Allocation of Relief Centre for Flood Victims Using Location Set Covering Problem (LSCP)

The frequency of natural disaster has been increasing over the years, resulting in loss of life, damage to properties and destruction of the environment. Compared to other natural disaster, floods are the most significant natural hazard in Malaysia that affect thousands of people. It becomes of great concern especially, during these recent floods, several relief centers were experiencing problems when most of the flood’s victims needed to move to the other relief centers after the existing shelters were also affected by flood water. Furthermore, the relief centers were congested due to the large number of flood evacuees that were over the capacity provided. This study attempts to adapt an advance mathematical location-allocation models in making decision to locate and allocate flood victims during flood event. The Location Set Covering Problem (LSCP), with capacitated constraint is considered and solved using Excel Solver. The determination of safe locations for relief centres with respect to prone area is proposed by adding three new constraints which are the distance from river, elevation from sea level and rainfall amount. A flood case of Kuala Kuantan, Pahang in 2013 was used as the main study area for the analysis as well as the whole Kuantan district. Two scenarios of split and non-split allowed were tested for allocation process and result showed that the percentage allocation and number of relief centers involved increases when the distance increases accordingly. Hence, the model can be considered for better evacuation plan.

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

Database 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.

Using machine learning to predict the number of alternative solutions to a minimum cardinality set covering problem

Although the characterization of alternative optimal solutions for linear programming problems is well known, such characterizations for combinatorial optimization problems are essentially non-existent. This is the first article to qualitatively predict the number of alternative optima for a classic NP-hard combinatorial optimization problem, namely, the minimum cardinality (also called unicost) set covering problem (MCSCP). For the MCSCP, a set must be covered by a minimum number of subsets selected from a specified collection of subsets of the given set. The MCSCP has numerous industrial applications that require that a secondary objective is optimized once the size of a minimum cover has been determined. To optimize the secondary objective, the number of MCSCP solutions is optimized. In this article, for the first time, a machine learning methodology is presented to generate categorical regression trees to predict, qualitatively (extra-small, small, medium, large, or extra-large), the number of solutions to an MCSCP. Within the machine learning toolbox of MATLAB®, 600,000 unique random MCSCPs were generated and used to construct regression trees. The prediction quality of these regression trees was tested on 5000 different MCSCPs. For the 5-output model, the average accuracy of being at most one off from the predicted category was 94.2%.

MODEL JARINGAN RANTAI PASOK UNTUK POS PEMADAM KEBAKARAN DI KAWASAN SIER

Perancangan jaringan rantai pasok bisa diaplikasikan dalam industri manufaktur maupun jasa, misalnya dalam menentukan lokasi pos pemadam kebakaran. Perancangan jaringan rantai pasok penting dilakukan khususnya mengenai pos pemadam kebakaran karena belum terdapat pelayanan yang optimal di hampir seluruh kawasan perindustrian di Indonesia. Metode yang digunakan dalam penelitian adalah set covering problem. Set covering problem memuat variabel keputusan tentang pos pemadam kebakaran mana yang nantinya akan digunakan untuk menjangkau seluruh area SIER. Pos pemadam kebakaran yang digunakan ada 4 yaitu Pos UPTD III, Pos Kali Rungkut, Pos Kepuh, dan Pos Sukolilo. Hasil dari penelitian ini adalah bahwa untuk jarak jangkauan 100-1000 meter ke empat pos pemadam kebakaran semuanya bertugas untuk mengamankan. Sedangkan untuk jarak jangkauan 2000 meter hanya 3 pos pemadam kebakaran yang bertugas. Jarak jangkauan 3000 meter, 2 pos pemadam kebakaran yang bertugas. Jarak jangkauan 4000-5000 meter hanya 1 pos pemadam kebakaran yang bertugas.