using genetic algorithm
(2018-08-15)
Lin, Chun-Cheng
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Chin, Hui-Hsin
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Chen, Wei-Bo
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工業工程與管理學系
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Department of Industrial Engineering and Management
Software-defined vehicular network (SDVN) effectively improves programmability and flexibility of VANET through software-defined network (SDN) features. To address the latency problem, the previous work considered that vehicles access the IP network through either cellular links or ad hoc links of vehicular networks in an SDVN, in which the SDN controller can rebate the bandwidth of cellular links allocated to vehicles to reduce latency, but the cost of renting the rebated bandwidth is paid by the network provider. Then, it proposed a two stage game to optimize the rebating strategy to balance the latency requirement and the cost. However, optimization of each of the two stages may influence optimization of the other stage. As a consequence, this work proposes an improved genetic algorithm (IGA) to optimize the rebating stage in a single stage, which includes a dynamic mutation adjustment scheme to ensure solution diversity, and keeps the best chromosome so far to avoid solution damage owing to the dynamic mutation. Through simulation, the number of packets transmitted through cellular lines is positively correlated with the rebate ratio and the other parameters. In addition, the proposed IGA can significantly improve performance of searching solutions, and obtain better results than the previous work.
Distribution planning for perishable foods in cold chains with quality concerns: Formulation and solution procedure
(2017-03-01)
Hsiao, Yu-Hsiang
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Chen, Mu-Chen
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Chin, Cheng-Lin
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
Background: Food safety and quality management is attracting more and more attention. Fresh foods are characterized by their perishable and temperature-sensitive nature. Thus, there is a need for cold chain management. In order to ensure delivery, of safe, fresh, high-quality foods to customers, strict time and temperature control are special requirements which must be incorporated during distribution planning. Scope and approach: This study modeled a cold chain food distribution planning problem, aiming to generate a distribution plan for fulfilling customer requirements for various foods with pre-appointed quality levels at the lowest distribution cost. The quality level was defined based on the estimated shelf life, which varies by food type and storage temperature, and is characterized by a stepped decrease as time goes on. Upward substitution of quality levels is implemented in the shipment of customer orders to ensure customers receiving the foods with ordered quality levels. The optimization of temperature setting for food storage in multi-item-multi-temperature vehicles was also involved in distribution planning. To solve the problem, an algorithm based on adapting Biogeography-based Optimization (BBO) was developed. The genetic algorithm was employed as a benchmarking method. Two designed examples regarding chilled meat distribution are illustrated. Key findings and conclusions: The results indicate that the generated distribution plan can ensure the fulfillment of customer requirements for various foods and food quality levels at the lowest cost. The superiority of the proposed adaptive BBO in both the solution quality and stability was also demonstrated. (C) 2016 Elsevier Ltd. All rights reserved.

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