Fuzzy Multi-objective Optimization Research Articles

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

Implementation of Fuzzy Multi-Objective Optimization On The Basic Of Ratio Analysis (Fuzzy-MOORA) In Determining The Eligibility Of Employee Salary

Purpose: CV. Harmoni Permata has several employees, and each employee has a bonus salary. However, in determining who is eligible for the employee salary bonus at CV. Harmoni Permata is still done manually assessment. This causes an error in the calculation because the decision-maker must look at previous historical data to decide.Design/methodology/approach: System design includes systems that can manage user data, position data, criteria data, criteria description data, absences data, task data, and assessment data, which will produce an assessment report. The MOORA method approach is used because it has calculations with minimum and simple math and has a good level of selectivity. Findings/result: The normalization comparison test of the manual calculation of the MOORA method with the system calculation results is the same, with the best five alternative employees who deserve a salary bonus.Originality/value/state of the art: Based on previous research reviews, this study uses the criteria for performance, honesty, attendance, and accuracy by determining the weight based on the type of benefit or cost and the MOORA method in calculating the final value of alternative ranking.

Http://pu.edu.pk/images/journal/maths/PDF/Paper_3_53_9_2021.pdf

Decision-making is one of the contemporary issues in this modern era due to the interaction of risk and uncertainties in every aspect of the daily lives of human beings. Accordingly, solving practical optimization problems tends to be more challenging. The fundamental reason for this investigation is to explore an effective solution technique for multi-objective optimization problems (MOOPs) in an intuitionistic fuzzy environment (IFE) addressing the issue of determining proper violation parameters and tolerances to the objectives and constraints. The other significant characteristic of this study is the consideration of the decisionmaker’s perspective, namely, optimistic, pessimistic and mixed views in the solution procedure. In the proposed method, compared to the existing study, the required number of iterations and stages are considerably reduced in solving intuitionistic fuzzy multi-objective optimization problems (IFMOOPs). Hence it has imperative advantages in solving complex real-world problems without much difficulty. One problem is solved to demonstrate the competency of the planned approach. A comparative analysis is also undertaken to ascertain the efficiency of the technique.

MORPHOFUNCTIONAL FEATURES OF PLACENTA AT WOMEN WITH PRIMARY AND SECONDARY INFERTILITY.

Water scarcity is a significant global challenge. Water resources planning and management are vital to all sectors, primarily for the agricultural sector identified as a significant consumer. Conjunctive water use management is ideal for decreasing water scarcity, especially in arid and semiarid regions. More importantly, considering uncertainties in water management is critical to planning and management issues. This study develops a fuzzy optimization model aiming at a qualitative-quantitative conjunctive reallocation of reclaimed wastewater and groundwater resources. Three objectives, including maximizing economic benefit, minimizing fertilizer consumption, and groundwater withdrawal, were assessed in Iran's Varamin Irrigation Network. This research uses fuzzy and non-fuzzy optimization models for a quantitative-qualitative conjunctive use of reclaimed wastewater and groundwater to meet multi-objective functions. These single and multi-objective fuzzy and non-fuzzy optimization models are compared. The particle swarm optimization (PSO) and multi-objective PSO (MOPSO) algorithms optimized single and multi-objective problems. A fuzzy optimization model is superior to the non-fuzzy optimization model regarding optimizing multi-objective functions. The results indicated that the net benefit increased up to 16% without increasing the cultivated area in the fuzzy MOPSO model. Moreover, groundwater withdrawal was reduced by using a larger share of reclaimed wastewater in total water consumption. This increased the use of potential nitrate in reclaimed wastewater and reduced fertilizer consumption. This study highlights achieving socio-economic and environmental goals under uncertain circumstances using agricultural water strategies through reclaimed wastewater. Also, a general improvement in water consumption efficiency was achieved through the optimal cropping pattern.

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

Water scarcity is a significant global challenge. Water resources planning and management are vital to all sectors, primarily for the agricultural sector identified as a significant consumer. Conjunctive water use management is ideal for decreasing water scarcity, especially in arid and semiarid regions. More importantly, considering uncertainties in water management is critical to planning and management issues. This study develops a fuzzy optimization model aiming at a qualitative-quantitative conjunctive reallocation of reclaimed wastewater and groundwater resources. Three objectives, including maximizing economic benefit, minimizing fertilizer consumption, and groundwater withdrawal, were assessed in Iran's Varamin Irrigation Network. This research uses fuzzy and non-fuzzy optimization models for a quantitative-qualitative conjunctive use of reclaimed wastewater and groundwater to meet multi-objective functions. These single and multi-objective fuzzy and non-fuzzy optimization models are compared. The particle swarm optimization (PSO) and multi-objective PSO (MOPSO) algorithms optimized single and multi-objective problems. A fuzzy optimization model is superior to the non-fuzzy optimization model regarding optimizing multi-objective functions. The results indicated that the net benefit increased up to 16% without increasing the cultivated area in the fuzzy MOPSO model. Moreover, groundwater withdrawal was reduced by using a larger share of reclaimed wastewater in total water consumption. This increased the use of potential nitrate in reclaimed wastewater and reduced fertilizer consumption. This study highlights achieving socio-economic and environmental goals under uncertain circumstances using agricultural water strategies through reclaimed wastewater. Also, a general improvement in water consumption efficiency was achieved through the optimal cropping pattern.

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-criteria framework to identify barriers and enablers of the next-generation vaccine supply chain

PurposeDue to the introduction of new vaccines in the child immunization program and inefficient vaccine supply chain (VSC), the universal immunization program (UIP), India is struggling to provide a full schedule of vaccination to the targeted children. In this paper, the authors investigate the critical factors for improving the performance of the existing VSC system by implementing the next-generation vaccine supply chain (NGVSC) in India.Design/methodology/approachThe authors design a fuzzy multi-criteria framework using a fuzzy analytical hierarchical process (FAHP) and fuzzy multi-objective optimization on the basis of ratio analysis (FMOORA) to identify and analyze the critical barriers and enablers for the implementation of NGVSC. Further, the authors carry out a numerical simulation to validate the model.FindingsThe outcome of the analysis contends that demand forecasting is the topmost supply chain barrier and sustainable financing is the most important/critical enabler to facilitate the implementation of the NGVSC. In addition, the simulation reveals that the results of the study are reliable.Social implicationsThe findings of the study can be useful for the child immunization policymakers of India and other developing countries to design appropriate strategies for improving existing VSC performance by implementing the NGVSC.Originality/valueTo the best of the authors’ knowledge, the study is the first empirical study to propose the improvement of VSC performance by designing the NGVSC.

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.

A fuzzy multi-objective multiple-pollutant model for rivers using an ant colony algorithm

A fuzzy multi-objective optimisation model was investigated for water quality management in a river under uncertain conditions. In this study, to deal with multiple pollutants simultaneously, the National Sanitation Foundation's water quality index was considered as one of the model's objective functions based on fuzzy set theory. This made it possible to investigate the overall effect of uncertainties on simultaneous changes. Another objective function was the total treatment costs for wastewater discharged into the river. A water quality simulation model and a non-dominated-archiving ant colony optimisation algorithm were used to determine the values of water quality parameters and the model's optimal solutions, respectively. Furthermore, a simulation–optimisation approach was adopted for facilitating the problem-solving process and applied to a hypothetical case study resembling a river system in Iran. The results show that the proposed model significantly reduced the total wastewater treatment costs compared with a similar single-objective model with a more cautious and a cost-effective approach. Although the treatment costs were increased compared with the similar deterministic model, more feasible approach was taken by considering the uncertainties associated with the objectives. With suitable modifications, the model could be easily adapted for other river systems.

A fuzzy scenario-based optimisation of supply network cost, robustness and shortages

Supply network (SN) robustness has become an important issue in SN management. In this paper, we refer to SN as robust, if it maintains its performance in the presence of uncertainty in SN parameters, namely uncertain changes in customer demand. A customer forecasts its demand in terms of requested quantity and time of delivery. This forecasted demand can be changed until a certain time. After that, the customer is committed to its demand. However, a manufacturer has to order materials in advance to produce its product without knowing the exact changes in customer demand. The materials can be ordered either from a standard supplier, or, from an emergency supplier, if there is not enough material in stock and/or there is not enough time for a delivery from the standard supplier. We define a new concept of fuzzy scenarios that comprise uncertain changes in customer demand. These changes are specified by linguistic terms and modelled using fuzzy numbers. The robustness of an SN is measured in a novel way as the variance of costs incurred in all fuzzy scenarios. This means that the robust SN maintains its cost in the presence of uncertain changes in customer demand. A novel fuzzy multi-objective optimisation model is developed, which determines quantities of materials to be ordered by a manufacturer from a standard supplier and times of ordering. The objectives considered simultaneously embed all fuzzy scenarios and include the minimisation of total SN cost, the maximisation of robustness and the minimisation of shortages. Various experiments are carried out to analyse the relationship between SN parameters and SN performance. Results obtained by applying the SN model demonstrate that robustness can be increased and shortages can be decreased, but, as expected, at a higher SN cost. In the case of the high ratio of the unit purchase cost from the emergency supplier to the unit surplus cost, a considerable increase of robustness and a decrease of shortages can be achieved. Finally, it is shown that the model can be applied to large-scale SNs.

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.

Robust and resilient supply chain network design considering risks in food industry: flavour industry in Iran

ABSTRACT Nowadays, shareholders are being encouraged to enhance supply chain risk management in order to deal with several disruptive and operational risks. Producing spice and flavor is one of the high-risk industries as the main raw material is spice and its quality is variable and strongly depends on suppliers, seeds, and climate. Having a constant quality of spices and producing a flavor that satisfies every customer’s demand is intricate. Therefore, these companies encountered a variety of goods which increased complexity and uncertainty in supply, processing time, and demand. To come up with risks and uncertainties, in this paper a robust bi-objective multi-product mathematical model is developed to consider resiliency and uncertainty in a multi-period, multi-item supply chain network design simultaneously. The first objective function maximizes total profits when the second one maximizes the total score of resiliency in which Fuzzy Analytic Hierarchy Process (AHP) and Fuzzy Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) have been used to obtain the four resiliency factors weights for the objective function. We proposed an approach for making coordination between production planning, distribution, supplier selection, and order allocation in a flavor plant. The -constraint method is used to obtain Pareto solutions for a real case in Iran. Finally, sensitivity analysis has been done for tracking the trade-off between optimization and robustness with different robust parameters and investigating unmet demand and benefits of resilience consideration.

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.

Shapley-Shubik Index incorporated debottlenecking framework for sustainable food-energy-water nexus optimised palm oil-based complex

Palm oil mill effluent (POME) elimination has been considered recently as the alternative to expensive biogas recovery to conquer environmental criticisms in the palm oil industry involving the release of biogas (i.e., a primary contributor towards climate change), during POME treatment. With POME elimination and mill-refinery integration considered, the integrated palm oil-based complex (POBC) serves as an appropriate system for sustainable food-energy-water (FEW) nexus evaluations. In this respect, multi-objective optimisation and debottlenecking of POBC has been suggested to address the economic and environmental trade-offs in palm oil mill (POM) for cleaner production. In this study, a stepwise debottlenecking framework was proposed to include previous fuzzy multi-objective optimisation approach and a new debottlenecking method to improve the fuzzy optimal POBC design and evaluate the impact of process on FEW nexus contributions. Each process creates distinctive impact or influence on the POBC performance. Shapley-Shubik Power Index (SSI) has been applied in the notion of power for yes-no voting systems. By evaluating the operate-fail possibilities of internal processes, SSI can be utilised to allocate the power of each process in achieving or failing the POBC performance target, prior to identifying the system bottleneck (SB) in terms of process stage. Therefore, a novel process-level debottlenecking approach with SSI incorporation for a sustainable FEW system was proposed. The SB of greatest impact on overall POBC performance within the fuzzy optimal flowsheet was identified based on SSI allocation and weightage assignment considering decision-maker's interest in multiple goals. Subsequently, the debottlenecked POBC flowsheet with improvement in SB process parameter was generated subjected to profit maximisation and environmental constraints. In this study, the nut/kernel separation and biomass cogeneration system was identified as the SB of fuzzy optimal POBC. The debottlenecked results show 0.8% and 51% of profit and net energy improvement upon 30% increment of boiler efficiency. The debottlenecked flowsheet is validated in the benefit-to-drawback ratio analysis with a feasible score of 3.9. The POME-eliminated POBC generated via the integrated multi-objective optimisation and debottlenecking framework is applicable for POM retrofit to secure 13%, 97% and 47% of improvements in terms of economic potential, greenhouse gas emissions and water footprint compared to the status quo biogas utilisation scenario. The results of the study could aid palm oil holders in real-life planning of budget distribution and maintenance schedule for process stages in the retrofitted POM.

Construction project cost estimation model cost dependent on multi-objective fuzzy optimization calculation

With the development of the social economy, the number and scale of construction projects are also growing. How to control construction project costs has become the key to investment and construction. Based on the multi-objective fuzzy optimization computing, the completed project investment data in the actual project progress is used for empirical analysis of a construction project using the cost prediction model.

Robust neutrosophic programming approach for solving intuitionistic fuzzy multiobjective optimization problems

This study presents the modeling of the multiobjective optimization problem in an intuitionistic fuzzy environment. The uncertain parameters are depicted as intuitionistic fuzzy numbers, and the crisp version is obtained using the ranking function method. Also, we have developed a novel interactive neutrosophic programming approach to solve multiobjective optimization problems. The proposed method involves neutral thoughts while making decisions. Furthermore, various sorts of membership functions are also depicted for the marginal evaluation of each objective simultaneously. The different numerical examples are presented to show the performances of the proposed solution approach. A case study of the cloud computing pricing problem is also addressed to reveal the real-life applications. The practical implication of the current study is also discussed efficiently. Finally, conclusions and future research scope are suggested based on the proposed work.

Synthesis of a Sustainable Wastewater Treatment Plant for Sago Industry using Fuzzy Optimisation

Wastewater treatment plant (WWTP) is an essential process in the manufacturing industry. However, wastewater treatment process is not a profitable process as it requires a significant amount of investment. It is important to design a WWTP that meets wastewater discharge legalisation with low investment costs. To do this, area footprint (land area) occupied by technologies in a WWTP must be factored into the design decision. Unfortunately, the area footprint is yet to be studied. As wastewater treatment processes involve multiple treatment units, the different combinations of these treatment units will give a range of capital costs and costs associated with the area occupied. In addition, the carbon footprint of technology resulting from power consumption must be considered. Each technology possesses unique power consumption requirements and these requirements may influence the total carbon footprint for a given WWTP design. Investment costs, area footprint and carbon footprint must be considered simultaneously but are conflicting in nature. This work aims to present a multi-objective decision-making tool to screen wastewater treatment technologies and to synthesise a WWTP design with low investment cost, low area footprint, and low carbon footprint. Specifically, fuzzy multi-objective optimisation (FMOO) is used to determine a desirable trade-off between investment costs, area footprint, and carbon footprint. To demonstrate the developed approach, a sago-based WWTP case study is solved. Based on the results, a trade-off between these optimisation objectives had reduced 5.35 m2 of area footprint, 986 USD/d of total investment cost, and 108 kg CO2/d of carbon footprint of the synthesised WWTP.

Comparative Assessment for Mechanical Vapour Recompression and Multi-effect Evaporation Technology in Palm Oil Mill Effluent Elimination

Due to the alarming methane emission from anaerobic palm oil mill effluent (POME) treatment practices in palm oil mills, POME elimination strategy is considered as an alternative zero waste solution towards methane mitigation apart from biogas recovery. By dewatering the effluent, POME evaporation technology provides opportunities in water recycling, oil recovery, effluent elimination and process integration with downstream refinery. One of the challenges for implementing POME evaporation is the extensive thermal energy use, which threatens energy efficiency and carbon footprint of palm oil mill. Advanced technologies such as multi-effect evaporation (MEE) and mechanical vapour recompression (MVR) can be considered to reduce steam demand for POME evaporation. Recently, electrical-driven MVR evaporator emerges as a popular solution for solution concentration and food powder production. In MVR systems, evaporated vapour is recompressed by power-consuming mechanical energy to enable steam reuse and reduction in fresh steam demand. In this paper, MEE and MVR evaporation systems are considered for POME dewatering in the integrated palm-oil based complex (POBC) optimisation. A fuzzy multi-objective optimisation model was developed to evaluate the trade-offs of economic benefits, energy consumption and environmental impacts between thermal and electrical driven evaporation techniques for sustainable POBC design. MEE was selected in the fuzzy optimal POBC with aggregate satisfaction of four objectives and improved the EP and GHG reduction of baseline study by 12 % and 61 %. Single-effect MVR POME evaporator contributed to 64 % thermal cost savings with overall 1.5 % reduction in POBC profitability. The optimal results and electricity-to-steam (ETS) price analysis have provided critical insights into the feasibility of MEE and MVR evaporation in POMs. Reducing electricity tariff to achieve ETS ratio below 1.27 could increase the economic favourability of MVR-implemented POBC.

Multi-objective Optimization for Roadway Maintenance Engineering Based on ev-MOGA

The performance of roadway maintenance is a highly correlated factor on national socio-economic issues. Roadway maintenance work is a complex task, involving market game, engineering, planning and scheduling. Taking such aspects as quality, schedule and cost into account, this paper develops a multi-objective roadway maintenance system, in which ev-MOGA is adopted to optimize the decisions concerning roadway maintenance. The experimental results can provide a references for fuzzy multi-objective optimization (MOP) problems.