Search

In the context of project management, the attention given to project portfolio management has increased in recent years. The use of mathematical programming for portfolio management is also on the rise, because it integrates the project interactions with the multiple objectives of portfolio management into a single model. Among the possible objectives, recent studies have paid special attention to the emerging objective of future preparedness, which has not yet been incorporated into the existing mathematical models. This paper presents a mixed-integer nonlinear optimization model for portfolio selection that considers four main performance measures for project management, namely, value maximization, strategic alignment, balance, and future preparedness. Given the importance of the last measure, the purpose of this paper is to provide a more complete model that provides the marginal contribution and the best combination of projects according to the needs of the company. The model is tested using real data from two companies, one in Brazil and one in Canada, and the results obtained are coherent with their respective practices. [ABSTRACT FROM AUTHOR]

In this paper, we propose a channel allocation algorithm for heterogeneous multi-cell cognitive radio 5G networks. The proposed algorithm considers channel-availability and channel-demand heterogeneity across CR cells. We use the theory of continuous-time Markov chains to develop mathematical expressions for channel availability and channel demand. Both expressions are incorporated in a binary integer program to find the optimal channel allocation. Further, we equip the proposed channel allocation algorithm with a novel interference domain identification scheme to detect interference range overlaps between cognitive radio cells. This scheme is essential for channel allocation, as it plays an important role in avoiding inter-cell interference. Our simulation results show significant performance gains compared to existing algorithms overlooking channel-availability. Furthermore, we use the theory of Dantzig-Wolfe decomposition to develop a framework for transforming centralized channel allocation algorithms into distributed ones. Through simulation experiments, we demonstrate the efficacy of the proposed framework. [ABSTRACT FROM AUTHOR]

The annual output value of Taiwan’s wafer foundry and IC packaging test ranks first worldwide. The electronics industry has a complete industrial ecological chain in the supply chain system of global information and communication technology. In order to increase market competitiveness and increase the operational flexibility, manufacturing process outsourcing has become a trend in the business model of the electronics industry. The quality of the outsourcing affects the quality and function of the final product, the outsourcer selection is crucial to ensure the quality of the final product. This article takes the wire bonding process as an example, and proposes a fuzzy selection model of quality-based IC packaging process outsourcers. Then, this article selects the six standard deviation indicator that can fully reflect the process yield and quality level as the evaluation tool. Since the indicator contains unknown parameter, in order to improve the accuracy of estimation and overcome the uncertainty of measurement data, this paper applies mathematical programming to derive the confidence interval of six sigma quality index, and proposes a confidence-interval-based fuzzy testing meth as a green outsourcer selection tool to reduce the risk of misjudgment due to sampling errors and improve the accuracy of the selection. [ABSTRACT FROM AUTHOR]

Consumption patterns of electric power systems are important for distribution companies, because of their significant impact on energy losses amount. Therefore, some incentives are suggested by distribution companies to energy consumers for correcting their consumption manner. For a specific load pattern, distribution system reconfiguration (DSR) is an effective way to mitigate energy losses. Hence, some research works have included load variations in the DSR problem to show the importance of consumption patterns in reconfiguration decisions. However, some of the specialized literature has ignored load changes in their reconfiguration models due to the high computational burden and processing time. On the other hand, the energy losses are calculated inaccurately if the consumption pattern is neglected. Consequently, the main goal of this article is to investigate load pattern impact on switching sequences to find out how much is load profile important for minimization of energy losses in DSR. The evaluations were carried out for three well-known distribution systems using a classic optimization tool, the A Mathematical Programming Language. [ABSTRACT FROM AUTHOR]

Nowadays, high-throughput screening (HTS) systems are widely used in pharmaceutical industries and laboratories for the discovery of new drugs and biomedical substances. It is important to efficiently schedule them so as to reduce the cost. In the operation of an HTS system, a microplate may visit some resources more than once and complex time window constraints are imposed on some activities and activity sequences. Moreover, with the consistency requirement, a one-microplate cyclic schedule is necessary. Thus, its scheduling problem is very challenging. This article studies the scheduling problem of an HTS system for an enzymatic assay, a typical application of HTSs, from the perspective of control theory. The system is modeled by resource-oriented Petri nets (ROPNs). With the model, necessary and sufficient conditions under which a feasible cyclic schedule exists are established. Then, we determine how many microplates should be in the system for concurrent processing and the transition firing sequence to obtain the activity sequence for an optimal and feasible schedule. In this way, a feasible and optimal cyclic schedule can be found by very simple computations, which shows that polynomial algorithms for an optimal schedule exist, while the existing studies apply mathematical programming with exponential computation complexity. Also, its efficient implementation is given. [ABSTRACT FROM AUTHOR]

Since the torque characteristic of a surface permanent magnet synchronous motor (SPMSM) is strongly dependent on its magnetization distribution (MD), the prior estimation of MD is essential for its practical design. An effective method for estimating MD involves minimizing the squared function of the difference between the measured magnetic flux density and the flux density derived from the numerical analysis of the region surrounding the permanent magnet. However, as there are countless MDs for reconstructing the specified magnetic flux density, it is important to estimate the realistic MD. In this article, we proposed an estimation method to derive the realistic MD based on the Biot–Savart law incorporated with mathematical programming (steepest descent method and quasi-Newton method). In this method, MD was formulated in the spherical coordinate system. Since the constraints regarding magnetization intensity and angle were defined by the sigmoid function, both quantities were definitely distributed within the constraints. Consequently, the generation of nonphysical MD was efficiently suppressed. Furthermore, the validity of the proposed method was illustrated in comparison with other estimation methods, such as the least-square method, Tikhonov regularization, and the truncated singular value decomposition (TSVD) method. [ABSTRACT FROM AUTHOR]

Deep learning has been widely applied in many fields. Efficient optimization algorithms contribute a lot to the enhancement of deep learning. However, reverse studies on how deep learning can solve optimization problems, especially mathematical programming, are relatively scarce. In this work, we aim to initiate a discussion on using deep learning to solve parametric mathematical programming, which can be converted to find a mapping from parameter space to solution space. Given that deep convolutional neural networks (DCNNs) have been successfully applied in the field of computer vision, converting a mathematical programming problem into an image representation may provide the solution. This work takes the 0–1 knapsack problem (KP) as an example to build an original image representation method. After modifying the image details, the proposed method is extended to the general 0–1 linear programming (LP) problem, and a deep learning architecture is designed to solve the transferred computer vision problem. The efficiency of the proposed DCNNs method is validated on a large number of problems, and results show that this method can solve the 0–1 LP accurately and efficiently without iteration. The solution speed is 20 times faster than that of traditional optimization solvers. [ABSTRACT FROM AUTHOR]

The cluster analysis of real-life data often encounters the challenges of noisy data or may rely heavily on the uncertainty of the main clustering variable owing to its stochastic nature, which has a potential influence on its performance. In this paper, we propose a novel clustering technique that is efficient in dealing with noise and uncertainty in a dataset by adopting a stochastic approach that uses realistic values of data points by assuming a continuous probability distribution instead of exact values. By estimating the best-fit probability distribution of the clustering variable, the proposed method formulates the problem of determining the most homogeneous clusters by determining the optimum cluster partitions (OCP) as a mathematical programming problem (MPP). A computer-intensive dynamic programming technique was used to solve the MPP and determine the OCP, which minimized the sum of the weighted intracluster standard deviations. The proposed technique is then demonstrated in this study using univariate data that follows a normal distribution, which is a symmetric distribution, as well as the Weibull distribution, which is a skewed distribution. Numerical examples were also presented to illustrate the computational details of the proposed method. Finally, using both simulated and real datasets, a comparative analysis of the effectiveness of the proposed technique was performed against four advanced clustering methods: k-means, fuzzy c-means, expectation maximization, and Genie++ hierarchical clustering. The results reveal that the proposed method works well and produces more efficient clusters than other methods.

Mimicking the biological visual attention mechanism to detect salient objects in images has been widely studied in recent years. Most of the existing computational models rely on external learning for saliency prediction, which however lack robustness in diversified scenes. In this paper, an unsupervised learning model is proposed to detect salient objects by fully exploiting the internal information of the scene. Specifically, we formulate saliency detection as a mathematical programming problem with which to learn a nonlinear feature mapping from multi-view features to saliency scores. The optimization objective is to maximize the between-class variance of the attended and background regions in the resulting saliency maps, which is statistically optimal. Moreover, to solve the non-convex constrained mathematical programming problem, a hybrid external point method based particle swarm optimization algorithm is developed to find the optimal solution in a rapid manner. Finally, extensive experiments are conducted on four classical saliency benchmark datasets to test the effectiveness of the proposed method and it shows superior qualitative and quantitative performance than the other 16 state-of-the-art unsupervised saliency models.

The emergence of distributed energy resources in the electricity system involves new scenarios in which domestic consumers can be aggregated in virtual power plants to participate in energy markets. In this paper, a reconfigurable hierarchical multi-time scale framework is developed by combining the concepts of dynamic storage virtualization and intent profiling with model predictive control. The combined implementation of these concepts allows the simultaneous weighted participation in different energy markets, not only according to some aggregators’ criteria, but also to several risk factors. In a first stage, the framework optimizes the strategy for bidding in day-ahead market whereas the second one consists of a control stage to mitigate deviations and potential penalties. The smart management of individual storage virtualization enables the participation in the demand-response program, which improves the forecasted economical profit related to the day-ahead participation. The changes in the schedule are performed considering new potential penalties. The framework is reconfigurable at every sample time at control stage. This enables to make dynamic participations depending on node availability or system peaks. The proposed case studies cover day-ahead and demand-response participations, but the framework is open to other multi-service configurations. The results have been assessed with satisfactory conclusions.