scholarly journals A Parallel Heuristic Method for Optimizing a Real Life Problem (Agricultural Land Investment Problem)

2018 ◽  
Vol 7 (4) ◽  
pp. 168
Author(s):  
Sagvan A. Saleh
Keyword(s):  
Parallel Computing ◽  
Message Passing Interface ◽  
Agricultural Land ◽  
Programming Model ◽  
Heuristic Method ◽  
Real Life ◽  
Neighborhood Search ◽  
Parallel Method ◽  
Real Life Problem ◽  
Investment Problem

This paper proposed a parallel method for solving the Agricultural Land Investment Problem (ALIP), the problem that has an important impact on the agriculture issues. The author is first represent mathematically the problem by introducing a mathematical programming model. Then, a parallel method is proposed for optimizing the problem. The proposed method based on principles of parallel computing and neighborhood search methods. Neighborhood search techniques explore a series of solutions spaces with the aim of finding the best one. This is exploited in parallel computing, where several search processes are performed simultaneously. The parallel computing is designed using Message Passing Interface (MPI) which allows to build a flexible parallel program that can be executed in multicore and/or distributed environment. The method is competitive since it is able to solve a real life problem and yield high quality results in a fast solution runtime.

scholarly journals Hybrid MPI and CUDA Parallelization for CFD Applications on Multi-GPU HPC Clusters

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jianqi Lai ◽  
Hang Yu ◽  
Zhengyu Tian ◽  
Hua Li
Keyword(s):  
Parallel Computing ◽  
Parallel Algorithm ◽  
Graphics Processing Units ◽  
Message Passing Interface ◽  
Gpu Computing ◽  
Programming Model ◽  
Domain Decomposition Method ◽  
Parallel Execution ◽  
Performance Measurements ◽  
Cfd Applications

Graphics processing units (GPUs) have a strong floating-point capability and a high memory bandwidth in data parallelism and have been widely used in high-performance computing (HPC). Compute unified device architecture (CUDA) is used as a parallel computing platform and programming model for the GPU to reduce the complexity of programming. The programmable GPUs are becoming popular in computational fluid dynamics (CFD) applications. In this work, we propose a hybrid parallel algorithm of the message passing interface and CUDA for CFD applications on multi-GPU HPC clusters. The AUSM + UP upwind scheme and the three-step Runge–Kutta method are used for spatial discretization and time discretization, respectively. The turbulent solution is solved by the K−ω SST two-equation model. The CPU only manages the execution of the GPU and communication, and the GPU is responsible for data processing. Parallel execution and memory access optimizations are used to optimize the GPU-based CFD codes. We propose a nonblocking communication method to fully overlap GPU computing, CPU_CPU communication, and CPU_GPU data transfer by creating two CUDA streams. Furthermore, the one-dimensional domain decomposition method is used to balance the workload among GPUs. Finally, we evaluate the hybrid parallel algorithm with the compressible turbulent flow over a flat plate. The performance of a single GPU implementation and the scalability of multi-GPU clusters are discussed. Performance measurements show that multi-GPU parallelization can achieve a speedup of more than 36 times with respect to CPU-based parallel computing, and the parallel algorithm has good scalability.

Modelling and analysis of uncertain hub maximal covering location problem in the presence of partial coverage

2021 ◽  
Vol 40 (5) ◽  
pp. 9987-10002
Author(s):  
Junbin Wang ◽  
Zhongfeng Qin
Keyword(s):  
Location Problem ◽  
Programming Model ◽  
Real Life ◽  
Expected Value ◽  
Neighborhood Search ◽  
Chance Constrained Programming ◽  
Travel Times ◽  
Maximal Covering ◽  
Partial Coverage ◽  
Deterministic Equivalent

The hub maximal covering location problem aims to find the best locations for hubs so as to maximize the total flows covered by predetermined number of hubs. Generally, this problem is defined in the framework of binary coverage. However, there are many real-life cases in which the binary coverage assumption may yield unexpected decisions. Thus, the partial coverage is considered by stipulating that the coverage of an origin-destination pair is determined by a non-increasing decay function. Moreover, as this problem contains strategic decisions in long range, the precise information about the parameters such as travel times may not be obtained in advance. Therefore, we present uncertain hub maximal covering location models with partial coverage in which the travel times are depicted as uncertain variables. Specifically, the partial coverage parameter is introduced in uncertain environment and the expected value of partial coverage parameter is further derived and simplified with specific decay functions. Expected value model and chance constrained programming model are respectively proposed and transformed to their deterministic equivalent forms. Finally, a greedy variable neighborhood search heuristic is presented and the efficiency of the proposed models is evaluated through computational experiments.

scholarly journals Using a Citizen Science Approach with German Horse Owners to Study the Locomotion Behaviour of Horses on Pasture

2020 ◽  
Vol 12 (5) ◽  
pp. 1835
Author(s):  
Anja Schmitz ◽  
Bettina Tonn ◽  
Ann-Kathrin Schöppner ◽  
Johannes Isselstein
Keyword(s):  
Land Use ◽  
Citizen Science ◽  
Agricultural Land ◽  
Agricultural Research ◽  
Real Life ◽  
Sustainable Land Use ◽  
Walking Distance ◽  
Pasture Management ◽  
Science Approach ◽  
Simple Technology

Engaging farmers as citizen scientists may be a cost-efficient way to answering applied research questions aimed at more sustainable land use. We used a citizen science approach with German horse farmers with a dual goal. Firstly, we tested the practicability of this approach for answering ‘real-life’ questions in variable agricultural land-use systems. Secondly, we were interested in the knowledge it can provide about locomotion of horses on pasture and the management factors influencing this behaviour. Out of 165 volunteers, we selected 40 participants to record locomotion of two horses on pasture and provide information on their horse husbandry and pasture management. We obtained complete records for three recording days per horse from 28 participants, resulting in a dataset on more individual horses than any other Global Positioning System study published in the last 30 years. Time spent walking was greatest for horses kept in box-stall stables, and walking distance decreased with increasing grazing time. This suggests that restrictions in pasture access may increase stress on grass swards through running and trampling, severely challenging sustainable pasture management. Our study, involving simple technology, clear instructions and rigorous quality assessment, demonstrates the potential of citizen science actively involving land managers in agricultural research.

scholarly journals Modelling contractor’s bidding decision

2017 ◽  
Vol 9 (1) ◽  
pp. 64-73 ◽  
Author(s):  
Sławomir Biruk ◽  
Piotr Jaśkowski ◽  
Agata Czarnigowska
Keyword(s):  
Programming Model ◽  
Real Life ◽  
Cash Flows ◽  
Managerial Decision ◽  
Single Measure ◽  
Construction Enterprises ◽  
Pricing Process ◽  
Bid Price ◽  
Total Price ◽  
Bidding Process

AbstractThe authors aim to provide a set of tools to facilitate the main stages of the competitive bidding process for construction contractors. These involve 1) deciding whether to bid, 2) calculating the total price, and 3) breaking down the total price into the items of the bill of quantities or the schedule of payments to optimise contractor cash flows. To define factors that affect the decision to bid, the authors rely upon literature on the subject and put forward that multi-criteria methods are applied to calculate a single measure of contract attractiveness (utility value). An attractive contract implies that the contractor is likely to offer a lower price to increase chances of winning the competition. The total bid price is thus to be interpolated between the lowest acceptable and the highest justifiable price based on the contract attractiveness. With the total bid price established, the next step is to split it between the items of the schedule of payments. A linear programming model is proposed for this purpose. The application of the models is illustrated with a numerical example.The model produces an economically justified bid price together with its breakdown, maintaining the logical proportion between unit prices of particular items of the schedule of payment. Contrary to most methods presented in the literature, the method does not focus on the trade-off between probability of winning and the price but is solely devoted to defining the most reasonable price under project-specific circumstances.The approach proposed in the paper promotes a systematic approach to real-life bidding problems. It integrates practices observed in operation of construction enterprises and uses directly available input. It may facilitate establishing the contractor’s in-house procedures and managerial decision support systems for the pricing process.

PARALLEL COMPUTING OF NUMERICAL SCHEMES AND BIG DATA ANALYTIC FOR SOLVING REAL LIFE APPLICATIONS

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Norma Alias ◽  
Nadia Nofri Yeni Suhari ◽  
Hafizah Farhah Saipan Saipol ◽  
Abdullah Aysh Dahawi ◽  
Masyitah Mohd Saidi ◽  
...  
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
Parallel Algorithm ◽  
Sparse Matrices ◽  
Real Life ◽  
Poor Performance ◽  
Equation System ◽  
Numerical Schemes ◽  
Linear Equation System ◽  
Data Analytic

This paper proposed the several real life applications for big data analytic using parallel computing software. Some parallel computing software under consideration are Parallel Virtual Machine, MATLAB Distributed Computing Server and Compute Unified Device Architecture to simulate the big data problems. The parallel computing is able to overcome the poor performance at the runtime, speedup and efficiency of programming in sequential computing. The mathematical models for the big data analytic are based on partial differential equations and obtained the large sparse matrices from discretization and development of the linear equation system. Iterative numerical schemes are used to solve the problems. Thus, the process of computational problems are summarized in parallel algorithm. Therefore, the parallel algorithm development is based on domain decomposition of problems and the architecture of difference parallel computing software. The parallel performance evaluations for distributed and shared memory architecture are investigated in terms of speedup, efficiency, effectiveness and temporal performance.

scholarly journals A Generalized Minimum Cost Flow Model for Multiple Emergency Flow Routing

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jianxun Cui ◽  
Shi An ◽  
Meng Zhao
Keyword(s):  
Flow Model ◽  
Time Window ◽  
Programming Model ◽  
Real Life ◽  
Minimum Cost ◽  
Emergency Evacuation ◽  
Mixed Integer ◽  
Fixed Cost ◽  
Minimum Cost Flow ◽  
Cost Flow

During real-life disasters, that is, earthquakes, floods, terrorist attacks, and other unexpected events, emergency evacuation and rescue are two primary operations that can save the lives and property of the affected population. It is unavoidable that evacuation flow and rescue flow will conflict with each other on the same spatial road network and within the same time window. Therefore, we propose a novel generalized minimum cost flow model to optimize the distribution pattern of these two types of flow on the same network by introducing the conflict cost. The travel time on each link is assumed to be subject to a bureau of public road (BPR) function rather than a fixed cost. Additionally, we integrate contraflow operations into this model to redesign the network shared by those two types of flow. A nonconvex mixed-integer nonlinear programming model with bilinear, fractional, and power components is constructed, and GAMS/BARON is used to solve this programming model. A case study is conducted in the downtown area of Harbin city in China to verify the efficiency of proposed model, and several helpful findings and managerial insights are also presented.

scholarly journals Modeling the Scheduling Problem in Cellular Manufacturing Systems Using Genetic Algorithm as an Efficient Meta-Heuristic Approach

2021 ◽  
Author(s):  
Amin Rezaeipanah ◽  
Musa Mojarad
Keyword(s):  
Genetic Algorithm ◽  
Manufacturing Systems ◽  
Cellular Manufacturing ◽  
Programming Model ◽  
Heuristic Method ◽  
Cell Formation ◽  
Mixed Integer ◽  
Test Problems ◽  
Cellular Manufacturing Systems ◽  
Machine Grouping

This paper presents a new, bi-criteria mixed-integer programming model for scheduling cells and pieces within each cell in a manufacturing cellular system. The objective of this model is to minimize the makespan and inter-cell movements simultaneously, while considering sequence-dependent cell setup times. In the CMS design and planning, three main steps must be considered, namely cell formation (i.e., piece families and machine grouping), inter and intra-cell layouts, and scheduling issue. Due to the fact that the Cellular Manufacturing Systems (CMS) problem is NP-Hard, a Genetic Algorithm (GA) as an efficient meta-heuristic method is proposed to solve such a hard problem. Finally, a number of test problems are solved to show the efficiency of the proposed GA and the related computational results are compared with the results obtained by the use of an optimization tool.

scholarly journals Art of Formulating LPs and IPs from Real Life Problems

2013 ◽  
Vol 61 (2) ◽  
pp. 185-191
Author(s):  
Md Hasib Uddin Molla ◽  
M Babul Hasan
Keyword(s):  
Mathematical Model ◽  
Objective Function ◽  
Real Life ◽  
Linear Constraints ◽  
Decision Problems ◽  
System Of Equations ◽  
Present Formulation ◽  
Real Life Problem ◽  
Life Problems ◽  
Life Decision

Formulation of LPs and IPs is a technique to convert real life decision problems into a mathematical model. This model consists of a linear objective function and a set of linear constraints expressed in the form of a system of equations or inequalities. In this paper, we present formulation from real life problem as an art. We discuss formulation through real life example and solve them using computer techniques AMPL and LINDO. DOI: http://dx.doi.org/10.3329/dujs.v61i2.17068 Dhaka Univ. J. Sci. 61(2): 185-191, 2013 (July)

scholarly journals Comparative study of the implementation of the Lagrange interpolation algorithm on GPU and CPU using CUDA to compute the density of a material at different temperatures

2021 ◽  
Vol 119 ◽  
pp. 07002
Author(s):  
Youness Rtal ◽  
Abdelkader Hadjoudja
Keyword(s):  
Parallel Computing ◽  
Graphics Processing Units ◽  
Lagrange Interpolation ◽  
Polynomial Interpolation ◽  
Programming Model ◽  
Interpolation Method ◽  
Processing Unit ◽  
Central Processing ◽  
Computational Performance ◽  
Different Temperatures

Graphics Processing Units (GPUs) are microprocessors attached to graphics cards, which are dedicated to the operation of displaying and manipulating graphics data. Currently, such graphics cards (GPUs) occupy all modern graphics cards. In a few years, these microprocessors have become potent tools for massively parallel computing. Such processors are practical instruments that serve in developing several fields like image processing, video and audio encoding and decoding, the resolution of a physical system with one or more unknowns. Their advantages: faster processing and consumption of less energy than the power of the central processing unit (CPU). In this paper, we will define and implement the Lagrange polynomial interpolation method on GPU and CPU to calculate the sodium density at different temperatures Ti using the NVIDIA CUDA C parallel programming model. It can increase computational performance by harnessing the power of the GPU. The objective of this study is to compare the performance of the implementation of the Lagrange interpolation method on CPU and GPU processors and to deduce the efficiency of the use of GPUs for parallel computing.

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