A fuzzy multi-objective programming optimization model for emergency resource dispatching under equitable distribution principle

To integrate the combination of electric vehicles and wind power of the transmission system, a wind farm environment multi-objective transmission model considering plug-in electric vehicles was established. The fuzzy optimal scheduling model for the environment and economy target of wind farm power system considering grid interconnection of electric vehicles designed by the author has the following characteristics: A fuzzy multi-objective dynamic scheduling model for the design of wind farm power system considering grid connection of plug-in electric vehicle is established, and the original 10 systems are taken as an example for power system design. The results show that the better model shows that the electric vehicle grid connection can greatly reduce the disadvantage of wind power stochastic uncertainties of system scheduling. The model can be used under different conditions further analyzing the rationality of the model determined by the author. An improved particle swarm optimization algorithm is used to solve this problem. The model takes the environmental and economic benefits into account, which improves the efficiency of the model.

Fuzzy multi-objective decision model for calibration supplier selection problem

Fuzzy multi-objective modeling of effectiveness and user experience in online advertising

Fuzzy particle swarm optimization for conjunctive use of groundwater and reclaimed wastewater under uncertainty

Developing a sustainable manufacturing system is a progressively challenging issue as governments across the world have been enforcing increasingly severe regulations by promoting the reduction of environmental waste for manufacturing and energy-saving production activities. Thus, there is a need for developing a sustainable manufacturing system that can be fully examined by incorporating ecological aspects (e.g., consumed energy) for related operations of a manufacturing system using computer-based discrete event simulation tools. In this study, a combined framework of a novel hybrid fuzzy multi-objective optimization and discrete event simulation approach is presented. We combine ecological and economic data and optimization techniques that are aimed at minimizing economic and ecological objectives in a manufacturing system at an early design phase. Hence, the fuzzy multi-objective optimization model is formulated by incorporating economic and ecological parameters. Again, the discrete event simulation model is established based on a comprehensive performance evaluation of the production system. This study also supports design decisions in determining optimum machine numbers, lighting, and cooling equipment required for the production processes within the sustainable manufacturing in conjunction with the most effective level of material flows. In addition, an integrated Decision-Making Trial and Evaluation Laboratory (DEMATEL)-epsilon constraint approach is applied to handle the multiple-objective optimization problem towards a set of trade-offs among the optimization objectives. A real-life application is carried out for investigating the applicability of the created hybrid framework. The findings of this study demonstrate that this framework is useful as a decision-making tool since it can develop a sustainable manufacturing system design considering an optimal solution associated with amounts of energy usage and CO2 emissions under economic constraints.

Purpose The purpose of this paper is to develop a combined intuitionistic fuzzy analytic hierarchy process (IFAHP) and fuzzy multi-objective optimization approach to select suppliers and allocate the orders to them in the bottled water production context. Design/methodology/approach First, the primary weights of criteria associated with the supplier selection problem are calculated using the IFAHP technique. Then a fuzzy multi-objective optimization model is developed to allocate the appropriate amount of orders to each supplier. Findings The proposed methodology has been successfully implemented in the case of an Iranian food company in its bottled water factory. Results demonstrate our model is capable of practically handling the uncertainty in DMs’ preference that leads to effective and efficient supplier selection and order allocation decisions. Originality/value The authors develop a novel hybrid decision-making tool to tackle the uncertainty in decision-makers’ opinions with a demonstrated applicability and some promising outcomes in efficiently allocating the order quantity to suppliers in the area of bottled water production.

Based on the characteristics and requirements of railway emergency resources dispatch, the paper established two optimal models respectively, the single objective model is aiming at minimizing the time of emergency resource dispatch, the multi objective model is for the sake of minimizing the time of emergency resource dispatch and the number of emergency rescue base, and the paper used voting analytic hierarchy process of operations research to solve the model. The study shows that the multi objective optimization model can reduce the number of emergency rescue base and the cost of rescue while it can meet the demand of the shortest allocation time. By using Matlab software, the decision-making process of emergency resource optimize dispatch was designed, making the emergency decision fast and scientific, meanwhile, the paper made an empirical analysis on one railway bureau. The research results reduce the rescue time and cost of the railway accident, and provide reference for implement the rapidness response to make decision on emergency resources allocation, scientific resource allocation decisions.

This paper investigates a multi-objective supplier selection and order allocation problem under quantity discounts in a fuzzy environment. Prior research on supplier selection and order allocation with quantity discounts mainly considered partial fuzziness of the decision problem; a situation where both the objectives of the decision maker and the constraints are fuzzy has not been studied up to now. This paper closes this gap by integrating both aspects into a single model. First, a combination of fuzzy preference programming and interval-based TOPSIS is proposed for evaluating suppliers. Secondly, based on the scores obtained in the first step, a fuzzy multi-objective linear programming model is developed. Subsequently, a new solution procedure for solving the fuzzy multi-objective linear programming model is presented. The procedure first transforms fuzzy constraints and coefficients into deterministic coefficients, and then three different fuzzy programming approaches, namely interactive fuzzy multi-objective linear programming, and the weighted additive as well as the weighted max-min method are implemented. Finally, the performance of each method is evaluated by computing the distance between each solution and the preferred solution.

Groundwater management involves conflicting objectives as maximization of discharge contradicts the criteria of minimum pumping cost and minimum piping cost. In addition, available data contains uncertainties such as market fluctuations, variations in water levels of wells and variations of ground water policies. A fuzzy model is to be evolved to tackle the uncertainties, and a multiobjective optimization is to be conducted to simultaneously satisfy the contradicting objectives. Towards this end, a multiobjective fuzzy optimization model is evolved. To get at the upper and lower bounds of the individual objectives, particle Swarm optimization (PSO) is adopted. The analytic element method (AEM) is employed to obtain the operating potentio metric head. In this study, a multiobjective fuzzy optimization model considering three conflicting objectives is developed using PSO and AEM methods for obtaining a sustainable groundwater management policy. The developed model is applied to a case study, and it is demonstrated that the compromise solution satisfies all the objectives with adequate levels of satisfaction. Sensitivity analysis is carried out by varying the parameters, and it is shown that the effect of any such variation is quite significant. Copyright (c) 2015 John Wiley & Sons, Ltd.

This thesis establishes the functional relationship between the position, pricing and completion of the task, the position, reputation value of the member, the starting time and the reservation limit of the task given by referring to its reputation. In addition, this thesis also establishes multi-objective programming model and logistic regression model, and writes out the task pricing rule and designs the pricing scheme, realizes the model of obtaining the best pricing scheme by analyzing the task information and member information. Firstly, the data in the attachment are plotted, the outliers are removed and the error values are modified. In addition, by plotting the pre-processed data, we can extract the characteristics of the region and find out the various factors that affect the pricing law and the general relationship. At the same time, on the basis of theoretical analysis and data availability, this paper establishes a task pricing model by using multiple binomial regression algorithm, obtains the law of task pricing in Dongguan, Shenzhen and Guangzhou, and analyzes the reasons for the low completion rate of tasks. In addition, considering from the point of view of task publisher, we should not only make the order rate sufficiently high, but also make the order cost not too high. On this basis, the thesis puts forward a minimum-maximum bi-objective optimization model. In order to solve this model, this paper creatively puts forward the cost performance function, and designs the corresponding solving algorithm to solve the problem and give the pricing scheme. Finally, based on the consideration of jointly packaging and publishing tasks with similar distances, the multi-objective optimization model is adjusted, and two restrictions are given: the distance between packaged tasks is less than a fixed value, and the number of packaged tasks is less than a fixed value.

An integrated methodology for the selection of sustainable suppliers and order allocation problem with quantity discounts, lost sales and varying supplier availabilities

Stochastic multi-objective models for network design problem

In an increasingly competitive global environment, more and more engineering designers are aware of the importance of facility layout on the entire manufacturing system. With the diversification of products, the proportion of multi-species and one-piece small batch production systems increase, this study focuses on small and medium enterprises as the main target, and researches its facility layout of manufacturing system and design of optimization model. In this paper, we discuss the layout of manufacturing system according to the characteristics of small and medium enterprises, and give a framework process for the layout design. Then learning from the existing planning model, we establish an optimization model for multi-objective non-linear programming to reflect the true environment of layout. By improving the original multi-objective non-linear programming model, we make this multi-objective non-linear programming model more consistent with requirements of facility layout

In this paper, computational models of environmental pollution and energy consumption of urban multimodal traffic network are proposed according to pertinent research and a multi-objective programming model is then developed to formulate optimization problem for such a system. Simultaneously, the main factors, such as travel time, pricing and convenience which influence travelers’ choice behaviors are all considered and a combined assignment model is proposed to simulate travelers’ mode and route choices. A bi-level programming model, in which the multi-objective optimization model is treated as the upper-level problem and the combined assignment model is processed as the lower-level problem, is then presented to solve multi-criterion system optimization problem for urban multimodal traffic network. The solution algorithms of the proposed models are also presented. Finally, the model and its algorithms are illustrated through a simple numerical example.

A multi-objective fuzzy linear programming model for optimization of natural gas supply chain through a greenhouse gas reduction approach