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The study presents the integration of Topology Optimization (TO) as a design strategy and Wire Arc Additive Manufacturing (WAAM) as a fabrication strategy. Although TO is well-known in the discipline, a gap in implementing this technique in architectural design due to fabrication limitations remains. In this research, an integrated framework is proposed to obtain a design where both TO and WAAM printing are implemented simultaneously. This framework is divided into five main phases, (1) checking suitability, (2) preprocessing, (3) processing, (4) post-processing, and (5) printing. The framework is then applied to the design of a topology-optimized spiral staircase and studied for fabrication using WAAM metal 3D printing. The results conclude that avoiding the usual standardization of design can be realized by implementing a framework that takes the fabrication strategies into consideration throughout the design process. [ABSTRACT FROM AUTHOR]

This paper models the resource allocation problem arising in multilevel marketing (i.e., network) operations. The supervisor of a network of salespersons has a limited resource (her own time). She must decide on the (i) optimal number of "direct contacts" to recruit, train and develop; (ii) optimal number of lower levels she should be responsible for helping to hire, train and develop their own direct contacts, and (iii) optimal allocation of her time at each level in the network. We use tools from branching processes and find general results for the probability distribution of the number of lower level contacts with non‐identical distributions for any given number of initial contacts. Using these results, we present an optimization model for contacts with different characteristics and determine the optimal number of initial contacts, the number of lower levels and the supervisor's optimal effort at each level using tools from nonlinear programming, in particular, Kuhn‐Tucker conditions and Lagrangian duality. We generalize our models, (i) to allow for the randomness of time spent by the supervisor; and (ii) the possibility of supervisor generating her own direct sales. Several examples illustrate our findings. [ABSTRACT FROM AUTHOR]

Healing of various ailments using herbal medicines is gaining much interest. Plants classified as grasses, specifically Cynodon dactylon, are an appreciated group of monocots used in many herbal remedies. In this work, C. dactylon, is grown naturally and also under market available LED Luminaires with different lighting conditions. Until 2010, most of the plants are grown under conventional lamps that are not spectrally tunable. Cynodon dactylon, the grass is grown under two different light spectrum, two light levels and three photoperiods (9hours, 12 hours, 15 hours) to extend our experiential knowledge. The biomass accumulation was the highest when grown under a lower RB ratio-12-hour- $163\mu $ mol/s, and phenolic content was the highest at 92.8 mg/g wt Gallic Acid Equivalents under combined light source at 15-hour photoperiod. A spectrally tunable LED light source with an optimal quantity of LEDs saves cost, space and energy. Considering the light parameters from the light sources used for growing C. dactylon, Levenberg–Marquardt (LM) algorithm is implemented to select an optimal LED quantity that composes the light source. The algorithm simulates the given target spectrum with minimum fitness error. The method applied to model LEDs, its validation against the practical LED spectrum, spectrum matching and computation of Luminous flux, Photosynthetic Photon Flux and efficacy are also presented. Many spectrums are simulated to validate the performance of the algorithm. A solution of optimal LEDs for three Photosynthetic Photon Flux (PPF) levels with LEDs is derived, and it is observed that the number of LEDs increased with PPF.

Critical infrastructure from a cross-section of sectors has become increasingly reliant on cyber systems and cyberinfrastructure. Increasing risks to these cyber components, including cyber-physical systems, have highlighted the importance of cybersecurity in protecting critical infrastructure. The need to cost-effectively improve cyberinfrastructure security has made this topic suitable for optimization research. In this survey, we review studies in the literature that apply optimization to enhance or improve cyberinfrastructure security and were published or accepted before the end of the year 2019. We select 68 relevant peer-reviewed scholarly works among 297 studies found on Scopus and provide an overview of their application areas, mission areas, and optimization models and methods. Finally, we consider gaps in the literature and possible directions for future research. [ABSTRACT FROM AUTHOR]

This research addresses the problem of dividing a group of people into a collection of teams that need to be "balanced" across a variety of different attributes. This type of problem arises, for example, in an academic setting where it is necessary to partition students into a number of balanced study teams and also in a youth camp in which children need to be formed into sports teams that are competitive with each other. Recent work has resulted in both linear and nonlinear integer programing models for solving this problem. In the research here, improvements to the models are made together with a linear approximation to the nonlinear objective function that significantly reduce the number of integer variables and constraints. Computational experiments are performed on random instances of the problem, as well as on instances for which there are almost perfectly balanced teams, the latter providing a way to determine the quality of the optimal solution obtained by the heuristics. These tests show that the approach developed here almost always obtain better balanced teams than those from prior research. [ABSTRACT FROM AUTHOR]

Production planners in the non-ferrous metal industry face an inherent combinatorial complexity of the metal production process within a fast-changing market environment. Herein, we study the benefit of an integrated optimization-based planning approach. We present the first value-based optimization approach for operational planning in the non-ferrous metal industry that yields high economic and technical benefits. We present a mixed-integer linear program for non-ferrous metal operational production planning that covers the complexity of material flows and the entire production process and is amenable for real-time application. We give insights into the practical implementation and evaluation of our modeling approach at a plant of Aurubis, a large European non-ferrous metal producer. Our results show that an optimization and value-based production planning approach provides significant benefits, including a 38% better planning solution in practice. In addition to economic benefits, we highlight the technical advantages that result from a detailed techno-economic representation of the entire production process. [ABSTRACT FROM AUTHOR]

Traditional building design is often done in a (pseudo-) sequential manner: the architect defines the form, the structural engineer defines the material and member dimensions, and the mechanical engineer defines the openings, clearances, and additional spaces that ensure proper operation of the building. The design process should ideally be linear, where each discipline receives a complete design from the previous. In reality, however, upstream revisions are usually substantive: significant work in the schematic design and design development phases are due to resolving upstream issues. That said, within the conceptual design and initial phase, the process is mostly linear. This work presents a set of tools that move towards an integrated design optimization, where the building's form and structure are optimized together and not as separate stages in the design. This approach often results in a higher impact/gain in efficiency, safety, cost-savings, and ultimately results in innovative designs. This industrial application manuscript provides specific details on the implementation and experience gained from the development of various topology optimization tools for use in building engineering. These are all accompanied by examples of their use in applied building projects or more general structural engineering problems. Part of the success of this effort is attributed to the environment in which these tools are implemented, which is friendly to architects. In contrast, commercial tools for this purpose tend to cater to engineers instead. [ABSTRACT FROM AUTHOR]

Through Central Procurement Organizations (CPOs), large firms with multiple divisions have begun adopting a center‐led sourcing approach that allows firms to centralize strategic sourcing activities, while permitting decentralized execution by divisions, allowing the firm to leverage large purchase volumes with vendors. This new center‐led procurement environment has brought a new decision requirement: How should a CPO select vendors for each division's requirements to minimize the firm's total procurement cost and simultaneously develop a fair and alignment‐inducing mechanism to allocate the costs (and savings) of company‐wide procurement to the divisions? Past research and current practice have not addressed this linkage between vendor selection and cost allocation in multi‐division firms. This work models this sourcing and procurement cost allocation (SPC) problem facing CPOs of large firms as a mixed‐integer optimization problem. This model is flexible and can incorporate several commonly‐used cost allocation rules. We show that the SPC problem is NP‐hard. Therefore, to support practical decision‐making in this context, we develop a tailored solution approach for the SPC problem. Our approach enhances the base model by adding strong valid inequalities. We tested our model and its enhancements in an extensive numerical study, solving over 160 instances. Results reveal that (a) the CPO can ensure fair cost allocation at a modest cost (relative to the centralized solution) with our model, and (b) the proposed tailored approach is effective and necessary in solving a wide variety of instances. [ABSTRACT FROM AUTHOR]

Abstract Generation expansion planning is the framework under which power grid capacity expansions are made. Under this framework, mathematical optimization tools are used to determine the type of generation technology to invest in, and when and where these investments should be made in order to minimize market costs such as investment costs, fixed and variable operating & maintenance costs, and fuel costs over a long term planning horizon. Given the current infrastructure and policies, fossil fuels (such as coal, oil, and natural gas) are among the most economical sources of electricity. Thus, under these assumptions, these energy sources dominate the resulting expansion plans. However, fossil fuel combustion creates by-products contributing to ground-level ozone, particulates, and acid rain, which have harmful health implications such as premature death, respiratory-related illnesses, cardiovascular injuries, pulmonary disorders, and autism leading to lost days at school or work on a daily basis. In this research, we formulate a linear program to solve a multi-period generation expansion planning problem minimizing market costs for a centrally dispatched power system. We can then assess the human health externalities of the resulting expansion plan by studying the model output with an Environmental Protection Agency (EPA) screening tool that determines the human health externalities from the electricity sector. Results with and without emission limits and other policies can then be evaluated and compared based on predicted societal costs including human health externalities. This research enables policy makers to directly assess the health implications of power grid expansion decisions by explicitly estimating the total societal costs by quantifying externalities as part of the investment strategy. Highlights • Generation expansion planning models for centralized networks often invest in fossil fuel energy sources, which produce harmful emissions. • We solve a generation expansion planning model and evaluate the output in an economic screening tool to assess the resulting health damages. • Emissions limits and renewable portfolio standards mitigate these damages by making large capital investments in wind capacity. • A case study for the Northeastern United States is presented to demonstrate our results. [ABSTRACT FROM AUTHOR]

• We analyze time-of-use rates with respect to model predictive controls. • We show that thermal storage potential can vary greatly for differing climates. • We build a model predictive control framework for indoor temperature setpoints. • We analyze of the impacts of different variable electric rates. • The framework is capable of reducing cooling electricity costs by 30%. Heating, ventilation and air conditioning for residential and commercial buildings requires a substantial share of electric energy, and ultimately drives summer peak demand in the United States. Variable electric rates are becoming more common in the residential market, as utilities try to encourage users to shift their energy demand. Model predictive controls, one method of reducing energy usage, employ an optimization model to minimize peak demand, energy usage, or electricity costs. This paper details the development of a co-simulation framework to rapidly model and simulate building energy use and optimize cooling setpoint controls. The framework integrates commercially available software to: (i) simulate all energy interactions between the building, internal gains, outdoor environment, and heating and cooling systems via a building energy simulation program (EnergyPlus), (ii) algebraically formulate an optimization problem (with AMPL) using a black-box, reduced-order model for rapid calculations, (iii) employ Simulink as the environment that links calls to EnergyPlus and AMPL, and (iv) solve the optimization model (with CPLEX) to minimize electricity costs and user discomfort. Variable electric time-of-use rates are analyzed in the context of total cooling electricity costs, thermal comfort of users, and peak demand shedding. The framework uses a model predictive control formulation capable of reducing cooling electricity costs by up to 30%; however, cost savings and peak demand shedding are highly dependent on the time-of-use electricity rate schedule. [ABSTRACT FROM AUTHOR]

Timely access of care has been widely recognized as an important dimension of health care quality. Waiting times can affect patient satisfaction and quality of care in the emergency department (ED). This study analyzes a general patient waiting policy such that ED patients who wait beyond a threshold have their wait shortened. Assuming that the policy is implemented to accelerate the long-waiting cases within a short time interval, we transform the original waiting distribution to a piecewise distribution. The objective of this paper is to examine the reliability of the induced waiting system by minimizing the coeffcient of variation (CV) of waiting time. We convert the CV minimization problem to an approximation counterpart using the sampling technique. With patient waiting time data from an emergency department in Singapore, we derive the optimal values of parameters, such as the threshold and the length of the underlying time interval, needed in the policy. Numerical results show that CV and variance of new waiting time will be reduced remarkably by 38% and 58% respectively, in comparison with the original ones. [ABSTRACT FROM AUTHOR]

We present a mathematical programming approach for simulation and optimization of a general continuous-flow production system with an intermediate finite buffer. In this system, each station is represented with a discrete state space-continuous time process with given transition time distributions between the states and a set of flow rates associated with each discrete state. We develop a mathematical programming formulation to determine the critical time instances of the sample trajectory of the buffer that correspond to state transitions, buffer dynamics, and changing flowrates. We showthat a simulated sample realization of the systemis obtained by solving amixed-integer linear program. The mathematical programming representation is also used to show that the production rate is a monotonically increasing function of the buffer capacity. We analyze the buffer capacity determination problem with the objective of determining the minimum buffer capacity that achieves a desired production rate and also with the objective of maximizing the profit. It is shown that the computational performance depends on the rates of change among system states and not on the number of states at each stage and on the buffer capacity. Our numerical results show a significant computational improvement compared with using a discrete-event simulation. As a result, the mathematical programming approach is proposed as a viable alternative method for performance evaluation and optimization of continuous-flow systems with a finite buffer. [ABSTRACT FROM AUTHOR]

Analysts predict impending shortages in the health care workforce, yet wages for health care workers already account for over half of U.S. health expenditures. It is thus increasingly important to adequately plan to meet health workforce demand at reasonable cost. Using infinite linear programming methodology, we propose an infinite-horizon model for health workforce planning in a large health system for a single worker class; e.g., nurses. We give a series of common-sense conditions that any system of this kind should satisfy and use them to prove the optimality of a natural lookahead policy. We then use real-world data to examine how such policies perform in more complex systems; in particular, our experiments show that a natural extension of the lookahead policy performs well when incorporating stochastic demand growth. [ABSTRACT FROM AUTHOR]

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Neural network is one of the branches with the most active research, development and application in computational intelligence and machine study. Radial basis function neural network (RBFNN) has achieved some success in more than one application field, especially in pattern recognition and functional approximation. Due to its simple structure, fast training speed and excellent generalization ability, it has been widely used. Artificial fish swarm algorithm (AFSA) is a new swarm intelligent optimization algorithm derived from the study on the preying behavior of fish swarm. This algorithm is not sensitive to the initial value and the parameter selection, but strong in robustness and simple and easy to realize and it also has parallel processing capability and global searching ability. This paper mainly researches the weight and threshold of AFSA in optimizing RBFNN. The simulation experiment proves that AFSA-RBFNN is significantly advantageous in global optimization capability and that it has outstanding global optimization ability and stability. [ABSTRACT FROM AUTHOR]

Even though epidemiological evidence links specific workplace stressors to health outcomes, the aggregate contribution of these factors to overall mortality and health spending in the United States is not known. In this paper, we build a model to estimate the excess mortality and incremental health expenditures associated with exposure to the following 10 workplace stressors: unemployment, lack of health insurance, exposure to shift work, long working hours, job insecurity, work-family conflict, low job control, high job demands, low social support at work, and low organizational justice. Our model uses input parameters obtained from publicly accessible data sources. We estimated health spending from the Medical Expenditure Panel Survey and joint probabilities of workplace exposures from the General Social Survey, and we conducted a meta-analysis of the epidemiological literature to estimate the relative risks of poor health outcomes associated with exposure to these stressors. The model was designed to overcome limitations with using inputs from multiple data sources. Specifically, the model separately derives optimistic and conservative estimates of the effect of multiple workplace exposures on health, and uses optimization to calculate upper and lower bounds around each estimate, which accounts for the correlation between exposures. We find that more than 120,000 deaths per year and approximately 5%-8% of annual healthcare costs are associated with and may be attributable to how U.S. companies manage their work forces. Our results suggest that more attention should be paid to management practices as important contributors to health outcomes and costs in the United States. [ABSTRACT FROM AUTHOR]

Abstract: During phonation the human vocal folds exhibit a complex self-sustained oscillation which is a result of the transglottic pressure difference, of the characteristics of the tissue of the folds and of the flow in the gap between the vocal folds (Van den Berg J. Myoelastic-aerodynamic theory of voice production. J Speech Hearing Res 1958;1:227–44 ). Obviously, extensive experiments cannot be performed in vivo. Therefore, in literature a variety of model experiments that try to replicate the vocal folds kinematics for specific studies within the vocal tract can be found. Here, we present an experimental model to visualize the fluid dynamics which result from the complex motions of real human vocal folds. An existing up-scaled glottal cam model with approximate glottal kinematics is extended to replicate more realistically observed glottal closure types. This extension of the model is a further step in understanding the fluid dynamical mechanisms contributing to the quality of human voice during phonation, in particular the cause (changed glottal kinematics) and its effect (changed aero-acoustic field). For four typical glottal closure types cam geometries of varying profile are generated. Two counter rotating cams covered with a silicone membrane reproduce as well as possible the observed glottal movements. [Copyright &y& Elsevier]

Driven by market pressures, financial service firms are increasingly partnering with independent vendors to create service networks that deliver greater profits while ensuring high service quality. In the management of call center networks, these partnerships are common and form an integral part of the customer care and marketing strategies in the financial services industry. For a financial services firm, configuring such a call center service network entails determining which partners to select and how to distribute service requests among vendors, while incorporating their capabilities, costs, and revenue-generating abilities. Motivated by a problem facing a Fortune 500 financial services provider, we develop and apply a novel mixed integer programming model for the service network configuration problem. Our tactical decision support model effectively accounts for the firm's costs by capturing the impact of service requirements on vendor staffing levels and seat requirements, and permits imposing call routing preferences and auxiliary service costs. We implemented the model and applied it to data from an industry partner. Results suggest that our approach can generate considerable cost savings and substantial additional revenues, while ensuring high service quality. Results based on test instances demonstrate similar savings and outperform two rule-based methods for vendor assignment. [ABSTRACT FROM AUTHOR]

The supply chain in the food and agribusiness sector is characterized by long supply lead times combined with significant supply and demand uncertainties, and relatively thin margins. These challenges generate a need for management efficiency and the use of modern decision technology tools. We review some of the literature on applications of decision technology tools for a selected set of agribusiness problems and conclude by outlining what we see as some of the significant new problems facing the industry. It is our hope that we will stimulate interest in these problems and encourage researchers to work on solving them. [ABSTRACT FROM AUTHOR]

Higher education institutions are among the many public and private sector entities that have committed to long-term sustainability goals in response to the threat of climate change. A key challenge for these institutions is establishing a commitment to make targeted investments in renewable energy technologies in support of emissions reduction goals. Such strategies require a vision to simultaneously coordinate strategic investments in renewable energy technologies with tactical operational decisions to achieve the desired benefits. In this paper, we formulate and solve a least-cost renewable energy capacity investment planning model to determine pathways to achieve emissions reduction strategies. Specifically, we apply our model to Rutgers University to evaluate its target of 100 % carbon neutrality. Using these insights, we share recommendations on how these strategies can be executed. This research serves as a springboard for administrators to assess and deploy their emissions reduction strategies, while ensuring system and financial constraints are satisfied. [ABSTRACT FROM AUTHOR]