不确定偏好信息下质量管理的含糊群体决策

为实现质量管理的定量决策和信息集结,将质量管理决策问题归结为偏好信息不确定下群体决策问题.建立了质量管理多属性群体决策模型,分析了决策中语言偏好信息和权重偏好信息的不确定性.采用双重模糊的含糊集表达决策语言的不确定性,同时将模糊集的基本运算引入到含糊集中,定义了四种处理权重偏好信息的决策函数,分别实现了质量管理决策量化结果的集结处理.比较了这些决策函数在权重偏好信息不同的情况下的集结行为,通过案例计算验证了这些决策函数对信息集结的不同效果和应用场合.     

Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection

Facility location selection is a multi-criteria decision problem and has a strategic importance for many companies. The conventional methods for facility location selection are inadequate for dealing with the imprecise or vague nature of linguistic assessment. To overcome this difficulty, fuzzy multi-criteria decision-making methods are proposed. The aim of this study is to use fuzzy analytic hierarchy process (AHP) and the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) methods for the selection of facility location. The proposed methods have been applied to a facility location selection problem of a textile company in Turkey. After determining the criteria that affect the facility location decisions, fuzzy AHP and fuzzy TOPSIS methods are applied to the problem and results are presented. The similarities and differences of two methods are also discussed.     

A practical taxonomy of methods and literature for managing uncertain spatial data in geographic information systems

Perfect information is seldom available to man or machines due to uncertainties inherent in real world problems. Uncertainties in geographic information systems (GIS) stem from either vague/ambiguous or imprecise/inaccurate/incomplete information and it is necessary for GIS to develop tools and techniques to manage these uncertainties. There is a widespread agreement in the GIS community that although GIS has the potential to support a wide range of spatial data analysis problems, this potential is often hindered by the lack of consistency and uniformity. Uncertainties come in many shapes and forms, and processing uncertain spatial data requires a practical taxonomy to aid decision makers in choosing the most suitable data modeling and analysis method. In this paper, we: (1) review important developments in handling uncertainties when working with spatial data and GIS applications; (2) propose a taxonomy of models for dealing with uncertainties in GIS; and (3) identify current challenges and future research directions in spatial data analysis and GIS for managing uncertainties.     

A fuzzy logic-based approach in capacity-planning problems

In capacity-planning systems, various sources of uncertainty and imprecision are encountered. In most cases, the uncertainty is determined by the subjective beliefs of managers linguistically. However, the measurement of mangers’ judgments is difficult and vague. Therefore, a fuzzy logic-based approach is proposed to deal with capacity-planning problems in the presence of the uncertain demand, set-up resources, and the capacity constraints. Firstly, fuzzy numbers are used to represent uncertain data. Secondly, fuzzy if-then rules are employed to model vaguely defined relations between fuzzy numbers. Then, the computational aspects of fuzzy models and interpretations of inference results are illustrated by a numerical case. Finally, three examples are used to verify the proposed representation and inference mechanism.     

A general fuzzy TOPSIS model in multiple criteria decision making

Decision making is the process of finding the best option among the feasible alternatives. In classical multiple criteria decision-making (MCDM) methods, the ratings and the weights of the criteria are known precisely. Owning to vagueness of the decision data, the crisp data are inadequate for real-life situations. Since human judgments including preferences are often vague and cannot be expressed by exact numerical values, the application of fuzzy concepts in decision making is deemed to be relevant. In this paper, we proposed the application of a fuzzy distance formula in order to compute a crisp value for the standard deviation of fuzzy data. Then, we use this crisp value of the standard deviation to normalize the fuzzy data using the distance formula again. In our normalization approach, we have enough flexibility to consider various types of fuzzy numbers (such as triangular, trapezoidal, and interval). Finally, we use the technique for order preference by similarity to an ideal solution to determine the ranking order of the alternatives. A numerical example from the literature is solved to demonstrate this applicability of the proposed model. We also compare our proposed approach with similar methods in the literature using some examples with known results and a number of randomly generated test problems. The results point to the applicability of our method and signify its effectiveness in identifying solutions.     

Unified synthesis of Watt-I six-link mechanisms using evolutionary optimization

A Watt-I mechanism can operate in eight different combinations of assembly modes and output link. In this paper, a novel approach is presented for carrying out unified optimum synthesis of various combination types of Watt-I mechanism, irrespective of whether identical or different ranges of variables are specified for different combination types. By carrying out unified synthesis the less suited combination types can be identified, leading to their elimination from the synthesis process. This results in a saving of the overall computational time. The presented approach can be implemented with most of the evolutionary optimization methods. In this paper, the Differential Evolution algorithm is chosen as the optimization method. Unified optimization results are presented for two problems. The proposed approach is general and can be used, with suitable modifications, to carry out unified optimum design of alternate mechanical systems which can perform a given task.     

A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach

A supply chain (SC) distribution network design model is developed in this paper. The goal of the model is to select the optimum numbers, locations and capacity levels of plants and warehouses to deliver products to retailers at the least cost while satisfying desired service level to retailers. A maximal covering approach is used in statement of the service level. The model distinguishes itself from other models in this field in the modeling approach used. Because of somewhat imprecise nature of retailers’ demands and decision makers’ (DM) aspiration levels for the goals, a fuzzy modeling approach is used. Additionally, a novel and generic interactive fuzzy goal programming (IFGP)-based solution approach is proposed to determine the preferred compromise solution. To explore the viability of the proposed model and the solution approach, computational experiments are performed on realistic scale case problems.     

A queuing approach for a tri-objective manufacturing problem with defects: a tuned Pareto-based genetic algorithm

In this research, a manufacturing facility with independent workstations to remanufacture nonconforming products is investigated. Each workstation is first modeled as an M/M/m queuing system with m being a decision variable. Then, a tri-objective integer nonlinear programming models is developed to formulate the problem. The first objective tries to minimize the waiting times of products, while the second one tries to maximize the minimum reliability of machines at the workstations. Since minimization of the waiting times results in using a large number of machines with higher idle times, the third objective is considered to minimize the mean idle time of the machines. The aim is to determine optimal number of machines at each workstation. Since the problem belongs to the class of NP-hard problems, the non-dominated sorting genetic algorithm-II (NSGA-II) is utilized to find Pareto fronts. Because there is no benchmark available in the literature to validate the results obtained, the non-dominated ranked genetic algorithm (NRGA) is used as well. In both algorithms, not only the best operators are selected but also all of their important parameters are calibrated using statistical analysis. The performances of the algorithms are statistically compared using the t test. Besides, the multiple attribute decision-making method of TOPSIS is used to determine the better algorithm. The applicability of the proposed model and the solution algorithms is demonstrated via some illustrative examples.     

The future trend on system-wide modelling in supply chain studies

Although supply chain (SC) has gained increasing attention in the last decade and numerous SC problems have been identified and solved, there is a lack of systematic approach to solve SC problems at a system level. SC can be viewed as a network of organisations including, but not restricted to, the suppliers (source of raw materials), manufacturers, distributors, retailers, and end customers. Each entity is able to process different tasks individually in order to satisfy the global goal of the network, which is usually the total cost or lead time of a set of products. If only inter-entity problems are solved, like inventory level, production planning, etc., the performance of the whole system is not guaranteed to be acceptable. The main objective of this paper is to review contemporary SC problems and the methodologies in solving them, and to suggest future research direction for solving supply chains problems. From the analysis of the review, it can be concluded that there is limited research to solve SC problems from a system point of view. In this connection, future direction in solving system-wide SC, and the potential difficulties, are discussed. This allows readers to gain insights in the future research direction in the area of supply chain management.     

基于偏好聚合不精确法的湿式制动器稳健设计

针对不确定性对产品性能的影响,以湿式多盘制动器设计为例,利用响应面模型,建立了设计变量和制动性能的关系.根据不精确法,采用整体偏好聚合函数,将随机、模糊、未确知三种不确定性对系统性能均值和波动性的影响综合起来考虑,改善了传统稳健设计的线性加权和法的缺陷.     

A hybrid cuckoo search and variable neighborhood descent for single and multiobjective scheduling problems

Cuckoo search (CS) is a relatively new meta-heuristic that has proven its strength in solving continuous optimization problems. This papers applies cuckoo search to the class of sequencing problems by hybridizing it with a variable neighborhood descent local search for enhancing the quality of the obtained solutions. The Lévy flight operator proposed in the original CS is modified to address the discrete nature of scheduling problems. Two well-known problems are used to demonstrate the effectiveness of the proposed hybrid CS approach. The first is the NP-hard single objective problem of minimizing the weighted total tardiness time ( \(1|| \sum {T_{w}}\) ) and the second is the multiobjective problem of minimizing the flowtime \(\overline {C}\) and the maximum tardiness T _{m a x} for single machine ( \(1|| (\frac {1}{n}\sum {C}, T_{max})\) ). For the first problem, computational results show that the hybrid CS is able to find the optimal solutions for all benchmark test instances with 40, 50, and 100 jobs and for most instances with 150, 200, 250, and 300 jobs. For the second problem, the hybrid CS generated solutions on and very close to the exact Pareto fronts of test instances with 10, 20, 30, and 40 jobs. In general, the results reveal that the hybrid CS is an adequate and robust method for tackling single and multiobjective scheduling problems.     

多产品采购条件下的供应商选择与订购量分配问题研究

为了解决随机性需求和价格折扣并存条件下的多产品采购供应商选择问题,建立了相应的多目标混合整数随机规划模型.该模型的特点是：①模型的约束条件中兼具确定性和随机性;②通过约束条件方程式准确地表现随机性需求和价格折扣两大假设条件.根据该模型的特殊结构,提出了一种适用的求解策略：首先,通过把机会约束转化为确定性等价类,而将多目标混合整数随机规划模型转化为多目标混合整数规划模型;然后,采用最大满意度法,将体现决策者偏好和目标模糊性的加权模糊多目标混合整数规划模型转化为求解等价的多个单目标混合整数规划问题;最后,在确定每个模糊目标的隶属度函数表达式的基础上求得问题的最优解.另外,通过应用算例说明了模型的有效性和可行性.     

An experimental study on crack healing of various glassy polymers

The validity of a simple tube model proposed by the senior author for crack healing recovery of various polymeric materials such as PMMA(Polymethyl-methacrylate) and Homalite-100(A Polyester) has been tested. The experimental results of recovery in terms of healing fracture toughness were compared with the theoretically predicted trends of recovered fracture toughness. The agreements between these results were found to be reasonable within the limitation of experimental method. After healing the PMMA CT (compact tension) specimens with various solvents having different solubility parameters(δ), the healing recovery (R) was measured by both the method of maximum load and the method of caustics. The relationship between R and solubility differences (Δδ) between a solven and a glass polymer was found to be R∝(Δδ)?1/4.     

基于交互双层模糊规划的敏捷供应链构建与优化

分析了供应链中不确定性的来源,并用梯形模糊数表达了供应链参数中的不确定性,用梯级平均综合法将模糊数转换为精确数。在分析现有各种合作伙伴决策和供应链构建方法的基础上,提出了基于交互双层模糊规划方法,利用软件LINGO 13求解该模糊规划,得出最终的精选合作伙伴及最优订货量。该方法兼顾了上下层各自的利益需求,并经过上下层决策者的反复交互协商,最终得到上下层决策者均可接受的妥协优化解。通过一个实例进一步阐述了该方法的决策步骤,并指出了该方法相较于其他方法的优缺点及下一步的研究思路。     

A multicriteria intuitionistic fuzzy group decision making for plant location selection with ELECTRE method

Plant location selection, the process of finding a suitable location for plant of a project has a strong influence on the success of industrial venture with the goal to minimize cost and maximize the use of resources. Thus, plant location selection problem is a multicriteria decision-making problem involving several conflicting criteria on which decision makers' knowledge is vague and imprecise. Therefore, in this study, the elimination and choice translating reality (ELECTRE) method is proposed with intuitionistic fuzzy sets for selection of appropriate plant location in group decision-making environment to tackle uncertainty of the information provided by decision makers and a plant location selection problem is considered to illustrate the proposed intuitionistic fuzzy-ELECTRE method. The ratings of alternatives with respect to each criterion and the weights of each criterion are taken as linguistic terms further characterized by triangular intuitionistic fuzzy sets.     

A mixed-integer linear programming model along with an electromagnetism-like algorithm for scheduling job shop production system with sequence-dependent set-up times

In this paper, we consider the job shop scheduling problem (JSS) with non-anticipatory, per-machine, sequence-dependent setup times (SDST). The contributions of this paper are twofold. First, we propose a formulation in the form of a mixed-integer linear programming (MILP) model to modelize the aforementioned problem. Second, we play a pioneering effort for the effective adaptation of a novel metaheuristic known as electromagnetism-like algorithm (EMA) to solve the foregoing problem under the minimization of makespan. Afterwards, we evaluate the performance of the proposed MILP model, the EMA, and other effective metaheuristic algorithms from the literature on two different sets of benchmarks: small-sized and large-sized instances. The rationale behind applying the MILP model and the other algorithms at the small-sized instances is to compare the solutions obtained by the metaheuristic algorithms and the optimal solutions obtained by the MILP model (optimality gap analysis). Subsequently, to demonstrate the competitiveness of the EMA against some effective algorithms in the literature, we conduct an experimental design based on Taillard's benchmark, which is considered as large-sized instances. The purpose of conducting this very experiment is to show whether the acceptable performance of the EMA is transferrable to large-sized instances. The computational evaluations simply manifest the superiority of our proposed algorithm vs the other high-performing algorithms over both small and large instances.     

Fuzzy preference based Interactive Fuzzy Physical Programming and its application in multi-objective optimization

Interactive Fuzzy Physical Programming (IFPP) developed in this paper is a new efficient multi-objective optimization method, which retains the advantages of physical programming while considering the fuzziness of the designer’s preferences. The fuzzy preference function is introduced based on the model of linear physical programming, which is used to guide the search for improved solutions by interactive decision analysis. The example of multi-objective optimization design of the spindle of internal grinder demonstrates that the improved preference conforms to the subjective desires of the designer.     

A fuzzy hybrid group decision support system approach for the supplier evaluation process

The quality and delivery performance of a typical company depends largely on the capability of its suppliers. It is therefore important for a company to evaluate the performance of its existing suppliers. However, the problem of supplier evaluation is rather complex when a group of decision makers responsible for these suppliers are geographically dispersed. Evaluating decisions requiring the elicitation of vague and imprecise linguistic information expressed in fuzzy terms. For this purpose, a hybrid fuzzy group decision-making approach for supplier evaluation is proposed, integrating the fuzzy analytic hierarchy process (F-AHP) and the fuzzy preference-ranking-organization-method-for-enrichment-of-evaluation (PROMETHEE) group decision support system (F-PROMETHEE GDSS) method. F-AHP is used to determine the weight of multiple criteria while F-PROMETHEE GDSS is applied for the final ranking of suppliers from a group decision viewpoint. The approach advances the supplier evaluation context in two aspects. First, it treats each decision maker uniquely in terms of the preference function and threshold levels. Second, it provides rapid communication between teams through video conferencing. The hybrid approach is used to evaluate the suppliers of a dairy company.     

Realistic variant of just-in-time flowshop scheduling: integration of L p -metric method in PSO-like algorithm

This study strives to schedule a just-in-time hybrid flowshop with sequence-dependent setup times by considering two performance measures, namely makespan and sum of the earliness and tardiness, simultaneously. The paper proposes a mixed integer programming model. However, since the simpler case with a single stage and with a single machine per stage is NP-hard, the utilization of the exact algorithms for the real-life problems is limited. Thus, this paper proposes a novel solving algorithm with a weighted L _p -metric-based framework. Since the particle swarm optimization is originally designed for continuous solution space, in this study, we modify the particle position based on our representation so that a particle position is decoded into a schedule using the largest processing time algorithm, Hadamard product, and swap operator. Furthermore, we apply a variable neighborhood search and a tabu search to improve the solution quality. This hybridization which combines the advantages of the individual components is the key innovative aspect of the approach. We investigate the performance of our algorithm in the comparison with several algorithms and show that it has a good performance.     

不确定信息的机械产品设计方案评价方法

机械产品设计方案评价中,针对评价指标值具有模糊性、指标权重具有不完全性的问题,引入模糊数据包络分析(DEA),分析模糊DEA在不完全权重信息处理方面存在的不足,提出改进的模糊DEA评价方法。该方法首先将指标值转化为无量纲的梯形模糊数,然后以梯形模糊数为基础,以不完全权重信息为约束,建立只有输出指标且含有偏好信息的模糊DEA模型,最后,基于去模糊化方法,将模糊DEA模型转化为确定性模型并求解。通过实验分析,验证该方法的合理性和可行性。