scholarly journals Laplace Distribution And Probabilistic (bi) In Linear Programming Model

2011 ◽  
Vol 17 (61) ◽  
pp. 20
Author(s):  
Ali Khaleel Al-zubidi
Keyword(s):  
Linear Programming ◽  
Nonlinear Programming ◽  
Programming Problem ◽  
Programming Model ◽  
Random Variable ◽  
Laplace Distribution ◽  
Probabilistic Programming ◽  
Economic Agents ◽  
Specification Errors ◽  
Nonlinear Programming Model

The theory of probabilistic programming  may be conceived in several different ways. As a method of programming it analyses the implications of probabilistic variations in the parameter space of linear or nonlinear programming model. The generating mechanism of such probabilistic variations in the economic models may be due to incomplete information about changes in demand, pro­duction and technology, specification errors about the econometric relations presumed for different economic agents, uncertainty of various sorts and the consequences of imperfect aggregation or disaggregating of economic variables. In this Research we discuss the probabilistic programming problem when the coefficient bi is random variable with given Laplace distribution.

scholarly journals An integrated decision support system for ERP implementation in small and medium sized enterprises

2014 ◽  
Vol 27 (4) ◽  
pp. 358-384 ◽  
Author(s):  
Ying Xie ◽  
Colin James Allen ◽  
Mahmood Ali
Keyword(s):  
Decision Support ◽  
Nonlinear Programming ◽  
Decision Support System ◽  
Support System ◽  
Regression Models ◽  
Programming Model ◽  
Resource Allocations ◽  
Erp Implementation ◽  
Content Type ◽  
Nonlinear Programming Model

Purpose – Implementing enterprise resource planning (ERP) is a challenging task for small- and medium-sized enterprises (SMEs). The purpose of this paper is to develop an integrated decision support system (DSS) for ERP implementation (DSS_ERP) to facilitate resource allocations and risk analysis. Design/methodology/approach – Analytical regression models are developed using data collected through a survey conducted on 400 SMEs that have implemented ERP systems, and are validated by a simulation model. The validated analytical regression models are used to construct a nonlinear programming model that generates solutions for resource allocations, such as time and budget. Findings – ERP implementation cost increases along the time horizon, while performance level increases up to a point and remains unchanged. To maximise or achieve a certain level of performance within a budget limitation, CSFs are prioritised as: project management (highest), top management, information technology, users and vendor support (lowest). SMEs are recommended to concentrate effort and resources on CSFs that have a greater impact on achieving their desired goals while optimising utilisation of resources. Research limitations/implications – DSS_ERP proves to be beneficial to SMEs in identifying required resources and allocating resources, but could be further tested in case studies for its practical use and benefits. Practical implications – DSS_ERP serves as a useful tool for SMEs to predict required resources and allocate them prior to ERP implementation, which maximises the probability of achieving predetermined targets. It also enables SMEs to analyse risk caused by changes to resources during ERP implementation, and helps them to be better prepared for the risks. Originality/value – The research contributes to the scarce research on ERP implementation using scientific methods. A novel nonlinear programming model is constructed for ERP implementation under time and budget limitations, facilitating resource allocations in an ERP implementation, which has not been reported in any previous research. The research offers a theoretical basis for empirical studies of resource allocations in ERP implementation.

Minimax Input Shaper Design Using Linear Programming

2008 ◽  
Vol 130 (5) ◽  
Author(s):  
Tarunraj Singh
Keyword(s):  
Linear Programming ◽  
Nonlinear Programming ◽  
Programming Problem ◽  
Linear Programming Problem ◽  
Barycentric Coordinates ◽  
Nonlinear Programming Problem ◽  
Dome A ◽  
Input Shaper ◽  
Maximum Value ◽  
The Cost

The focus of this paper is on the design of robust input shapers where the maximum value of the cost function over the domain of uncertainty is minimized. This nonlinear programming problem is reformulated as a linear programming problem by approximating a n-dimensional hypersphere with multiple hyperplanes (as in a geodesic dome). A recursive technique to approximate a hypersphere to any level of accuracy is developed using barycentric coordinates. The proposed technique is illustrated on the spring-mass-dashpot and the benchmark floating oscillator problem undergoing a rest-to-rest maneuver. It is shown that the results of the linear programming problem are nearly identical to that of the nonlinear programming problem.

A multibelief analytic hierarchy process and nonlinear programming approach for diversifying product designs: Smart backpack design as an example

2020 ◽  
Vol 234 (6-7) ◽  
pp. 1044-1056 ◽  
Author(s):  
Yu-Cheng Lin ◽  
Toly Chen
Keyword(s):  
Nonlinear Programming ◽  
Analytic Hierarchy Process ◽  
Programming Model ◽  
Programming Approach ◽  
Analytic Hierarchy ◽  
Nonlinear Programming Model ◽  
Judgment Matrix ◽  
The Cost ◽  
Analytic Hierarchy Process Method ◽  
Hierarchy Process

Owing to the cost constraint, it is difficult to incorporate all critical features into a single product design. To deal with this issue, analytic hierarchy process is a well-known method that compares the relative priorities of critical features. This study aims to illustrate that the judgment of a designer can be used to generate multiple diversified product designs. To this end, this study proposed a multibelief analytic hierarchy process and nonlinear programming approach. In the proposed methodology, a decision maker’s judgment matrix is decomposed into several single-belief judgment matrices that are more consistent than the original judgment matrix and represent diversified points of view regarding the relative priorities of factors. To this end, a nonlinear programming model is established and optimized. The proposed methodology was applied to a smart backpack design problem. It was concluded that a designer’s judgment was often inconsistent, which was ignored in the conventional analytic hierarchy process method but could be employed to diversify product designs.

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