Multicriteria Optimization Techniques Research Articles

Optimization of a Real Municipal Sewage Treatment Plant Using CRFSMA Algorithm and a Mathematical Model

This study presents the development, calibration, and validation of a mathematical model tailored for biological wastewater treatment at an actual urban sanitation facility. Utilizing multi-criteria optimization techniques, the research identified the most effective MCO algorithm by assessing Pareto optimal solutions. The model incorporated three primary performance measures energy consumption, overall volume, mean quality of effluent, and optimized 12 process parameters. Three algorithms, CRFSMA, particle swarm algorithm, and adaptive non-dominated sorting genetic algorithm III, were rigorously tested using MATLAB. The CRFSMA method emerged as superior, achieving enhanced Pareto optimal solutions for three-dimensional optimization. Quantitative improvements were observed with a 14.8% increase in wastewater quality and reductions in total nitrogen (TN), chemical oxygen demand (COD), total phosphorus (TP), and ammonium nitrogen (-) concentrations by 0.95, 2.38, 0.04, and 0.14 mg/L, respectively. Additionally, the total cost index and overall volume were decreased, contributing to an 18.27% reduction in overall volume and an 18.83% decrease in energy utilization. The adapted anaerobic-anoxic-Oxic (A2O) framework, based on real-world wastewater treatment plants, demonstrated compatibility with observed effluent variables, signifying the potential for energy savings, emission reductions, and urban sanitation enhancements.

Multi-Objective Optimization of the Recovery of Base and Precious Metals from Waste Printed Circuit Boards by Two-Stage Hydrometallurgical Process Using Taguchi-Based Grey Relationship Analysis

AbstractThis study focused on determining the optimum conditions for the maximum recovery of base and precious metals from printed circuit boards of end-of-life desktop computer motherboards using Taguchi-based grey relation analysis. In the first stage of the two-stage study, optimum conditions were investigated for the dissolution of base metals (copper and zinc) in waste printed circuit boards under high-pressure leaching. The dissolution of base metals was performed based on the L25 orthogonal array designed by Taguchi method. In the second step, designed according to Taguchi L9 orthogonal array to recover gold and silver from the solid remaining from the pressure-leaching process. Optimum combinations of parameters in both stages were determined using the multi-criteria optimization technique grey relationship analysis. In the experiments carried out in the determined optimum combinations, 99.62% of copper, 98.76% of zinc, 99.15 of silver and 85.82% of gold in waste printed circuit boards were recovered. Graphical Abstract

Environmental assessment and CO2emissions of Brayton cycle configurations based on exergo-sustainability, economic and ecological efficiency using multi-criteria optimization technique

The performance indicators of Brayton cycle configurations (BCY) for the topping cycle application are presented. The indicators include exergy efficiency, ecological efficiency, sustainability index, environmental impact, and economic parameters. The study's objective is to access the key indicators of sustainability and investment cost to provide valid information for the choice of GT configuration as topping cycles. Five BCY configurations (Model 1 to Model 5) were studied. The maximum exergy efficiency of 28 % was obtained across the studied models. In addition, the waste exergy ratios, environmental effect factors, and CO2 emissions were determined for each model. The CO2 emissions were found to vary from 102.8 to 168 kg/MWh. Model 1 and Model 5 had the highest payback periods of 2.3 and 3.6 years, respectively, with the least unit cost of energy. Similarly, the highest cost of investment was obtained with Model 5. Results from the TOPPIS analysis show that the closeness to the final positive ideal solution varied from 0.218 to 0. 56 across the BCYs. The best model close to ideality was model 5 and thus ranked first and based principally on economic, technical, and environmental sustainability. Furthermore, the optimization results show that there are prospects for system retrofitting.

Portfolio optimization based on bi-objective linear programming

In this study, we deal with a portfolio optimization problem including both risky and risk-free assets. We use the infinity norm criterion to measure portfolio risk and formulate the problem as a bi-objective linear optimization problem. Then, a single objective linear program is considered related to the bi-objective optimization problem. Using the well-known Karush-Kuhn-Tucker optimality conditions, we obtain analytic formula for an optimal solution. Moreover, we determine the whole efficient frontier by multi-criteria optimization techniques. Based on the theoretical results, two algorithms are proposed for finding the portfolio weights and the efficient frontier. Numerical examples are given for illustrating the new models and algorithms. Additionally, a simulation study has been conducted to assess the performance of the proposed method.

Personalized Day-Trip Planning: A TSP-TW-Based Multimodal Multicriteria Optimisation Approach

In this paper, we present a novel approach for computing personalized itineraries for individual travel plans throughout one day, considering the wide variety of mobility preferences individuals consider when making itinerary choices. We extend the Traveling Salesman Problem with Time Windows (TSP-TW) by integrating multi-criteria optimization techniques, flexible activities, park-and-ride options, and various transport modes to provide a more comprehensive representation of transportation options. We assess travelers’ mobility preferences, selecting a relevant subset for a real-world itinerary optimization scenario, and employ choice experiments to identify the importance of these preferences for individual decision-makers. The utility functions derived from these experiments are then used for itinerary optimization. We validated our method through simulations in a medium-sized German city, which demonstrated a significant improvement of 16.19% in travel utility when incorporating a utility function into itinerary optimization compared to plans based solely on travel time.

Protection system planning in distribution networks with microgrids using a bi-level multi-objective and multi-criteria optimization technique

Microgrids are able to improve several features of power systems, such as energy efficiencies, operating costs and environmental impacts. Nevertheless, microgrids’ protection must work congruently with power distribution protection to safely take all advantages. This research contributes to enable their protection by proposing a bi-level method to simultaneously solve the allocation and coordination problems, where the proposed scheme also includes local protections of distributed energy resources. The uncertainties associated with generation and loads are categorized by the k-means method, as well. The non-dominated sorting genetic algorithm II is employed in the upper-level task to solve the protection and control devices allocation problem with two opposing objectives. In the lower-level task, a genetic algorithm ensures their coordination. Protection devices include reclosers and fuses from the network, and directional relays for the point of common coupling of microgrids, while control devices consist of remote-controlled switches. In contrast to related works, local devices installed at the point of coupling of distributed generation units are considered as well, such as voltage-restrained overcurrent relays and frequency relays. The optimal solution for the decision-maker is achieved by utilizing the compromise programming technique. Results show the importance of solving the allocation and coordination problems simultaneously, achieving up to $25,000 cost savings compared to cases that solve these problems separately. The integrated strategy allows the network operator to select the optimum solution for the protective system and avoid corrective actions afterward. The results also show the viability of the islanding operation depending on the decision maker's criteria.

Experimental investigation on microhardness, surface roughness, and white layer thickness of dry EDM

In this research, the environment-friendly dry electrical discharge machining (EDM) process is investigated to improve the microhardness, surface finish, and white layer thickness of the machined surfaces using graphite-argon gas as a dielectric medium. The graphite powder, mixed with compressed argon gas, has been used to replace the existing dielectric medium in the EDM process. Gas pressure, discharge current, pulse width, and gap voltage were working as input parameters to reduce surface roughness and enhance the microhardness and white layer thickness. The Taguchi L16 orthogonal array is applied to the design and analysis of the experimental results. The minimum surface roughness (2.23 μm) of the HN31 steel has been attained by increasing the gas pressure up to 1.0 MPa and the minimum values of pulse width (40 μs), gap voltage (40 V), and discharge current (6 A). The maximum microhardness (501.04 HV) has been obtained at 1.2 MPa of gas pressure, 120 μs of pulse width, 60 V of gap voltage, and 18 A of discharge current. The maximum white layer thickness (16.24 μm) is achieved by the maximum values of gas pressure (1.2 MPa), pulse width (160 μs), gap voltage (70 V) and discharge current (18 A). The SEM analysis had been done to reveal the white recast layer thickness and surface roughness of the machined surfaces of the dry EDM process. The SR is increased by the recast layer, pores, and microcracks on the machined surfaces. Finally, the multi-criteria optimization technique: Weight Product Method (WPM) is applied to predict optimum process parameter settings: GP: 1.2 MPa, PW: 120 μs, GV: 50 V, and DC: 18 A to meet the best machining performances (MH = 493.32 HV, WLT = 14.28 μm, and SR = 3.82 μm). The validation tests were done to confirm the predicted results obtained by both the Taguchi and WSM methods.

Solar PV power plant site selection using a GIS-based non-linear multi-criteria optimization technique.

The ongoing rise in energy consumption imposed serious environmental challenges by using fossil fuels. The use of renewable energy sources is being increasingly explored as a potential answer for achieving sustainable energy production and minimizing adverse environmental effects. In the modern day, photovoltaic (PV) systems are viewed as a possible replacement for fossil fuels as a clean energy source. The installation of solar PV power plants requires vast land and huge investment. Therefore, it is necessary to select a suitable site to achieve maximum efficiency and low cost. A feasible location of photovoltaic (PV) system must consider certain criteria including land restrictions, access to roads, and transmission lines. This study analyzed ten factors grouped into four categories: geographic, technical, economic, and flood susceptibility criterion. The data of each factor is extracted from various governments, United Nation (UN), and non-government organizational bodies. Weights were assigned to ten factors by using a non-linear multi-criteria optimization technique called full consistency method (FUCOM). A geographic information system (GIS) software, ESRI ArcGIS pro, performs the weighted overlay analysis of the ten factors with weighted importance calculated by the above technique. A suitability map is created showing that a total of 2.02% of the country's area is suitable for PV power plants, which are further divided into five suitability classes. The results highlight the distribution of suitable sites for the construction of solar PV power plant throughout the country. A sensitivity analysis is performed to highlight the impact of the factor on the final suitability map. These findings can promote the future widespread development and application of solar energy resources.

Optimization of Process Parameters in Electrochemical Micromachining of AMCs by Using Different Techniques of Weight Evaluation

The application of electrode heating is proposed in electrochemical micromachining (EMM). In the EMM process, the temperature of the electrode, voltage, duty cycle, and electrolyte concentration are considered process parameters. Taguchi L18 mixed-level orthogonal array (OA) design was adopted for designing the experiments and this study highlights the effect of temperature on responses such as radial overcut (ROC), material removal rate (MRR), and conicity factor (CF). In addition, multicriterion design making (MCDM) with VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje, i.e., multi-criteria optimization and compromise solution) technique is used for finding the best alternatives based on the distinct weight assessing methods such as equal weights method (EWM), analytic hierarchy process (AHP), and entropy-based weights method (EBWM). Furthermore, a confirmation test is also conducted to find the best optimal parameters and their levels among the EWM-VIKOR, AHP-VIKOR, and EBWM-VIKOR. The results revealed that AHP-VIKOR provides a maximum improvement of 0.945 among the three methods.

3D-flight route optimization for air-taxis in urban areas with Evolutionary Algorithms and GIS

Electric aviation is being developed as a new mode of transportation for the urban areas of the future. This requires urban air space management that considers these aircraft. Flight routes need to be determined that avoid no-fly areas, and minimize flight time, energy consumption and added noise. Yet, no method currently exists for optimizing urban flight routes under multiple conflicting objectives while avoiding three-dimensional restricted areas. In our work, this research gap is overcome by optimizing 3D-routes with the multi-criteria optimization technique called Non-dominated Sorting Genetic Algorithm II. We propose a novel procedure in the optimization process to incorporate geographical representations. Furthermore, we include a seeding procedure for initializing the flight routes and repair methods for invalid flight routes that may arise during the optimization process. We apply the optimization to a case study in Manhattan (New York City) for two different aircraft types, the Lilium Jet (vectored thrust) and the EHANG 184 (wingless multicoptor), under three objectives concerning flight time, energy consumption and added noise. Compared to a least-distance path, flight routes were obtained with maximum improvements of 38% in added noise, 65% in flight time and 52% in energy consumption for the EHANG 184. For the Lilium jet, maximum improvements of 43% in added noise, 47% in flight time and 47% in energy consumption were obtained. Still, the obtained noise addition levels by the aircraft in New York City exceed 5 dB, which is considered as long-term noise annoyance. We illustrated that minimizing the added noise requires high search effort compared to the other two objectives. Upon further analysis of the optimization results, we conclude that the Lilium jet as representative of the eVTOL type vectored thrust is more sensitive to flight route changes than the multicoptor EHANG 184. This information may help in air taxi type choice for a certain region as well as in flight route planning.

A Novel Multicriteria Optimization Technique for VLSI Floorplanning Based on Hybridized Firefly and Ant Colony Systems

In VLSI circuit design, physical design is one of the main steps in placing the circuit into the chip area. Floorplanning is a crucial step in the physical design of IC design, which generates a blueprint for the placement of circuit modules into the chip. A floorplanning step accepts a netlist as its input, given by the circuit-partitioning step of physical design. The floorplanning step generates optimal placements for the circuit modules. The netlist contains the modules’ dimensions, size, and interconnect information. During the floorplan generation, the chip area, wire length required for connecting modules and the heat generated by the chips can be estimated. A good floorplan makes placements and routing simple. In order to improve the circuit performance by minimizing chip area, wire length, and peak temperature, it is essential to generate an optimized floorplan by developing metaheuristic optimization algorithms. A novel Hybridized Multicriteria Ant Colony and Firefly Optimization (HMAC-FO) technique is introduced to generate an optimized floorplan. The primary focus of HMAC-FO technique is to design a model for generating efficient floorplanning. The standard MCNC benchmark dataset has a number of modules with their connections. Firefly optimization is the main algorithm in HMAC-FO, which has been used in generating efficient floorplan with the optimized area, wire length and thermal. The firefly algorithm initially requires some number of solutions as the fireflies’ population. In the firefly algorithm, usually, the populations are generated randomly. But, to improve the firefly optimization algorithm’s performance and obtain better optimum results, the proposed technique uses ACO to generate the initial population, which are all optimal solutions. The firefly algorithm uses the set of optimal solutions as the initial population and generates the globally optimal result. The proposed technique has experimented with the standard MCNC benchmark circuits, and the results prove that the proposed algorithm reduces the area by 3.48%, the wire length by 0.64% and the temperature by 3.33% than the best existing methodology. The proposed methodology generates a good floorplan with all the required optimization, such as area, wire length and heat generation.

Multicriteria optimal design of emamectin benzoate microparticles obtained by spray drying and ionic gelation

Emamectin benzoate (EB) is an antiparasitic used to control Caligus rogercresseyi in Chile. However, it has lost efficacy, and the parasite has been exposed to a sublethal dose. Microencapsulation has been suggested as an alternative method to protect and control the release of poorly absorbed drugs to ensure their lethal doses. Accordingly, design of experiments (DOE) and response surface methodology (RSM) were applied to find optimal conditions for EB's ionic gelation (IG) and spray drying (SD) microencapsulation. We used multiobjective (MOO) and multi-response optimization techniques as the desirability function (DFA) to obtain optimal conditions that produce microparticles that satisfy several criteria, such as low gastric digestion (GD) and high yield (Y), encapsulation efficiency (EE), load capacity (LC), and intestinal digestion (ID). The optimization process prioritized the digestion responses and was constrained according to a mass balance. MOO generated theoretical solutions that were better than any of the DOE experimental solutions. Both optimization methods achieved a more balanced performance than the responses obtained in the experimental design. Each optimization method produced better experimental responses than the other in some responses. In SD, DFA yielded higher LC, GD, and ID than MOO by 7.5%, 9.3%, and 2.1%, respectively. In contrast, MOO obtained higher Y and EE than DFA by 6.2% and 10.1%, respectively. In IG, the DFA method yielded a solution with better responses than MOO in LC (3.7%), GD (7.4%), and ID (3.2%), while the MOO solution was better in Y (14.2%) and EE (19.3%). Both multicriteria optimization techniques were suitable for obtaining optimal solutions; however, neither proved superior in all cases.

Enhancing deep learning algorithm accuracy and stability using multicriteria optimization: an application to distributed learning with MNIST digits

The training phase is the most crucial stage during the machine learning process. In the case of labeled data and supervised learning, machine learning entails minimizing the loss function under various constraints. We provide an innovative model for learning with numerous data sets, resulting from the application of multicriteria optimization techniques to existing deep learning algorithms. Data fitting is formulated as a multicriteria model in which each criterion measures the data fitting error on a specific data set. This is an optimization model involving a vector-valued function, and it has to be analyzed using the notion of Pareto efficiency. We present stability results for efficient solutions in the presence of input and output data perturbations. The multiple data set environment comes into play to eliminate the bias caused by the selection of a specific training set. To apply this concept, we present a scalarization strategy as well as numerical experiments in digit classification using MNIST data.

Analysing supply chain coordination mechanisms dealing with repurposing challenges during Covid-19 pandemic in an emerging economy: a multi-layer decision making approach

Following the outbreak of the Covid-19 pandemic, there was a serious need for the pharmaceutical industry to combat the disease more quickly and effectively. In this regard, numerous companies set out to repurpose current drugs. The noticed decision has major challenges in various dimensions, including the creation and management of an efficient supply chain. The present study attempts to examine the significance and relationships of the repurposing challenges and analyze the effectiveness of supply chain coordination contracts confronting them. In this regard, a combination of Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) named DANP method is applied to investigate the relationships and extracting the weights of the mentioned challenges and the multi-criteria optimization and compromise solution technique called VIKOR is employed to prioritize the supply chain coordination contracts found on their impact facing with repurposing challenges. The mentioned techniques have been conducted under the condition of linguistic Z-numbers. The results demonstrated that financial support and digitalization are the most influential challenges. Moreover, collaboration and data availability have the most weight. In addition, four contracts including effort sharing, cost-sharing, credit option and buyback are the best contracts that companies in the merging economy of Iran should concentrate on them. This research proposes a novel framework of decision-making by integrating DANP and VIKOR with linguistic Z-numbers. Additionally, this study takes a new look at the use of coordination contracts from the viewpoint of repurposing challenges which is highlighted particularly during the Covid-19 pandemic.

Seismic response sensitivity and optimal design of an isolated multi-span continuous highway bridge with self-centering SMA RC bridge piers and superelastic SMA restrainers

The use of superelastic shape memory alloy (SMA) in reinforced concrete (RC) pier and restrainers between girder and pier/abutment has been proposed in an earlier study to improve the seismic performance of highway bridges. This paper aims at identifying the significant factors that affect the seismic response of such a novel bridge system. A sequential fractional factorial design method is performed to statistically evaluate the effects of six design factors (three geometry-related and three material-related) as well as their interactions. Additionally, a multi-criteria optimization technique is implemented to determine the most efficient combination of the design parameters for SMA RC piers and SMA restrainers. Results demonstrate that the geometry-related factors and their interactions have large effects (with a contribution greater than 91%) on the relative displacement between the girder and pier. The target residual drift of the pier, design target displacement of the restrainer, and their interaction are the three most significant factors (with contributions approximately 30%–68%) affecting the base shear of the pier. The residual drift of the pier is sensitive to the design target displacement of the restrainer, its interaction with the forward transformation stress of SMA, and material-related factors with regard to the energy dissipation of SMA.

Optimal thermochemical material selection for a hybrid thermal energy storage system for low temperature applications using multi criteria optimization technique

Thermochemical energy storage is one of the viable solutions for the increasing energy demand because of its high energy density and less heat loss during the storage phase comparing to the other methods. The selection of proper thermochemical material (TCM) is then become important in this type of thermal energy storage. This paper describes the use of Multi Criteria Decision Making (MCDM) techniques to select the optimum thermochemical material from the alternatives to be used for low temperature (<150 °C) energy storage. The best thermochemical material is selected using different MCDM techniques like simple additive method, weighted product model, Technique for Order Preference by Similarity to Ideal Solution, Evaluation based on Distance from Average Solution, Multi-Objective Optimization on The Basis of Ratio Analysis, Preference Ranking Organization Method for Enrichment Evaluation, and VIKOR. The criteria weights for optimization are evaluated by different methods such as mean weight method, standard deviation method, analytic hierarchy process, Entropy method, criteria importance through inter-criteria correlation and Compromised weight method. In all MCDM-Weighting method combinations MgCl2·6H2O ranked first (optimum material) and Na2S·9H2O ranked last. The ranking obtained from various MCDM-weighting method combination are compared with each other using average Spearman rank correlation coefficient and found that Evaluation based on Distance from Average Solution (EDAS), Multi-Objective Optimization on The Basis of Ratio Analysis (MOORA), Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II) and VIKOR with AHP weighting method have the highest correlation coefficient (rs = 0.9857) and CRITIC-VIKOR method (rs = 0.5143) has the lowest correlation coefficient.

Study of a Multicriterion Decision-Making Approach to the MQL Turning of AISI 304 Steel Using Hybrid Nanocutting Fluid.

The enormous use of cutting fluid in machining leads to an increase in machining costs, along with different health hazards. Cutting fluid can be used efficiently using the MQL (minimum quantity lubrication) method, which aids in improving the machining performance. This paper contains multiple responses, namely, force, surface roughness, and temperature, so there arises a need for a multicriteria optimization technique. Therefore, in this paper, multiobjective optimization based on ratio analysis (MOORA), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and technique for order of preference by similarity to ideal solution (TOPSIS) are used to solve different multiobjective problems, and response surface methodology is also used for optimization and to validate the results obtained by multicriterion decision-making technique (MCDM) techniques. The design of the experiment is based on the Box–Behnken technique, which used four input parameters: feed rate, depth of cut, cutting speed, and nanofluid concentration, respectively. The experiments were performed on AISI 304 steel in turning with minimum quantity lubrication (MQL) and found that the use of hybrid nanofluid (Alumina–Graphene) reduces response parameters by approximately 13% in forces, 31% in surface roughness, and 14% in temperature, as compared to Alumina nanofluid. The response parameters are analyzed using analysis of variance (ANOVA), where the depth of cut and feed rate showed a major impact on response parameters. After using all three MCDM techniques, it was found that, at fixed weight factor with each MCDM technique, a similar process parameter was achieved (velocity of 90 m/min, feed of 0.08 mm/min, depth of cut of 0.6 mm, and nanoparticle concentration of 1.5%, respectively) for optimum response. The above stated multicriterion techniques employed in this work aid decision makers in selecting optimum parameters depending upon the desired targets. Thus, this work is a novel approach to studying the effectiveness of hybrid nanofluids in the machining of AISI 304 steel using MCDM techniques.

Multi-criteria Optimization Technique with Complex Pythagorean Fuzzy N-soft Information

The main objective of this article is to lay the foundations of a novel multi-criteria optimization technique, namely, the complex Pythagorean fuzzy N-soft VIKOR (CPFNS-VIKOR) method that is highly proficient to express a great deal of linguistic imprecision and vagueness inherent in human assessments. This strategy provides a versatile decision-making tool for the ranking-based fuzzy modeling of two-dimensional parameterized data. The CPFNS-VIKOR method integrates the ground-breaking specialities of the VIKOR method with the outstanding parametric structure of the complex Pythagorean fuzzy N-soft model. It is exclusively designed for the specification of a compromise optimal solution having maximum group utility and minimum individual regret of the opponent by analyzing their weighted proximity from ideal solutions. The developed strategy factually permits specific linguistic terms to demystify the individual perspectives of the decision-making experts regarding the efficacy of the alternatives and the priorities of the applicable criteria. We comprehensively assemble these independent appraisals of all the experts using the complex Pythagorean fuzzy N-soft weighted averaging operator. Moreover, we calibrate the ranking measure by utilizing group utility measure and regret measure in order to specify the hierarchical outranking of the feasible alternatives. We demonstrate the systematic methodology and framework of the proposed method with the assistance of an explicative flow chart. We skilfully investigate an empirical analysis related to selection of constructive industrial robots for the modernization of a manufacturing industry which really justifies the remarkable accountability of the proposed strategy. Furthermore, we validate this technique by a comparative study with the existing complex Pythagorean fuzzy TOPSIS (CPF-TOPSIS) method, complex Pythagorean fuzzy VIKOR (CPF-VIKOR) method and Pythagorean fuzzy TOPSIS (PF-TOPSIS) method. The comparative study is exemplified with an illustrative bar chart that visually endorses the rationality of the proposed methodology by interpreting highly compatible and accurate final outcomes. Finally, we holistically analyze the functionality of the developed strategy to enlighten its merits and prominence over other available competent approaches.

Multi-Criteria Optimization of a PCM based Heat Sink—Effect of Geometric Parameters on Operational Time and Melt Fraction

Abstract Computational fluid dynamic analysis of a phase change material (PCM) based heat sink has been carried out in the present study. The PCM used is RT44HC. Longitudinal fins made of aluminum have been considered. The influence of pertinent parameters viz., fin number, fin thickness, orientation and base thickness on melt fraction and operational time have been analyzed. The critical temperature considered for the study is 54.8 °C. The melting behavior of the PCM is simulated by using the volume of fluid (VOF) method. The design of the experiment has been performed using the Taguchi method. By using gray relational multicriteria optimization technique and multi-objective optimization on the basis of ratio analysis (MOORA) method, the best thermally performing configuration has been attained through the optimum values of operational time and melt fraction. In addition to the above, analysis of variance (ANOVA) is performed to find the most significant parameter. Based on the investigation, fin thickness and number of fins are observed to significantly influence the thermal transport.

Selection of appropriate maintenance strategy using fuzzy VIKOR technique: application in paper industry

PurposeThe industry is relying on the preventive maintenance techniques that can minimize failures and provide industrial plants with effective equipment, but in many companies the maintenance tasks are performed very frequently and not as per plan and do not take into consideration the conditions of the plant and equipments. The failure of each and every component needs to be studied in order to choose the best maintenance strategy. This paper presents a fuzzy VIKOR (Multicriteria Optimization and Compromise Solution) technique which is used in developing a comprehensive approach for maintenance strategy selection in the Deinking plant of the paper industry to choose the appropriate maintenance strategy thereby reducing the unnecessary cost incurred on the maintenance.Design/methodology/approachIn this paper, the Fuzzy VIKOR based methodology was applied for determining the maintenance criticality index of the deinking plant of the paper industry. The effect of failure of components were evaluated by three maintenance experts on five performance criteria that is chance of failure, chance of non-detection, downtime length, severity, spare part criticality. The components were ranked according to the maintenance criticality index and thereby implementing the appropriate maintenance strategy.FindingsThe Fuzzy VIKOR technique was applied to calculate the ranking of various components of paper industry based on the views and judgment of three maintenance experts. The proposed technique suggested the appropriate maintenance strategy for various components taking into consideration the maintenance criticality index of the components.Originality/valueThe proposed technique will help the maintenance managers to solve a discrete problem with non-commensurable and conflicting criteria. The study will help the industries to reduce the unnecessary maintenance tasks and thereby reduce the maintenance cost. This will help the maintenance practitioners in choosing the best and most effective strategy for the organization with regard to the market and company situation especially in the changing business requirement of Industry 4.0.