Browsing by Author "Wu, Pei-Ju"

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A hybrid fuzzy integral decision-making model for locating manufacturing centers in China: A case study
(2010-01-01)
Feng, Cheng-Min
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Wu, Pei-Ju
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Chia, Kai-Chieh
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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
This study presents a hybrid fuzzy integral decision-making model that integrates factor analysis, interpretive structural modeling, Markov chain, fuzzy integral and the simple additive weighted method for selecting locations of high-tech manufacturing centers in China. The analytical results of this case study demonstrate the feasibility of the proposed model for solving fuzzy multiple attribute decision-making problems, especially when criteria are interdependent. Further, the empirical study brings out some properties that are crucial for high-tech manufacturing centers to invest in China. (C) 2008 Elsevier B.V. All rights reserved.
A tax savings model for the emerging global manufacturing network
(2009-12-01)
Feng, Cheng-Min
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Wu, Pei-Ju
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運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
The emerging global manufacturing network involves nodal location features of tax areas and international logistics zones, manufacturing procedures of simple process and deep process, as well as transportation arcs. Since choosing tax savings locations and manufacturing procedures that increase after-tax profit is important to global manufacturers, this study aims to present several tax savings approaches and to develop a tax savings model for maximizing after-tax profit in the emerging network. Numerical illustration demonstrates that the proposed model is an effective approach for global manufacturers to achieve tax savings. The proposed model elucidates the crucial logistics behavior associated with tax savings. (C) 2009 Elsevier B.V. All rights reserved.
An effective pricing model for the congestion alleviation of e-commerce logistics
(2019-03-01)
Chen, Mu-Chen
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Wu, Pei-Ju
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Hsu, Yu-Han
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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
E-retailers and home delivery companies have struggled with logistics congestion. Hence, this study aims to develop a pricing model for alleviation of congestion in e-commerce logistics. The analytical results reveal that a congestion surcharge mechanism influences the behavior of a home delivery company and e-commerce consumers in terms of early delivery and delayed delivery, cargo volume, and consumers' favorite delivery time. Moreover, e-commerce logistics fees in practice are established by negotiation between e-retailers and home delivery companies. The proposed model can be treated as a reference for e-retailers and home delivery companies to determine appropriate logistics fees. This pioneering study develops a congestion pricing model to deal with congestion problems in e-commerce logistics, which can effectively reflect the impact of various congestion fees on the behavior of e-commerce stakeholders.
Modeling Resilience Enhancement Strategies for International Express Logistics
(2013)
Chen, Cheng-Chieh (Frank)
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Feng, Cheng-Min
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Tsai, Ya-Hsuan
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Wu, Pei-Ju
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運輸與物流管理系註：原交通所+運管所
International express is a most time-sensitive industry, and members of this industry must be able to respond to disruptions quickly to ensure service quality and to avoid a loss of their competitiveness with other logistics service providers. Instead of a method that arbitrarily makes rushed decisions during the postdisruption phase, this paper describes a method for quantifying and optimizing resilience strategies based on concepts of integrated resource assignment, regardless of where the available resources are located in the logistics network studied or how much capacity can be rented from others. The study started with the use of a typical transportation network modeling approach and then incorporated nonlinear time-dependent cargo value functions into a multiobjective mixed-integer nonlinear programming problem. A set of optimal actions from resilience strategies, such as the selection of alternative routes, switching of shipping modes, rental of other carriers' capacities, reallocation of local trucks, and prioritization of the order of shipments because of limited capacities, was considered. Decisions should be based on overall trade-off considerations and, at the same time, joint maximization of the product of the total time-dependent cargo value and the corresponding throughput and minimization of the costs incurred with resilience enhancement strategies.
The data-driven analytics for investigating cargo loss in logistics systems
(2017-01-01)
Wu, Pei-Ju
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Chen, Mu-Chen
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Tsau, Chih-Kai
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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
Purpose - Cargo loss has been a major issue in logistics management. However, few studies have tackled the issue of cargo loss severity via business analytics. Hence, the purpose of this paper is to provide guidance about how to retrieve valuable information from logistics data and to develop cargo loss mitigation strategies for logistics risk management. Design/methodology/approach - This study proposes a research design of business analytics to scrutinize the causes of cargo loss severity. Findings - The empirical results of the decision tree analytics reveal that transit types, product categories, and shipping destinations are key factors behind cargo loss severity. Furthermore, strategies for cargo loss prevention were developed. Research limitations/implications - The proposed framework of cargo loss analytics provides a research foundation for logistics risk management. Practical implications - Companies with logistics data can utilize the proposed business analytics to identify cargo loss factors, while companies without logistics data can employ the proposed cargo loss mitigation strategies in their logistics systems. Originality/value - This pioneer empirical study scrutinizes the critical cargo loss issues of cargo damage, cargo theft, and cargo liability insurance through exploiting real cargo loss data.