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Junhu Ruan
College of Economics and Management, Northwest A&F University, Xianyang 712100, China

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Journal article
Published: 08 August 2021 in Sustainability
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The Internet of Things technology (IoT) in food traceability provides new ideas to solve the problem of smart production and offers new ideas for the formation of safe and high-quality markets for meat products. However, scholars have studied the combination of blockchain and IoT technology. There is a lack of research on the combination of IoT and food traceability technology. Moreover, previous studies focused on the application of IoT traceability technology, taking farmers’ adoption willingness as an exogenous variable while ignoring its endogeneity. Therefore, it is essential to study farmers’ willingness to adopt IoT traceability technology and find the factors that influence farmers’ adoption intention. Based on survey data from 264 pig farmers in Shaanxi Province, this paper discussed the factors which influence pig farmers’ adoption of the technology by using the Unified Theory of Acceptance and Use of Technology (UTAUT). The results showed that farmers’ adoption intention was influenced by a combination of farmers’ performance expectancy, effort expectancy, social influence, personal innovation, and perceived risk. Personal innovation played a mediating role in effort expectancy and adoption willingness and perceived risk played a moderating role in personal innovation and adoption willingness.

ACS Style

Ruiyu Sun; Siyao Zhang; Tianyu Wang; Jiarui Hu; Junhu Ruan; Junyong Ruan. Willingness and Influencing Factors of Pig Farmers to Adopt Internet of Things Technology in Food Traceability. Sustainability 2021, 13, 8861 .

AMA Style

Ruiyu Sun, Siyao Zhang, Tianyu Wang, Jiarui Hu, Junhu Ruan, Junyong Ruan. Willingness and Influencing Factors of Pig Farmers to Adopt Internet of Things Technology in Food Traceability. Sustainability. 2021; 13 (16):8861.

Chicago/Turabian Style

Ruiyu Sun; Siyao Zhang; Tianyu Wang; Jiarui Hu; Junhu Ruan; Junyong Ruan. 2021. "Willingness and Influencing Factors of Pig Farmers to Adopt Internet of Things Technology in Food Traceability." Sustainability 13, no. 16: 8861.

Journal article
Published: 15 June 2021 in IEEE Internet of Things Journal
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The production and distribution planning of fresh produce is a complex optimization problem, which is affected by many factors including its perishable characteristics. Farmers cannot guarantee the efficiency and accuracy of production and distribution decisions. Given the close relationship between the production and distribution of annual fresh produce, the intention of our research is to solve the two-stage joint planning problem, and maximize the revenue of farmers ultimately. The internal relationship matrix between the two links of production and distribution is established. On this basis, we propose a mixed integer programming (MIP) model, which covers the constraints of labor and capital. The decisions obtained are not only based on price estimation and resource availability, but also on the impact of the agricultural IoT technology and the special requirements of each distribution channel. Numerical experiments demonstrate that when the planting area is 1 hectare, 4 hectares, and 6 hectares, the proposed joint planning model can improve the distribution revenue of farmers by 7.92%, 4.15%, and 4.94%, respectively, compared with the traditional separate decision-making approach of distribution. According to different decision scenarios, management insights have been obtained. For example, farmers should carefully sort and package products as well as choose a timely and safe third-party express delivery company. Additionally, the proposed strategy can evaluate the impact of distribution channels on farmers’ revenue.

ACS Style

Jiliang Han; Na Lin; Junhu Ruan; Xuping Wang; Wei Wei; Huimin Lu. A Model for Joint Planning of Production and Distribution of Fresh Produce in Agricultural Internet of Things. IEEE Internet of Things Journal 2021, 8, 9683 -9696.

AMA Style

Jiliang Han, Na Lin, Junhu Ruan, Xuping Wang, Wei Wei, Huimin Lu. A Model for Joint Planning of Production and Distribution of Fresh Produce in Agricultural Internet of Things. IEEE Internet of Things Journal. 2021; 8 (12):9683-9696.

Chicago/Turabian Style

Jiliang Han; Na Lin; Junhu Ruan; Xuping Wang; Wei Wei; Huimin Lu. 2021. "A Model for Joint Planning of Production and Distribution of Fresh Produce in Agricultural Internet of Things." IEEE Internet of Things Journal 8, no. 12: 9683-9696.

Journal article
Published: 10 September 2020 in Journal of Cleaner Production
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Internet of Things (IoT) has played a key role in developing sustainable precision agriculture. This study addresses water and fertilizer allocation issues derived from the IoT-enabled precision agriculture for achieving sustainable irrigation and fertilization management. Existing studies on irrigation and fertilization management have more focused on short-term management and valued the timeliness of resource scheduling. However, short-term management is unsustainable since it ignores the economic and environmental goals of production activities and not applicable when the resources are limited. To fill this gap, this study develops a framework for the IoT-based irrigation and fertilization system in which both long-term and short-term planning are considered. Based on the framework, an integer linear programming model is developed for allocating limited resources among multiple crops with the goal of maximizing the economic profits and environmental benefits. After that, a hybrid genetic algorithm is designed to solve the optimization model. Finally, numerical experiments based on a case study are conducted to test the effectiveness of the proposed model and solving method. Results have confirmed that the optimization model presented in this study can promote sustainable irrigation and fertilization management in precision agriculture by offering more economic and environmental benefits than empirical models. Also, related management implications are obtained from sensitivity analysis to support the decision-making of managers, involving planting structure design, strategies selection of water and fertilizer storage and replenishment.

ACS Style

Na Lin; Xuping Wang; Yihao Zhang; Xiangpei Hu; Junhu Ruan. Fertigation management for sustainable precision agriculture based on Internet of Things. Journal of Cleaner Production 2020, 277, 124119 .

AMA Style

Na Lin, Xuping Wang, Yihao Zhang, Xiangpei Hu, Junhu Ruan. Fertigation management for sustainable precision agriculture based on Internet of Things. Journal of Cleaner Production. 2020; 277 ():124119.

Chicago/Turabian Style

Na Lin; Xuping Wang; Yihao Zhang; Xiangpei Hu; Junhu Ruan. 2020. "Fertigation management for sustainable precision agriculture based on Internet of Things." Journal of Cleaner Production 277, no. : 124119.

Journal article
Published: 07 August 2020 in Sustainability
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The agricultural economy, as an integral branch of the global economy, covering the whole supply chain of agricultural production including cultivation, processing, distribution and consumption, is of great importance to realizing a sustainable circular economy and ecological development. As a traditional agricultural country, China has experienced a series of problems such as a serious waste of resources and a fragile ecological environment during its agricultural economic development. With the background of “the Belt and Road Initiative”, major progress has been witnessed in both ecological development and agricultural circular economy in China. However, the development of circular agriculture in China has to deal with barriers from different stakeholders. This research identifies critical barriers for the government, farmers, and the enterprises to develop circular agriculture. The causal factors, effect factors, and the center of factors are identified and the correlation between the barriers is described using the Gray-DEMATEL method. Based on the analysis results, several policy suggestions are proposed for the government. This paper provides a feasible framework for decision-making to support the development of a sustainable circular economy in agriculture in China.

ACS Style

Xiqiang Xia; Junhu Ruan. Analyzing Barriers for Developing a Sustainable Circular Economy in Agriculture in China Using Grey-DEMATEL Approach. Sustainability 2020, 12, 6358 .

AMA Style

Xiqiang Xia, Junhu Ruan. Analyzing Barriers for Developing a Sustainable Circular Economy in Agriculture in China Using Grey-DEMATEL Approach. Sustainability. 2020; 12 (16):6358.

Chicago/Turabian Style

Xiqiang Xia; Junhu Ruan. 2020. "Analyzing Barriers for Developing a Sustainable Circular Economy in Agriculture in China Using Grey-DEMATEL Approach." Sustainability 12, no. 16: 6358.

Article
Published: 17 July 2020 in Electronic Commerce Research
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For B2C (Business to Customer) commerce platforms, quickly attracting enough consumers is an extremely important issue. However, existing studies mainly analyze whether consumers make online purchase and its influencing factors, but pay less attention to the changes in consumer size. Therefore, this paper aims to study the changing law of consumer quantity from the macro level, which may help E-commerce platforms reasonably predict it. Firstly, we point out the unique feature of the B2C commerce platforms compared with traditional products or technologies, namely indirect network externality. And we combine this feature and the factors that influence consumers and enterprises’ adoption of B2C commerce to build an extended Bass Model. Finally, we verify the validity of our model with the data of Chinese online shoppers. In addition, we put forward some suggestions on the future research of this extended Bass Model.

ACS Style

Xiaoyu Li; Jiahong Yuan; Yan Shi; TianTeng Wang; Xiangpei Hu; Felix Tung Sun Chan; Junhu Ruan. An extended Bass Model on consumer quantity of B2C commerce platforms. Electronic Commerce Research 2020, 20, 609 -628.

AMA Style

Xiaoyu Li, Jiahong Yuan, Yan Shi, TianTeng Wang, Xiangpei Hu, Felix Tung Sun Chan, Junhu Ruan. An extended Bass Model on consumer quantity of B2C commerce platforms. Electronic Commerce Research. 2020; 20 (3):609-628.

Chicago/Turabian Style

Xiaoyu Li; Jiahong Yuan; Yan Shi; TianTeng Wang; Xiangpei Hu; Felix Tung Sun Chan; Junhu Ruan. 2020. "An extended Bass Model on consumer quantity of B2C commerce platforms." Electronic Commerce Research 20, no. 3: 609-628.

Journal article
Published: 20 May 2020 in IEEE Access
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This paper selects the daily data of the exchange rates of Chinese Yuan (CNY) over the currencies of 14 countries along the Belt and Road, Shanghai composite index and Shenzhen composite index to study the influence of the Belt and Road Initiative on the linkages between exchange rates and Chinese stock index based on the flow-oriented model and the stock-oriented model. To reflect the fluctuations in daily data and reduce the central bank’s interference with the exchange rate, two fuzzy techniques are used to process data, that is, the centroid based measure and the integral based measure. Then we judge the relationship between exchange rate and stock index through the Pearson correlation coefficient and the Granger causality test. Besides, we further compare the results and their differences by the classic crisp method and our two fuzzy techniques, which enable us to judge their correlation more accurately, and provide a reference for a wider application of the proposed fuzzy methods. We find that there is a correlation between exchange rate and stock index under certain conditions, and the Belt and Road initiative strengthens the relationship between the Chinese foreign exchange market and the stock market, more importantly, the fuzzy techniques are effective to judge this relation.

ACS Style

Jiahong Yuan; Xiaoyu Li; Yan Shi; Felix T. S. Chan; Junhu Ruan; Yuchun Zhu. Linkages Between Chinese Stock Price Index and Exchange Rates-An Evidence From the Belt and Road Initiative. IEEE Access 2020, 8, 95403 -95416.

AMA Style

Jiahong Yuan, Xiaoyu Li, Yan Shi, Felix T. S. Chan, Junhu Ruan, Yuchun Zhu. Linkages Between Chinese Stock Price Index and Exchange Rates-An Evidence From the Belt and Road Initiative. IEEE Access. 2020; 8 (99):95403-95416.

Chicago/Turabian Style

Jiahong Yuan; Xiaoyu Li; Yan Shi; Felix T. S. Chan; Junhu Ruan; Yuchun Zhu. 2020. "Linkages Between Chinese Stock Price Index and Exchange Rates-An Evidence From the Belt and Road Initiative." IEEE Access 8, no. 99: 95403-95416.

Journal article
Published: 06 May 2020 in IEEE Internet of Things Journal
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Cyber-physical systems and data-driven techniques have potentials to facilitate the prediction and control of product quality, which is one of the two most important issues in modern industries. In this study, we integrate Random Forest with Bayesian Optimization for quality prediction with large scale dimensions data, selecting crucial production elements by Information Gain, and then utilizing sensitivity analysis to maintain product quality. Horizonal empirical experiments are performed to verify the superiorities of Random Forest embedded within Bayesian Optimization over classical Random Forest, Support Vector Machine, Logistic Regression, Decision Tree and even Background Propagation Neural Network. Besides, we find fewer but critical features handled by Random Forest-Bayesian Optimization can realize satisfactory forecast accuracy as well as cost-effective computing time, where we interpret it with Herbert A. Simon’s management decision theory and Pareto principle. Consequently, the results could provide managerial insights and operational guidance for product quality prediction and control at real-life process industry.

ACS Style

TianTeng Wang; Xuping Wang; Ruize Ma; Xiaoyu Li; Xiangpei Hu; Felix T. S. Chan; Junhu Ruan. Random Forest-Bayesian Optimization for Product Quality Prediction With Large-Scale Dimensions in Process Industrial Cyber–Physical Systems. IEEE Internet of Things Journal 2020, 7, 8641 -8653.

AMA Style

TianTeng Wang, Xuping Wang, Ruize Ma, Xiaoyu Li, Xiangpei Hu, Felix T. S. Chan, Junhu Ruan. Random Forest-Bayesian Optimization for Product Quality Prediction With Large-Scale Dimensions in Process Industrial Cyber–Physical Systems. IEEE Internet of Things Journal. 2020; 7 (9):8641-8653.

Chicago/Turabian Style

TianTeng Wang; Xuping Wang; Ruize Ma; Xiaoyu Li; Xiangpei Hu; Felix T. S. Chan; Junhu Ruan. 2020. "Random Forest-Bayesian Optimization for Product Quality Prediction With Large-Scale Dimensions in Process Industrial Cyber–Physical Systems." IEEE Internet of Things Journal 7, no. 9: 8641-8653.

Review
Published: 16 March 2020 in Future Internet
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Internet finance is a financial mode combining traditional financial industry with Internet technologies, which has become a crucial part of the financial field. Due to the rapid change of information technologies and public financial needs, Internet finance has produced quite a few specific operation modes, which have interested many scholars. To better appreciate its development process and innovation modes, we used bibliometrics to analyze 2,877 articles on Internet finance in Web of Science. Through the co-word network, co-citation network and various results generated by CiteSpace, we recognized six main modes of Internet finance, that is, Internet bank, peer to peer lending (P2P lending), crowdfunding, big data finance, digital currency and fintech. Emerging research topics and the development history of each mode are also detected. We find that the mainstream modes in current research are P2P lending and crowdfunding and the research on fintech and digital currency has just begun. Through the review, we also suggest some research directions for the research direction of each mode. These results will help to deepen relevant scholars’ understanding of Internet finance and provide guidance for them to choose research directions.

ACS Style

Xiaoyu Li; Jiahong Yuan; Yan Shi; Zilai Sun; Junhu Ruan. Emerging Trends and Innovation Modes of Internet Finance—Results from Co-Word and Co-Citation Networks. Future Internet 2020, 12, 52 .

AMA Style

Xiaoyu Li, Jiahong Yuan, Yan Shi, Zilai Sun, Junhu Ruan. Emerging Trends and Innovation Modes of Internet Finance—Results from Co-Word and Co-Citation Networks. Future Internet. 2020; 12 (3):52.

Chicago/Turabian Style

Xiaoyu Li; Jiahong Yuan; Yan Shi; Zilai Sun; Junhu Ruan. 2020. "Emerging Trends and Innovation Modes of Internet Finance—Results from Co-Word and Co-Citation Networks." Future Internet 12, no. 3: 52.

Journal article
Published: 01 November 2019 in Processes
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The evaluation of vegetable production process efficiency is of great significance for energy saving and waste reduction in production processes. However, few studies have considered the effect of greenhouse vegetable production process efficiency on energy saving and waste reduction. In this paper, data envelopment analysis (DEA) is used to analyze the production process efficiency and the effective use of input elements of greenhouse vegetables at the provincial level in China. The results reveal that many chemical fertilizers, farmyard manure, and pesticides in China are inefficient. On the other hand, the pure technical efficiency of greenhouse tomatoes and cucumbers is low in most areas of China. Meanwhile, the scale efficiency of greenhouse eggplants and greenhouse peppers is low in most areas of China. In order to save energy and develop green sustainable agriculture, we put forward some suggestions to improve the production efficiency of greenhouse vegetables in different provinces.

ACS Style

Yuhu Liang; Xu Jing; Yanan Wang; Yan Shi; Junhu Ruan. Evaluating Production Process Efficiency of Provincial Greenhouse Vegetables in China Using Data Envelopment Analysis: A Green and Sustainable Perspective. Processes 2019, 7, 780 .

AMA Style

Yuhu Liang, Xu Jing, Yanan Wang, Yan Shi, Junhu Ruan. Evaluating Production Process Efficiency of Provincial Greenhouse Vegetables in China Using Data Envelopment Analysis: A Green and Sustainable Perspective. Processes. 2019; 7 (11):780.

Chicago/Turabian Style

Yuhu Liang; Xu Jing; Yanan Wang; Yan Shi; Junhu Ruan. 2019. "Evaluating Production Process Efficiency of Provincial Greenhouse Vegetables in China Using Data Envelopment Analysis: A Green and Sustainable Perspective." Processes 7, no. 11: 780.

Journal article
Published: 09 July 2019 in IEEE Transactions on Industrial Informatics
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In the era of Internet of people and things, big data are merging. Conventional computation algorithms including correlation measures become inefficient to deal with big data problems. Motivated by this observation, we present three fuzzy correlation measurement algorithms, that is, the centroid based measure, the integral based measure, and the a-cut based measure using fuzzy techniques. Data of Shanghai stock price index (SSI) and exchange rates of main foreign currencies over China Yuan from 22 January 2013 to 17 May 2018 are used to check the effectiveness of our algorithms, and, more importantly, to observe the causality relationship between Shanghai stock price index and these main exchange rates. We observed some findings, as below. (1) The usage of the highest, lowest or closing values in daily exchange rates and stock prices has impact on the significant Granger causes of exchange rates over SSI, but does not produce any opposite cause from SSI to exchange rates; (2) No matter which of our fuzzy measurement algorithms is used, HKDCNY and USDCNY are positively related with SSI, and EURCNY negatively correlated with SSI is always recognized as a Granger cause to Shanghai stock price index with the significance level being 1%; (3) Both the optimism level and the uncertainty level are observed having impact on the correlation coefficients, but the later brings more significant changes to results of Granger causality tests.

ACS Style

Junhu Ruan; Hua Jiang; Jiahong Yuan; Yan Shi; Yuchun Zhu; Felix T. S. Chan; Weizhen Rao. Fuzzy Correlation Measurement Algorithms for Big Data and Application to Exchange Rates and Stock Prices. IEEE Transactions on Industrial Informatics 2019, 16, 1296 -1309.

AMA Style

Junhu Ruan, Hua Jiang, Jiahong Yuan, Yan Shi, Yuchun Zhu, Felix T. S. Chan, Weizhen Rao. Fuzzy Correlation Measurement Algorithms for Big Data and Application to Exchange Rates and Stock Prices. IEEE Transactions on Industrial Informatics. 2019; 16 (2):1296-1309.

Chicago/Turabian Style

Junhu Ruan; Hua Jiang; Jiahong Yuan; Yan Shi; Yuchun Zhu; Felix T. S. Chan; Weizhen Rao. 2019. "Fuzzy Correlation Measurement Algorithms for Big Data and Application to Exchange Rates and Stock Prices." IEEE Transactions on Industrial Informatics 16, no. 2: 1296-1309.

Journal article
Published: 30 April 2019 in IEEE Transactions on Industrial Informatics
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The connection of the physical agriculture with corresponding cyber systems is helpful to achieve precision agriculture. Real-time data from agriculture sensors can provide decision supports to improve the yields and quality of agri-products, but also bring about challenges one of which is how to mine useful information from these vast amounts of data at acceptable computation costs. To deal with the dimension disaster problem faced by most conventional mining algorithms, we combine granulation techniques and genetic algorithm (GA) with support vector machine (SVM) to propose a granular GA-SVM. In the integrated predictor, three granulation methods, that is, Min-Median-Max granulation, Quartile-Median granulation and Fuzzy granulation, are introduced to break down big data in agricultural cyber-physical systems into small-scale granules, and GA is used to find the optimal values of SVM penalty parameter and kernel parameter from the reduced granules. IoT data from Luochuan Apple Experimental Demonstration Station in Shaanxi Province, China verified that the proposed granular GA-SVM predictor is effective to make big data prediction with reduced computation time and equivalent accuracy. Moreover, the predicted environment information could provide guidance for growers achieving precise management of apple planting.

ACS Style

Junhu Ruan; Hua Jiang; Xiaoyu Li; Yan Shi; Felix T. S. Chan; Weizhen Rao. A Granular GA-SVM Predictor for Big Data in Agricultural Cyber-Physical Systems. IEEE Transactions on Industrial Informatics 2019, 15, 6510 -6521.

AMA Style

Junhu Ruan, Hua Jiang, Xiaoyu Li, Yan Shi, Felix T. S. Chan, Weizhen Rao. A Granular GA-SVM Predictor for Big Data in Agricultural Cyber-Physical Systems. IEEE Transactions on Industrial Informatics. 2019; 15 (12):6510-6521.

Chicago/Turabian Style

Junhu Ruan; Hua Jiang; Xiaoyu Li; Yan Shi; Felix T. S. Chan; Weizhen Rao. 2019. "A Granular GA-SVM Predictor for Big Data in Agricultural Cyber-Physical Systems." IEEE Transactions on Industrial Informatics 15, no. 12: 6510-6521.

Journal article
Published: 02 February 2019 in Computers & Industrial Engineering
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To improve the sustainable rescue ability in emergency relief management, this paper addresses the emergency resource allocation problem by simultaneously considering primary and secondary disasters. A conditional probability based scenario tree is proposed to define the correlation between primary and secondary disasters. A multi-objective three-stage stochastic programming model to minimize transportation time, transportation cost and unsatisfied demand. An alternative single-objective model based on fuzzy auxiliary variables of membership is employed to cope with the multi-objective functions. To improve the computational tractability for large-scale cases, we propose an approximation single-stage stochastic programming by taking the worst-case scenario. Our results based on Wenchuan Earthquake show that the solution in this paper outperforms some normal ways. Moreover, by considering secondary disasters, we find that the sustainable rescue ability can be greatly improved than others only considering primary disasters.

ACS Style

Jianghua Zhang; Haiyue Liu; Guodong Yu; Junhu Ruan; Felix T.S. Chan. A three-stage and multi-objective stochastic programming model to improve the sustainable rescue ability by considering secondary disasters in emergency logistics. Computers & Industrial Engineering 2019, 135, 1145 -1154.

AMA Style

Jianghua Zhang, Haiyue Liu, Guodong Yu, Junhu Ruan, Felix T.S. Chan. A three-stage and multi-objective stochastic programming model to improve the sustainable rescue ability by considering secondary disasters in emergency logistics. Computers & Industrial Engineering. 2019; 135 ():1145-1154.

Chicago/Turabian Style

Jianghua Zhang; Haiyue Liu; Guodong Yu; Junhu Ruan; Felix T.S. Chan. 2019. "A three-stage and multi-objective stochastic programming model to improve the sustainable rescue ability by considering secondary disasters in emergency logistics." Computers & Industrial Engineering 135, no. : 1145-1154.

Journal article
Published: 09 August 2018 in Sustainability
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Helicopters and vehicles are often jointly used to transport key relief supplies and respond to disaster situations when supply nodes are far away from demand nodes or the key roads to affected areas are cut off. Emergency transfer centers (ETCs) are often changed due to secondary disasters and further rescue, so the extant intermodal transportation plan of helicopters and vehicles needs to be adjusted accordingly. Disruption management is used to re-plan emergency intermodal transportation with updated ETCs in this study. The basic idea of disruption management is to minimize the negative impact resulting from unexpected events. To measure the impact of updated ETCs on the extant plan, the authors consider three kinds of rescue participators, that is, supply recipients, rescue drivers, and transport schedulers, whose main concerns are supply arrival time, intermodal routes and transportation capacity, respectively. Based on the measurement, the authors develop a recovery model for minimizing the disturbance caused by the updated ETCs and design an improved genetic algorithm to generate solutions for the recovery model. Numerical experiments verify the effectiveness of this model and algorithm and discern that this disruption management method could produce recovery plans with shorter average waiting times, smaller disturbances for all the supply arrival times, intermodal routes and transportation capacity, and shorter running times. The comparison shows the advantage of this disruption management method over the rescheduling method.

ACS Style

Junhu Ruan; Felix T. S. Chan; Xiaofeng Zhao. Re-Planning the Intermodal Transportation of Emergency Medical Supplies with Updated Transfer Centers. Sustainability 2018, 10, 2827 .

AMA Style

Junhu Ruan, Felix T. S. Chan, Xiaofeng Zhao. Re-Planning the Intermodal Transportation of Emergency Medical Supplies with Updated Transfer Centers. Sustainability. 2018; 10 (8):2827.

Chicago/Turabian Style

Junhu Ruan; Felix T. S. Chan; Xiaofeng Zhao. 2018. "Re-Planning the Intermodal Transportation of Emergency Medical Supplies with Updated Transfer Centers." Sustainability 10, no. 8: 2827.

Journal article
Published: 29 January 2018 in IEEE Access
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Internet of Things (IOT) is being widely used especially in industry sectors. The IOT techniques provide more information for the inventory control. With the increased fierce competition in market economy, the supply chain is at the core of a successful enterprise. In today’s context, it is an inevitable trend to optimize the inventory cost of supply chains. Separating all aspects of the supply chain impedes controlling inventory costs of the whole system with traditional approaches. Therefore, in this work we consider supply chains consisting of multiple suppliers, a manufacturer and multiple distributors. The time cost of delayed transportation is integrated into previous studies to construct a new model, which is solved with an immune genetic algorithm. Unlike the genetic algorithm, the memory function and adjustment function of the immune algorithm are included in this algorithm. Different from the immune algorithm, genetic operators of the genetic algorithm are included. The immune genetic algorithm effectively overcomes the disadvantages of the genetic algorithm, improving global search ability and search efficiency. The validity and rationality of the optimized model are assessed in comparison with previous results.

ACS Style

Yingchen Wang; Xiaoxiao Geng; Fan Zhang; Junhu Ruan. An Immune Genetic Algorithm for Multi-Echelon Inventory Cost Control of IOT Based Supply Chains. IEEE Access 2018, 6, 8547 -8555.

AMA Style

Yingchen Wang, Xiaoxiao Geng, Fan Zhang, Junhu Ruan. An Immune Genetic Algorithm for Multi-Echelon Inventory Cost Control of IOT Based Supply Chains. IEEE Access. 2018; 6 (99):8547-8555.

Chicago/Turabian Style

Yingchen Wang; Xiaoxiao Geng; Fan Zhang; Junhu Ruan. 2018. "An Immune Genetic Algorithm for Multi-Echelon Inventory Cost Control of IOT Based Supply Chains." IEEE Access 6, no. 99: 8547-8555.

Research article
Published: 15 February 2017 in Mathematical Problems in Engineering
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Freshness of products and timeliness of delivery are two critical factors which have impact on customer satisfaction in terminal delivery of perishable products. This paper investigates how to make a cost-saving vehicle scheduling for perishable products by maximizing customer satisfaction. Customer satisfaction is defined from the two aspects of freshness and time window. Then we develop a priority function based on customer satisfaction and use the hierarchical clustering method to identify customer service priority. Based on the priority, a multiobjective vehicle scheduling optimization model for perishable products is formulated to maximize customer satisfaction and minimize total delivery costs. To solve the proposed model, a priority-based genetic algorithm (PB-GA) is designed. Numerical experiments and sensitivity analysis are performed to show the validity and advantage of our approach. Results indicate that PB-GA can achieve better solutions than traditional genetic algorithm. The improvement of customer satisfaction is higher than the decrease rate of total costs within a certain shelf life range, which reveals that the proposed method is applicable to the terminal delivery of perishable products.1. IntroductionWith the popularization of online shopping and the improvement of delivery service, more and more customers buy perishable products on Business-to-Customer (B2C) platforms. However, freshness of products and timeliness of delivery are two critical factors affecting customer satisfaction in B2C experience [1]. Thus, it is an important issue to make reasonable delivery plans for perishable products with the consideration of customer satisfaction in terminal delivery.Perishable products, such as fruits, vegetables, and meat, have short delivery timespans. These products may start deteriorating from the moment they are produced and the freshness appears to be decreasing as transportation time elapses until spoilt [2, 3]. Freshness is one of the primary concerns when customers buy perishable products. However, in China, the damage rate of perishable products reaches up to 30%, much higher than the 5% in developed countries. Especially for terminal delivery, it makes great influences on customer’s overall satisfaction. Therefore, distributors require to take into account the freshness factor in terminal delivery planning. Moreover, timeliness is another important factor of customer satisfaction. If distributors are not able to deliver products on time, the customer satisfaction probably decreases.Taking these two factors into consideration, it is a difficulty for logistics service providers to ensure freshness and timeliness during terminal delivery because perishable products need to be handled in a special way not the traditional cost-saving way. From the customer satisfaction view, we study a multiobjective vehicle scheduling problem for perishable products in terminal delivery.To sum up, our contribution includes three aspects. () We define a customer priority function considering the freshness and time window to qualify customer satisfaction and use the hierarchical clustering method to classify customers into different service priorities. () Taking customer service priority as one of constraints, we establish a multiobjective vehicle scheduling optimization model for perishable products. The objective functions consist of maximizing customer satisfaction and minimizing total costs. () Then we design a priority-based genetic algorithm for the proposed model, which could produce satisfactory terminal delivery plans.The remainder of this paper is organized as follows. Section 2 presents a brief review on related studies. In Section 3, a mathematical model of multiobjective vehicle scheduling optimization for perishable products is formulated. To solve the model, a priority-based genetic algorithm is designed in Section 4. In Section 5, numerical experiments and sensitivity analysis are presented to show the validity and advantage of our work. The paper is concluded in Section 6.2. Literature ReviewConsidering customer satisfaction in perishable products delivery has been continuously a concern in both academic research and industry application. In this review, we focus on two parts directly related to delivery problems for perishable products. Firstly, we look into what has been done in perishable products distribution with time windows and the definition of customer satisfaction. Secondly, some related studies on the multiobjective modelling for VRP are briefly reviewed.2.1. VRPTW for Perishable Products and Definition of Customer SatisfactionThe well-known vehicle routing problem with time windows (VRPTW) has been discussed deeply in the literature. In recent years, many scholars have studied VRPTW for perishable products in various aspects. Osvald and Stirn [4] extended a heuristic algorithm for distributing fresh vegetables where perishability was set as a critical factor. The problem was formulated as a VRPTW with time-dependent travel time (VRPTWTD). Considering the randomness of perishable products in delivery process, Hsu et al. [5] extended the VRPTWTD model that the perishability cost was served as a stochastic manner. Chen et al. [6] focused on production scheduling and vehicle routing with time windows to maximize the expected total profit of supplier. Ahumada and Villalobos [7] and Yan et al. [8] followed a produce through all stages of production and distribution to build an integrated model for perishable products. Wang and Yu [9] took different delivery modes into consideration and established a perishable product delivery network to minimize the total costs. Coelho and Laporte [10] compared two suboptimal policies and computed optimal joint replenishment and delivery decisions for perishable products effectively. Firoozi et al. [11] developed an efficient network for storage and perishable products distribution. Ruan and Shi [12] formulated an Internet of Things-based framework for monitoring and assessing the freshness of in-transit fruits. These studies mainly belonged to traditional VRPTW, and most researches took the minimization of total costs as the optimization objective. They converted the damage rate of perishable products into corresponding damage cost and entrusted with different weights for each cost. However, the weighting method has a certain degree of subjectivity. Moreover, perishable products have the characteristic of perishability and the spoilt products can decrease customer satisfaction tremendously. In this sense, the above studies only simply considered the economic perspective and ignored customer satisfaction for distributing perishable products.Some researchers took customer satisfaction into consideration. Rong et al. [13] determined product deterioration by time and temperature. If the customer requirement was not satisfied, a penalty cost would be incurred for the spoilt food. Considering quality time window, Jia et al. [14] formulated a production-distribution-inventory model to control product quality and satisfy customer requirement. Amorim and Almada-Lobo [15] examined relationship between distribution scenarios and the cost-freshness trade-off. Cao et al. [16] introduced fuzzy appointment time to reflect customer preference time window and defined service start time of fuzzy membership functions as customer satisfaction. The above studies play an important role for the following research works, but the studies mainly describe customer satisfaction from the view of delivery timeliness. For perishable products, freshness is a key factor affecting customer satisfaction while these studies do not take into account the perishability.2.2. Multiobjective Modelling for VRPMultiobjective modelling for VRP is commonly used in fields of microcalamities management and emergency logistics, which take humanitarian factors into consideration [17–22]. With perishability concerned in this paper, we mainly concentrate on multiobjective VRP for perishable products. Amorim et al. [23] developed a novel model that decoupled the minimization of delivery costs from the maximization of the freshness state of delivered products. Atashbar and Baboli [24] designed a multiobjective routing problem for perishable items in a disaster relief. In this situation, the first objective was to maximize satisfaction and demands coverage, and the second objective was to minimize the total time needed to transfer perishable products. Bortolini et al. [25] constructed a multiobjective perishable distribution model considering distribution costs, delivery time, and carbon emissions. Rahimi et al. [26] applied social issue to perishable products delivery, aiming at minimizing distribution costs and maximizing social issue which was calculated by vehicle accident rate and number of expired products. Wang et al. [27] proposed an effective distribution route that minimized the total costs and maximized freshness state of delivered products. We noted that the above studies enhanced the traditional multiobjective models for VRP to make them more adaptable for perishable products delivery. However, these studies formulated multiobjective models from different perspectives which made objective functions different from each other. In this paper, our focus is mainly on maximizing customer satisfaction and minimizing total logistics costs.According to the above analysis, we try to define customer satisfaction from the aspects of freshness and time window. Moreover, we combine customer satisfaction with terminal delivery which can make a reasonable vehicle scheduling for perishable products. A multiobjective mathematical model and hybrid algorithm are proposed to solve the problem.3. Problem Description and Formulation3.1. Problem DescriptionThe delivery processes of perishable products are as follows: customers buy perishable products on B2C platform and submit orders to suppliers. When vehicles s

ACS Style

Xuping Wang; Xiaoyu Sun; Jie Dong; Meng Wang; Junhu Ruan. Optimizing Terminal Delivery of Perishable Products considering Customer Satisfaction. Mathematical Problems in Engineering 2017, 2017, 1 -12.

AMA Style

Xuping Wang, Xiaoyu Sun, Jie Dong, Meng Wang, Junhu Ruan. Optimizing Terminal Delivery of Perishable Products considering Customer Satisfaction. Mathematical Problems in Engineering. 2017; 2017 ():1-12.

Chicago/Turabian Style

Xuping Wang; Xiaoyu Sun; Jie Dong; Meng Wang; Junhu Ruan. 2017. "Optimizing Terminal Delivery of Perishable Products considering Customer Satisfaction." Mathematical Problems in Engineering 2017, no. : 1-12.

Research article
Published: 09 February 2017 in Scientific Programming
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As product returns are eroding Internet retail profit, managers are continuously striving for a more scientific and efficient network layout to arrange the returned goods. Based on a three-echelon product returns network, this paper proposes a mixed integer nonlinear programming model with the aim of minimizing total cost and creates a high-efficiency method, the Modified Plant Growth Simulation Algorithm (MPGSA), to optimize the problem. The algorithm handles the objective function and the constraints, respectively, requiring no extrinsic parameters and provides a guiding search direction generated from the assessment of the current solving state. Above all, MPGSA keeps a great balance between concentrating growth opportunities on the outstanding growth points and expanding the searching scope. The improvements give the revaluating and reselecting chances to all growth points in each iteration, enhancing the optimization efficiency. A case study illustrates the effectiveness and robustness of MPGSA compared to its original version, Plant Growth Simulation Algorithm, and other approaches, namely, Genetic Algorithm, Artificial Immune System, and Simulated Annealing. 1. IntroductionProduct returns have been one of the main sources resulting in inefficiency in Internet retail market, eroding retailers’ profits. According to a survey [1], the products returned have occupied 22 percent averagely of the total online retailing amount. Most retailers have viewed this series of costs resulting from product returns as unavoidable items. However, few of them get a clear understanding of the composition of product return losses and cannot master an effective method to avoid the losses.The design of a reverse logistics network for product returns has become an important research content since it grasps the attention of the logistics industry increasingly. Designing a proper returns network can reduce the total cost of the network to provide more space for augmenting the retailing profits and provide customers with satisfactory experiences through the after-sale links and help producers arrange the following works concerning recovering, refreshing, and reproducing. The content of reverse logistics typically includes transporting, storing, recovering, recycling, remanufacturing, redistributing, and discarding. Product returning is a necessary step in the process. Rabinovich et al. [2] and Wood [3] separately found that product type and product return policy would probably influence customers’ decisions on returning in the Internet retail market. Furthermore, retailers can take actions to reduce product returns, such as selling the products that customers can easily retrieve online and evaluate and setting more rigorous return policies [4, 5]. Although it is true that these measures can reduce the amount of returns, the scope of applying them in the actual marketing operation is very narrow, running in the opposite direction of diversification trend of online retail commodity. On the other hand, retailers reluctant to make a strict return policy alienate themselves from the customers. Therefore, retailers should do some self-reflection, looking for a way to improve their service capability and market competitiveness. Undoubtedly, it is one of the important tasks to build a scientific and reasonable reverse logistics network.This paper proposes a mixed integer nonlinear programming model, which corresponds to a three-echelon product returns network, to find out the number and location of initial collection points (ICPs) and centralized return centers (CRCs) required in efficient collection and return systems and the maximum holding time of each ICP for clustering small volumes of returned products into a large shipment. Moreover, this research for the first time introduces the Plant Growth Simulation Algorithm (PGSA) to address the aforementioned problem and transforms it into the Modified Plant Growth Simulation Algorithm (MPGSA) with three improvements. Under the same condition of experiment data, having compared the results using Genetic Algorithm (GA), Artificial Immune System (AIS), and Simulated Annealing (SA), the comparison result shows that the average total cost of the product returns network is reduced significantly using MPGSA, and the solutions have good stability.This paper is organized as follows. In Section 2, relevant studies are reviewed. Section 3 proposes a mathematical model minimizing the total cost of the product return network. Section 4 introduces the MPGSA to solve the problem. In Section 5, we carry out an experiment and analyze the theoretical results and then examine the stability of MPGSA. We carry out a sensitivity analysis to identify the factors influencing the total cost in Section 6 and conclude the work in Section 7.2. Literature ReviewResearches on reverse logistics network design can be categorized into two types [6, 7]: Closed-Loop Reverse Logistics Network Design (CLRLND) and Open-Loop Reverse Logistics Network Design (OLRLND). OLRLND can be called reverse logistics network design in a narrow sense, which encompasses the reverse logistics tasks like reverse distribution planning and returns management. Combination of forward and reverse logistics networks, which concentrates on the design of reverse logistics network, will structure a closed-loop reverse logistics network [7]. Relevant studies of these two types are summarized separately in this section.2.1. Closed-Loop Reverse Logistics Network DesignConsideration of researches on CLRLND is comprehensive, covering all the fields such as the return of goods, product maintenance, product refurbishment, component reuse, refabrication, and discard [6]. Important topics in this area are collection and distribution of products and the coordination between producing plan and reverse logistics. Guide Jr. and van Wassenhove [8] tracked the development of closed-loop supply chain and found that the rise of remanufacturing is the first stage of it. The concept of remanufacturing in the early days was put forward for prolonging the service life of the high-value and low-volume items, such as locomotive engines and aircraft. At that time, the hardest initially was the scale of the problem. Products were composed of tens of thousands of components and parts, which brought about incredible challenges for the arrangement of disassembly, remanufacturing, and reassembly. In Europe, the Waste Electrical and Electronic Equipment Directive (WEEE Directive) became European Law in 2003, setting collection, recycle, and recovery targets for various electrical goods [9]. Environmental and social pressures are forcing managers to improve the holistic operation of their logistics networks.For CLRLND, different researchers have various thoughts for specific problems. Consumer durables generally adopted modular structured design; in other words, they were composed of many modules which are characterized by distinct life cycles. Then, the modules required different recycling and recovering processes. For the modular products such as personal computers, Kaya et al. [10] found that correctly estimating the amount of returns is much more important than correctly estimating the market demand, and they made a suggestion: in the first phase, the manager should decide the optimal number of disassembly and refurbishing sites to open while considering the actual situation of market demand and returns level; in the second phase, the manager should give the operational decision for fixed capacities, like production and inventory rates. Jeihoonian et al. [11] introduced the disassembly tree in the process of assessing the quality status of return streams of durable products. The analysis tool showed decision-makers with a plain tree diagram to evaluate the qualities of modules, parts, and residues, even raw materials. Min et al. [12] studied the spatial and temporal consolidation of returns in a closed-loop supply chain network, which has been widely applied in the forward supply chain design, with the aim of providing a minimum-cost solution for the network design. With the consideration of the probable gain achieved from selling the maintained products, Eskandarpour et al. [13] believed that this part of profit should be incorporated into the overall plan. The work represented a classical direction in CLRLND research changing people’s conception.With the problems being gradually prominent, like environmental degradation and resource scarcity, more and more researchers have taken the environmental factors into account while designing a closed-loop supply chain. Fleischmann et al. [14] proposed a model with the aim of finding a balance between reducing the cost of the recycling network and decreasing the environmental impact. More recently, to find a tradeoff between economic benefit and environmental influence, Chaabane et al. [15] introduced the life-cycle assessment principles in the model and assessed the environmental impact with carbon dioxide emission; the conclusion suggested that the environmental strategy would be meaningful if current legislation and Emission Trading Schemes are strengthened and harmonized at the global level. Guide Jr. et al. [16] made an investigation and then realized that most studies had been seeking the lowest operation cost and set a loose demand on the processing period of returns, which made the reuse ratio of returns very limited. Due to the return delay, the loss of the short-life-cycle and time-sensitive goods was more than 30%, which was contrary to the original intention of pursuing cost optimization. For that, Guide Jr. et al. [16] designed a model to minimize the operation cost of the returns network and maintain the recoverable product value and recommended the time-sensitive industry to react in a timely manner and the time-insensitive industry to pursue profit. Combination of the reverse logistics and the closed-loop supply chain

ACS Style

Xuping Wang; Jian Qiu; Tong Li; Junhu Ruan. A Network Optimization Research for Product Returns Using Modified Plant Growth Simulation Algorithm. Scientific Programming 2017, 2017, 1 -14.

AMA Style

Xuping Wang, Jian Qiu, Tong Li, Junhu Ruan. A Network Optimization Research for Product Returns Using Modified Plant Growth Simulation Algorithm. Scientific Programming. 2017; 2017 ():1-14.

Chicago/Turabian Style

Xuping Wang; Jian Qiu; Tong Li; Junhu Ruan. 2017. "A Network Optimization Research for Product Returns Using Modified Plant Growth Simulation Algorithm." Scientific Programming 2017, no. : 1-14.

Editorial
Published: 25 January 2017 in Scientific Programming
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Junhu Ruan; Xuping Wang; Chengyan Yue; Guo Chen; Minsoo Kim. Optimization Models and Algorithms for Operation and Control with Advanced Information Technologies. Scientific Programming 2017, 2017, 1 -2.

AMA Style

Junhu Ruan, Xuping Wang, Chengyan Yue, Guo Chen, Minsoo Kim. Optimization Models and Algorithms for Operation and Control with Advanced Information Technologies. Scientific Programming. 2017; 2017 ():1-2.

Chicago/Turabian Style

Junhu Ruan; Xuping Wang; Chengyan Yue; Guo Chen; Minsoo Kim. 2017. "Optimization Models and Algorithms for Operation and Control with Advanced Information Technologies." Scientific Programming 2017, no. : 1-2.

Research article
Published: 11 June 2014 in Mathematical Problems in Engineering
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“Last mile” delivery has become one of the bottlenecks of e-logistics. This paper aims to explore the competitiveness of three “Last mile” delivery modes—attended home delivery (AHD), reception box (RB), and collection-and-delivery points (CDPs) in different scenarios, especially in high population density scenario. The advantages and disadvantages of each mode are introduced first. Then each mode’s operation efficiency is solved with different kinds of vehicle routing problem (VRP) models and genetic algorithm (GA). Finally the cost of each mode is calculated on the basis of cost structures and operation efficiencies. The results show that different modes are suitable for different scenarios: (i) AHD and independent reception box work better in a scenario with sparse population or small order quantity; (ii) shared reception box and CDPs are more appropriate in the scenario with high population density and large order quantity, and the better one depends on the cost of labors and facilities; (iii) RB is desirable in some circumstances as delivering fresh vegetables and fruits to the ones living in high-grade communities.

ACS Style

Xuping Wang; Linmin Zhan; Junhu Ruan; Jun Zhang. How to Choose “Last Mile” Delivery Modes for E-Fulfillment. Mathematical Problems in Engineering 2014, 2014, 1 -11.

AMA Style

Xuping Wang, Linmin Zhan, Junhu Ruan, Jun Zhang. How to Choose “Last Mile” Delivery Modes for E-Fulfillment. Mathematical Problems in Engineering. 2014; 2014 ():1-11.

Chicago/Turabian Style

Xuping Wang; Linmin Zhan; Junhu Ruan; Jun Zhang. 2014. "How to Choose “Last Mile” Delivery Modes for E-Fulfillment." Mathematical Problems in Engineering 2014, no. : 1-11.