A multi-objective optimization approach for exploring the cost and makespan trade-off in additive manufacturing

2021 ◽  
Author(s):  
F. Tevhide Altekin ◽  
Yossi Bukchin
Keyword(s):  
Additive Manufacturing ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Trade Off ◽  
Multi Objective ◽  
The Cost

scholarly journals A COST-SENSITIVE LOGISTIC REGRESSION CREDIT SCORING MODEL BASED ON MULTI-OBJECTIVE OPTIMIZATION APPROACH

2019 ◽  
Vol 26 (2) ◽  
pp. 405-429 ◽  
Author(s):  
Feng Shen ◽  
Run Wang ◽  
Yu Shen
Keyword(s):  
Logistic Regression ◽  
Credit Scoring ◽  
Classification Error ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Scoring Model ◽  
Multi Objective ◽  
Proposed Model ◽  
The Cost ◽  
Credit Scoring Model

Credit scoring is an important process for peer-to-peer (P2P) lending companies as it determines whether loan applicants are likely to default. The aim of most credit scoring models is to minimize the classification error rate, which implies that all classification errors bear the same cost; however, in reality, there is a significant cost-sensitive problem in credit scoring methods. Therefore, in this paper, a new cost-sensitive logistic regression credit scoring model based on a multi-objective optimization approach is proposed that has two objectives in the cost-sensitive logistic regression process. The cost-sensitive logistic regression parameters are solved using a multiple objective particle swarm optimization (MOPSO) algorithm. In the empirical analysis, the proposed model was applied to the credit scoring of a Chinese famous P2P company, from which it was found that compared with other common credit scoring models, the proposed model was able to effectively reduce type II error rates and total classification error costs, and improve the AUC, the F1 values (reconciliation average of Recall and Precision), and the G-means. The proposed model was compared with other multi-objective optimization algorithms to further demonstrate that MOPSO is the best approach for cost-sensitive logistic regression credit scoring models.

scholarly journals A Decision-Support Model for Additive Manufacturing Scheduling Using an Integrative Analytic Hierarchy Process and Multi-Objective Optimization

2020 ◽  
Vol 10 (15) ◽  
pp. 5159
Author(s):  
Kasin Ransikarbum ◽  
Rapeepan Pitakaso ◽  
Namhun Kim
Keyword(s):  
Decision Support ◽  
Additive Manufacturing ◽  
Analytic Hierarchy Process ◽  
Supply Chain Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Analytic Hierarchy ◽  
Multi Objective ◽  
Trade Offs ◽  
Hierarchy Process

Additive manufacturing (AM) became widespread through several organizations due to its benefits in providing design freedom, inventory improvement, cost reduction, and supply chain design. Process planning in AM involving various AM technologies is also complicated and scarce. Thus, this study proposed a decision-support tool that integrates production and distribution planning in AM involving material extrusion (ME), stereolithography (SLA), and selective laser sintering (SLS). A multi-objective optimization approach was used to schedule component batches to a network of AM printers. Next, the analytic hierarchy process (AHP) technique was used to analyze trade-offs among conflicting criteria. The developed model was then demonstrated in a decision-support system environment to enhance practitioners’ applications. Then, the developed model was verified through a case study using automotive and healthcare parts. Finally, an experimental design was conducted to evaluate the complexity of the model and computation time by varying the number of parts, printer types, and distribution locations.

scholarly journals Multi-Objective Accelerated Process Optimization of Part Geometric Accuracy in Additive Manufacturing

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Amir M. Aboutaleb ◽  
Mark A. Tschopp ◽  
Prahalad K. Rao ◽  
Linkan Bian
Keyword(s):  
Experimental Data ◽  
Additive Manufacturing ◽  
Process Optimization ◽  
Process Parameters ◽  
Process Conditions ◽  
Optimization Approach ◽  
Geometric Accuracy ◽  
Multi Objective Optimization ◽  
Fused Filament Fabrication ◽  
Multi Objective

The goal of this work is to minimize geometric inaccuracies in parts printed using a fused filament fabrication (FFF) additive manufacturing (AM) process by optimizing the process parameters settings. This is a challenging proposition, because it is often difficult to satisfy the various specified geometric accuracy requirements by using the process parameters as the controlling factor. To overcome this challenge, the objective of this work is to develop and apply a multi-objective optimization approach to find the process parameters minimizing the overall geometric inaccuracies by balancing multiple requirements. The central hypothesis is that formulating such a multi-objective optimization problem as a series of simpler single-objective problems leads to optimal process conditions minimizing the overall geometric inaccuracy of AM parts with fewer trials compared to the traditional design of experiments (DOE) approaches. The proposed multi-objective accelerated process optimization (m-APO) method accelerates the optimization process by jointly solving the subproblems in a systematic manner. The m-APO maps and scales experimental data from previous subproblems to guide remaining subproblems that improve the solutions while reducing the number of experiments required. The presented hypothesis is tested with experimental data from the FFF AM process; the m-APO reduces the number of FFF trials by 20% for obtaining parts with the least geometric inaccuracies compared to full factorial DOE method. Furthermore, a series of studies conducted on synthetic responses affirmed the effectiveness of the proposed m-APO approach in more challenging scenarios evocative of large and nonconvex objective spaces. This outcome directly leads to minimization of expensive experimental trials in AM.

A PARETO MULTI-OBJECTIVE OPTIMIZATION APPROACH FOR SOLVING TIME-COST-QUALITY TRADEOFF PROBLEMS

2011 ◽  
Vol 17 (1) ◽  
pp. 22-41 ◽  
Author(s):  
Xundi Diao ◽  
Heng Li ◽  
Saixing Zeng ◽  
Vivian Wy Tam ◽  
Hongling Guo
Keyword(s):  
Project Planning ◽  
Optimization Approach ◽  
Time Cost ◽  
Multi Objective Optimization ◽  
Quality Performance ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Computer Based ◽  
The Cost ◽  
Optimal Set

Speeding up a project's duration will definitely increase the cost and decrease the quality. The previous literatures were mainly related to project planning and controlling which mainly focus on cost-time tradeoff. However, limited researches have been referred to project quality based on mathematical methodologies. This paper proposes a tradeoff problem on time-cost-quality performance. A computer-based Pareto multi-objective optimization approach is utilized for solving the tradeoff problems. The approach can help searching near the reality Pareto-optimal set while not receiving any information on the stakeholders’ preference for time, cost and quality. Based on the developed approach, decision-making can become easy according to the sorted non-dominated solutions and project preferences.

scholarly journals Silhouetting the Cost-Time Front: Multi-objective Resource Optimization in Business Processes

2021 ◽  
pp. 92-108
Author(s):  
Orlenys López-Pintado ◽  
Marlon Dumas ◽  
Maksym Yerokhin ◽  
Fabrizio Maria Maggi
Keyword(s):  
Resource Utilization ◽  
Cycle Time ◽  
Search Space ◽  
Hill Climbing ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Resource Allocations ◽  
Nsga Ii ◽  
Multi Objective ◽  
The Cost

AbstractThe allocation of resources in a business process determines the trade-off between cycle time and resource cost. A higher resource utilization leads to lower cost and higher cycle time, while a lower resource utilization leads to higher cost and lower waiting time. In this setting, this paper presents a multi-objective optimization approach to compute a set of Pareto-optimal resource allocations for a given process concerning cost and cycle time. The approach heuristically searches through the space of possible resource allocations using a simulation model to evaluate each allocation. Given the high number of possible allocations, it is imperative to prune the search space. Accordingly, the approach incorporates a method that selectively perturbs a resource utilization to derive new candidates that are likely to Pareto-dominate the already explored ones. The perturbation method relies on two indicators: resource utilization and resource impact, the latter being the contribution of a resource to the cost or cycle time of the process. Additionally, the approach incorporates a ranking method to accelerate convergence by guiding the search towards the resource allocations closer to the current Pareto front. The perturbation and ranking methods are embedded into two search meta-heuristics, namely hill-climbing and tabu-search. Experiments show that the proposed approach explores fewer resource allocations to compute Pareto fronts comparable to those produced by a well-known genetic algorithm for multi-objective optimization, namely NSGA-II.

A Review of Multi-Objective Evolutionary Based Fuzzy Classifiers

2020 ◽  
Vol 13 (1) ◽  
pp. 77-85
Author(s):  
Praveen Kumar Dwivedi ◽  
Surya Prakash Tripathi
Keyword(s):  
Fuzzy Systems ◽  
Evolutionary Process ◽  
Fuzzy Rule ◽  
Fuzzy Classification ◽  
Multi Objective Optimization ◽  
Fuzzy Classifier ◽  
Rule Based ◽  
Trade Off ◽  
Multi Objective ◽  
Rule Based Systems

Background: Fuzzy systems are employed in several fields like data processing, regression, pattern recognition, classification and management as a result of their characteristic of handling uncertainty and explaining the feature of the advanced system while not involving a particular mathematical model. Fuzzy rule-based systems (FRBS) or fuzzy rule-based classifiers (mainly designed for classification purpose) are primarily the fuzzy systems that consist of a group of fuzzy logical rules and these FRBS are unit annexes of ancient rule-based systems, containing the "If-then" rules. During the design of any fuzzy systems, there are two main objectives, interpretability and accuracy, which are conflicting with each another, i.e., improvement in any of those two options causes the decrement in another. This condition is termed as Interpretability –Accuracy Trade-off. To handle this condition, Multi-Objective Evolutionary Algorithms (MOEA) are often applied within the design of fuzzy systems. This paper reviews the approaches to the problem of developing fuzzy systems victimization evolutionary process Multi-Objective Optimization (EMO) algorithms considering ‘Interpretability-Accuracy Trade-off, current research trends and improvement in the design of fuzzy classifier using MOEA in the future scope of authors. Methods: The state-of-the-art review has been conducted for various fuzzy classifier designs, and their optimization is reviewed in terms of multi-objective. Results: This article reviews the different Multi-Objective Optimization (EMO) algorithms in the context of Interpretability -Accuracy tradeoff during fuzzy classification. Conclusion: The evolutionary multi-objective algorithms are being deployed in the development of fuzzy systems. Improvement in the design using these algorithms include issues like higher spatiality, exponentially inhabited solution, I-A tradeoff, interpretability quantification, and describing the ability of the system of the fuzzy domain, etc. The focus of the authors in future is to find out the best evolutionary algorithm of multi-objective nature with efficiency and robustness, which will be applicable for developing the optimized fuzzy system with more accuracy and higher interpretability. More concentration will be on the creation of new metrics or parameters for the measurement of interpretability of fuzzy systems and new processes or methods of EMO for handling I-A tradeoff.

Pareto Set as a Model for Dispatching Resources in Emergency Centres (Preprint)

2018 ◽  
Author(s):  
Ricardo Guedes ◽  
Vasco Furtado ◽  
Tarcísio Pequeno ◽  
Joel Rodrigues
Keyword(s):  
Operational Research ◽  
Real Data ◽  
Pareto Set ◽  
Multi Objective Optimization ◽  
Pareto Dominance ◽  
Four Dimensions ◽  
Multi Objective ◽  
The Best Approximation ◽  
One Year ◽  
The Cost

UNSTRUCTURED The article investigates policies for helping emergency-centre authorities for dispatching resources aimed at reducing goals such as response time, the number of unattended calls, the attending of priority calls, and the cost of displacement of vehicles. Pareto Set is shown to be the appropriated way to support the representation of policies of dispatch since it naturally fits the challenges of multi-objective optimization. By means of the concept of Pareto dominance a set with objectives may be ordered in a way that guides the dispatch of resources. Instead of manually trying to identify the best dispatching strategy, a multi-objective evolutionary algorithm coupled with an Emergency Call Simulator uncovers automatically the best approximation of the optimal Pareto Set that would be the responsible for indicating the importance of each objective and consequently the order of attendance of the calls. The scenario of validation is a big metropolis in Brazil using one-year of real data from 911 calls. Comparisons with traditional policies proposed in the literature are done as well as other innovative policies inspired from different domains as computer science and operational research. The results show that strategy of ranking the calls from a Pareto Set discovered by the evolutionary method is a good option because it has the second best (lowest) waiting time, serves almost 100% of priority calls, is the second most economical, and is the second in attendance of calls. That is to say, it is a strategy in which the four dimensions are considered without major impairment to any of them.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

Sign in / Sign up

Export Citation Format

Share Document