Automobile manufacturers’ strategic choice in applying green practices: joint application of evolutionary game theory and fuzzy rule-based approach

Satisfying the end-of-life vehicle (ELV) legislations is essential for business activities in some markets. However, with the lack of similar regulations for the treatment of ELV, automotive manufacturers follow different green practices. These practices can be a complex task such as an eco-design strategy or issuing a guideline or manual for the end-of-life phase of the product. Each of these practices also has different impacts on the other players in the market. Several factors contribute to manufacturers’ gain as a result of applying green practices. The uncertainties in these features and the absence of data availability raise the difficulty in the manufacturers’ profit estimation. This paper proposes a new approach to analysing automotive manufacturers’ strategic choice in applying ELV practices considering the competitive advantages of performing these practices and the interaction between players. A joint application of evolutionary game theory and fuzzy rule-based approach is proposed to analyse the strategic behaviour of automotive manufacturers. The proposed model provides a simulation environment for testing the interaction of the different market factors and players’ action. The experimental design highlights the validity of the model and its implications. The result reveals that when we have two populations of players including the market leader in recycling initiatives and market reader; the market elements such as ‘elasticity of demand to price’ and ‘loyalty of consumers to market leader’ play an essential role in determining the stable strategy of the game.     

Robust mixed strategies in fuzzy non-cooperative Nash games

Game theory has traditionally used real-valued utility functions in decision-making problems. However, the real information available to assess these utility functions is normally uncertain, suggesting the use of uncertainty distributions for a more realistic modelling. In this sense, utilities results or pay-offs have been normally modelled with probability distributions, assuming random uncertainty. However, when statistical information is unavailable, probability may not be the most adequate paradigm, and can lead to very large execution times when some real complex problems are addressed. In this article possibility distributions are used to model the uncertainty of utility functions when the strategies are probability distributions (mixed strategies) over a set of original and discrete strategies (pure strategies). Two dual approaches to solve the resulting non-cooperative fuzzy games are proposed: modelling players’ risk aversion, and thus providing realistic conservative strategies. Two examples show the robustness of the strategies obtained with the proposed approaches.     

不完备模糊信息系统

以不完备模糊信息系统为研究对象，建立了其中的模糊相容关系及模糊粗糙上、下近似集。在此基础上，探讨了论域上的模糊覆盖问题并提出了覆盖的3种运算形式；定义了2种新的模糊粗糙熵以讨论不完备模糊信息系统中的不确定性因素，证明了不确定因素的变化与度量强度之间的重要关系；建立了一种度量部分模糊知识依赖的新方法，获得了一些新的定理结果证明。     

基于模糊Kano模型与熵权TOPSIS的产品设计研究

目的 为提供以玩家体验为中心的严肃游戏产品,提出一种融合学习方法(游戏化机制模型、模糊Kano模型、熵权TOPSIS法)的严肃游戏设计方法。方法 首先,根据游戏教育目标结合相关成熟范式建立有针对性的学习机制——游戏化机制模型(Learning Mechanics-Game Mechanics Model),然后对模型中指标进行相关性评价,筛选得到初步符合游戏主题与教育目的的设计要素。其次,使用模糊Kano模型设计问卷调查得到需求分类,以必备需求为核心设计要素,提取期望需求、兴奋需求进行再次筛选排序,计算满意度指标。再次,通过李克特量表收集对满意度指标的评价信息结合熵值法得到具有客观性的设计要素权重,使用逼近理想解法(TOPSIS)确定各设计要素重要性排序。最后,以宣传网络信息安全的实体严肃游戏“DataCapital”作为设计案例,验证了该设计方法的可行性与优越性。结论 认为该方法能较好地帮助严肃游戏的开发、切实提高游戏的玩家满意度,能为其他类似产品提供设计方法借鉴。     

利用模糊模式识别诊断内燃机失火故障的研究

提出了一种基于模糊模式识别的内燃机失火故障综合诊断方法。通过建立简明实用的瞬时转速诊断模型并深入分析瞬时转速信号的波形特点,定义并提取了１０种无量纲的诊断特征参数。在此基础上,利用模糊模式识别技术对这些参数进行信息融合,首先快速识别整机有无失火故障,然后在有必要时再进行故障气缸定位。实验结果表明,该方法可以充分利用多种特征参数的信息互补性,提高了诊断效率和识别正确率,并能满足在线监测和实时诊断的要     

Game theoretic analysis of horizontal carrier coordination with revenue sharing in E-commerce logistics

This paper proposes comparisons among two different competitive power structures with and without horizontal coordination. We conduct a game theoretic analysis on the situation where two logistics service providers compete in an e-commerce logistics market with respect to the order quantity and service level decision on a particular logistics service product. Under a consumer utility-based demand, we acquire the equilibrium order quantity decisions and optimal profits under the centralised setting and decentralised setting. An analytical model is established to examine the existence of the Nash equilibrium in the service game. We then prove that the logistics service demand depends on different competitive power structures with the establishment of Cournot game and Stackelberg game model. The analytical results and the equilibrium analysis of the e-commerce logistics system are investigated. After that, we compare the equilibrium results among the centralised system, Cournot game, and Stackelberg game. To empower the horizontal carrier coordination, we design a revenue sharing contract for the decentralised model. A numerical study illustrates the impact of parameters on the optimal decision variables. In this paper, we prove the effectiveness of horizontal carrier collaboration and explicate the reason for the lower marginal profit in logistics theoretically.     

The Coalition Cooperative Game Method and Its Application in Multi-objective Optimization Design

This paper proposes a multi-objective optimization design method based on the coalition cooperative game theory where the three design goals have been seen as three game players.By calculating the affecting factors and fuzzy clustering,the design variables are divided into different strategic spaces which belong to each player,then it constructs a payoff function based on the coalition mechanism.Each game player takes its own revenue function as a target and obtains the best strategy versus other players.Th...     

e-Commerce and supply chains: Modelling of dynamics through fuzzy enhanced high level petri net

Although information plays a major role in effective functioning of supply chain networks (SCNs), studies that deal specifically with the dynamics of supply chains are few. This problem is relatively new since fast communications and the means to employ it for effective management of supply chains did not exist till recently. In order to provide a vehicle for dynamic modelling and analysis of supply chain operations in vague and uncertain environments, we propose a fuzzy enhanced high level petri net (FEHLPN) model. The proposed model captures the capability of petri nets for graphical and analytical representation of dynamic SCNs with the management of uncertain information provided by fuzzy logic. The dynamics associated with two production planning and control policies are modelled, viz. make-to-stock and assemble-to-order in vague and ambiguous situations in electronic commerce environment. A fuzzy set and fuzzy truth-values are attached to an uncertain fuzzy token to model imprecision and uncertainty. The proposed FEHLPN incorporates essential aspects of rule-based systems, such as conservation of facts, refraction, and closed-world assumption.     

带有模糊参数的电子废弃物回收网络优化设计模型

考虑到不确定的回收量会影响回收网络的稳健性,提出了一种带有模糊参数的逆向物流网络优化设计模型.该模型针对不确定的回收量,借助模糊线性规划的思想和解决方法,用模糊参数来描述网络中不确定的回收量,并利用容差法将模型清晰化求解.通过该模型,管理者可以由模糊的回收量得到优化的回收网络.通过算例验证了该方法的可行性.     

温黄平原防汛调度模糊控制方法

 针对温黄平原河网调度所具有的影响因素多、存在范围广、工程难度大、地形复杂、信息采集不完备等特点，提出一种基于数据挖掘的模糊分类算法。该算法极大程度地利用有限的历史数据，对模型输入输出量进行抽象，根据对历史主干河道水位、降雨等记录值的学习并建模，解决了存在的调度问题。通过工程实践，对温黄平原各个主要闸门进行实时调度，调整流域内主要河道水位，取得了较好的工程效果，同时验证了工程的实用性与有效性。     

Optimal vehicle-dispatch decisions for cement silos using a fuzzy multi-objective approach

In real dispatch problems for cement-silo trucks, input data or parameters, such as forecasting demand, resources, costs, and the objective function, are often imprecise or fuzzy because some information is incomplete, unavailable, or unobtainable. This work presents a novel fuzzy multi-objective linear programming (FMOLP) model that solves the cement-silo vehicle-dispatch problem in a fuzzy environment. This model is applied to solve multi-source, multi-product, multi-vehicle, and multi-ready-mixed-concrete (RMC) plant vehicle-dispatch problems with imprecise goals, input data, and parameters. This work elucidates the relationship between dispatch planning and RMC plants with a focus on the allocation of cement-silo trucks. This work uses a real cement study case to demonstrate the feasibility of the proposed model. The main contribution of this work is its fuzzy mathematical programming methodology for solving the cement-silo vehicle-dispatch problem in a fuzzy environment. The analytical results can help dispatchers analyse systematically the cost-effectiveness of vehicle-dispatch planning in practical applications.     

一种粗模糊神经分类器

介绍一种新的粗集编码模糊神经分类器。基于粗集理论的概念，讨论了知识编码、属性简化、分类系统简化的方法；并利用模糊隶属度函数将输入精确信息映射为模糊变量信息，解决分类中病态定义的数据问题和提高系统非线性映射的分类能力；提出了结合系统参数的重要性因子的网络的模糊推理方法和粗模糊神经分类器的网络结构以及有导师的最小平方误差学习训练算法。实现的粗集编码模糊神经分类器具有网络结构空间维数低、学习算法简单、网络训练时间短、非线性特性丰富等优点。     

Integrated fuzzy preference relations with decision utilities for construction contractor selection

The construction contractor selection process aims to choose the most appropriate bidder to complete construction projects within predefined constraints and achieve expected economic advantages. How to avoid elevated risk of not implementing contracted obligation is an important task in contractor selection. This article proposes a ‘Multi-Criteria Prospect Model’ to support a construction contractor selection process in which two considered criteria are the same as defined in the European Union Legislation and the construction cost and completion time are taken into account. Through this transparent competition model, not only utility but also implementation probability (as provided in the bidder's tender promise) can be effectively evaluated by clients. In the model, implementation probabilities are evaluated using fuzzy preference relations, and predefined decision utility curves allow each bid to provide utility to the client. Both probability and utility are integrated using the cumulative prospect theorem. Finally, an empirical case is studied to illustrate the proposed model.     

Comparison of Bertrand and Cournot competitions under random yield

We look at a Bertrand model in which each firm may be unreliable with random yield, so the total quantity brought into market is uncertain. Under mild conditions, the Bertrand model with random yield has a unique Nash equilibrium, in which the prices and production quantities are determined by each firm’s production cost and reliability. In the case of symmetric firms, we compare Bertrand competition with Cournot competition by numerical examples, and find that Bertrand competition yields lower prices and less profits than Cournot competition. Furthermore, in the case of symmetric firms with 0–1 yields, we explicitly show that Bertrand competition yields lower prices and less profits than Cournot competition, and the comparison between the quantities of Bertrand and Cournot competition is dependent on the value of reliability. When the reliability is high, Cournot competition yields less quantities than Bertrand competition. Otherwise, the other hand holds.     

Hesitant fuzzy programming technique for multidimensional analysis of hesitant fuzzy preferences

The linear programming technique for multidimensional analysis of preferences (LINMAP) is the most representative method for handling the multiple criteria decision making (MCDM) problems with respect to the preference information over alternatives. This paper utilizes the main structure of LINMAP to develop a novel hesitant fuzzy mathematical programming technique to handle MCDM problems within the decision environment of hesitant fuzzy elements (HFEs). Considering the hesitancy of the decision maker, both the pair-wise comparison preference information over alternatives and the evaluation information of alternatives with criteria are represented by the HFEs. Based on the incomplete pair-wise preference judgments over alternatives, we propose the concepts of the hesitant fuzzy consistency and inconsistency indices. Furthermore, we construct a hesitant fuzzy mathematical programming model to derive the weights of criteria and the positive-ideal solution. In this hesitant fuzzy programming model both the objective function and partial constraints’ coefficients take the form of HFEs, and an effective approach based on the ranking method of HFEs is further developed to solve the new derived model. To address the incomplete and inconsistent preference structures of criteria weights, we introduce several deviation variables and establish the bi-objective nonlinear programming model. At length, we employ a green supplier selection problem to illustrate the feasibility and applicability of the proposed technique and conduct a comparison analysis to validate its effectiveness.     

Mixed model disassembly line balancing problem with fuzzy goals

The collection of used products is the driving force of remanufacturing systems and enterprises can gain significant economic, technical and social benefits from recycling. All products are disassembled up to some level in remanufacturing systems. The best way to disassemble returned products is valid by a well-balanced disassembly line. In this paper, a mixed integer programming (MIP) model is proposed for a mixed model disassembly line balancing (MMDLB) problem with multiple conflicting objectives: (1) minimising the cycle time, (2) minimising the number of disassembly workstations and (3) providing balanced workload per workstation. In most real world MMDLB problems, the targeted goals of decision makers are frequently imprecise or fuzzy because some information may be incomplete and/or unavailable over the planning horizon. This study is the first in the literature to offer the binary fuzzy goal programming (BFGP) and the fuzzy multi-objective programming (FMOP) approaches for the MMDLB problem in order to take into account the vague aspirations of decision makers. An illustrative example based on two industrial products is presented to demonstrate the validity of the proposed models and to compare the performances of the BFGP and the FMOP approaches.     

Fuzzy FMEA in risk assessment for test and calibration laboratories

In this study, it is aimed to compare traditional and fuzzy FMEA in identifying areas that may pose risks and need improvement in Test and Calibration Laboratories. Within this scope, FMEA is used in ranking the possible risks. One hundred ninety-nine failures are detected in 91 inspections, carried out in the Test and Calibration Laboratories. Since FMEA uses experts’ evaluations, which are considered subjective, fuzzy logic is implemented to the approach where the evaluations are presented with linguistic variables. The comparison of FMEA and fuzzy FMEA showed that there exists a high correlation between these two analyses and the order of priority based on the Fuzzy Risk Priority Number calculation is overlapping with the Risk Priority Number sequence. Fuzzy FMEA can also be considered when the evaluations are not trustworthy or incomplete. Therefore, this study can be addressed as an example of how fuzzy implementation to FMEA substantially be used instead of traditional FMEA when there exist qualitative, subjective or incomplete evaluations, or in cases where traditional FMEA has troubles in practice.     

Fermatean Hesitant Fuzzy Prioritized Heronian Mean Operator and Its Application in Multi-Attribute Decision Making

In real life, incomplete information, inaccurate data, and the preferences of decision-makers during qualitative judgment would impact the process of decision-making. As a technical instrument that can successfully handle uncertain information, Fermatean fuzzy sets have recently been used to solve the multi-attribute decision-making (MADM) problems. This paper proposes a Fermatean hesitant fuzzy information aggregation method to address the problem of fusion where the membership, non-membership, and priority are considered simultaneously. Combining the Fermatean hesitant fuzzy sets with Heronian Mean operators, this paper proposes the Fermatean hesitant fuzzy Heronian mean (FHFHM) operator and the Fermatean hesitant fuzzy weighted Heronian mean (FHFWHM) operator. Then, considering the priority relationship between attributes is often easier to obtain than the weight of attributes, this paper defines a new Fermatean hesitant fuzzy prioritized Heronian mean operator (FHFPHM), and discusses its elegant properties such as idempotency, boundedness and monotonicity in detail. Later, for problems with unknown weights and the Fermatean hesitant fuzzy information, a MADM approach based on prioritized attributes is proposed, which can effectively depict the correlation between attributes and avoid the influence of subjective factors on the results. Finally, a numerical example of multi-sensor electronic surveillance is applied to verify the feasibility and validity of the method proposed in this paper.     

Constructing a new fuzzy classifier based on hierarchical fuzzy entropy

Abstract

In an earlier work, Lee et al. (Lee et al., 2001) presented a simple and fast fuzzy classifier that employed fuzzy entropy to evaluate pattern distribution information in a pattern space. In this paper, we extend his work to propose a new fuzzy classifier based on hierarchical fuzzy entropy (FC‐HFE). We retained the main parts of the original structure and modified some methods (e.g., methods for deciding the number of intervals in each dimension and for assigning class labels). In addition, the hierarchical fuzzy entropy is proposed for partitioning the decision region. The proposed FC‐HFE improves classification accuracy and overcomes some of the drawbacks in the Lee et al method (Lee et al., 2001). The simulation results show that the classification rate of the proposed FC‐HFE is better than earlier methods.