Multiobjective Fuzzy Research Articles

Configuring a Trusted Cloud Service Model for Smart City Exploration Using Hybrid Intelligence

Emerging research concerns about the authenticated cloud service with high performance of security and assuring trust for distributed clients in a smart city. Cloud services are deployed by the third-party or web-based service providers. Thus, security and trust would be considered for every layer of cloud architecture. The principle objective of cloud service providers is to deliver better services with assurance of trust about clients' information. Cloud's users recurrently face different security challenges about the use of sharable resources. It is really difficult for Cloud Service Provider for adapting varieties of security policies to sustain their enterprises' goodwill. To make an optimistic decision that would be better suitable to provide a trusted cloud service for users' in smart city. Statistical method known as Multivariate Normal Distribution is used to select different attributes of different security entities for developing the proposed model. Finally, fuzzy multi objective decision making and Bio-Inspired Bat algorithm are applied to achieve the objective.

A Novel Solution to the Cognitive Radio Decision Engine Based on Improved Multi-Objective Artificial Bee Colony Algorithm and Fuzzy Reasoning

Targeting at the parameters reconfiguration of a cognitive radio system, a novel cognitive decision engine (CDE), based on an improved multi-objective artificial bee colony (IMOABC) algorithm and f...

Procurement scheduling in engineer procure construct projects: a comparison of three fuzzy modelling approaches

ABSTRACTProcurement stage connects engineering and construction stages of engineer procure construct (EPC) projects. Scientific materials management is vital to ensure timely material availability to reduce inventory shortage and holding costs. Procurement scheduling of such projects is challenging due to imprecise estimation of activity durations and lead times and stage budget constraints. Fuzzy modelling can incorporate and analyse such imprecise data. In literature multiple fuzzy approaches are applied in different contexts. But a study which provides roadmap to select best solution approach in absence of standardized methodologies and benchmark results in EPC projects is missing. This study compares three fuzzy modelling approaches: crisp approach, fuzzy multi-objective (FMO) approach and crisp multi-objective (CMO) approach based on manner and extent to which they exploit available fuzzy information, computational efficiency and concurrence of results. Results reveal that crisp approach is computationally efficient, FMO approach leverages fuzzy information maximum and CMO approach closely aligns with managerial intuition.

A Novel Technique for Solving Multiobjective Fuzzy Linear Programming Problems

This study considers multiobjective fuzzy linear programming (MFLP) problems in which the coefficients in the objective functions are triangular fuzzy numbers. The study proposing a new technique to transform MFLP problems into the equivalent single fuzzy linear programming problem and then solving it via linear ranking function using the simplex method, supported by numerical example.

English

English

Agricultural Land Allocation for Crop Planning in a Canal Command Area Using Fuzzy Multiobjective Goal Programming

AbstractReal-world problems in the agricultural sector are usually multiobjective in nature. More than one objective needs to be fulfilled simultaneously to incorporate economic and social requirem...

A Modified Differential Evolution based Fuzzy Multi-objective Approach for Clustering

Many evolutionary-based metaheuristics have been proposed for minimisation of intra cluster distance for better clustering, however the effect of inter cluster distances on clustering is ignored in most of the cases. Considering this issue a modified differential evolution-based fuzzy multi-objective (MDEFM) approach is proposed in this study where effect of both intra and inter cluster distance on clustering is analysed. This way of considering both the objectives and assigning different weighting factors according to their priority results in well separate clusters with greater accuracy. Furthermore, a centroid rearrangement scheme has been proposed for getting a consistent result. A comparative analysis of the proposed approach with another five population-based methods on eight real datasets is carried out to justify the efficacy of the model. The results reveal that the proposed approach can be considered as one of the alternate powerful methods for data clustering applications in various fields.

An interactive possibilistic programming for fuzzy multi objective solid transportation problem

An interactive possibilistic programming for fuzzy multi objective solid transportation problem

Solution of Fuzzy Multi-objective Nonlinear Programming Problem Using Fuzzy Programming Techniques Based on Hyperbolic Membership Functions

The main objective of this paper is three folds: (i) introduction of hyperbolic membership function in Zimmerman’s fuzzy programming technique to handle multi-objective nonlinear programming problems (ii) reduction of fuzzy multi objective nonlinear programming problem to crisp one using ranking function (iii) reduction of complexity of nonlinear problem occurring in course of application of Zimmerman’s technique by the process of linearization.

Solution of fuzzy multi objective non-linear programming problem (fmonlpp) using fuzzy programming techniques based on exponential membership functions

Solution of fuzzy multi objective non-linear programming problem (fmonlpp) using fuzzy programming techniques based on exponential membership functions

Supply Chain Network Optimization Based on Fuzzy Multiobjective Centralized Decision-Making Model

Supply chain cooperation strategy believes that the integration of the operation process can produce value for customers, optimize the supply chain of the connection between the vertical nodes, and constantly strengthen the performance of the advantages, so as to achieve mutual benefit and win-win results. Under fuzzy uncertain environment and centralized decision-making mode, we study multiobjective decision-making optimization, which focuses on equilibrium and compensation of multiobjective problems; that is to say, the proper adjustment of the individual goal satisfaction level will make other goals’ satisfaction levels change greatly. Through coordination among the multiobjectives, supply chain system will achieve global optimum. Finally, a simulation experiment from Shaoxing textile case is used to verify its efficiency and effectiveness.

A Multiobjective Fuzzy Chance Constrained Programming Model for Land Allocation in Agricultural Sector: A case study

In this article a fuzzy multiobjective chance constrained programming model is used for modeling and solving land allocation problems efficiently with the help of fuzzy goal programming. Optimal production of seasonal crops and related expenditures are considered from the viewpoint of proper utilization of total cultivating land and different farming resources. Some resource parameters associated with the probabilistic constraints are taken as normally distributed fuzzy random variables. The potential use of this methodology is illustrated by a case example.

Novel fuzzy multi objective DVFS-aware consolidation heuristics for energy and SLA efficient resource management in cloud data centers

Cloud computing is an attractive solution for managing IT infrastructures due to its numerous beneficiaries such as automatic optimum resource management as well as modern service delivery models. Cloud computing relies on huge energy hungry data centers to supply resources for diverse range of users with dynamic workloads. Hence, reducing the ever-increasing energy consumption of Cloud data centers as well as their carbon footprints is a crucial issue for Cloud providers. This paper takes advantage of dynamic voltage and frequency scaling method as well as consolidation approach to propose a novel fuzzy multi criteria and objective resource management solution. The extensive evaluation of proposed algorithms using Cloudsim simulator shows notable reductions in energy consumption, SLA violation, and number of migrations in comparison with state of the art.

Sustainable and Resilient Garment Supply Chain Network Design with Fuzzy Multi-Objectives under Uncertainty

Researchers and practitioners are taking more interest in developing sustainable garment supply chains in recent times. On the other hand, the supply chain manager drops sustainability objectives while coping with unexpected natural and man-made disruption risks. Hence, supply chain managers are now trying to develop sustainable supply chains that are simultaneously resilient enough to cope with disruption risks. Owing to the importance of the considered issue, this study proposed a network optimization model for a sustainable and resilient supply chain network by considering sustainability via embodied carbon footprints and carbon emissions and resilience by considering resilience index. In this paper, initially, a possibilistic fuzzy multi-objective sustainable and resilient supply chain network model is developed for the garment industry considering economic, sustainable, and resilience objectives. Secondly, a possibilistic fuzzy linguistic weight-based interactive solution method is proposed. Finally, a numerical case example is presented to show the applicability of the proposed model and solution methodology.

Multiobjective Adaptive Fuzzy C-Means Algorithm for Mining Frequent Closed Itemset: A Lattice Based Approach for Time Series Data

Of late, frequent itemset mining from the data bases has emerged as a highly challenging task. And, there is a feast of various approaches launched with an eye on extracting the frequent rules from the database. In the erstwhile frequent itemset mining technique, the frequent itemset are extracted from the time series data by means of Adaptive Fuzzy C-Means (AFCM) clustering, though it is saddled with several setbacks in the clustering procedure. This arises on account of the fact that in clustering, the number of itemset is mammoth, and hence the method consumes a lot of time in addition to spending enhanced scanning time. Therefore, with a view tackling these adverse features, an innovative frequent itemset mining method is suggested in this document. At the outset, the time sequence database values are grouped by means of the Multiobjective AFCM method. Subsequently, the frequent itemset are extracted from the grouped outcomes by employing the lattice based method. The novel technique is performed in the MATLAB platform and its excellence is appraised by means of rainfall database.

A Multi-objective Fuzzy Goal Programming P-hub Location and Protection Model with Back-up Hubs Considering Hubs Establishment Fixed Costs

The critical and undeniable role of hubs in telecommunication and networking brings about some precautions to be taken to protect networks against any disruption. In this paper, a multi-objective model in a group of hub location problems, referred to as protection models for survivable network design, is developed. In fact, this model is a hub location problem that aims to maximize the potential flow between the origin and destination node pairs in the network, which has the minimum potential flow among all O-D pairs, including multiple assignment and back-up routes. In addition, it concerns the fixed cost of installation of the hub facilities. A fuzzy goal programming method is applied to solve the model. Different scenarios are implemented using Turkish data set and also numerical experiments are presented to illustrate the advantages of the proposed model in some aspects, compared to previous models. The results can give useful insights into telecommunication network design.

A Multi-Objective Fuzzy Ant Colony Optimization Algorithm for Virtual Machine Placement

In cloud computing, the most important challenge is to enforce proper utilization of physical resources. To accomplish the mentioned challenge, the cloud providers need to take care of optimal mapping of virtual machines to a set of physical machines. In this paper, the authors address the mapping problem as a multi-objective virtual machine placement problem (VMP) and propose to apply multi-objective fuzzy ant colony optimization (F-ACO) technique for optimal placing of virtual machines in the physical servers. VMP-F-ACO is a combination of fuzzy logic and ACO, where we use fuzzy transition probability rule to simulate the behaviour of the ants and the authors apply the same for virtual machine placement problem. The results of fuzzy ACO techniques are compared with five variants of classical ACO, three bin packing heuristics and two evolutionary algorithms. The results show that the fuzzy ACO techniques are better than the other optimization and heuristic techniques considered.

Evaluation of trapezoidal fuzzy numbers on AHP based solution of multi-objective programming problems

Multi-criteria decision making method is used in such cases having multiple targets. Multi-objective linear programming problems, which deal with uncertain measurements for both objectives and constraints, are solved by fuzzy multi-objective linear programming methods. Analytic hierarchy process (AHP), one of the multi-criteria decision-making methods based on weighting of objectives, is designed to solve complex problems. In this study, multiple criteria decision making problems and decision makers’ opinions on these problems as well as their solution processes are discussed. Firstly, a fuzzy multi objective linear programming problem is solved using the approaches of Zimmerman and Hybrid. In the literature, these methods are applied using triangular fuzzy numbers based on decision makers’ opinions only in cases where the objective function is minimization. In this study, as an alternative to the triangular fuzzy numbers, trapezoidal fuzzy numbers are defined, and both minimization and maximization of objective functions are examined. Finally, a solution algorithm for a fuzzy multi-objective linear programming problem is proposed under some given conditions. The algorithm is applied to a sample problem that involves modeling of a congress organizing which aims to place attendees to accommodations under specific objectives and constraints. In the evaluation phase of the algorithm, targets are weighted by using triangular and trapezoidal fuzzy numbers. At the end, the results of the proposed algorithm are compared with the results of other methods in the literature.

Fuzzy Multiobjective Modeling and Optimization for One-Shot Multiattribute Exchanges With Indivisible Demand

The modern economy involves a variety of marketplaces, and the Internet has led to the development of a new efficient marketplace—the one-shot multiattribute exchange. It is an important decision problem for a matchmaker (or broker) to achieve the optimal trade matching in one-shot multiattribute exchanges; however, to the best of our knowledge, there has been little work on this issue under fuzzy environments. This paper proposes an optimal matching approach for one-shot multiattribute exchanges with simultaneous fuzzy information and indivisible demand considerations. First, we employ fuzzy set theory to represent the traders’ orders with fuzzy information and then put forward a calculation method of the matching degree based on the improved fuzzy information axiom. Second, on the basis of the matching degree, we construct a fuzzy multiobjective programming model for one-shot multiattribute exchanges with indivisible demand. Afterward, the credibility measure is introduced to convert the model into a crisp one. The crisp model belongs to a class of multiobjective nonlinear general assignment problems and has NP-hard complexity. In order to solve the crisp model effectively, we develop a problem-specified metaheuristic algorithm, i.e., multiobjective discrete differential evolution. Finally, we conduct comprehensive computational experiments on numerical examples to illustrate the application and performance of the proposed model and algorithm.

Fuzzy Multi-objective Expected Value Optimization Models for Locating an Automotive Service Enterprise

Automotive service enterprise location is an interesting and important issue in the logistic field. In practice, some factors of its facility location allocation (FLA) problem, i.e., customer demands, allocations, even locations of customers and facilities, are usually changing, and thus FLA problem features with uncertainty. To account for this uncertainty, some researchers have addressed the fuzzy time and cost issues for locating an automotive service enterprise. However, a decision-maker hopes to minimize the transportation time of customers meanwhile minimizing their transportation cost when locating a facility. Also, they prefer to arrive at the destination within the specific time and cost. To handle this issue via a more practical manner, by taking the vehicle inspection station as a typical automotive service enterprise example, this work presents a fuzzy multi-objective expected value optimization approach to address it. Moreover, some region constraints can greatly influence FLA and travel velocity is also an uncertain variable due to the influence of some unpredictable factors in the location process. To do so, this work builds two practical fuzzy multi-objectives programming models of its location with regional constraints, fuzzy inspection demand, and varying velocity. A hybrid algorithm integrating fuzzy simulation, neural networks (NN), and Genetic Algorithms (GA), namely a random weight based multi-objective NN-GA, is proposed to solve the proposed models. A numerical example is given to illustrate the proposed models and the effectiveness of the proposed algorithm.