Multi-objective Optimization of Data Placement in a Storage-as-a-Service Federated Cloud

Cloud federation enables service providers to collaborate to provide better services to customers. For cloud storage services, optimizing customer object placement for a member of a federation is a real challenge. Storage, migration, and latency costs need to be considered. These costs are contradictory in some cases. In this article, we modeled object placement as a multi-objective optimization problem. The proposed model takes into account parameters related to the local infrastructure, the federated environment, customer workloads, and their SLAs. For resolving this problem, we propose CDP-NSGAII IR , a Constraint Data Placement matheuristic based on NSGAII with Injection and Repair functions. The injection function aims to enhance the solutions’ quality. It consists to calculate some solutions using an exact method then inject them into the initial population of NSGAII. The repair function ensures that the solutions obey the problem constraints and so prevents from exploring large sets of unfeasible solutions. It reduces drastically the execution time of NSGAII. Experimental results show that the injection function improves the HV of NSGAII and the exact method by up to 94% and 60%, respectively, while the repair function reduces the execution time by an average of 68%.

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

Journal of Humanitarian Logistics and Supply Chain Management ◽

10.1108/jhlscm-11-2020-0108 ◽

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.

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Multi-Objective Optimization Model for P + R and K + R Facilities’ Collaborative Layout Decision

Sustainability ◽

10.3390/su12218833 ◽

2020 ◽

Vol 12 (21) ◽

pp. 8833

Author(s):

Wei Wang ◽

Zhentian Sun ◽

Zhiyuan Wang ◽

Yue Liu ◽

Jun Chen

Keyword(s):

Optimization Model ◽

Road Traffic ◽

Supply And Demand ◽

Optimal Decision ◽

Multi Objective Optimization ◽

Service Capacity ◽

Location Selection ◽

Multi Objective ◽

Proposed Model ◽

Collaborative Decision

In order to reduce the pressure on urban road traffic, multi-modal travel is gradually replacing single-modal travel. Park and ride (P + R) and kiss and ride (K + R) are effective methods to integrate car transportation and rail transit. However, there is often an imbalance between supply and demand in existing car occupant transfer facilities, which include both P + R and K + R facilities. Therefore, we aim to conduct a research on P + R and K + R facilities’ collaborative decision. It first classifies car occupant transfer facilities into types and levels and sets the service capacity of each category. On the premise of ensuring the occupancy of parking spaces, our model aims to maximize the intercepted vehicle mileage and transfer utility and establishes an optimal decision model for car occupant transfer facilities. The model collaboratively decides the facilities in terms of location selection, layout arrangement, and overflow demand conversion to balance the supply and demand. We choose Chengdu as an example, apply the multi-objective optimization model of car occupant transfer facilities, give improved schemes, and further explore the influence of the quantity of facilities on the optimization objectives. The results show that the scheme obtained by the proposed model is significantly better than the existing scheme.

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A Novel Multi-Efficiency Optimization Method for Disassembly Line Balancing Problem

Sustainability ◽

10.3390/su11246969 ◽

2019 ◽

Vol 11 (24) ◽

pp. 6969 ◽

Cited By ~ 2

Author(s):

Jianhua Cao ◽

Xuhui Xia ◽

Lei Wang ◽

Zelin Zhang ◽

Xiang Liu

Keyword(s):

Optimization Model ◽

Optimization Method ◽

High Energy ◽

Line Balancing ◽

Multi Objective Optimization ◽

Automotive Engine ◽

Multi Objective ◽

Proposed Model ◽

Disassembly Line Balancing ◽

Disassembly Line

Disassembly is an indispensable part in remanufacturing process. Disassembly line balancing and disassembly mode have direct effects on the disassembly efficiency and resource utilization. Recent researches about disassembly line balancing problem (DLBP) either considered the highest productivity, lowest disassembly cost or some other performance measures. No one has considered these metrics comprehensively. In practical production, ignoring the ratio of resource input and value output within remanufacturing oriented disassembly can result in inefficient or pointless remanufacturing operations. To address the problem, a novel multi-efficiency DLBP optimization method is proposed. Different from the conventional DLBP, destructive disassembly mode is considered not only on un-detachable parts, but also on detachable parts with low value, high energy consumption, and long task time. The time efficiency, energy efficiency, and value efficiency are newly defined as the ultimate optimization objectives. For the characteristics of the multi-objective optimization model, a dual-population discrete artificial bee colony algorithm is proposed. The proposed model and algorithm are validated by different scales examples and applied to an automotive engine disassembly line. The results show that the proposed model is more efficient, and the algorithm is well suited to the multi-objective optimization model.

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Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment

Journal of Industrial Engineering and Management ◽

10.3926/jiem.2078 ◽

2017 ◽

Vol 10 (2) ◽

pp. 188 ◽

Cited By ~ 5

Author(s):

Muhammad Hashim ◽

Muhammad Nazam ◽

Liming Yao ◽

Sajjad Ahmad Baig ◽

Muhammad Abrar ◽

...

Keyword(s):

Genetic Algorithm ◽

Supply Chain ◽

Supplier Selection ◽

Decision Makers ◽

Quality Level ◽

Multi Objective Optimization ◽

Multi Objective ◽

Fuzzy Environment ◽

Proposed Model ◽

Textile Sector

Purpose: The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM). Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS) problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model is developed to tackle this problem.Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis.Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability.Research limitations/implications: Although the fuzzy expected value model (EVM) with fuzzy coefficients constructed in present research should be helpful for solving real world problems. A detailed comparative analysis by using other algorithms is necessary for solving similar problems of agriculture, pharmaceutical, chemicals and services sectors in future.Practical implications: It can help the decision makers for ordering to different supplier for managing supply chain performance in efficient and effective manner. From the procurement and engineering perspectives, minimizing cost, sustaining the quality level and meeting production time line is the main consideration for selecting the supplier. Empirically, this can facilitate engineers to reduce production costs and at the same time improve the product quality.Originality/value: In this paper, we developed a novel multi-objective programming model based on genetic algorithm to select sustainable strategic supplier (SSSS) under fuzzy environment. The algorithm was tested and applied to solve a real case of textile sector in Pakistan. The experimental results and comparative sensitivity analysis illustrate the effectiveness of our proposed model.

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Improved NSGA-III using transfer learning and centroid distance for dynamic multi-objective optimization

Complex & Intelligent Systems ◽

10.1007/s40747-021-00570-z ◽

2021 ◽

Author(s):

Haijuan Zhang ◽

Gai-Ge Wang

Keyword(s):

Transfer Learning ◽

Pareto Front ◽

Optimization Problems ◽

Prediction Method ◽

Reference Points ◽

Initial Population ◽

Multi Objective Optimization ◽

Multi Objective ◽

Centroid Distance ◽

Dynamic Strategy

AbstractMulti-objective problems in real world are often contradictory and even change over time. As we know, how to find the changing Pareto front quickly and accurately is challenging during the process of solving dynamic multi-objective optimization problems (DMOPs). In addition, most solutions obey different distributions in decision space and the performance of NSGA-III when dealing with DMOPs should be further improved. In this paper, centroid distance is proposed and combined into NSGA-III with transfer learning together for DMOPs, called TC_NSGAIII. Centroid distance-based strategy is regarded as a prediction method to prevent some inappropriate individuals through measuring the distance of the population centroid and reference points. After the distance strategy, transfer learning is used for generating an initial population using the past experience. To verify the effectiveness of our proposed algorithm, NSGAIII, Tr_NSGAIII (NSGA-III combining with transfer learning only), Ce_NSGAIII (NSGA-III combining with centroid distance only), and TC_NSGAIII are compared. Seven state-of-the-art algorithms have been used for comparison on CEC 2015 benchmarks. Besides, transfer learning and centroid distance are regarded as a dynamic strategy, which is incorporated into three static algorithms, and the performance improvement is measured. What’s more, twelve benchmark functions from CEC 2015 and eight sets of parameters in each function are used in our experiments. The experimental results show that the performance of algorithms can be greatly improved through the proposed approach.

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A Decision-Making Tool for a Complex Post Enrollment-Based Course Timetabling

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.55.6.2 ◽

2020 ◽

Vol 55 (6) ◽

Author(s):

Ngo Tung Son

Keyword(s):

Combinatorial Problems ◽

Mixed Integer ◽

Objective Functions ◽

Multi Objective Optimization ◽

Spring Semester ◽

Multi Objective ◽

Training Costs ◽

Proposed Model ◽

Decision Making Tool ◽

Single Objective

The article describes a new method to construct an enrollment-based course timetable in universities, based on a multi-objective optimization model. The model used mixed-integer and binary variables towards creating a schedule. It satisfies students' preferences for study time, with the number of students in the same class being optimal for training costs while ensuring timetabling business constraints. We use a combination of compromise programming and linear scalarizing to transform many objective functions into single-objective optimization. A scheme of the Genetic Algorithm was developed to solve the proposed model. The proposed method allows approaching several types of multi-objective combinatorial problems. The algorithm was tested by scheduling a study schedule for 3,000 students in the spring semester of 2020 at FPT University, Hanoi, Vietnam. The obtained results show the average students' preference level of 69%. More than 30% of students have a satisfaction level of more than 80% of the timetable after two hours of execution time.

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

Technological and Economic Development of Economy ◽

10.3846/tede.2019.11337 ◽

2019 ◽

Vol 26 (2) ◽

pp. 405-429 ◽

Cited By ~ 2

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.

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Some Preliminary Results on the Development and Comparison of a New Multi-Objective Genetic Algorithm

Volume 2A: 27th Design Automation Conference ◽

10.1115/detc2001/dac-21021 ◽

2001 ◽

Author(s):

A. Farhang-Mehr ◽

J. Wu ◽

S. Azarm

Keyword(s):

Genetic Algorithm ◽

Optimization Problem ◽

Quality Metrics ◽

Initial Population ◽

Multi Objective Optimization ◽

Pareto Solutions ◽

Preliminary Results ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Speed Reducer

Abstract Some preliminary results for a new multi-objective genetic algorithm (MOGA) are presented. This new algorithm aims at obtaining the fullest possible representation of observed Pareto solutions to a multi-objective optimization problem. The algorithm, hereafter called entropy-based MOGA (or E-MOGA), is based on an application of the concepts from the statistical theory of gases to a MOGA. A few set quality metrics are introduced and used for a comparison of the E-MOGA to a previously published MOGA. Due to the stochastic nature of the MOGA, confidence intervals with a 95% confidence level are calculated for the quality metrics based on the randomness in the initial population. An engineering example, namely the design of a speed reducer is used to demonstrate the performance of E-MOGA when compared to the previous MOGA.

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Multi-Objective Optimization of Nutritional, Environmental and Economic Aspects of Diets Applied to the Spanish Context

Foods ◽

10.3390/foods9111677 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1677

Author(s):

Ricardo Abejón ◽

Laura Batlle-Bayer ◽

Jara Laso ◽

Alba Bala ◽

Ian Vazquez-Rowe ◽

...

Keyword(s):

Food Consumption ◽

Dietary Patterns ◽

Food Product ◽

Economic Consequences ◽

Eating Patterns ◽

Multi Objective Optimization ◽

Multi Objective ◽

Proposed Model ◽

Additional Costs ◽

Optimization Methodology

Current food consumption patterns must be revised in order to improve their sustainability. The nutritional, environmental, and economic consequences of these dietary patterns must be taken into consideration when diet guidelines are proposed. This study applied a systematic optimization methodology to define sustainable dietary patterns complying with nutritional, environmental, and economic issues. The methodology was based on a multi-objective optimization model that considered a distance-to-target approach. Although the three simultaneous objectives (maximal nutritional contribution, minimal greenhouse gas emissions, and minimal costs) could be divergent, the proposed model identified the optimal intake of each food product to achieve the maximal level of nutritional, environmental, and economic diets. This model was applied to six different eating patterns within the Spanish context: one based on current food consumption and five alternative diets. The results revealed that dietary patterns with improved nutritional profiles and reduced environmental impacts could be defined without additional costs just by increasing the consumption of vegetables, fruits, and legumes, while reducing the intake of meat and fish.

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Multi-Objective Optimization for the Force System of Orthodontic Retraction Spring Using Genetic Algorithms

Journal of Medical Devices ◽

10.1115/1.4000494 ◽

2009 ◽

Vol 3 (4) ◽

Author(s):

Bahaa I. Kazem

Keyword(s):

Design Parameters ◽

Element Formulation ◽

Geometrical Shape ◽

Multi Objective Optimization ◽

Current Application ◽

Force System ◽

Multi Objective ◽

Proposed Model ◽

Analytical Formulas ◽

Section Type

In this study, Castigliano’s second theorem is applied to predict the force and moment system produced by orthodontics T-spring. The developed analytical formulas include all spring design parameters (material, geometrical shape and wire cross section, type and position of spring end mounting system, and direction and magnitude of the activation forces). The analytical results are compared with those obtained by nonlinear finite element formulation with nonlinear capabilities as a large deflection and showed an acceptable agreement for the current application. The reliability of the proposed model is successfully tested to predict the effect of some design parameters on spring stiffness. The developed analytic force system formulas are used as an objective function for solving a multi-objective optimization problem to produce the required force and moment at spring ends. A new genetic algorithm scheme is developed to obtain optimal spring design parameters according to design objectives and constraints. The results show that depending on the above methodology we can make a good estimation of the required design parameters of the T-spring for a specific application.

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