Fuzzy Multi-objective Optimization Model Research Articles

A fuzzy multi-objective optimization model for sustainable healthcare supply chain network design

Healthcare supply chains play a crucial role, which enables the implementation of optimization strategies that have rapidly emerged as highly effective means for improving the overall structure of pharmaceutical and healthcare supply chains.. In the healthcare industry, parameters such as increasing the quality of service, as well as optimizing costs, environmental, and social factors play a unique role in supply chain management. To improve the healthcare supply chain network, this study proposed a novel optimization model to optimize multiple objectives, including minimizing the total costs and environmental impacts, while maximizing the social factors by creating jobs simultaneously. To address the effects of uncertain parameters, a fuzzy optimization method alongside the multi-objective gray wolf optimizer (MOGWO), non-dominated sorting genetic algorithm II (NSGA-II), multi-objective differential evolution algorithm (MODEA), and ε-constraint are applied to optimize the model. Also, a case study of the pharmaceutical industry demonstrates the model's efficacy in a real-life context. The numerical results show the MOGWO manages to create high-quality Pareto solutions with a good spread at the Pareto boundary within a short time compared to the ε-constraint approach. Further, it has shown a more robust performance compared to MODEA and NSGA-II, indicating the efficiency of MOGWO, among other solution methods and other objective indicators.

Manufacturing system reconfiguration towards sustainable production: a novel hybrid optimization methodology.

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.

A fuzzy multi-objective optimization model for sustainable location planning using volumetric fuzzy sets

A fuzzy multi-objective optimization model for sustainable location planning using volumetric fuzzy sets

Construction of an economic environment scheduling system based on the multi-objective fuzzy optimization model

Abstract 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.

Influence and optimization of long-time superfine grinding on the physicochemical features of green tea powder

Superfine grinding is a crucial step in green tea powder (GTP) processing. In this work, the time-resolved changes of physicochemical properties (physical properties, main active components, etc.) of GTPs were investigated, and ball milling time (BMT) for tea grinding was optimized. Variable importance in projection was used to evaluate the importance of modification of each physicochemical parameter on BMT, and a multi-objective fuzzy optimization model was used to obtain the optimal BMT. The BMT range was 2–30 h, and the results showed that BMT had a significant influence on the physicochemical properties of GTP. The mean particle size decreased from 257 to 10.6 μm and the color became lighter as the BMT increased. The main active components decreased markedly when BMT increased form 18–24 h. Mean particle size and color difference dramatically correlated with BMT and could be described by a one-phase exponential decay function. The optimal BMT was 18 h and high quality GTP could be obtained under this condition with small particle size, bright green color and rich in theanine and total tea polyphenols (TTPs).

A Multi-Objective Fuzzy Optimization Model for Electricity Generation and Consumption Management in a Micro Smart Grid

This manuscript proposes an intelligent supply and demand management system in a complete network of electricity production and consumption. A micro smart grid (MSG), which includes a solar cell, a wind turbine, a diesel generator, and battery storage system capable of trading energy with the smart gride (SG), connected to smart buildings with different types of loads is modelled. Different types of intelligent fuzzy controllers for distributed management were proposed and optimized via the non-dominated sorting genetic algorithm-II (NSGAII), which is a multi-objective optimization method. Maximum user comfort, the amount of renewable energy employment, minimum total power consumption cost, total energy consumption at peak time, and MSG loss of power supply probability are the five objective functions of the optimization process. Various uncertainties of the real world have also been considered. The most crucial distinguishing feature of this proposed method is the design of controllers to manage the demand and supply of electricity without the need for daily optimization. Comparison experiments with other methods presented in the field of electricity supply and demand management are conducted to show the superiority of this method in terms of optimality of the results, low processing volume required to implement real controllers, and its resilience to changing conditions.

Sustainable supply chain partner selection and order allocation: A hybrid fuzzy PL-TODIM based MCGDM approach

Sustainability, as a trend of social development and the embodiment of corporate social responsibility, has begun to receive more attention. To achieve this goal, sustainable supplier selection (SSS) and order allocation (OA) are seen as the crucial activities in corporate management. In the process of SSS, the psychological behavior of decision-makers (DMs) could play a critical role in the evaluation results. Therefore, introducing it into the decision-making process may lead to decision in line with the actual situation. In the uncertain multi-criteria group decision-making (MCGDM) problem described by probability linguistic term sets (PLTS), the DMs can evaluate the criteria of each supplier based on his own preference and hesitation, which is useful to avoid the loss of information. For this reason, this study develops a novel multi-criteria group decision-making combined with fuzzy multi-objective optimization (MCGDM-FMOO) model for SSS/OA problems by considering the triple bottom line (TBL) in which includes economic, environmental and social factors. The proposed method includes four stages. (1) the best-worst method (BWM) and entropy weight method are utilized to assign the weights of criteria to obtain the comprehensive weight. According to the output weights, the an acronym for interactive and multi-criteria decision-making in Portugese (TODIM) approach is applied to rank the suppliers under PLTS environment; (2) a FMOO model that can effectively deal with uncertainties and dynamic nature of parameter is formulated for allocating optimal order quantities; (3) two novel approaches are utilized to solve the FMOO model in order to obtain the richer Pareto frontier; and (4) the final OA solution is achieved by technique for order preference by similarity to ideal solution (TOPSIS) method. Finally, the validity and practicability of proposed MCGDM-FMOO model are verified by an example and comparative analysis with other classical MCGDM methods.

Distribution of Irrigated and Rainfed Agricultural Land in a Semi-Arid Sandy Area

Under water resource and terrain constraints, a certain scale threshold of irrigated and rainfed agricultural areas exists in semi-arid sandy areas. If this threshold is exceeded, water and soil resources will be unbalanced, and the ecological environment will deteriorate. Accurate assessment of the suitable scale of cultivated land in semi-arid sandy areas is of great significance for sustainable utilization of cultivated land resources and regional ecological security. Most existing research methods are based on water resource constraints and rarely consider terrain factors. Therefore, based on the principle of water balance and with the Horqin Left Wing Rear Banner as the research area, this study adopted a multi-objective fuzzy optimization model and relative terrain index analysis method to explore the appropriate spatial ratio of irrigation and rainfed agriculture. The results show that the area of irrigated agriculture in the study area is 77,700 hm2, and the appropriate scale is 91,700 hm2. The current area of dry farming is 184,600 hm2, and the suitable scale is 117,100 hm2. The results also show that the utilization efficiency of water and soil resources in irrigated agriculture was not optimal, rainfed agriculture exceeded its suitable scale, and water and soil resources were seriously unbalanced. However, the region of cultivated land that exceeds the appropriate scale is mostly located in an area with poor terrain, less precipitation, and other unsuitable conditions for cultivation, which is prone to abandonment, resulting in deterioration of the ecological environment. Therefore, the spatial layout of agricultural land use in the study area should be adapted to local conditions, and the water-saving structure of irrigated agriculture should be optimized to achieve the maximum comprehensive benefits. Dry farming should be controlled on a reasonable scale, and the part exceeding the appropriate scale should be returned to grassland to ensure sustainable development.

A discrete multi-objective grey wolf optimizer for the home health care routing and scheduling problem with priorities and uncertainty

In China, with the acceleration of the population aging, the demand for home health care is growing rapidly. However, the available caregivers are insufficient to serve all the customers who make an appointment in advance. Besides, there are actually some uncertain parameters such as travel and service times when implementing the given schedule. Hence, this paper presents a home health care routing and scheduling problem (HHCRSP) by considering customers’ priorities and times uncertainty. A fuzzy multi-objective optimization model is established to maximize the total priority of all visited customers and minimize the total service cost. Jimenez’s method is utilized to transform the fuzzy parameters in the model. A discrete multi-objective grey wolf optimizer (DMOGWO) is proposed to solve the model. In the DMOGWO, the heuristic rule of priority maximum-earliest service is designed to generate the initial solution. An individual position updating mechanism is developed to make the algorithm suitable for discrete search space. Subsequently, three neighborhood structures are designed to enhance the local search ability of the algorithm. Finally, the DMOGWO is compared with CPLEX, MOGWO, MOPSO, NSGA-II and NSGA-III. Experimental results indicate that the proposed DMOGWO is competitive in convergence, diversity and distribution of the solutions on many instances. This shows that the proposed DMOGWO can effectively solve the home care routing and scheduling problem.

The attitude of MCDM approaches versus the optimization model in finding the safest shortest path on a fuzzy network

This paper examines the performances of the multi-criteria decision-making (MCDM) methods and optimization model in solving multi-attribute shortest path problems such as the safest shortest path under a fuzzy environment. To the best of the knowledge of the authors, this is the first study performing comparative analysis on finding the multi-attribute shortest path by employing well-known techniques in terms of computational effort and results in a fuzzy environment. To this end, the safest shortest path problem, where the risk and distance values concerning arcs on a directed network are defined as triangular fuzzy numbers, is handled. The solution process is carried out under two main headings: (i) To start the solution with MCDM methods, an auxiliary algorithm that constructs a fuzzy decision matrix is proposed. Then, Fuzzy-Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS), Fuzzy Simple Additive Weighting (F-SAW), and Fuzzy Evaluation Based on Distance from Average Solution (F-EDAS), that are fuzzy-based MCDM methods, are employed to rank the alternative paths. (ii) A multi-objective fuzzy optimization model is formulated, and the most reasonable paths are obtained considering different α-cut levels. Following that, comparative analysis is performed through a set of scenarios considering the different weights of the criteria to see the variability in the rankings. Besides, the addressed fuzzy-based MCDM methods are compared in terms of computational complexity. Overall, the main findings and managerial insights regarding the effectiveness and performance of the methods discussed in the solution process are provided.

Green road\u2013rail intermodal routing problem with improved pickup and delivery services integrating truck departure time planning under uncertainty: an interactive fuzzy programming approach

This paper addresses the multi-objective optimization for the road–rail intermodal routing problem that aims to minimize the total costs and carbon dioxide emissions of the routes. To achieve high timeliness of the entire transportation process, pickup and delivery services are simultaneously improved based on the employment of fuzzy soft time windows to measure their service levels. The modeling of road–rail intermodal routing considers fixed schedules of rail and time flexibility of road to match the real-world transportation scenario, in which travel times and carbon dioxide emission factors of road services are considered to be time-varying. To improve the feasibility of the routing, uncertainty of travel times and carbon dioxide emission factors of road services and capacities of rail services are incorporated into the problem. By applying trapezoidal fuzzy numbers to formulate the uncertainty, we propose a fuzzy multi-objective nonlinear optimization model for the routing problem that integrates the truck departure time planning for road services. After processing the model with fuzzy chance-constrained programming and linearization, we obtain an auxiliary equivalent crisp linear model and solve it by designing an interactive fuzzy programming approach with the Bounded Objective Function method. Based on an empirical case study, we demonstrate the validity of the proposed approach and discuss the effects of improving the confidence levels and service levels on the optimization results. The case analysis reveals several managerial insights that help to realize an efficient transportation organization by making effective trade-offs among lowering costs, reducing emissions, improving service levels, and enhancing feasibility.

A fuzzy multi-objective optimization model for sustainable closed-loop supply chain network design in food industries

A fuzzy multi-objective optimization model for sustainable closed-loop supply chain network design in food industries

A deep neural network with a fuzzy multi-objective optimization model for fault analysis in an elastic optical network

The elastic optical network (EON) is the most attractive architecture for the next generation of optical networks. Dealing with high bit-rate traffic, EON faces the challenge of ensuring survivability to operate with stringent Service Level Agreements. This article proposes a Deep Neural Network model with a multi-objective Fuzzy Inference System (FIS) to solve the Routing and Spectrum Assignment problem with Shared Backup Path Protection. The algorithm aims to optimize the trade-off between blocking probability (BP) and fault restoration ratio (FRR). It uses a new spectrum-fragmentation metric to improve the FRR of affected connections. The FIS adds features of load balancing and alignment of allocation path solutions. We use figures of merit as BP of connection requests, FRR, spectrum utilization ratio, and connection downtime to evaluate the algorithm performance. The proposed algorithm organizes traffic in a less fragmented way, efficiently uses routing and protection resources, and performs well compared to similar algorithms related in the literature.

A fuzzy multi-objective optimization model for a sustainable reverse logistics network design of municipal waste-collecting considering the reduction of emissions

The most important reason for waste collection is the protection of the environment and the health of the population. Reverse logistics is applied in the sustainable management of municipal waste and is used in the collection, recycling and reuse, as well as the reduction of consumables and environmental compatibility. One of the challenges of sustainable management is costs and customer demand must be considered simultaneously. In this paper, we try to address a comprehensive approach by applying fuzzy mathematical programming to design a multi-objective model for a reverse logistics network. To cover all aspects of this system, we tried to minimize the cost of facility construction, vehicle fuel and environmental damage from the emission of polluting gases, as well as minimize the sum of the ratio of unanswered customer demand to the amount of their demand for all periods, as objective functions of the model. In order to obtain solutions on the Pareto front, a customized multi-objective genetic algorithm (NSGAII) and a customized bee colony algorithm (BCO) were applied. The results of the two algorithms according to the indicators of quality comparison, spacing, diversification and solution time have been compared. The results showed that in all cases, the bee colony algorithm was better able to explore and extract the area to a feasible solution and to achieve near-optimal answers. In terms of spacing metric and resolution time, the genetic algorithm performed better than the bee algorithm.

Provide a multi-purpose fuzzy model for stock portfolio optimization

Researchers in the field of portfolio optimization made efforts to decrease uncertainty in future returns. Any disturbance in the parameter values causes the solution to be non-optimal or impossible. This study designs a strong fuzzy-multipurpose model for stock portfolio optimization based on Tehran Stock Exchange market data. At the end of the paper, the created model is compared with the results of the multi-objective model. The results show that the fuzzy multi-objective optimization model has relative stability and model compared to the multi-purpose optimization model is strong.

Textile supply chain management considering carbon emissions and apparel demand fuzziness: a fuzzy mathematical programming approach

PurposeThis paper studies the textile supply chain tactical planning under demand fuzziness through considering environmentally friendly and social responsibility. Hence, carbon emission in textile production and transportation is considered along with supply chain profitability.Design/methodology/approachThe authors present a fuzzy multi-objective mathematical optimization model with credibilistic chance constraints to determine the fabric procurement quantities and production plan under uncertainty. The solution procedure makes use of credibility measure and fuzzy aggregation operator to attain compromise solutions.FindingsA trade-off among carbon emissions, social performance and supply chain total profit is conducted. The analyses indicate the importance of transportation costs and carbon emission while determining the supply chain's tactical plan.Originality/valueThe textile supply chain's social sustainability alongside carbon emissions of textile operations is contemplated to provide apparel production and distribution logistics planning under uncertainty. In doing so, the authors propose a hybrid credibility-possibility mathematical optimization model to determine a compromise solution for textile managers.

Individual privacy in data mining using fuzzy optimization

This article proposes the individual data privacy during collaborative computation in data mining method using an optimization model. The privacy problem is solved using different methodologies. The solution for individual privacy is considered as a multi-objective optimization model. Practically, the requirement for privacy varies from user to user. Therefore, it generates inherent vagueness for individual privacy. In this article, the vagueness is considered and the privacy problem is solved by a fuzzy optimization method. The fuzzy multi-objective optimization model is proposed to be used as a supplementary privacy method to address individual privacy issues. The fuzzy constraints are generated to solve the models on the basis of the privacy requirements of users. The fuzzy set domain for the optimization problem is used to fulfil the individual privacy requirements in a computing environment. The proposed solution allows data owners to choose their own privacy level on demand, with maximum flexibility.

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

In the initial stage of emergency rescue for major railway emergencies, there may be insufficient emergency resources. In order to ensure that all the emergency demand points can be effectively and fairly rescued, considering the fuzzy properties of the parameters, such as the resource demand quantity, the dispatching time and the satisfaction degree, the railway emergency resources dispatching optimization model is studied, with multi- demand point, multi-depot, and multi-resource. Based on railway rescue features, it was proposed that the couple number of relief point - emergency point is the key to affect railway rescue cost and efficiency. Under the premise of the maximum satisfaction degree of quantity demanded at all emergency points, a multi-objective programming model is established by maximizing the satisfaction degree of dispatching time and the satisfaction degree of the couple number of relief point - emergency point. Combined with the ideal point method, a restrictive parameter interval method for optimal solution was designed, which can realize the quick seek of Pareto optimal solution. Furthermore, an example is given to verify the feasibility and effectiveness of the method.

Multi-objective fuzzy optimization model of power system dynamic environment economy based on coordinated dispatching of wind power and electric vehicles

In order to coordinate the impact of the integration of electric vehicles and wind power on the economic dispatch of power systems, a multi-objective dispatch model for the environmental economy of wind farms with PHEVs connected to the grid was constructed. The uncertain factors of wind power generation and load will increase the difficulty of dispatching. The fuzzy theory will be used to realize the fuzzy modelling of the coordinated connection of wind power and electric vehicles. The satisfaction index will be used to transform the multi-objective problems of deterministic environment and economy into single For the objective optimization problem, based on the improvement of the traditional particle swarm optimization algorithm using linear descent search ideas and the particle information sharing method considering boundary constraints, the improved PSO algorithm is used to solve the proposed dynamic environment and economic dispatch model. From the grid operation and vehicle owners from the perspective of demand, the constraints of the system’s rotation reserve and the daily travel of the owner are considered. The comparison and analysis of the simulation of algorithms and calculation examples and multiple scheduling schemes in different scenarios verify the rationality of the model and the superiority of the improved PSO algorithm for solving such dynamic scheduling problems.

Fuzzy Multi-Objective Optimization Model of Basketball Lineup

Fuzzy Multi-Objective Optimization Model of Basketball Lineup