Multi-objective temporal bin packing problem : an application in cloud computing

Improving energy efficiency and lowering operational costs are the main challenges faced in systems with multiple servers. One prevalent objective in such systems is to minimize the number of servers required to process a given set of tasks under server capacity constraints. This objective leads to the well-known bin packing problem. In this study, we consider a generalization of this problem with a time dimension, where the tasks are to be performed with predefined start and end times. This new dimension brings about new performance considerations, one of which is the uninterrupted utilization of servers. This study is motivated by the problem of energy efficient assignment of virtual machines to physical servers in a cloud computing service. We address the virtual machine placement problem and present a binary integer programming model to develop different assignment policies. By analyzing the structural properties of the problem, we propose an efficient heuristic method based on solving smaller versions of the original problem iteratively. Moreover, we design a column generation algorithm that yields a lower bound on the objective value, which can be utilized to evaluate the performance of the heuristic algorithm. Our numerical study indicates that the proposed heuristic is capable of solving large-scale instances in a short time with small optimality gaps

Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies

This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points

Hybridizing the electromagnetism-like algorithm with descent search for solving engineering design problems

In this paper, we present a new stochastic hybrid technique for constrained global optimization. It is a combination of the electromagnetism-like (EM) mechanism with a random local search, which is a derivative-free procedure with high ability of producing a descent direction. Since the original EM algorithm is specifically designed for solving bound constrained problems, the approach herein adopted for handling the inequality constraints of the problem relies on selective conditions that impose a sufficient reduction either in the constraints violation or in the objective function value, when comparing two points at a time. The hybrid EM method is tested on a set of benchmark engineering design problems and the numerical results demonstrate the effectiveness of the proposed approach. A comparison with results from other stochastic methods is also included

Build orientation optimization problem in additive manufacturing

Additive manufacturing (AM) is an emerging type of production technology to create three-dimensional objects layer-by-layer directly from a 3D CAD model. AM is being extensively used by engineers and designers. Build orientation is a critical issue in AM since it is associated with the object accuracy, the number of supports required and the processing time to produce the object. Finding the best build orientation in the AM will reduced significantly the building costs and will improve the object accuracy. This paper presents an optimization approach to solve the part build orientation problem considering the staircase effect, support area characteristics and the build time. Two global optimization methods, the Electromagnetism-like and the Stretched Simulated Annealing algorithms, are used to study the optimal orientation of four models. Preliminary experiments show that both optimization methods can effectively solve the build orientation problem in AM, finding several global solutions.This work has been supported and developed under the FIBR3D project - Hybrid processes based on additive manufacturing of composites with long or short fibers reinforced thermoplastic matrix (POCI-01-0145-FEDER-016414), supported by the Lisbon Regional Operational Programme 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Memetic electromagnetism algorithm for surface reconstruction with rational bivariate Bernstein basis functions

Surface reconstruction is a very important issue with outstanding applications in fields such as medical imaging (computer tomography, magnetic resonance), biomedical engineering (customized prosthesis and medical implants), computer-aided design and manufacturing (reverse engineering for the automotive, aerospace and shipbuilding industries), rapid prototyping (scale models of physical parts from CAD data), computer animation and film industry (motion capture, character modeling), archaeology (digital representation and storage of archaeological sites and assets), virtual/augmented reality, and many others. In this paper we address the surface reconstruction problem by using rational Bézier surfaces. This problem is by far more complex than the case for curves we solved in a previous paper. In addition, we deal with data points subjected to measurement noise and irregular sampling, replicating the usual conditions of real-world applications. Our method is based on a memetic approach combining a powerful metaheuristic method for global optimization (the electromagnetism algorithm) with a local search method. This method is applied to a benchmark of five illustrative examples exhibiting challenging features. Our experimental results show that the method performs very well, and it can recover the underlying shape of surfaces with very good accuracy.This research is kindly supported by the Computer Science National Program of the Spanish Ministry of Economy and Competitiveness, Project #TIN2012-30768, Toho University, and the University of Cantabria. The authors are particularly grateful to the Department of Information Science of Toho University for all the facilities given to carry out this work. We also thank the Editor and the two anonymous reviewers who helped us to improve our paper with several constructive comments and suggestions

Improving additive manufacturing performance by build orientation optimization

Additive manufacturing (AM) is an emerging type of production technology to create three-dimensional objects layer-by-layer directly from a 3D CAD model. AM is being extensively used in several areas by engineers and designers. Build orientation is a critical issue in AM since it is associated with the part accuracy, the number of supports required and the processing time to produce the object. This paper presents an optimization approach to solve the part build orientation problem taking into account some characteristics or measures that can affect the accuracy of the part, namely the volumetric error, the support area, the staircase effect, the build time, the surface roughness and the surface quality. A global optimization method, the Electromagnetism-like algorithm, is used to solve the part build orientation problem.The authors are grateful to the anonymous referees for their fruitfulcomments and suggestions. This work has been supported and developed under the FIBR3Dproject - Hybrid processes based on additive manufacturing of composites with long or shortfibers reinforced thermoplastic matrix (POCI-01-0145-FEDER-016414), supported by theLisbon Regional Operational Programme 2020, under the PORTUGAL 2020 PartnershipAgreement, through the European Regional Development Fund (ERDF). This work hasbeen also supported by national funds through FCT - Funda ̧c ̃ao para a Ciˆencia e Tecnologiawithin the Project Scope: UID/CEC/00319/201

A deterministic inventory/production model with general inventory cost rate function and piecewise linear concave production costs

We present a thorough analysis of the economic production quantity model with shortages under a general inventory cost rate function and piecewise linear concave production costs. Consequently, an effective solution procedure, particularly useful for an approximation scheme, is proposed. A computational study is appended to illustrate the performance of the proposed solution procedure

Reassessment and monitoring of loan applications with machine learning

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Credit scoring and monitoring are the two important dimensions of the decision-making process for the loan institutions. In the first part of this study, we investigate the role of machine learning for applicant reassessment and propose a complementary screening step to an existing scoring system. We use a real data set from one of the prominent loan companies in Turkey. The information provided by the applicants form the variables in our analysis. The company’s experts have already labeled the clients as bad and good according to their ongoing payments. Using this labeled data set, we execute several methods to classify the bad applicants as well as the significant variables in this classification. As the data set consists of applicants who have passed the initial scoring system, most of the clients are marked as good. To deal with this imbalanced nature of the problem, we employ a set of different approaches to improve the performance of predicting the applicants who are likely to default. In the second part of this study, we aim to predict the payment behavior of clients based on their static (demographic and financial) and dynamic (payment) information. Furthermore, we analyze the effect of the length of the payment history and the staying power of the proposed prediction models