Application of grey-fuzzy logic technique for parametric optimization of non-traditional machining processes

2018 ◽  
Vol 8 (1) ◽  
pp. 46-68 ◽  
Author(s):  
Shankar Chakraborty ◽  
Partha Protim Das ◽  
Vidyapati Kumar
Keyword(s):  
Fuzzy Logic ◽  
Parametric Optimization ◽  
Electrochemical Machining ◽  
Multi Objective Optimization ◽  
Machining Performance ◽  
Machining Processes ◽  
Content Type ◽  
Multi Objective ◽  
Developed Surface ◽  
Fuzzy Logic Technique

Purpose The purpose of this paper is to exploit the fullest potential and capability of different non-traditional machining (NTM) processes, it is often recommended to operate them at their optimal parametric combinations. There are several mathematical tools and techniques that have been effectively deployed for identifying the optimal parametric mixes for the NTM processes. Amongst them, grey relational analysis (GRA) has become quite popular due to its sound mathematical basis, ease to implement and apprehensiveness for multi-objective optimization of NTM processes. Design/methodology/approach In this paper, GRA is integrated with fuzzy logic to present an efficient technique for multi-objective optimization of three NTM processes (i.e. abrasive water-jet machining, electrochemical machining and ultrasonic machining) while identifying their best parametric settings for enhanced machining performance. Findings The derived results are validated with respect to technique for order preference by similarity to ideal solution (TOPSIS), and analysis of variance is also performed so as to identify the most significant control parameters in the considered NTM processes. Practical implications This grey-fuzzy logic approach provides better parametric combinations for all the three NTM processes with respect to the predicted grey-fuzzy relational grades (GFRG). The developed surface plots help the process engineers to investigate the effects of various NTM process parameters on the predicted GFRG values. Originality/value The adopted approach can be applied to various machining (both conventional and non-conventional) processes for their parametric optimization for achieving better response values.

A multi-objective optimization model for logistic planning in the crisis response phase

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Mahdi Ershadi ◽  
Hossein Shams Shemirani
Keyword(s):  
Objective Function ◽  
Optimization Model ◽  
Crisis Response ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model ◽  
Different Types ◽  
Response Phase ◽  
Logistic Planning

PurposeProper planning for the response phase of humanitarian relief can significantly prevent many financial and human losses. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of injured people, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified injured people.Design/methodology/approachThe main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized injured people in the network. Besides, the total transportation activities of different types of vehicles are considered as another objective function. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize transportation activities as the second objective function while maintaining the optimality of the first objective function.FindingsThe performances of the proposed model were analyzed in different cases and its robust approach for different problems was shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.Practical implicationsThe proposed methodology can be applied to find the best response plan for all crises.Originality/valueIn this paper, we have tried to use a multi-objective optimization model to guide and correct response programs to deal with the occurred crisis. This is important because it can help emergency managers to improve their plans.

Multi-objective optimization of hydro-viscous flexible drive for dynamic characteristics using genetic algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jianzhong Cui ◽  
Hu Li ◽  
Dong Zhang ◽  
Yawen Xu ◽  
Fangwei Xie
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Characteristics ◽  
Transmission Efficiency ◽  
Outer Radius ◽  
Transmission Model ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Dynamic Transmission Model

Purpose The purpose of this study is to investigate the flexible dynamic characteristics about hydro-viscous drive providing meaningful insights into the credible speed-regulating behavior during the soft-start. Design/methodology/approach A comprehensive dynamic transmission model is proposed to investigate the effects of key parameters on the dynamic characteristics. To achieve a trade-off between the transmission efficiency and time proportion of hydrodynamic and mixed lubrication, a multi-objective optimization of friction pair system by genetic algorithm is presented to obtain the optimal combination of design parameters. Findings Decreasing the engagement pressure or the ratio of inner and outer radius, increasing the lubricating oil viscosity or the outer radius will result in the increase of time proportion of hydrodynamic and mixed lubrication, as well as the transmission efficiency and its maximum value. After optimization, main dynamic parameters including the oil film thickness, angular velocity of the driven disk, viscous torque and total torque show remarkable flexible transmission characteristics. Originality/value Both the dynamic transmission model and multi-objective optimization model are established to analyze the effects of main design parameters on the dynamic characteristics of hydro-viscous flexible drive.

Exploring governments' role in mergers and acquisitions using IVIF MULTIMOORA-COPRAS technique

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Evans Opoku-Mensah ◽  
Yuming Yin ◽  
Love Offeibea Asiedu-Ayeh ◽  
Dennis Asante ◽  
Priscilla Tuffour ◽  
...  
Keyword(s):  
Assessment Method ◽  
Ratio Analysis ◽  
Multi Objective Optimization ◽  
Government Officials ◽  
Content Type ◽  
Training Support ◽  
Multi Objective ◽  
Tax Relief ◽  
Specific Order ◽  
The Government

PurposeExisting studies have found that most merger and acquisition (M&A) activities do not create the intended synergy. These studies have mainly investigated how firms' internal factors contribute to M&A successes or failures. The current study differs from the earlier ones by exploring how governments' activities can contribute to the creation of acquisition synergy.Design/methodology/approachA novel technique based on multi-objective optimization by ratio analysis and complex proportional assessment method under an interval-valued intuitionistic fuzzy (IVIF) environment is proposed to prioritize these government roles needed during the M&A process focusing on the Chinese M&A market.FindingsEnactments of regulations and loan guarantees are the most important strategies to help Chinese acquirers overcome acquisition failures. While tax relief ranks third, government training support ranks fourth. Finally, the result shows that government institutional support is the least to help acquirers overcome acquisition failures.Practical implicationsThe government has a role to play in the acquisition success. Although this study has prioritized governments' role in relative importance order, the authors recommend that governments capable of providing all these strategies should do so without any specific order. However, if otherwise, governments should not neglect the strategies with less weight completely but rather consider reducing capital allocations to such strategies. Moreover, this study shows how firms with stronger business ties with government officials may enjoy success during acquisition activities. The authors recommend that firms intending to make acquisitions develop stronger ties with governments in order to benefits from governments.Originality/valueThis is the first study to develop a theoretical framework showing how government can contribute to M&A success. The study achieves this by extending Keynesian's arguments and identifies five (5) ways in which governments can ensure acquisition success. Second, within fuzzy multi-criteria decision-making (F-MCDM) research, this study is the first to show the applicability of integrated multi-objective optimization by ratio analysis (MULTIMOORA) and complex proportional assessment (COPRAS) techniques in an IVIF environment. The novel methodology proposed in this study offers an insightful research method to future studies focusing on group decision problems.

Parametric optimization in modified air abrasive jet machining

2019 ◽  
Vol 15 (3) ◽  
pp. 617-629
Author(s):  
S. Rajendra Prasad ◽  
K. Ravindranath K. Ravindranath ◽  
M.L.S. Devakumar M.L.S. Devakumar
Keyword(s):  
Optimization Technique ◽  
Mesh Size ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Abrasive Jet Machining ◽  
Alloy Material ◽  
Size Measure ◽  
First Time

Purpose The choice of best machining parameters is an extremely basic factor in handling of any machined parts. The purpose of this paper is to exhibit a multi-objective optimization technique; in view of weighted aggregate sum product assessment (WASPAS) technique toward upgrade the machining parameters in modified air abrasive jet machining (MAAJM) process: injecting pressure, stand-off distance (SOD), and abrasive mesh size measure with 100 rpm rotatable worktable on Nickel 233 alloy material. Three conflicting destinations, material removal rate (MRR), surface roughness (SR) and taper angles (Ta), respectively, are considered at the same time. The proposed procedure uses WASPAS, which is the examination of parametric optimization of the abrasive jet machining (AJM) process. The results was used any scopes of reactions in MAAJM process is the ideal setting of parameters are resolved through investigations represented. There is wide utilization of Nickel 233 in aviation enterprises; machining information on producing a hole utilizing MAAJM for the first time is given in this work, which will be helpful different industries. Design/methodology/approach This paper exhibits a multi-objective optimization technique; in view of WASPAS technique toward upgrade the machining parameters in MAAJM process: injecting pressure, SOD, and abrasive mesh size measure with 100 rpm rotatable worktable on Nickel 233 alloy material. Findings As an outcome of using the tool in any ranges of responses in the AJM process, the optimal setting of parameters is determined through experiments illustrated. The machining data of generating a hole using AJM are studied for the first time in this work, which will be useful for aerospace industries, where Nickel 233 is used broadly. Originality/value A new material in unconventional machining process and also a multi-objective optimization technique are adopted.

On preventive maintenance policies: a selection framework

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Imad Alsyouf ◽  
Sadeque Hamdan ◽  
Mohammad Shamsuzzaman ◽  
Salah Haridy ◽  
Iyad Alawaysheh
Keyword(s):  
Preventive Maintenance ◽  
Fuzzy Ahp ◽  
Critical Component ◽  
Multi Objective Optimization ◽  
Maintenance Policy ◽  
Coolant Pump ◽  
Content Type ◽  
Multi Objective ◽  
Maintenance Policies ◽  
Preventive Maintenance Policy

PurposeThis paper develops a framework for selecting the most efficient and effective preventive maintenance policy using multiple-criteria decision making and multi-objective optimization.Design/methodology/approachThe critical component is identified with a list of maintenance policies, and then its failure data are collected and the optimization objective functions are defined. Fuzzy AHP is used to prioritize each objective based on the experts' questionnaire. Weighted comprehensive criterion method is used to solve the multi-objective models for each policy. Finally, the effectiveness and efficiency are calculated to select the best maintenance policy.FindingsFor a fleet of buses in hot climate environment where coolant pump is identified as the most critical component, it was found that block-GAN policy is the most efficient and effective one with a 10.24% of cost saving and 0.34 expected number of failures per cycle compared to age policy and block-BAO policy.Research limitations/implicationsOnly three maintenance policies are compared and studied. Other maintenance policies can also be considered in future.Practical implicationsThe proposed methodology is implemented in UAE for selecting a maintenance scheme for a critical component in a fleet of buses. It can be validated later in other Gulf countries.Originality/valueThis research lays a solid foundation for selecting the most efficient and effective preventive maintenance policy for different applications and sectors using MCDM and multi-objective optimization to improve reliability and avoid economic loss.

Multi-objective optimization for repetitive scheduling under uncertainty

2019 ◽  
Vol 26 (7) ◽  
pp. 1294-1320 ◽  
Author(s):  
Tarek Salama ◽  
Osama Moselhi
Keyword(s):  
Cost Optimization ◽  
Indirect Costs ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Software Module ◽  
Content Type ◽  
Project Duration ◽  
Multi Objective ◽  
Work Interruptions ◽  
Cost Calculations

Purpose The purpose of this paper is to present a newly developed multi-objective optimization method for the time, cost and work interruptions for repetitive scheduling while considering uncertainties associated with different input parameters. Design/methodology/approach The design of the developed method is based on integrating six modules: uncertainty and defuzzification module using fuzzy set theory, schedule calculations module using the integration of linear scheduling method (LSM) and critical chain project management (CCPM), cost calculations module that considers direct and indirect costs, delay penalty, and work interruptions cost, multi-objective optimization module using Evolver © 7.5.2 as a genetic algorithm (GA) software, module for identifying multiple critical sequences and schedule buffers, and reporting module. Findings For duration optimization that utilizes fuzzy inputs without interruptions or adding buffers, duration and cost generated by the developed method are found to be 90 and 99 percent of those reported in the literature, respectively. For cost optimization that utilizes fuzzy inputs without interruptions, project duration generated by the developed method is found to be 93 percent of that reported in the literature after adding buffers. The developed method accelerates the generation of optimum schedules. Originality/value Unlike methods reported in the literature, the proposed method is the first multi-objective optimization method that integrates LSM and the CCPM. This method considers uncertainties of productivity rates, quantities and availability of resources while utilizing multi-objective GA function to minimize project duration, cost and work interruptions simultaneously. Schedule buffers are assigned whether optimized schedule allows for interruptions or not. This method considers delay and work interruption penalties, and bonus payments.

A multi-objective optimization model for sustainable supply chain network with using genetic algorithm

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Reza Ehtesham Rasi ◽  
Mehdi Sohanian
Keyword(s):  
Supply Chain ◽  
Performance Indicators ◽  
Supplier Selection ◽  
Supply Chain Network ◽  
Sustainable Supply Chain ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Sustainability Performance ◽  
Multi Objective ◽  
Sustainable Supplier Selection

Purpose The purpose of this paper is to design and optimize economic and environmental dimensions in a sustainable supply chain (SSC) network. This paper developed a mixed-integer linear programing (MILP) model to incorporate economical and environmental data for multi-objective optimization of the SSC network. Design/methodology/approach The overall objective of the present study is to use high-quality raw materials, at the same time the lowest amount of pollution emission and the highest profitability is achieved. The model in the problem is solved using two algorithms, namely, multi-objective genetic and multi-objective particle swarm. In this research, to integrate sustainable supplier selection and optimization of sustainability performance indicators in supply chain network design considering minimization of cost and time and maximization of sustainability indexes of the system. Findings The differences found between the genetic algorithms (GAs) and the MILP approaches can be explained by handling the constraints and their various logics. The solutions are contrasted with the original crisp model based on either MILP or GA, offering more robustness to the proposed approach. Practical implications The model is applied to Mega Motor company to optimize the sustainability performance of the supply chain i.e. economic (cost), social (time) and environmental (pollution of raw material). The research method has two approaches, namely, applied and mathematical modeling. Originality/value There is limited research designing and optimizing the SSC network. This study is among the first to integrate sustainable supplier selection and optimization of sustainability performance indicators in supply chain network design considering minimization of cost and time and maximization of sustainability indexes of the system.

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