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In competitive global markets, sustainable suppliers are critical success factors for sustainable supply chain operations. Sustainable supplier selection must be based on a complex network of numerous indicators and experts’ fuzzy linguistic terms. Considering the correlation between the evaluation criteria and the ambiguity of the criteria values, this paper proposes combining the rough DEMATEL method and the fuzzy VIKOR (FVIKOR) method to solve sustainable supplier selection problem. We determine 15 sustainable supplier evaluation criteria from economic, environmental and social dimensions. We also apply the rough DEMATEL method to determine the weight of evaluation indicators that are interrelated or even conflicting and use the FVIKOR method to determine supplier rankings by converting the fuzzy linguistic terms into precise information. The practicability of the proposed method is verified by an example of sustainable supplier selection.
Jing Zhang; Dong Yang; Qiang Li; Benjamin Lev; Yanfang Ma. Research on Sustainable Supplier Selection Based on the Rough DEMATEL and FVIKOR Methods. Sustainability 2020, 13, 88 .
AMA StyleJing Zhang, Dong Yang, Qiang Li, Benjamin Lev, Yanfang Ma. Research on Sustainable Supplier Selection Based on the Rough DEMATEL and FVIKOR Methods. Sustainability. 2020; 13 (1):88.
Chicago/Turabian StyleJing Zhang; Dong Yang; Qiang Li; Benjamin Lev; Yanfang Ma. 2020. "Research on Sustainable Supplier Selection Based on the Rough DEMATEL and FVIKOR Methods." Sustainability 13, no. 1: 88.
Alleviating poverty is a critical problem in many developing countries such as China. In this paper, we consider a poverty-alleviation supply chain composed of one supplier in a poor area and one producer helping the supplier reduce poverty by fulfilling Corporate Social Responsibility (CSR). Our work aims at examining the impacts of government subsidies and Corporate Social Responsibility (CSR) on the poverty-alleviation operations. Four game-theoretic models are constructed and analyzed to investigate the impacts of coefficients of government subsidies and CSR cost sharing on the supplier’s and producer’s profits, social welfare growth, CSR level, wholesale price, output of the supplier, and retail price. Our findings suggest that the most effective poverty-alleviation mechanism in most cases is the combination of government subsidies and market efforts. Contrary to common beliefs that companies have to sacrifice profit for social responsibility, we show that poverty alleviation is reconcilable with profit maximization and social welfare improvement, and companies can achieve a win-win situation of both poverty alleviation and profitability. Our work provides new insights for sustainable poverty alleviation and socially sustainable operations.
Kai Kang; Xinfeng Luan; Wenjing Shen; Yanfang Ma; Xuguang Wei. The Strategies of the Poverty-Alleviation Supply Chain with Government Subsidies and Cost Sharing: Government-Led or Market-Oriented? Sustainability 2020, 12, 4050 .
AMA StyleKai Kang, Xinfeng Luan, Wenjing Shen, Yanfang Ma, Xuguang Wei. The Strategies of the Poverty-Alleviation Supply Chain with Government Subsidies and Cost Sharing: Government-Led or Market-Oriented? Sustainability. 2020; 12 (10):4050.
Chicago/Turabian StyleKai Kang; Xinfeng Luan; Wenjing Shen; Yanfang Ma; Xuguang Wei. 2020. "The Strategies of the Poverty-Alleviation Supply Chain with Government Subsidies and Cost Sharing: Government-Led or Market-Oriented?" Sustainability 12, no. 10: 4050.
Group decision making (GDM) problems require consensus reaching processes; however, these can be time consuming and costly. As experts change their evaluations after exchanging opinions and being influenced by others, these influences are spread across the various expert trust relationships. Because of the experts’ knowledge limits, the evaluations on the alternatives and the trust relationships are generally described using probabilistic linguistic terms. Therefore, to simplify the decision making process and avoid decision bias, this paper proposes a particle swarm optimization method that incorporates a trust relationship based social network for GDM under a probabilistic linguistic environment. Each expert is regarded as a particle that moves toward the final evaluation and reaches the threshold. A fitness function is built to measure the consensus levels, and the updated function is improved by the trust relationships to derive the new evaluations. A numerical example is then given to illustrate the feasibility of the proposed approach and comparisons given to further elucidate its novelty and validity.
Xiaoyang Zhou; Feipeng Ji; Liqin Wang; Yanfang Ma; Hamido Fujita. Particle swarm optimization for trust relationship based social network group decision making under a probabilistic linguistic environment. Knowledge-Based Systems 2020, 200, 105999 .
AMA StyleXiaoyang Zhou, Feipeng Ji, Liqin Wang, Yanfang Ma, Hamido Fujita. Particle swarm optimization for trust relationship based social network group decision making under a probabilistic linguistic environment. Knowledge-Based Systems. 2020; 200 ():105999.
Chicago/Turabian StyleXiaoyang Zhou; Feipeng Ji; Liqin Wang; Yanfang Ma; Hamido Fujita. 2020. "Particle swarm optimization for trust relationship based social network group decision making under a probabilistic linguistic environment." Knowledge-Based Systems 200, no. : 105999.
Purpose The purpose of this paper is to study a transportation service procurement bid construction problem from a less than a full truckload perspective. It seeks to establish stochastic mixed integer programming to allow for the proper bundle of loads to be chosen based on price, which could improve the likelihood that carrier can earn its maximum utility. Design/methodology/approach The authors proposes a bi-level programming that integrates the bid selection and winner determination and a discrete particle swarm optimization (PSO) solution algorithm is then developed, and a numerical simulation is used to make model and algorithm analysis. Findings The algorithm comparison shows that although GA could find a little more Pareto solutions than PSO, it takes a longer time and the quality of these solutions is not dominant. The model analysis shows that compared with traditional approach, our model could promote the likelihood of winning bids and the decision effectiveness of the whole system because it considers the reaction of the shipper. Originality/value The highlights of this paper are considering the likelihood of winning the business and describing the conflicting and cooperative relationship between the carrier and the shipper by using a stochastic mixed integer programming, which has been rarely examined in previous research.
Fang Yan; Yanfang Ma; Cuiying Feng. A bi-level programming for transportation services procurement based on combinatorial auction with fuzzy random parameters. Asia Pacific Journal of Marketing and Logistics 2018, 30, 1162 -1182.
AMA StyleFang Yan, Yanfang Ma, Cuiying Feng. A bi-level programming for transportation services procurement based on combinatorial auction with fuzzy random parameters. Asia Pacific Journal of Marketing and Logistics. 2018; 30 (5):1162-1182.
Chicago/Turabian StyleFang Yan; Yanfang Ma; Cuiying Feng. 2018. "A bi-level programming for transportation services procurement based on combinatorial auction with fuzzy random parameters." Asia Pacific Journal of Marketing and Logistics 30, no. 5: 1162-1182.
Although construction supply chain management has attracted significant research attention, this field remains somewhat fragmented. This paper examines an integrated production-distribution-construction system consisting of the construction department and material suppliers under a fuzzy random environment with the aim of optimizing the global equilibrium. A novel bi-level multistage programming method with multiple objective optimization is developed to examine the inherent conflicts and complex interactions among decision makers in order to obtain the Stackelberg-Nash equilibrium solution, in which the construction department, as the leader, decides on the material allocations to construction sites, while the material supplier, as the follower, produces and transports the corresponding materials. For dealing with uncertainties, a hybrid crisp approach with an expected value operator is proposed to convert the fuzzy random parameters into definitive parameters. A hybrid algorithm combining an evolved genetic algorithm and particle swarm optimization is developed to solve this novel Stackelberg game model. The results from a practical example demonstrate the practicality and efficiency of the proposed optimization method, highlight the significance of quantitative analysis for the construction supply chain, and provide objective guidelines for its real-world application.
Cuiying Feng; Yanfang Ma; Gengui Zhou; Ting Ni. Stackelberg game optimization for integrated production-distribution-construction system in construction supply chain. Knowledge-Based Systems 2018, 157, 52 -67.
AMA StyleCuiying Feng, Yanfang Ma, Gengui Zhou, Ting Ni. Stackelberg game optimization for integrated production-distribution-construction system in construction supply chain. Knowledge-Based Systems. 2018; 157 ():52-67.
Chicago/Turabian StyleCuiying Feng; Yanfang Ma; Gengui Zhou; Ting Ni. 2018. "Stackelberg game optimization for integrated production-distribution-construction system in construction supply chain." Knowledge-Based Systems 157, no. : 52-67.
There is a growing concern that business enterprises focus primarily on their economic activities and ignore the impact of these activities on the environment and the society. This paper investigates a novel sustainable inventory-allocation planning model with carbon emissions and defective item disposal over multiple periods under a fuzzy random environment. In this paper, a carbon credit price and a carbon cap are proposed to demonstrate the effect of carbon emissions’ costs on the inventory-allocation network costs. The percentage of poor quality products from manufacturers that need to be rejected is assumed to be fuzzy random. Because of the complexity of the model, dynamic programming-based particle swarm optimization with multiple social learning structures, a DP-based GLNPSO, and a fuzzy random simulation are proposed to solve the model. A case is then given to demonstrate the efficiency and effectiveness of the proposed model and the DP-based GLNPSO algorithm. The results found that total costs across the inventory-allocation network varied with changes in the carbon cap and that carbon emissions’ reductions could be utilized to gain greater profits.
Kai Kang; Wei Pu; Yanfang Ma. A Dynamic Programming-Based Sustainable Inventory-Allocation Planning Problem with Carbon Emissions and Defective Item Disposal under a Fuzzy Random Environment. Mathematical Problems in Engineering 2018, 2018, 1 -18.
AMA StyleKai Kang, Wei Pu, Yanfang Ma. A Dynamic Programming-Based Sustainable Inventory-Allocation Planning Problem with Carbon Emissions and Defective Item Disposal under a Fuzzy Random Environment. Mathematical Problems in Engineering. 2018; 2018 ():1-18.
Chicago/Turabian StyleKai Kang; Wei Pu; Yanfang Ma. 2018. "A Dynamic Programming-Based Sustainable Inventory-Allocation Planning Problem with Carbon Emissions and Defective Item Disposal under a Fuzzy Random Environment." Mathematical Problems in Engineering 2018, no. : 1-18.
This paper presents mixed integer programming for a transportation service procurement bid construction problem from a less than full truckload perspective, in which the bidders (carriers) generate their best bid (package) using a bundled price to maximize their utility and increase the chance of winning the business. The models are developed from both the carriers and shippers perspectives to establish a relationship between the quoted price and the likelihood of winning to assist the carriers in balancing the potential benefits and the possibility of winning the bid. An intelligent algorithm based on Particle Swarm Optimization is then designed to solve the proposed model and hypothetical data sets are used to test the effectiveness and efficiency of the proposed model and algorithm.
Fang Yan; Yanfang Ma; Manjing Xu; Xianlong Ge. Transportation Service Procurement Bid Construction Problem from Less Than Truckload Perspective. Mathematical Problems in Engineering 2018, 2018, 1 -17.
AMA StyleFang Yan, Yanfang Ma, Manjing Xu, Xianlong Ge. Transportation Service Procurement Bid Construction Problem from Less Than Truckload Perspective. Mathematical Problems in Engineering. 2018; 2018 ():1-17.
Chicago/Turabian StyleFang Yan; Yanfang Ma; Manjing Xu; Xianlong Ge. 2018. "Transportation Service Procurement Bid Construction Problem from Less Than Truckload Perspective." Mathematical Problems in Engineering 2018, no. : 1-17.
Recycling waste products is an environmental-friendly activity that can result in manufacturing cost saving and economic efficiency improving. In the beer industry, recycling bottles can reduce manufacturing cost and the industry’s carbon footprint. This paper presents a model for a collection-distribution center location and allocation problem in a closed-loop supply chain for the beer industry under a fuzzy random environment, in which the objectives are to minimize total costs and transportation pollution. Both random and fuzzy uncertainties, for which return rate and disposal rate are considered fuzzy random variables, are jointly handled in this paper to ensure a more practical problem solution. A heuristic algorithm based on priority-based global-local-neighbor particle swarm optimization (pb-glnPSO) is applied to ensure reliable solutions for this NP-hard problem. A beer company case study is given to illustrate the application of the proposed model and to demonstrate the priority-based global-local-neighbor particle swarm optimization.
Kai Kang; Xiaoyu Wang; Yanfang Ma. A Collection-Distribution Center Location and Allocation Optimization Model in Closed-Loop Supply Chain for Chinese Beer Industry. Mathematical Problems in Engineering 2017, 2017, 1 -15.
AMA StyleKai Kang, Xiaoyu Wang, Yanfang Ma. A Collection-Distribution Center Location and Allocation Optimization Model in Closed-Loop Supply Chain for Chinese Beer Industry. Mathematical Problems in Engineering. 2017; 2017 ():1-15.
Chicago/Turabian StyleKai Kang; Xiaoyu Wang; Yanfang Ma. 2017. "A Collection-Distribution Center Location and Allocation Optimization Model in Closed-Loop Supply Chain for Chinese Beer Industry." Mathematical Problems in Engineering 2017, no. : 1-15.
Recycling waste products is an environmental-friendly activity that can bring benefits to accompany, saving manufacturing costs and improving economic efficiency. For the beer industry, recycling bottles can reduce manufacturing costs and reduce the industry's carbon footprint. This paper presents a model for a multi-objective collection-distribution center location and allocation problem in a closed loop supply chain for the beer industry, in which the objective is to minimize total costs and transportation pollution. Uncertainties in the form of randomness and fuzziness are jointly handled in this paper to ensure a more practical problem solution, for which returned bottle sand unusable bottles are considered fuzzy random variables. A heuristic algorithm based on priority-based global-local-neighbor particle swarm optimization (pb-glnPSO) is applied to ensure reliable solutions for this NP-hard problem. A case study on a beer operation company is conducted to illustrate the application of the proposed model and demonstrate the priority-based global-local-neighbor particle swarm optimization.
Kang Kai; Xiaoyu Wang; Yanfang Ma. A Multi-Objective Collection-Distribution Center Location and Allocation Problem in a Closed-Loop Supply Chain for the Chinese Beer Industry. 2016, 1 .
AMA StyleKang Kai, Xiaoyu Wang, Yanfang Ma. A Multi-Objective Collection-Distribution Center Location and Allocation Problem in a Closed-Loop Supply Chain for the Chinese Beer Industry. . 2016; ():1.
Chicago/Turabian StyleKang Kai; Xiaoyu Wang; Yanfang Ma. 2016. "A Multi-Objective Collection-Distribution Center Location and Allocation Problem in a Closed-Loop Supply Chain for the Chinese Beer Industry." , no. : 1.
In this paper, a dynamic programming model is proposed for a joint pricing construction materials procurement problem with multiple suppliers (JPCMPPMS) in a fuzzy random environment. In this model, the objective of the leader is to minimize total costs by deciding the purchase quantity. Demand and transport price are assumed to be fuzzy random variables in this paper. A dynamic programming-based genetic algorithm (DP-based GA) is developed to find feasible solutions and a dynamic programming-based initialization, crossover and mutation are designed to avoid infeasible solutions. The model and the proposed solution procedure are very practical and effective, and raw material purchasing system will achieve the overall best economic interests.
Kai Kang; Shuyue Zhou; Yanfang Ma; Xuguang Wei. A Dynamic Programming-Based Genetic Algorithm for a Joint Pricing Construction Materials Procurement Problem with Uncertainties. Advances in Intelligent Systems and Computing 2016, 297 -305.
AMA StyleKai Kang, Shuyue Zhou, Yanfang Ma, Xuguang Wei. A Dynamic Programming-Based Genetic Algorithm for a Joint Pricing Construction Materials Procurement Problem with Uncertainties. Advances in Intelligent Systems and Computing. 2016; ():297-305.
Chicago/Turabian StyleKai Kang; Shuyue Zhou; Yanfang Ma; Xuguang Wei. 2016. "A Dynamic Programming-Based Genetic Algorithm for a Joint Pricing Construction Materials Procurement Problem with Uncertainties." Advances in Intelligent Systems and Computing , no. : 297-305.
This paper investigates a novel inventory and distribution planning model with non-conforming items disposal (NIDPNCID) under fuzzy random environment to minimize the whole process cost. In this process, a certain fraction or a random number of produced items are defective. These non-conforming items are rejected in order to improve the consumer satisfaction. To solve the problem, a dynamic programming-based particle swarm optimization (DP-based PSO) algorithm with fuzzy random simulation is proposed, which can be easy to implement. In more specific terms, DP-based PSO can reduce the dimensions of a particle by using the state equation, which significantly reduced the solution space.
Kai Kang; Wei Pu; Yanfang Ma; Xuguang Wei. A Novel Inventory and Distribution Planning Model with Non-conforming Items Disposal Under Fuzzy Random Environment. Advances in Intelligent Systems and Computing 2016, 1191 -1201.
AMA StyleKai Kang, Wei Pu, Yanfang Ma, Xuguang Wei. A Novel Inventory and Distribution Planning Model with Non-conforming Items Disposal Under Fuzzy Random Environment. Advances in Intelligent Systems and Computing. 2016; ():1191-1201.
Chicago/Turabian StyleKai Kang; Wei Pu; Yanfang Ma; Xuguang Wei. 2016. "A Novel Inventory and Distribution Planning Model with Non-conforming Items Disposal Under Fuzzy Random Environment." Advances in Intelligent Systems and Computing , no. : 1191-1201.
This paper considers fuzzy random shipments and seeks to optimize transportation service prices from a shipper’s perspective. To determine suitable prices, the carriers’ existing networks and possible reactions are considered, so that the shipper can easily and effectively purchase the required services while optimizing total cost. Two objectives are considered for the shippers, minimizing total cost and total risk. For the carrier, the objective is to maximize carrier profits. Multi-objective expected bi-level programming is developed to incorporate the decision makers’ preferences. After establishing the model with uncertain data, multi-objective particle swarm optimization is proposed to solve the model. A case study and analysis are then presented to illustrate the effectiveness of the proposed model and approach.
Fang Yan; Manjing Xu; Yanfang Ma; Haiyan Yu. Price optimization for transportation service procurement with fuzzy random shipments: from shipper’s perspective. Transportation Letters 2016, 9, 258 -275.
AMA StyleFang Yan, Manjing Xu, Yanfang Ma, Haiyan Yu. Price optimization for transportation service procurement with fuzzy random shipments: from shipper’s perspective. Transportation Letters. 2016; 9 (5):258-275.
Chicago/Turabian StyleFang Yan; Manjing Xu; Yanfang Ma; Haiyan Yu. 2016. "Price optimization for transportation service procurement with fuzzy random shipments: from shipper’s perspective." Transportation Letters 9, no. 5: 258-275.
In this paper, an integrated production-distribution planning model using bi-level programming is proposed for supply chain management. In the bi-level model, the core firm is the leader in the hierarchal process that decides which plants and warehouses to open to serve customers to minimize total global cost. In the lower level, the production branch and distribution branch managers aim to minimize costs in their respective branches and make decisions based on the core firm’s decisions. A hybrid priority-based two stage genetic algorithm with a fuzzy logic controller algorithm is developed to solve the proposed model. Finally, construction material transportation at the Lancang River Hydropower Base is taken as a real world example to demonstrate the practicality and efficiency of the optimization model and the algorithm.
Yanfang Ma; Fang Yan; Kai Kang; Xuguang Wei. A novel integrated production-distribution planning model with conflict and coordination in a supply chain network. Knowledge-Based Systems 2016, 105, 119 -133.
AMA StyleYanfang Ma, Fang Yan, Kai Kang, Xuguang Wei. A novel integrated production-distribution planning model with conflict and coordination in a supply chain network. Knowledge-Based Systems. 2016; 105 ():119-133.
Chicago/Turabian StyleYanfang Ma; Fang Yan; Kai Kang; Xuguang Wei. 2016. "A novel integrated production-distribution planning model with conflict and coordination in a supply chain network." Knowledge-Based Systems 105, no. : 119-133.