scholarly journals Scheduling Jobs with Linear Model of Simultaneous Ageing and Learning Effects

2011 ◽  
Vol 5 (1) ◽  
pp. 37-48 ◽  
Author(s):  
Adam Janiak ◽  
Maciej Lichtenstein ◽  
Agata Rusoń
Keyword(s):  
Heuristic Algorithms ◽  
Piecewise Linear ◽  
Piecewise Linear Function ◽  
Learning Effects ◽  
Aging Effects ◽  
Scheduling Model ◽  
Special Cases ◽  
Polynomially Solvable ◽  
Single Processor ◽  
Makespan Criterion

In the paper, we introduce some new scheduling model in which learning and aging effects are both considered simultaneously. In this model the actual processing time of the jobs depends only on its position in a schedule and can be described by the piecewise linear function. For single-processor problem with introduced model, we show that the problem of minimizing the makespan criterion for independent jobs with release dates is strongly NPhard, but some special cases of this problem are polynomially solvable. Based on those special cases, we propose 4 heuristic algorithms and we experimentally examine their usefulness for solving the general problem.

scholarly journals Group Scheduling with Learning Effect and Random Processing Time

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Dingyu Wang ◽  
Chunming Ye
Keyword(s):  
Objective Function ◽  
Completion Time ◽  
Processing Time ◽  
Learning Effect ◽  
Heuristic Algorithms ◽  
Random Variable ◽  
Group Scheduling ◽  
Learning Effects ◽  
Scheduling Model ◽  
Installation Time

In this paper, we establish a stochastic grouping scheduling model. In the model, there is no installation time between the jobs in the same group, but each group has an installation time before processing. There are learning effects between groups and within groups, and the completion time of jobs is a random variable. We take the long expected schedule and the expected total completion time as the objective function, and the noninterruptible static priority strategy is obtained. At the same time, heuristic algorithms and examples are given.

scholarly journals MINLP formulations for continuous piecewise linear function fitting

2021 ◽  
Author(s):  
Noam Goldberg ◽  
Steffen Rebennack ◽  
Youngdae Kim ◽  
Vitaliy Krasko ◽  
Sven Leyffer
Keyword(s):  
Linear Function ◽  
Piecewise Linear ◽  
Piecewise Linear Function ◽  
Mixed Integer ◽  
Function Fitting ◽  
Computational Performance ◽  
Integer Nonlinear Programming ◽  
Minlp Model ◽  
Necessary Constraints ◽  
Continuous Piecewise Linear Function

AbstractWe consider a nonconvex mixed-integer nonlinear programming (MINLP) model proposed by Goldberg et al. (Comput Optim Appl 58:523–541, 2014. 10.1007/s10589-014-9647-y) for piecewise linear function fitting. We show that this MINLP model is incomplete and can result in a piecewise linear curve that is not the graph of a function, because it misses a set of necessary constraints. We provide two counterexamples to illustrate this effect, and propose three alternative models that correct this behavior. We investigate the theoretical relationship between these models and evaluate their computational performance.

Scheduling Jobs with Truncated Exponential Sum-of-Logarithm-Processing-Times Based and Position-based Learning Effects

2015 ◽  
Vol 32 (04) ◽  
pp. 1550026 ◽  
Author(s):  
Yuan-Yuan Lu ◽  
Fei Teng ◽  
Zhi-Xin Feng
Keyword(s):  
Single Machine ◽  
Minimization Problem ◽  
Completion Time ◽  
Heuristic Algorithms ◽  
Exponential Sum ◽  
Total Weighted Completion Time ◽  
Learning Effects ◽  
Processing Times ◽  
Time Minimization ◽  
Weighted Completion Time

In this study, we consider a scheduling problem with truncated exponential sum-of-logarithm-processing-times based and position-based learning effects on a single machine. We prove that the shortest processing time (SPT) rule is optimal for the makespan minimization problem, the sum of the θth power of job completion times minimization problem, and the total lateness minimization problem, respectively. For the total weighted completion time minimization problem, the discounted total weighted completion time minimization problem, the maximum lateness minimization problem, we present heuristic algorithms (the worst-case bound of these heuristic algorithms are also given) according to the corresponding single machine scheduling problems without learning considerations. It also shows that the problems of minimizing the total tardiness, the total weighted completion time and the discounted total weighted completion time are polynomially solvable under some agreeable conditions on the problem parameters.

scholarly journals A Novel Approach for Solving Nonsmooth Optimization Problems with Application to Nonsmooth Equations

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Hamid Reza Erfanian ◽  
M. H. Noori Skandari ◽  
A. V. Kamyad
Keyword(s):  
Nonsmooth Optimization ◽  
Optimization Problems ◽  
Piecewise Linear ◽  
Linear Optimization ◽  
Optimal Solution ◽  
Piecewise Linear Function ◽  
Nonsmooth Equations ◽  
Smooth Functions ◽  
Generalized Derivative ◽  
Novel Approach

We present a new approach for solving nonsmooth optimization problems and a system of nonsmooth equations which is based on generalized derivative. For this purpose, we introduce the first order of generalized Taylor expansion of nonsmooth functions and replace it with smooth functions. In other words, nonsmooth function is approximated by a piecewise linear function based on generalized derivative. In the next step, we solve smooth linear optimization problem whose optimal solution is an approximate solution of main problem. Then, we apply the results for solving system of nonsmooth equations. Finally, for efficiency of our approach some numerical examples have been presented.

scholarly journals Actor-critic learning-based energy optimization for UAV access and backhaul networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yaxiong Yuan ◽  
Lei Lei ◽  
Thang X. Vu ◽  
Symeon Chatzinotas ◽  
Sumei Sun ◽  
...  
Keyword(s):  
Power Allocation ◽  
Heuristic Algorithms ◽  
Piecewise Linear ◽  
Access Network ◽  
Computation Time ◽  
Combinatorial Problem ◽  
Base Station ◽  
Piecewise Linear Approximation ◽  
Group Scheduling ◽  
User Group

AbstractIn unmanned aerial vehicle (UAV)-assisted networks, UAV acts as an aerial base station which acquires the requested data via backhaul link and then serves ground users (GUs) through an access network. In this paper, we investigate an energy minimization problem with a limited power supply for both backhaul and access links. The difficulties for solving such a non-convex and combinatorial problem lie at the high computational complexity/time. In solution development, we consider the approaches from both actor-critic deep reinforcement learning (AC-DRL) and optimization perspectives. First, two offline non-learning algorithms, i.e., an optimal and a heuristic algorithms, based on piecewise linear approximation and relaxation are developed as benchmarks. Second, toward real-time decision-making, we improve the conventional AC-DRL and propose two learning schemes: AC-based user group scheduling and backhaul power allocation (ACGP), and joint AC-based user group scheduling and optimization-based backhaul power allocation (ACGOP). Numerical results show that the computation time of both ACGP and ACGOP is reduced tenfold to hundredfold compared to the offline approaches, and ACGOP is better than ACGP in energy savings. The results also verify the superiority of proposed learning solutions in terms of guaranteeing the feasibility and minimizing the system energy compared to the conventional AC-DRL.

scholarly journals DESIGN OF TIME DELAYED CHAOTIC CIRCUIT WITH THRESHOLD CONTROLLER

2011 ◽  
Vol 21 (03) ◽  
pp. 725-735 ◽  
Author(s):  
K. SRINIVASAN ◽  
I. RAJA MOHAMED ◽  
K. MURALI ◽  
M. LAKSHMANAN ◽  
SUDESHNA SINHA
Keyword(s):  
Numerical Simulations ◽  
Linear Function ◽  
Piecewise Linear ◽  
Piecewise Linear Function ◽  
Operational Amplifiers ◽  
Phase Portraits ◽  
Chaotic Attractors ◽  
Chaotic Oscillator ◽  
Threshold Values ◽  
Chaotic Circuit

A novel time delayed chaotic oscillator exhibiting mono- and double scroll complex chaotic attractors is designed. This circuit consists of only a few operational amplifiers and diodes and employs a threshold controller for flexibility. It efficiently implements a piecewise linear function. The control of piecewise linear function facilitates controlling the shape of the attractors. This is demonstrated by constructing the phase portraits of the attractors through numerical simulations and hardware experiments. Based on these studies, we find that this circuit can produce multi-scroll chaotic attractors by just introducing more number of threshold values.

High transparency flexible sensor for pressure and proximity sensing

2018 ◽  
Vol 32 (32) ◽  
pp. 1850394 ◽  
Author(s):  
Dan Bu ◽  
Si Qi Li ◽  
Yun Ming Sang ◽  
Cheng Jun Qiu
Keyword(s):  
Indium Tin Oxide ◽  
Pressure Range ◽  
Piecewise Linear ◽  
High Sensitivity ◽  
Maximum Error ◽  
Piecewise Linear Function ◽  
Good Repeatability ◽  
Flexible Pressure Sensor ◽  
Human Finger ◽  
Proximity Sensing

A high-sensitivity and high-transmittance flexible pressure sensor is presented in this paper. Using polydimethylsiloxane (PDMS) sensing film to cover indium tin oxide (ITO) electrodes interdigitated on the polyethylene terephthalate (PET) substrate, an interdigital capacitance (IDC) structure is constructed. The pressure and proximity sensing characteristics of the fabricated IDC sensor are investigated. The experiment results show that the IDC sensor has the piecewise linear function in different pressure range, especially sensitive to the low-pressure range with the pressure sensitivity of 6.64 kPa[Formula: see text]. Moreover, it has a good repeatability with the maximum error rate of 2.73% and a high transmittance over 90% in the wavelength range from 400 nm to 800 nm. As a human finger approaches or leaves, the proximity sensing characteristic emerges, with a maximum sensing distance of about 20 cm.

scholarly journals Reliably Assessing Duration of Protection for COVID-19 Vaccines

2021 ◽  
Author(s):  
Danyu Lin ◽  
Donglin Zeng ◽  
Yu Gu ◽  
Thomas Fleming ◽  
Phillip Krause
Keyword(s):  
Piecewise Linear ◽  
Piecewise Linear Function ◽  
Vaccine Effectiveness ◽  
Vaccine Protection ◽  
Time Period ◽  
Duration Of Protection ◽  
Reliable Methods ◽  
Protective Effectiveness ◽  
Vaccine Effect ◽  
Observation Period

Decision-making about booster dosing for COVID-19 vaccine recipients hinges on reliable methods for evaluating the longevity of vaccine protection. We show that modeling of protection as a piecewise linear function of time since vaccination for the log hazard ratio of the vaccine effect provides more reliable estimates of vaccine effectiveness at the end of an observation period and also more reliably detects plateaus in protective effectiveness as compared with the traditional method of estimating a constant vaccine effect over each time period. This approach will be useful for analyzing data pertaining to COVID-19 vaccines and other vaccines where rapid and reliable understanding of vaccine effectiveness over time is desired.

Dynamic Portfolio Selection

2017 ◽  
pp. 116-140
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Piecewise Linear ◽  
Relevant Literature ◽  
Asset Price ◽  
Piecewise Linear Function ◽  
Simple Formulation ◽  
Wolfram Mathematica ◽  
Dynamic Portfolio ◽  
Algorithmic Construction

In this chapter I recognize the importance of the stochastic programming as a significant tool in financial planning. The current practice of portfolio optimization is still limited to the simple formulation of linear programming (LR) or quadratic programming (QR) type. For that reason, relevant literature on asset-liability management (ALM) model has been reviewed and two different ALM approaches are compared: first piecewise linear function; and second a nonlinear utility function. This chapter shows that the mathematical programming methodology is ready to challenge the huge problem arising from LP portfolio optimization. A special emphasis was put on the shape of the investors' payoff functions in asset price equilibrium. The results underpin our claim that the nonlinear ALM model generated better asset allocation. An algorithmic construction of ALM model is developed in Wolfram Mathematica 9.

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