A Decision Support System for Solving Linear Programming Problems

2014 ◽  
Vol 6 (2) ◽  
pp. 46-62
Author(s):  
Nikolaos Ploskas ◽  
Nikolaos Samaras ◽  
Jason Papathanasiou

Linear programming algorithms have been widely used in Decision Support Systems. These systems have incorporated linear programming algorithms for the solution of the given problems. Yet, the special structure of each linear problem may take advantage of different linear programming algorithms or different techniques used in these algorithms. This paper proposes a web-based DSS that assists decision makers in the solution of linear programming problems with a variety of linear programming algorithms and techniques. Two linear programming algorithms have been included in the DSS: (i) revised simplex algorithm and (ii) exterior primal simplex algorithm. Furthermore, ten scaling techniques, five basis update methods and eight pivoting rules have been incorporated in the DSS. All linear programming algorithms and methods have been implemented using MATLAB and converted to Java classes using MATLAB Builder JA, while the web interface of the DSS has been designed using Java Server Pages.

Author(s):  
LEV V. UTKIN ◽  
NATALIA V. SIMANOVA

An extension of the DS/AHP method is proposed in the paper. It takes into account the fact that the multi-criteria decision problem might have several levels of criteria. Moreover, it is assumed that expert judgments concerning the criteria are imprecise and incomplete. The proposed extension also uses groups of experts or decision makers for comparing decision alternatives and criteria. However, it does not require assigning favorability values for groups of decision alternatives and criteria. The computation procedure for processing and aggregating the incomplete information about criteria and decision alternatives is reduced to solving a finite set of linear programming problems. Numerical examples explain in detail and illustrate the proposed approach.


Author(s):  
Seyed Hadi Nasseri ◽  
Ali Ebrahimnejad

In the real word, there are many problems which have linear programming models and sometimes it is necessary to formulate these models with parameters of uncertainty. Many numbers from these problems are linear programming problems with fuzzy variables. Some authors considered these problems and have developed various methods for solving these problems. Recently, Mahdavi-Amiri and Nasseri (2007) considered linear programming problems with trapezoidal fuzzy data and/or variables and stated a fuzzy simplex algorithm to solve these problems. Moreover, they developed the duality results in fuzzy environment and presented a dual simplex algorithm for solving linear programming problems with trapezoidal fuzzy variables. Here, the authors show that this presented dual simplex algorithm directly using the primal simplex tableau algorithm tenders the capability for sensitivity (or post optimality) analysis using primal simplex tableaus.


2006 ◽  
Vol 8 (2) ◽  
pp. 91-100 ◽  
Author(s):  
Olfa Khelifi ◽  
Andrea Lodolo ◽  
Sanja Vranes ◽  
Gabriele Centi ◽  
Stanislav Miertus

Groundwater remediation operation involves several considerations in terms of environmental, technological and socio-economic aspects. A decision support tool (DST) becomes therefore necessary in order to manage problem complexity and to define effective groundwater remediation interventions. CCR (Credence Clearwater Revival), a decision support tool for groundwater remediation technologies assessment and selection, has been developed to help decision-makers (site owners, investors, local community representatives, environmentalists, regulators, etc.) to assess the available technologies and select the preferred remedial options. The analysis is based on technical, economical, environmental and social criteria. These criteria are ranked by all involved parties to determine their relative importance for a particular groundwater remediation project. The Multi-Criteria Decision Making (MCDM) is the core of the CCR using the PROMETHEE II algorithm.


Forests ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 438 ◽  
Author(s):  
Marco Marto ◽  
Keith Reynolds ◽  
José Borges ◽  
Vladimir Bushenkov ◽  
Susete Marques

This study examines the potential of combining decision support approaches to identify optimal bundles of ecosystem services in a framework characterized by multiple decision-makers. A forested landscape, Zona de Intervenção Florestal of Paiva and Entre-Douro and Sousa (ZIF_VS) in Portugal, is used to test and demonstrate this potential. The landscape extends over 14,388 ha, representing 1976 stands. The property is fragmented into 376 holdings. The overall analysis was performed in three steps. First, we selected six alternative solutions (A to F) in a Pareto frontier generated by a multiple-criteria method within a web-based decision support system (SADfLOR) for subsequent analysis. Next, an aspatial strategic multicriteria decision analysis (MCDA) was performed with the Criterium DecisionPlus (CDP) component of the Ecosystem Management Decision Support (EMDS) system to assess the aggregate performance of solutions A to F for the entire forested landscape with respect to their utility for delivery of ecosystem services. For the CDP analysis, SADfLOR data inputs were grouped into two sets of primary criteria: Wood Harvested and Other Ecosystem Services. Finally, a spatial logic-based assessment of solutions A to F for individual stands of the study area was performed with the NetWeaver component of EMDS. The NetWeaver model was structurally and computationally equivalent to the CDP model, but the key NetWeaver metric is a measure of the strength of evidence that solutions for specific stands were optimal for the unit. We conclude with a discussion of how the combination of decision support approaches encapsulated in the two systems could be further automated in order to rank several efficient solutions in a Pareto frontier and generate a consensual solution.


Author(s):  
Irma Becerra-Fernandez ◽  
Matha Del Alto ◽  
Helen Stewart

Today, organizations rely on decision makers to make mission-critical decisions that are based on input from multiple domains. The ideal decision maker has a profound understanding of specific domains coupled with the experience that allows him or her to act quickly and decisively on the information. Daily, decision makers face problems and failures that are too difficult for any individual person to solve; therefore, teams are now required who share their knowledge in spontaneous collaborations. Since requisite expertise may not all reside in the same organization, nor be geographically colocated, virtual networked teams are needed. This chapter presents a case study describing the development and use of Postdoc, the first Web-based collaborative and knowledge management platform deployed at NASA.


2018 ◽  
Vol 77 (22) ◽  
pp. 30035-30050 ◽  
Author(s):  
Lili He ◽  
Hongtao Bai ◽  
Yu Jiang ◽  
Dantong Ouyang ◽  
Shanshan Jiang

Author(s):  
Raju Prajapati ◽  
Om Prakash Dubey

Non Linear Programming Problems (NLPP) are tedious to solve as compared to Linear Programming Problem (LPP).  The present paper is an attempt to analyze the impact of penalty constant over the penalty function, which is used to solve the NLPP with inequality constraint(s). The improved version of famous meta heuristic Particle Swarm Optimization (PSO) is used for this purpose. The scilab programming language is used for computational purpose. The impact of penalty constant is studied by considering five test problems. Different values of penalty constant are taken to prepare the unconstraint NLPP from the given constraint NLPP with inequality constraint. These different unconstraint NLPP is then solved by improved PSO, and the superior one is noted. It has been shown that, In all the five cases, the superior one is due to the higher penalty constant. The computational results for performance are shown in the respective sections.


Author(s):  
Sanjay Jain ◽  
Adarsh Mangal

In this research paper, an effort has been made to solve each linear objective function involved in the Multi-objective Linear Programming Problem (MOLPP) under consideration by AHA simplex algorithm and then the MOLPP is converted into a single LPP by using various techniques and then the solution of LPP thus formed is recovered by Gauss elimination technique. MOLPP is concerned with the linear programming problems of maximizing or minimizing, the linear objective function having more than one objective along with subject to a set of constraints having linear inequalities in nature. Modeling of Gauss elimination technique of inequalities is derived for numerical solution of linear programming problem by using concept of bounds. The method is quite useful because the calculations involved are simple as compared to other existing methods and takes least time. The same has been illustrated by a numerical example for each technique discussed here.


Author(s):  
Seyed Hadi Nasseri ◽  
Ali Ebrahimnejad

In the real word, there are many problems which have linear programming models and sometimes it is necessary to formulate these models with parameters of uncertainty. Many numbers from these problems are linear programming problems with fuzzy variables. Some authors considered these problems and have developed various methods for solving these problems. Recently, Mahdavi-Amiri and Nasseri (2007) considered linear programming problems with trapezoidal fuzzy data and/or variables and stated a fuzzy simplex algorithm to solve these problems. Moreover, they developed the duality results in fuzzy environment and presented a dual simplex algorithm for solving linear programming problems with trapezoidal fuzzy variables. Here, the authors show that this presented dual simplex algorithm directly using the primal simplex tableau algorithm tenders the capability for sensitivity (or post optimality) analysis using primal simplex tableaus.


Author(s):  
Iftikhar U. Sikder ◽  
Aryya Gangopadhyay

There are numerous technical and organizational challenges in the design and implementation of spatial decision support systems. Part of the problem stems from the distributed and uncoordinated land management practices of individual decision-makers. For example, in environmental planning, multiple decision makers with conflicting goals may need to make collective decisions. This requires collaborative decision-making tools and conflict resolution capabilities. In this chapter, we identify the research issues related to the design and implementation of Web- based collaborative spatial decision-making support systems in the context of distributed environmental planning. We implemented a Web-based Spatial Decision Support System called GEO-ELCA (Exploratory Land Use Change Assessment) for typical decision-making tasks by urban or municipal planning agencies where resource managers or stakeholders of different interest groups can express their options for future land use changes and assess the resulting hydrological impacts in a collaborative environment.


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