Types Of Optimization Strategies Research Articles

Cancer Screening Benefits Maximization Using Markov Decision Process Models: A Systematic Review

Context: Due to the chronic nature of cancer, screening programs were a set of sequential decisions taken over time. Markov decision process (MDP) and partially observable Markov decision process (POMDP) models were the mathematical tools applied in studies, including sequential decision-making such as screening protocols for medical decision-making. Objectives: The main goal of this study was to investigate optimal policy for cancer screening using MDP and POMDP models. Methods: We performed a review of articles published within July 2000 to November 2022 in PubMed, Web of Science, and Scopus databases. The stopping age, the type of optimal strategy, the benefits of the optimal policy, and the relationship between age and risk threshold were extracted. Studies that did not use MDPs and POMDPs as the mathematical maximization models in cancer screening, review articles, editorials or commentaries, non-English articles, and those that did not focus on optimization were excluded. Results: From 532 articles, 6 studies met the study criteria. All studies suggested that the optimal policy was control-limit, and the cancer risk threshold was a non-decreasing function of age. Three studies specified a stopping age for cancer screening. In five studies, the optimal policies outperformed the guidelines or no screening strategy. Conclusions: Two essential factors in screening decisions were cancer risk and age, which were individual variables. The control-limit policy included these factors in decision-making for cancer screening. These policies highlighted personalized screening and showed that this type of screening could outperform cancer screening guidelines regarding economic and clinical benefits.

Design and optimization of nanooptical couplers based on photonic crystals involving dielectric rods of varying lengths

This study presents design and optimization of compact and efficient nanooptical couplers involving photonic crystals. Nanooptical couplers that have single and double input ports are designed to obtain efficient transmission of electromagnetic waves in desired directions. In addition, these nanooptical couplers are cascaded by adding one after another to realize electromagnetic transmission systems. In the design and optimization of all these nanooptical couplers, the multilevel fast multipole algorithm, which is an efficient full-wave solution method, is used to perform electromagnetic analyses and simulations. A heuristic optimization method based on genetic algorithms is employed to obtain effective designs that provide the highest efficiency values. Two types of optimization strategies are applied using nanorods with a fixed length and using nanorods with varying lengths. This way, photonic crystals consisting of irregular arrays of both identical and nonidentical dielectric elements are designed for the realization of nanooptical couplers. The designs and their numerical results show that it is possible to design and further improve efficient nanooptical couplers with simple and compact geometries based on the principles of photonic crystals. Using relatively simple geometries and a single material, the designed nanooptical couplers are more preferable than the available designs in the literature.

Enhancing an insurer's expected value by reinsurance and external financing

In this paper, we analyze a decision-making problem for an insurer with limited liability, who is subject to a solvency constraint and wants to maximize the expected value through reinsurance purchase and external financing. We impose mild conditions on the reinsurance premium principle, which are the axioms of law invariance, risk loading and preserving the convex order. These three axioms are satisfied by all the widely used premium principles, except the Esscher principle, listed in Young (2004). If value at risk (VaR) or conditional value at risk (CVaR) is adopted to calculate the insurer's regulatory capital, we show that the optimal reinsurance can be in the form of two layers. The optimal reinsurance form is reduced to one layer once the premium principle further satisfies a weak condition. In particular, under Wang's, expected value, variance and Dutch premium principles, we derive the insurer's optimal strategies of reinsurance and financing, which are obtained explicitly under the VaR risk measure but have to be solved numerically for the CVaR risk measure. Results indicate that the insurer has three types of optimal strategies: the reinsurance only strategy, external financing only strategy, and mixed strategy. Moreover, the insurer's attitude toward ceding the risk through reinsurance and increasing capital through external financing is often greatly affected by the regulatory regime, the financing cost and the reinsurance price.

Sharing Economy for Cost Reduction and Efficiency Increase: The Case of Sharing E-Commerce Logistics

In the real business environment, due to unpredictable market demand or high prediction difficulty and low prediction accuracy, there will be inevitably operational loss in the field of e-commerce logistics caused by undersupply or oversupply of express service capabilities. At present, China is deepening the supply-side structural reforms. Confronted with the growing demand for e-commerce logistics express delivery, especially the urgent demand for tackling orders piling up to 1 billion pieces during the recent “Double 11” shopping carnival, it is very important and practical for us to study how to make scientific decisions on the supply side in the field of e-commerce express delivery. Therefore, in this paper, we design a sharing logistics cooperation mechanism considering both the undersupply and oversupply of express delivery service capabilities under stochastic demand. By comparing the earnings data of several listed express companies, we analyze four types of optimization strategies: the order flow proportion revenue sharing strategy (RE-OFP), the combined factors revenue sharing strategy (RE-RSF), the order flow proportion risk sharing strategy (RS-OFP), and the combined factors risk sharing strategy (RS-RSF). The research results demonstrate that the four optimization strategies of RE-OFP, RE-RSF, RS-OFP, and RS-RSF could achieve Pareto improvements in the operational performance of e-commerce express service. The performance of four revenue sharing and risk sharing strategies varies with different revenue sharing or risk sharing factors. Under some certain combined factors, the revenue sharing contract could help realize the supply chain coordination of the sharing logistics service. The sharing logistics cooperation mechanism based on equity investment proposed hereafter provides a feasible solution to solve the problems of high empty driving rate and operational cost of e-commerce express delivery service in urban areas.

Numerical evaluation of gamma radiation monitoring

Abstract Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden–Fletcher–Goldfarb–Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and non-diagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

A robust fuzzy tree method with outlier detection for combustion models and optimization

The fuzzy tree (FT) method is a new efficient modeling algorithm. To improve the performance of detecting noise and outliers for most industrial applications, this paper proposes a robust fuzzy called weighted FT (W-FT). A typical nonlinear example is used in the numerical experiments to validate the proposed W-FT. Then, the W-FT is used for building the combustion models, mainly three soft sensor models are established considering boiler efficiency, NOx and SO2 emissions by using the historical data of a circulating fluidized bed (CFB) boiler. Compared with other methods, the W-FT method exhibits more robustness, higher prediction accuracy and better generalization capability. Moreover, in basis of above soft sensor models, three types of optimization strategies are proposed to optimize the adjustable parameters by using the modified fruit fly optimization algorithm. Simulation results validate the effectiveness of the proposed optimization strategies, and further demonstrate the practicability of soft sensor models by W-FT.

Regret-Based Automated Decision-Making Aids for Domain Search Tasks Using Human-Agent Collaborative Teams

In this paper, we consider domain search tasks using a human-agent collaborative team. An automated Bayesian sequential decision-making strategy is first proposed for the optimal allocation of manual and autonomous sensing modes in the presence of sensing uncertainty. An agent will generate a request for human intervention whenever the manual mode is deemed optimal. However, this type of optimal strategy is not always the style of human decision making under uncertainty. In particular, regret has been shown to play a critical role in human’s rational decision making. Humans experience regret when they perceive that they are better off with another option and tend to make choices to avoid such experience. Furthermore, it has been shown that team members who share the same mental model perform better than teams with a more accurate but less similar mental model. Therefore, to enable more effective human-agent collaboration (HAC) and improve joint performance, we embed regret analysis into the proposed decision-making framework to provide suboptimal but more humanlike automated decision aids. We will reconcile the dominant Bayesian methods with the analysis of human behaviors during decision making. We provide the simulation results to compare the decision-making strategies with and without regret analysis and show that regret-based decision making can integrate human tendency in risk averse and risk seeking for better HAC.

Centralized–decentralized optimization for refinery scheduling

This paper presents a novel decomposition strategy for solving large scale refinery scheduling problems. Instead of formulating one huge and unsolvable MILP or MINLP for centralized problem, we propose a general decomposition scheme that generates smaller sub-systems that can be solved to global optimality. The original problem is decomposed at intermediate storage tanks such that inlet and outlet streams of the tank belong to the different sub-systems. Following the decomposition, each decentralized problem is solved to optimality and the solution to the original problem is obtained by integrating the optimal schedule of each sub-systems. Different case studies of refinery scheduling are presented to illustrate the applicability and effectiveness of the proposed decentralized strategy. The conditions under which these two types of optimization strategies (centralized and decentralized) give the same optimal result are discussed.

An Abelian Limit Approach to a Singular Ergodic Control Problem

We consider an ergodic stochastic control problem for a class of one‐dimensional Itô processes where the available control is an added bounded variation process. The corresponding infinite horizon discounted control problem was solved in [A. Weerasinghe, SIAM J. Control Optim., 44 (2005), pp. 389–417]. Here, we show that as the discount factor approaches zero, the optimal strategies derived in [A. Weerasinghe, SIAM J. Control Optim., 44 (2005), pp. 389–417] “converge” to an optimal strategy for the ergodic control problem. Under different assumptions, two types of optimal strategies were derived. Also, the Abelian limit relationships among the ergodic control problem, the infinite horizon discounted control problem, and the finite time horizon control problem are established here. A solution to a constrained optimization problem is obtained as an application.

3D optimization of average torque of electrostatic micromotors

The paper presents a technique to optimize electrostatic micromotors. The finite element technique is combined with different types of optimization strategies, one of them, a version of the evolution strategy is discussed. It further involves an automated generation of 3D meshes where the dimensions and rotor positions of the motor models can be chosen arbitrarily. It also involves constructing equivalent circuit models for the electrostatic motors. Both, the automated 3D mesh generation and the construction of the equivalent circuit model are treated in detail in this paper. The possibilities of producing powerful tools in the field of optimization are described, but also some warnings and limitations concerning the automated optimization are mentioned.