Mathematica--A System for Doing Mathematics by Computer.

This study introduces a two-stage interval-stochastic programming (TISP) model for the planning of solid-waste management systems under uncertainty. The model is derived by incorporating the concept of two-stage stochastic programming within an interval-parameter optimization framework. The approach has the advantage that policy determined by the authorities, and uncertain information expressed as intervals and probability distributions, can be effectively communicated into the optimization processes and resulting solutions. In the modeling formulation, penalties are imposed when policies expressed as allowable waste-loading levels are violated. In its solution algorithm, the TISP model is converted into two deterministic submodels, which correspond to the lower and upper bounds for the desired objective-function value. Interval solutions, which are stable in the given decision space with associated levels of system-failure risk, can then be obtained by solving the two submodels sequentially. Two special characteristics of the proposed approach make it unique compared with other optimization techniques that deal with uncertainties. First, the TISP model provides a linkage to predefined policies determined by authorities that have to be respected when a modeling effort is undertaken; second, it furnishes the reflection of uncertainties presented as both probabilities and intervals. The developed model is applied to a hypothetical case study of regional solid-waste management. The results indicate that reasonable solutions have been generated. They provide desired waste-flow patterns with minimized system costs and maximized system feasibility. The solutions present as stable interval solutions with different risk levels in violating the waste-loading criterion and can be used for generating decision alternatives.

https://www.tandfonline.com/doi/pdf/10.1080/10473289.2003.10466195

A Two-Stage Interval-Stochastic Programming Model for Waste Management under Uncertainty

Wireless power transfer technology is considered as one of the promising solutions to address the energy limitation problems for end-devices, but its incurred potential risk of electromagnetic radiation (EMR) exposure is largely overlooked by most existing works. In this paper, we consider the Safe Charging with Adjustable PowEr (SCAPE) problem, namely, how to adjust the power of chargers to maximize the charging utility of devices, while assuring that EMR intensity at any location in the field does not exceed a given threshold $R_{t}$ . We present novel techniques to reformulate SCAPE into a traditional linear programming problem, and then remove its redundant constraints as much as possible to reduce computational effort. Next, we propose a series of distributed algorithms, including a fully distributed algorithm that provably achieves $(1-\epsilon)$ approximation ratio and requires only communications with neighbors within a constant distance for each charger. Through extensive simulation and testbed experiments, we demonstrate that our proposed algorithms can outperform the set-cover algorithm by up to 17.05%, and has an average performance gain of 41.1% over the existing algorithm in terms of the overall charging utility.

SCAPE: Safe Charging With Adjustable Power

An inexact dynamic optimization model for municipal solid waste management in association with greenhouse gas emission control

The objective function and the constraints can be formulated as linear functions of independent variables in most of the real-world optimization problems. Linear Programming (LP) is the process of optimizing a linear function subject to a finite number of linear equality and inequality constraints. Solving linear programming problems efficiently has always been a fascinating pursuit for computer scientists and mathematicians. The computational complexity of any linear programming problem depends on the number of constraints and variables of the LP problem. Quite often large-scale LP problems may contain many constraints which are redundant or cause infeasibility on account of inefficient formulation or some errors in data input. The presence of redundant constraints does not alter the optimal solutions(s). Nevertheless, they may consume extra computational effort. Many researchers have proposed different approaches for identifying the redundant constraints in linear programming problems. This paper compares five of such methods and discusses the efficiency of each method by solving various size LP problems and netlib problems. The algorithms of each method are coded by using a computer programming language C. The computational results are presented and analyzed in this paper.

/pdf/a-comparative-study-of-redundant-constraints-identification-43tkfpafck.pdf

A Comparative Study of Redundant Constraints Identification Methods in Linear Programming Problems

Organizations, as a complex systems, in the structure of their subsystems have the human or social subsystem. It is a subsystem that has a significant influence on the functioning of the organization and its competitiveness. The quality of the social organizational subsystem is determined by the quality of the different variables that the subsystem includes. Job satisfaction is one of these variables. It is a variable that can have an adequate impact on the effectiveness and efficiency of the organization, as well as on its performance. The aim of the authors of this paper is to point out the nature, the significance, causes and consequences of job satisfaction, as well as the role of managers in raising the level of these organizational variables. Key w ords :  job satisfaction, staff, management, organization

/pdf/manager-s-role-in-raising-the-level-of-job-satisfaction-gu5zftxl46.pdf

Manager’s role in raising the level of job satisfaction

In this paper 1 we compare two variants of Mehrotra’s primal dual algorithm which are based on the augmented and normal equations system, respectively. An implementation of corresponding algorithms in the package MATHEMATICA, version 4.1, is used for the comparison. Numerical examples are reported applying the program on some Netlib test problems.

Augmented and Normal Equations System in Mehrotra's Primal-dual Algorithm

We show how a computer algebra system in MATHEMATICA can be used in several elementary courses in mathematics for students. We have also developed an application in programming language DELPHI for testing students in MATHEMATICA.

Some useful MATHEMATICA teaching examples

We analyze the problem of finding the first basic solution in the two phases simplex algorithm. Also, a modification and several improvements of the simplex method are introduced. We report computational results on numerical examples from Netlib test set.

/pdf/several-modifications-of-simplex-method-2bt8ebztgo.pdf

Several modifications of simplex method

Employees’ satisfaction is one of the frequently analyzed subgroups of human attitudes connected to organizational environment. The authors’ aim is to study different categories of satisfaction or dissatisfaction of teachers in elementary schools in the Republic of Serbia, as a specific category of employees. The results of the research carried out have shown that teachers are dissatisfied with salaries, awards and benefits. The next category concerning the intensity of dissatisfaction is teachers’ dissatisfaction with operational procedures. On the other hand, teachers are mostly satisfied with the work they are doing, with their superiors, their colleagues and system of communicating within schools.

/pdf/research-on-elementary-school-teacher-satisfaction-in-the-17bys18udf.pdf

Research on Elementary School Teacher Satisfaction in the Republic of Serbia

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