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RISS 인기검색어
- 검색키워드
- 저자 : Kai-Ling Mak (검색결과 3 건)
- 무료
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Mak, Kai-Ling,Cui, Lixin,Su, Wei Korean Institute of Industrial Engineers 2012 Industrial Engineeering & Management Systems Vol.11 No.2
As the global market becomes more competitive, manufacturing industries face relentless pressure caused by a growing tendency of greater varieties of products, shorter manufacturing cycles and more sophisticated customer requirements. Efficient and effective supplier selection and order allocation decisions are, therefore, important decisions for a manufacturer to ensure stable material flows in a highly competitive supply chain, in particular, when customers are willing to accept products with less desirable product attributes (e.g., color, delivery date) for economic reasons. This paper attempts to solve optimally the challenging problem of supplier selection and order allocation, taking into consideration the customer flexibility for a manufacturer producing multi-products to satisfy the customers' demands in a multi period planning horizon. A new mixed integer programming model is developed to describe the behavior of the supply chain. The objective is to maximize the manufacturer's total profit subject to various operating constraints of the supply chain. Due to the complexity and non-deterministic polynomial-time (NP)-hard nature of the problem, an improved genetic approach is proposed to solve the problem optimally. This approach differs from a canonical genetic algorithm in three aspects: a new selection method to reduce the chance of premature convergence and two problem-specific repair heuristics to guarantee feasibility of the solutions. The results of applying the proposed approach to solve a set of randomly generated test problems clearly demonstrate its excellent performance. When compared with applying the canonical genetic algorithm to locate optimal solutions, the average improvement in the solution quality amounts to as high as ten percent.
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Kai-Ling Mak,Lixin Cui, Wei Su 대한산업공학회 2012 Industrial Engineeering & Management Systems Vol.11 No.2
As the global market becomes more competitive, manufacturing industries face relentless pressure caused by a growing tendency of greater varieties of products, shorter manufacturing cycles and more sophisticated customer requirements. Efficient and effective supplier selection and order allocation decisions are, therefore, important decisions for a manufacturer to ensure stable material flows in a highly competitive supply chain, in particular, when customers are willing to accept products with less desirable product attributes (e.g., color, delivery date) for economic reasons. This paper attempts to solve optimally the challenging problem of supplier selection and order allocation, taking into consideration the customer flexibility for a manufacturer producing multi-products to satisfy the customers’ demands in a multi period planning horizon. A new mixed integer programming model is developed to describe the behavior of the supply chain. The objective is to maximize the manufacturer’s total profit subject to various operating constraints of the supply chain. Due to the complexity and non-deterministic polynomial-time (NP)-hard nature of the problem, an improved genetic approach is proposed to solve the problem optimally. This approach differs from a canonical genetic algorithm in three aspects: a new selection method to reduce the chance of premature convergence and two problemspecific repair heuristics to guarantee feasibility of the solutions. The results of applying the proposed approach to solve a set of randomly generated test problems clearly demonstrate its excellent performance. When compared with applying the canonical genetic algorithm to locate optimal solutions, the average improvement in the solution quality amounts to as high as ten percent.
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Cheng Han Ng,Kai En Chan,Yip Han Chin,Rebecca Wenling Zeng,Pei Chen Tsai,Wen Hui Lim,Darren Jun Hao Tan,Chin Meng Khoo,Lay Hoon Goh,Zheng Jye Ling,Anand Kulkarni,Lung-Yi Loey Mak,Daniel Q Huang,Mark C 대한간학회 2022 Clinical and Molecular Hepatology(대한간학회지) Vol.28 No.3
Background/Aims: Nonalcoholic fatty liver disease (NAFLD) is closely associated with diabetes. The cumulative impact of both diseases synergistically increases risk of adverse events. However, present population analysis is predominantly conducted with reference to non-NAFLD individuals and has not yet examined the impact of prediabetes. Hence, we sought to conduct a retrospective analysis on the impact of diabetic status in NAFLD patients, referencing non-diabetic NAFLD individuals. Methods: Data from the National Health and Nutrition Examination Survey 1999–2018 was used. Hepatic steatosis was defined with United States Fatty Liver Index (US-FLI) and FLI at a cut-off of 30 and 60 respectively, in absence of substantial alcohol use. A multivariate generalized linear model was used for risk ratios of binary outcomes while survival analysis was conducted with Cox regression and Fine Gray model for competing risk. Results: Of 32,234 patients, 28.92% were identified to have NAFLD. 36.04%, 38.32% and 25.63% were non-diabetic, prediabetic and diabetic respectively. Diabetic NAFLD significantly increased risk of cardiovascular disease (CVD), stroke, chronic kidney disease, all-cause and CVD mortality compared to non-diabetic NAFLD. However, prediabetic NAFLD only significantly increased the risk of CVD and did not result in a higher risk of mortality. Conclusions: Given the increased risk of adverse outcomes, this study highlights the importance of regular diabetes screening in NAFLD and adoption of prompt lifestyle modifications to reduce disease progression. Facing high cardiovascular burden, prediabetic and diabetic NAFLD individuals can benefit from early cardiovascular referrals to reduce risk of CVD events and mortality.
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