The effects of qos level degradation cost on provider selection and task allocation model in telecommunication networks

Firms acquire network capacity from multiple suppliers which offer different Quality of Service (QoS) levels. After acquisition, day-to-day operations such as video conferencing, voice over IP and data applications are allocated between these acquired capacities by considering QoS requirement of each operation. In optimal allocation scheme, it is generally assumed each operation has to be placed into resource that provides equal or higher QoS Level. Conversely, in this study it is showed that former allocation strategy may lead to suboptimal solutions depending upon penalty cost policy to charge degradation in QoS requirements. We model a cost minimization problem which includes three cost components namely capacity acquisition, opportunity and penalty due to loss in QoS

Simulation-Optimization via Kriging and Bootstrapping:A Survey (Revision of CentER DP 2011-064)

Abstract: This article surveys optimization of simulated systems. The simulation may be either deterministic or random. The survey reflects the author’s extensive experience with simulation-optimization through Kriging (or Gaussian process) metamodels. The analysis of these metamodels may use parametric bootstrapping for deterministic simulation or distribution-free bootstrapping (or resampling) for random simulation. The survey covers: (1) Simulation-optimization through "efficient global optimization" (EGO) using "expected improvement" (EI); this EI uses the Kriging predictor variance, which can be estimated through parametric bootstrapping accounting for estimation of the Kriging parameters. (2) Optimization with constraints for multiple random simulation outputs and deterministic inputs through mathematical programming applied to Kriging metamodels validated through distribution-free bootstrapping. (3) Taguchian robust optimization for uncertain environments, using mathematical programming— applied to Kriging metamodels— and distribution- free bootstrapping to estimate the variability of the Kriging metamodels and the resulting robust solution. (4) Bootstrapping for improving convexity or preserving monotonicity of the Kriging metamodel.

Maximizing Activity in Ising Networks via the TAP Approximation

A wide array of complex biological, social, and physical systems have recently been shown to be quantitatively described by Ising models, which lie at the intersection of statistical physics and machine learning. Here, we study the fundamental question of how to optimize the state of a networked Ising system given a budget of external influence. In the continuous setting where one can tune the influence applied to each node, we propose a series of approximate gradient ascent algorithms based on the Plefka expansion, which generalizes the na\"{i}ve mean field and TAP approximations. In the discrete setting where one chooses a small set of influential nodes, the problem is equivalent to the famous influence maximization problem in social networks with an additional stochastic noise term. In this case, we provide sufficient conditions for when the objective is submodular, allowing a greedy algorithm to achieve an approximation ratio of $1-1/e$. Additionally, we compare the Ising-based algorithms with traditional influence maximization algorithms, demonstrating the practical importance of accurately modeling stochastic fluctuations in the system

Stochastic Model Predictive Control of Mixed-mode Buildings Based on Probabilistic Interactions of Occupants With Window Blinds

Between 4% to 20% of energy used for HVAC, lighting and refrigeration in a building is wasted due to issues associated with systems operations. It is estimated that proper building energy load control and operation can result in up to 40% utility cost savings. Current heuristic rules based on decision trees are difficult to define, manage and optimize as buildings become more complex. Advanced control strategies with weather forecast and cooling load anticipation, known as model predictive control (MPC), offer an attractive alternative for buildings with slow dynamics. However, MPC is mostly practiced through deterministic approaches. Deterministic MPC implicitly assumes that a dynamic model is able to perfectly predict the future behavior of the building over the desired control window, or prediction horizon. However, this assumption is clearly not rational because there will be both modeling errors and disturbances acting on the system over this period. One of these disturbances is associated with building occupant behaviors which interfere with deterministic assumptions. In this study, a probabilistic model of occupants’ behavior on window blind closing event is used to represent the disturbance coming from interactions of building residents with window blinds. This model is a multiple logistic regression analysis, based on a field study in an office building at the University of California, Berkeley (Inkarojrit, 2005). It considers the incident solar radiation on window surface and occupants’ self-reported brightness sensitivity as variable parameters to predict the closing event of blinds with 86.3% of accuracy. The probability of closing event is compared with a random number from the uniform distribution on the interval [0,1] at each time step and if it is greater than the random number, some indicator function will be equal to 1 (closing action) and vice versa. In order to implement the stochastic MPC, Monte Carlo simulation needs to be conducted due to the randomness of occupants’ behavior in closing the blinds. A test-building with mixed-mode cooling and high solar gains is considered as a test-bed. In our methodology, a detailed dynamic building model is developed and it is then used to identify the parameters of a 4th order linear time-variant state-space model. In the MPC formulation, the window opening schedule is optimized for the upcoming prediction horizon and the cost function is the minimization of energy usage subject to thermal comfort constraints during this horizon. Optimal control sequences based on the proposed stochastic MPC framework will be compared with deterministic MPC approaches to investigate possible advantages of considering uncertainties of occupant actions in model predictive controllers of buildings. References: Inkarojrit V., 2005. Balancing Comfort: Occupants’ Control of Window Blinds in Private Offices. PhD thesis, School of Architecture, University of California Berkeley

Short-Term Robustness of Production Management Systems: New Methodology

This paper investigates the short-term robustness of production planning and control systems. This robustness is defined here as the systems ability to maintain short-term service probabilities (i.e., the probability that the fill rate remains within a prespecified range), in a variety of environments (scenarios). For this investigation, the paper introduces a heuristic, stagewise methodology that combines the techniques of discrete-event simulation, heuristic optimization, risk or uncertainty analysis, and bootstrapping. This methodology compares production control systems, subject to a short-term fill-rate constraint while minimizing long- term work-in-process (WIP). This provides a new tool for performance analysis in operations management. The methodology is illustrated via the example of a production line with four stations and a single product; it compares Kanban, Conwip, Hybrid, and Generic production control schemes.manufacturing;inventory;risk analysis;robustness and sensitivity analysis;scenarios