Outsourcing logistics operations in circular economy towards to sustainable development goals

One of novel manners to achieve sustainable development is concentrating on circular economy in order to manage greenhouse gas emissions, energy consumption, and waste. It also helps to protect the environment and optimize usage of input resources. The main element of circular economy is closed-loop supply chain which covers both forward and reverse products flows. This study aims to outsource logistics operations in a closed-loop supply chain. An innovative analytical multi-step fuzzy decision-making method is proposed to rank sustainable third-party logistics service providers (3PLSPs). Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is first used to evaluate the relationships among the main criteria. Then, fuzzy analytic network process (ANP) is applied to weight the determined set of criteria. In order to develop the hybrid fuzzy DEMATEL-ANP method and to simplify the computation, the expert judgment method is used between the two techniques as a middle step to reduce the number of criteria. As many of the criteria overlap with each other on content and context, expert opinions are used to shortlist and rank the criteria. Finally, the 3PLSPs are ranked through the fuzzy complex proportional assessment (COPRAS) method. To validate the practicability and applicability of the improved decision-making model, a household appliance case study is applied with data obtained from industry experts. In this case, five service providers are considered and ranked, while the first one gets the best score. A sensitivity analysis is performed by altering the criteria weights to validate the proposed approach, data, and the obtained results. In terms business and the environment, this study provides success critical criteria for decision-making problems in circular economy for addressing sustainable development.     

Designing an integrated humanitarian logistics network for the preparedness and response phases under uncertainty

In the current paper, a model of possibilistic location-allocation is proposed for designing a multi-period bi-objective humanitarian logistics network that pursues a reduction in the total cost and maximizes the total network coverage. The model considers the inventory management of perishable relief items and the flow of affected and wounded people in pre-disaster and post-disaster phases, simultaneously. Moreover, wounded people are categorized according to the injury severity. The uncertainty associated with key parameters is also addressed. To handle the uncertainty, a fuzzy chance-constrained programming method originated in the Me measure is used. The purpose of this measure is to prevent the extreme attitudes of the decision maker by considering the combination of possibility and necessity measures and using the optimistic-pessimistic parameter. This paper presents a solution procedure derived from a fuzzy interactive programming approach and two meta-heuristic algorithms, imperialist competitive algorithm and invasive weed optimization, so as to solve the study model. As for the validation of the proposed model and solution procedures, a number of test problems have been generated. A real-life case study is also implemented to instantiate whether the proposed model is applicable or not.     

Redesign strategies of a comprehensive robust relief network for disaster management

We address the problem of designing as well as redesigning a relief network over multiple periods as a strategic decision which plays a critical role in the post-disaster management. Design of the relief network has a significant impact on the effective performance of disaster response operations. For considering both the uncertainty and dynamism of the decision-making environment, a comprehensive scenario-based robust approach embedded in the rolling horizon framework is proposed. The proposed mixed-integer linear programming model is inspired by a real case study of a disaster management in Iran, which aims to minimize the total cost of network management. Furthermore, restorative strategies are considered to increase the efficiency and robustness of the proposed relief network under disaster. To tackle the proposed optimization model, a heuristic solution algorithm is adopted. The results indicate that the proposed robust relief network provides an affordable access to its demand points in a sustainable manner under disaster. In addition, extensive computational results illustrate the efficiency of the proposed model in dealing with the considered disaster management issues.     

Reliable design of a congested disaster relief network: A two-stage stochastic-robust optimization approach

The preparedness of humanitarian relief networks can be enhanced by pre-positioning resources in strategic locations and using them when disasters strike, a strategy that gives rise to a two-stage planning problem. This paper presents a novel two-stage stochastic-robust optimization approach for integrated planning of pre- and post-disaster positioning and allocation of relief resources, while taking into consideration the uncertainty about demand for relief services and disruptions in the relief facilities and the transportation network. The proposed approach enables planners to effectively use limited historical data and imperfect experts’ opinions to obtain robust solutions while avoiding the over-conservatism of classical robust optimization methods. The objective sought is to minimize the expected total time victims need to receive assistance, including both access time to facilities and waiting/service time in them. Congestion in relief facilities is accounted for by modeling them as queuing systems and penalizing waiting time. A decomposition method based on column-and-constraint generation is implemented to solve the problem, whereas the nonlinear terms corresponding to queuing in the second-stage problem are handled using a direct search procedure. Applicability of the proposed approach is demonstrated through a real case study and the numerical results are analyzed to draw managerial insights.     

Neyman-Pearson Lemma for fuzzy hypotheses testing

In this paper a new approach is presented for testing statistical hypotheses when the hypotheses are fuzzy rather than crisp.  In order to establish optimality criteria, we first give new definitions for probability of type I and type II errors. Then, we state and prove the Neyman-Pearson Lemma, on the basis of these new errors, for testing fuzzy hypotheses, and we give a few examples. Received February 1998     

Fuzzy grey relation method for multiple criteria decision-making problems

Combining the concepts of fuzzy set theory, entropy, ideal, and grey relation analysis, a fuzzy grey relation method for multiple criteria decision-making problems is proposed. First, triangular fuzzy numbers and linguistic values characterized by triangular fuzzy numbers are used to evaluate the importance weights of all criteria and the superiority of all alternatives versus various criteria above the alternative level. Then, the concept of entropy is utilized to solve the adjusted integration weight of all objective criteria above the alternative level. Furthermore, using the concepts of ideal, the grey ration grades of various alternatives versus ideal solution are ranked to determine the best alternative. Finally, a numerical example of selecting most appropriate company to build a new highway is used to demonstrate the applicability of proposed method. The study results show that this method is an effective means for tackling MCDM problems in fuzzy and grey environments.     

GIS-based fuzzy multi-criteria approach for optimal site selection of fire stations in Istanbul,Turkey

Fire stations play a central role in protection and response activities as part of emergency management services in cases of fire incidences. With the rising urban populations and city expansions, the demand for more fire services resultantly increases. It then becomes critical to effectively plan the location of emergency facilities to adequately service the population and ensure the protection of lives and infrastructure. This study, therefore proposes the use of the fuzzy extension of the Multi-Criteria Decision-Making (MCDM) method of Analytical Hierarchical Process (AHP), hence called fuzzy AHP, integrated with Geographic Information Systems (GIS) approach to optimally site new fire stations for the case of Istanbul region. This proposed fuzzy approach simulates the subjective expert judgements for the preferences of the six criteria assessed for fire station suitability mapping and thereby accounted for the uncertainty of crisp comparison values via triangular fuzzy numbers (TFNs). The criteria weights evaluated from this procedure were used in a weighted overlay analysis of the reclassified criteria map layers in ArcGIS to generate a fire station suitability map. These resultant fuzzy AHP criteria weights were validated using another MCDM technique, called Best-Worst Method (BWM) and found to be comparable and consistent. The criteria that had the strongest influence on the selection of sites for fire stations were identified to be: the density of hazardous material facilities (DHM), a high population density (HPD) and proximity to main roads (PMR) with associated weights of 33.3%, 24.4% and 15.2%, respectively. Based on a thorough assessment within the areas represented by class values ranging from 3 to 5 on the suitability map, a total of 34 new fire station sites were selected complementing the existing 121 fire stations. Further, a prioritization analysis from low, medium to high, was performed to plan the phases for the construction of new fire stations in view of competing budgetary needs and resource constraints. The methodology to achieve this was proposed and modelled for enhancing the decision-making process in urban fire station site selection studies.     

A quality approach to airport management

This paper discusses aspects of airport service quality evaluation using fuzzy multicriteria analysis and the alpha-cut concept. It presents a fuzzy multicriteria methodology for analysing a complex set of quality variables. This methodology develops a step by step set of indicators that allow managers have a holistic view of the quality dimension. It also helps the understanding of cause and effect relation, it helps tracking service quality problems and benchmarking them. From the alpha-cut concept, by defining upper and lower limits, this approach seeks to assist managers in evaluating a complex number of quality criteria and decision-makers’ behaviour. This approach gives managers a broader view of comparative perceptions of quality among the airports in a given sample, by presenting the results of the analysis in fuzzy form and allowing managers to consider different styles of evaluation. The methodology discussed is applied to six Brazilian international airports.     

Pre-positioning hurricane supplies in a commercial supply chain

Inventory control for retailers situated in the projected path of an observed hurricane or tropical storm can be challenging due to the inherent uncertainties associated with storm forecasts and demand requirements. In many cases, retailers react to pre- and post-storm demand surge by ordering emergency supplies from manufacturers posthumously. This wait-and-see approach often leads to stockout of the critical supplies and equipment used to support post-storm disaster relief operations, which compromises the performance of emergency response efforts and proliferates lost sales in the commercial supply chain. This paper proposes a proactive approach to managing disaster relief inventories from the perspective of a single manufacturing facility, where emergency supplies are pre-positioned throughout a network of geographically dispersed retailers in anticipation of an observed storm's landfall. Once the requirements of a specific disaster scenario are observed, supplies are then transshipped among retailers, with possible direct shipments from the manufacturer, to satisfy any unfulfilled demands. The manufacturer's pre-positioning problem is formulated as a two-stage stochastic programming model which is illustrated via a case study comprised of real-world hurricane scenarios. Our findings indicate that the expected performance of the proposed pre-positioning strategy over a variety of hurricane scenarios is more effective than the wait-and-see approach; currently used in practice.     

Fuzzy modelling of static system optimum traffic assignment problem having multi origin-destination pair

Traffic congestion is an unpreventable problem to avoid in a transportation network and it has negative effects on traffic accident, time wasting, traffic delay and safety problem. Besides, in transportation networks, drivers do not want to deal with traffic jam while traversing between specified origin-destination pair. Therefore, traffic assignment (TA) is imperative to improve traffic management, transportation safety, time, and cost savings. System Optimum Traffic Assignment Problem (SOTAP) is a kind of TA model which aims to minimize the total system travel time on the network, and satisfies the flow conservation constraints. To model the SOTAP more realistically, the imprecise parameters can be taken as fuzzy. Therefore, in this paper, we focus on converting the conventional SOTAP to a fuzzy quadratic programming problem (QPP) which is named System Optimum Fuzzy Traffic Assignment Problem (SOFTAP). Here, link travel time is expressed with BPR function as generally used in the literature by converting to fuzzy except link-dependent parameters. Thus, the nonlinear objective function of SOFTAP is expressed in terms of fuzzy link flows and fuzzy link travel times. A solution approach from the literature is modified to the reconstructed SOFTAP.     

Assessment of CSR based supply chain performance system using an integrated fuzzy AHP-TOPSIS approach

The objective of this research was to identify Corporate Social Responsibility (CSR) criteria as well as propose and prioritise the alternatives to improve the supply chain performance system of an organisation. To meet the objective, firstly, CSR-based criteria, sub-criteria and alternatives were identified based on the literature review and discussion with the field experts, taken from automobile industries located at the Delhi region of India. Then, kappa statistics has been applied to check the internal consistency between identified criteria and sub-criteria. After finding the evidence of internal consistency, a hierarchical-type model has been developed and analysed using a multi-criteria decision-making as an integrated fuzzy analytical hierarchy process (AHP) and a fuzzy technique for order performance by similarity to the ideal solution (TOPSIS) approach. To make the results more robust and feasible, a sensitivity analysis has also been performed.     

Developing a multivariate approach to monitor fuzzy quality profiles

Referring to several applications in which the response quality characteristic is fuzzy, this paper studies how the profile functional relationship between a fuzzy response variable and a predictor variable can be monitored by using a fuzzy regression model which is referred to as profile. The purpose of this paper is to develop a multivariate approach for monitoring process/product fuzzy quality profiles in phase I for applications where the quality characteristic is linguistic, imprecise, vague or deficient. The multivariate approach includes three fuzzy multivariate control charts which are developed by using fuzzy set theory to monitor fuzzy profiles in order to achieve an in-control process. The performance of developed approach is investigated on the basis of signal probability in various out-of-control scenarios through a simulation study. Compared with univariate approach, the results indicate a good performance of our multivariate approach in detecting all sized shifts in process profiles. A real case in tourism industry is utilized to show the applicability of the proposed approach.     

Multi-stage evaluation and selection in the formation process of complex creative solution

In the case of limited organizational resources, smoothly and effectively completing the multi-stage evaluation and selection in the formation process of complex creative solution is vital for the organization to solve creative problems. In this paper, a multi-stage evaluation criteria system is constructed from the three dimensions of novelty, value and practicality, each of which is composed of three separate evaluation criteria systems, and the fuzzy AHP method is applied to obtain the weights of each dimensions and sub-dimensions, we find that the weights of the same dimensions and sub-dimensions are different in each evaluation criteria systems. The fuzzy TOPSIS method and the fuzzy AHP method are combined to build a multi-stage evaluation and selection model in the formation process of complex creative solution, then the complex creative solutions development activities of rural credit products, which we have been personally involved in, are selected as the analysis case to confirm the practical operability of this model.     

Sustainability in The Banking Industry: A Strategic Multi‐Criterion Analysis

The current paper aims to develop an effective and integrated MCDM model for the evaluation of the sustainability practices in the banking services, employing a multi‐stage, fuzzy MCDM model that integrates the Balanced Scorecard, fuzzy AHP and fuzzy TOPSIS. The approach aims to evaluate sustainability from the following four perspectives: financial stability, customer relationship management, internal business process and environment‐friendly management system. A real implementation dealing with the six largest commercial banks in India is discussed. The results highlights the critical aspects of the evaluation criteria and the issues in improving sustainable banking performances. Regarding the sustainability issues, it is shown that the environment‐friendly management system takes a back seat compared with the other criteria. Furthermore, the results show that there is a misunderstanding of the role that corporate social responsibility plays with respect to environmental issues. The developed evaluation model offers a valuable management tool for banks' administrators by assisting them in strategic choices in order to achieve their objective of sustainability and sustainable banking. Moreover, it offers a measuring tool with unique features that complements the emerging trend of integrated reporting considering uncertainty. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment     

基于模糊综合评价法的我国城市贫困人口医疗救助系统的评价

目的:对我国城市贫困人口医疗救助系统进行系统划分、绘出因果关系图,并对所构建的我国城市贫困人口医疗救助系统进行系统评价。方法:用VENSIM软件绘出系统因果关系图,采用模糊综合评判法评价系统。结果:成功的绘出了我国城市贫困人口医疗救助系统的因果关系图并对我国城市贫困人口医疗救助系统进行了评价。结论:所构建的我国城市贫困人口医疗救助系统是具有可行性和针对性的系统。     

An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption

This paper introduces an integrated algorithm for forecasting electricity consumption (EL) based on fuzzy regression, time series and principal component analysis (PCA) in uncertain markets such as Iran. The algorithm is examined by mean absolute percentage error, analysis of variance (ANOVA) and Duncan Multiple Range Test. PCA is used to identify the input variables for the fuzzy regression and time series models. Monthly EL in Iran is used to show the superiority of the algorithm. Moreover, it is shown that the selected fuzzy regression model has better estimated values for total EL than time series. The algorithm provides as good results as intelligent methods. However, it is shown that the algorithm does not require utilization of preprocessing methods but genetic algorithm, artificial neural network and fuzzy inference system require preprocessing which could be a cumbersome task to deal with ambiguous data. The unique features of the proposed algorithm are three fold. First, two type of fuzzy regressions with and without preprocessed data are prescribed by the algorithm in order to minimize the bias. Second, it uses PCA approach instead of trial and error method for selecting the most important input variables. Third, ANOVA is used to statistically compare fuzzy regression and time series with actual data.     

DE优化模糊神经网络算法在无刷直流电机控制系统中的应用

DE优化模糊神经网络算法是一种利用模糊神经网络控制器进行计算的方式,该算法避免了以往DE模糊神经网络连接权值和阈值选择上的随机性缺陷,从而能发挥DE模糊神经网络泛化的映射能力。本文就DE优化模糊神经系统在无刷直流电机控制系统中的应用,进行了系统的研究和探讨。     

A fuzzy service quality based approach for performance evaluation of educational units

Educational unites try to make efficient use of resources to achieve higher service qualities. This paper presents a new hybrid fuzzy approach based on service quality for performance evaluation of educational units. The proposed approach includes four stages: firstly, evaluation and gap analysis of students' expectations and perceptions in the service quality of educational units are performed based on fuzzy SERVQUAL questionnaires. In the second stage, the corresponding weights of dimensions and sub-dimensions of SERVQUAL are determined via fuzzy AHP method. In the third stage, the educational units are ranked using fuzzy TOPSIS method with respect to service quality sub-dimensions. Finally, a fuzzy DEA method is applied to determine the efficiency of educational units based on their service quality ratings. The approach is used for performance evaluation of eight higher education institutes in Iran. The proposed approach increased the accuracy of performance evaluation of educational units based on service quality.     

基于神经网络的模糊综合评判在传输网建设中的应用

本文以某地市传输网建设可行性研究报告为例,使用基于模糊神经网络的模糊综合评判方法对三个可研方案进行综合评价。提出了一种对传输网建设鉴定的新方法,该方法采用了人工智能技术来减少鉴定的主观性,由于对建设可行性的认识存在一定的模糊性,采用了模糊综合评判进行可行性鉴定。并且提出了模糊综合评判和模糊神经网络的集成对通信工程建设和网络评估时的指导作用。     

Decision making framework for tender evaluation and contractor selection in public organizations with risk considerations

Selecting the best construction contractor plays a critical role in the success of any construction project. Moreover, in the public sector, the traditional lowest bid method is still broadly used and contracts are often awarded based on the lowest price. However, this method has been criticized by many researchers because even if it might guarantee the lowest cost for a project, it does not guarantee the maximum value in terms of time and quality. More particularly, the risk exposure during the tendering process is usually very high and the success of the construction project is strongly related to managing this risk in an appropriate way. Therefore, the selection of the most appropriate contractor should be based on a set of criteria such as technical capability, financial stability, risk, safety, etc., in addition to cost in order to avoid problems that may happen after the contract is awarded. This study aims therefore to develop a decision making framework (DMF) to assist the public organizations in selecting the most appropriate construction contractor(s). The proposed DMF uses a combination of multi-criteria decision making (MCDM) tools and fuzzy logic theory and consists of three stages. In the first stage, data envelopment analysis (DEA) is used to reduce the number of contractors, based on multiple criteria, such that only efficient contractors are considered. In the second stage, the risk factors that are related to each efficient contractor are identified and assessed using a fuzzy logic based approach. Finally, in the third stage, a bi-objective mixed integer linear programming (MILP) model is developed to select the best contractor(s) based on cost and risk and to determine the optimal quantity of work to be assigned to each selected contractor. Indeed, the proposed approach considers the multiple award contract case in which more than one contractor can be selected to share the amount of work to be performed. We assume that prior to using the proposed DMF, a pre-qualification study is conducted using multiple criteria chosen by the public organization, and only pre-qualified contractors are included in the first stage of the proposed DMF. Moreover, a case study inspired from the contractor selection process in a public organization in the United Arab Emirates (UAE) is used to show the effectiveness of the proposed approach.