scholarly journals Solving fuzzy linear fractional set covering problem by a goal programming based solution approach

2017 ◽  
Vol 13 (5) ◽  
pp. 0-0
Author(s):  
Ali Mahmoodirad ◽  
◽  
Harish Garg ◽  
Sadegh Niroomand ◽  
◽  
...  
Keyword(s):  
Goal Programming ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Solution Approach

A Goal Programming Approach to Solve Linear Fractional Multi-Objective Set Covering Problem

OPSEARCH ◽  
2005 ◽  
Vol 42 (2) ◽  
pp. 112-125 ◽  
Author(s):  
S. R. Arora ◽  
Ravi Shanker ◽  
Neelam Malhotra
Keyword(s):  
Goal Programming ◽  
Set Covering ◽  
Covering Problem ◽  
Programming Approach ◽  
Set Covering Problem ◽  
Multi Objective

An OR Practitioner's Solution Approach for the Set Covering Problem

2015 ◽  
Vol 6 (4) ◽  
pp. 1-13 ◽  
Author(s):  
Yun Lu ◽  
Francis J. Vasko
Keyword(s):  
Industrial Applications ◽  
Fine Tuning ◽  
Set Covering ◽  
Covering Problem ◽  
Initial Population ◽  
Optimization Approach ◽  
Set Covering Problem ◽  
Two Phase ◽  
Solution Approach ◽  
Phase Approach

The set covering problem (SCP) is an NP-complete problem that has many important industrial applications. Since industrial applications are typically large in scale, exact solution algorithms are not feasible for operations research (OR) practitioners to use when called on to solve real-world problems involving SCPs. However, the best performing heuristics for the SCP reported in the literature are not usually straightforward to implement. Additionally, these heuristics usually require the fine-tuning of several parameters. In contrast, simple greedy or even randomized greedy heuristics typically do not give as good results as the more sophisticated heuristics. In this paper, the authors present a compromise; a straightforward to implement, population-based solution approach for the SCP. It uses a randomized greedy approach to generate an initial population and then uses a genetic-based two phase approach to improve the population solutions. This two-phase approach uses transformation equations based on a Teaching-Learning based optimization approach developed by Rao, Savsani and Vakharia (2011, 2012) for continuous nonlinear optimization problems. Empirical results using set covering problems from Beasley's OR-library demonstrate the competitiveness of this approach both in terms of solution quality and execution time. The advantage to this approach is its relative simplicity for the practitioner to implement.

scholarly journals Multi-objective database queries in combined knapsack and set covering problem domains

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sean A. Mochocki ◽  
Gary B. Lamont ◽  
Robert C. Leishman ◽  
Kyle J. Kauffman
Keyword(s):  
Relational Database ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Database Queries ◽  
Multi Objective ◽  
Data Representations ◽  
Np Hard Problem ◽  
Institute Of Technology ◽  
The One

AbstractDatabase queries are one of the most important functions of a relational database. Users are interested in viewing a variety of data representations, and this may vary based on database purpose and the nature of the stored data. The Air Force Institute of Technology has approximately 100 data logs which will be converted to the standardized Scorpion Data Model format. A relational database is designed to house this data and its associated sensor and non-sensor metadata. Deterministic polynomial-time queries were used to test the performance of this schema against two other schemas, with databases of 100 and 1000 logs of repeated data and randomized metadata. Of these approaches, the one that had the best performance was chosen as AFIT’s database solution, and now more complex and useful queries need to be developed to enable filter research. To this end, consider the combined Multi-Objective Knapsack/Set Covering Database Query. Algorithms which address The Set Covering Problem or Knapsack Problem could be used individually to achieve useful results, but together they could offer additional power to a potential user. This paper explores the NP-Hard problem domain of the Multi-Objective KP/SCP, proposes Genetic and Hill Climber algorithms, implements these algorithms using Java, populates their data structures using SQL queries from two test databases, and finally compares how these algorithms perform.

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