scholarly journals Optimization of Tower Crane Location and Material Quantity Between Supply and Demand Points: A Comparative Study

2018 ◽  
Author(s):  
Ali Kaveh ◽  
Yasin Vazirinia
Keyword(s):  
Construction Projects ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Supply And Demand ◽  
Location Optimization ◽  
Tower Crane ◽  
Colliding Bodies Optimization ◽  
Enhanced Colliding Bodies Optimization ◽  
Complex Optimization ◽  
Material Transportation

Location optimization of tower crane as an expensive equipment in the construction projects has an important effect on material transportation costs. Due to the construction site conditions, there are several tower crane location optimization models. Appropriate location of tower cranes for material supply and engineering demands is a combinatorial optimization problem within the tower crane layout problem that is difficult to resolve.  Meta-heuristics are popular and useful techniques to resolve complex optimization problems. In this paper, the performance of the Particle Swarm Optimization (PSO) and four newly developed meta-heuristic algorithms Colliding Bodies Optimization (CBO), Enhanced Colliding Bodies Optimization (ECBO), Vibrating Particles System (VPS), and Enhanced Vibrating Particles System (EVPS) are compared in terms of their effectiveness in resolving a practical Tower Crane Layout (TCL) problem. Results show that ECBO performs better than other three methods in both cases.

scholarly journals Experimental Study of a Hybrid Genetic Algorithm for the Multiple Travelling Salesman Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Maha Ata Al-Furhud ◽  
Zakir Hussain Ahmed
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Hybrid Genetic Algorithm ◽  
Travelling Salesman ◽  
Complex Optimization ◽  
Problem Instances ◽  
Search Approach ◽  
Multiple Travelling Salesman Problem

The multiple travelling salesman problem (MTSP), an extension of the well-known travelling salesman problem (TSP), is studied here. In MTSP, starting from a depot, multiple salesmen require to visit all cities so that each city is required to be visited only once by one salesman only. It is NP-hard and is more complex than the usual TSP. So, exact optimal solutions can be obtained for smaller sized problem instances only. For large-sized problem instances, it is essential to apply heuristic algorithms, and amongst them, genetic algorithm is identified to be successfully deal with such complex optimization problems. So, we propose a hybrid genetic algorithm (HGA) that uses sequential constructive crossover, a local search approach along with an immigration technique to find high-quality solution to the MTSP. Then our proposed HGA is compared against some state-of-the-art algorithms by solving some TSPLIB symmetric instances of several sizes with various number of salesmen. Our experimental investigation demonstrates that the HGA is one of the best algorithms.

scholarly journals Comparison of spreadsheet-based optimization tools applied to construction site layout problem

2021 ◽  
Vol 1209 (1) ◽  
pp. 012010
Author(s):  
B Dasović ◽  
U Klanšek
Keyword(s):  
Optimization Problems ◽  
Construction Site ◽  
Layout Problem ◽  
Modeling Tools ◽  
Tower Crane ◽  
Site Layout ◽  
Material Transportation ◽  
Excel Solver ◽  
Construction Site Layout ◽  
Construction Optimization

Abstract This paper compares different spreadsheet-based optimization tools applied to a practical example of a construction site layout problem. The objective of the optimization is to minimize the total time of material transportation by optimal positioning of tower crane and work facilities on the construction site with repetitive operations. Computer programs, such as MS Excel, LibreOffice Calc, and Google Sheets can be applied as modeling tools for a variety of construction optimization problems in addition to their usual functionalities. In this study, LibreOffice Calc Solver, MS Excel Solver, along with other MS Excel add-ins, i.e., OpenSolver, Evolver, and What’sBest, are analyzed. The capabilities of optimization tools mentioned above are examined on the problem of optimal positioning of tower crane and work facilities on the construction site. The results obtained by optimization tools are noted and discussed. The paper ends with conclusions and recommendations for further research.

scholarly journals Comprehensive optimization of passenger trains operation based on an automated system for managing the profitability of passenger traffic

2021 ◽  
Vol 79 (6) ◽  
pp. 343-350
Author(s):  
G. L. Venediktov ◽  
V. M. Kochetkov
Keyword(s):  
Optimization Problems ◽  
Supply And Demand ◽  
Optimization Procedure ◽  
Automated System ◽  
Analytical Models ◽  
Passenger Traffic ◽  
Passenger Demand ◽  
Transportation Process ◽  
Complex Optimization ◽  
Passenger Trains

The article is devoted to solving the problem of optimizing the tariff policy, which is relevant for the passenger complex, with the most rational use of the rolling stock. Principles of economic and mathematical modeling are presented in order to determine the optimal number of cars in passenger trains and prices for travel in a single calculation process called complex optimization. Developed models make it possible to form optimal train schemes in accordance with the predicted demand, balancing supply and demand for transportation, which, in turn, radically increases its economic efficiency. Simultaneously with finding the optimal schemes by the method of complex optimization, such fares are determined at which the balance of supply and demand is achieved with the maximum possible economic effect. The article discusses the analytical models of passenger demand, the principles of implementation of the complex optimization procedure, the problems of forecasting passenger demand and the features of calculating the forecast of demand for the example of train No. 17/18 “Karelia” on the Petrozavodsk—Moscow route, and also offers a solution to the problem of determining the optimal prices and train schemes. On the example of real data of the operation of this train, the effectiveness of the automated system for managing the profitability of passenger traffic, created on the basis of complex optimization algorithms, was evaluated. It is shown that the developed analytical models of passenger demand allow solving optimization problems that increase the efficiency of automated control systems for the economic indicators of the transportation process, and the multi-model forecasting system provides satisfactory accuracy with a high level of forecast detail. Due to the fact that the level of expenses for train journeys radically affects the result of optimization of the transportation process with variable train schemes, the adopted optimization criterion takes into account both income and transportation costs. The results of calculations are presented showing that the complex optimization procedure for the “Karelia” train could give a significant additional financial result.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

scholarly journals Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems

2021 ◽  
Vol 11 (8) ◽  
pp. 3430
Author(s):  
Erik Cuevas ◽  
Héctor Becerra ◽  
Héctor Escobar ◽  
Alberto Luque-Chang ◽  
Marco Pérez ◽  
...  
Keyword(s):  
Convergence Rates ◽  
Optimization Problems ◽  
Search Space ◽  
Second Order ◽  
Order System ◽  
Search Patterns ◽  
Multiple Behaviors ◽  
Complex Optimization ◽  
Second Order Systems ◽  
Order Algorithm

Recently, several new metaheuristic schemes have been introduced in the literature. Although all these approaches consider very different phenomena as metaphors, the search patterns used to explore the search space are very similar. On the other hand, second-order systems are models that present different temporal behaviors depending on the value of their parameters. Such temporal behaviors can be conceived as search patterns with multiple behaviors and simple configurations. In this paper, a set of new search patterns are introduced to explore the search space efficiently. They emulate the response of a second-order system. The proposed set of search patterns have been integrated as a complete search strategy, called Second-Order Algorithm (SOA), to obtain the global solution of complex optimization problems. To analyze the performance of the proposed scheme, it has been compared in a set of representative optimization problems, including multimodal, unimodal, and hybrid benchmark formulations. Numerical results demonstrate that the proposed SOA method exhibits remarkable performance in terms of accuracy and high convergence rates.

scholarly journals Chunking and cooperation in particle swarm optimization for feature selection

2021 ◽  
Author(s):  
Malek Sarhani ◽  
Stefan Voß
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Cooperative Learning ◽  
Optimization Problems ◽  
Practical Importance ◽  
Particle Swarm ◽  
Large Datasets ◽  
Social Phenomena ◽  
Swarm Optimization ◽  
Complex Optimization

AbstractBio-inspired optimization aims at adapting observed natural behavioral patterns and social phenomena towards efficiently solving complex optimization problems, and is nowadays gaining much attention. However, researchers recently highlighted an inconsistency between the need in the field and the actual trend. Indeed, while nowadays it is important to design innovative contributions, an actual trend in bio-inspired optimization is to re-iterate the existing knowledge in a different form. The aim of this paper is to fill this gap. More precisely, we start first by highlighting new examples for this problem by considering and describing the concepts of chunking and cooperative learning. Second, by considering particle swarm optimization (PSO), we present a novel bridge between these two notions adapted to the problem of feature selection. In the experiments, we investigate the practical importance of our approach while exploring both its strength and limitations. The results indicate that the approach is mainly suitable for large datasets, and that further research is needed to improve the computational efficiency of the approach and to ensure the independence of the sub-problems defined using chunking.

scholarly journals Healthcare Facility Location-Allocation Optimization for China’s Developing Cities Utilizing a Multi-Objective Decision Support Approach

2018 ◽  
Vol 10 (12) ◽  
pp. 4580 ◽  
Author(s):  
Li Wang ◽  
Huan Shi ◽  
Lu Gan
Keyword(s):  
Heuristic Algorithms ◽  
Programming Model ◽  
Rapid Development ◽  
Supply And Demand ◽  
Healthcare Facility ◽  
Allocation Model ◽  
Location Allocation ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Allocation Problems

With rapid development of the healthcare network, the location-allocation problems of public facilities under increased integration and aggregation needs have been widely researched in China’s developing cites. Since strategic formulation involves multiple conflicting objectives and stakeholders, this paper presents a practicable hierarchical location-allocation model from the perspective of supply and demand to characterize the trade-off between social, economical and environmental factors. Due to the difficulties of rationally describing and the efficient calculation of location-allocation problems as a typical Non-deterministic Polynomial-Hard (NP-hard) problem with uncertainty, there are three crucial challenges for this study: (1) combining continuous location model with discrete potential positions; (2) introducing reasonable multiple conflicting objectives; (3) adapting and modifying appropriate meta-heuristic algorithms. First, we set up a hierarchical programming model, which incorporates four objective functions based on the actual backgrounds. Second, a bi-level multi-objective particle swarm optimization (BLMOPSO) algorithm is designed to deal with the binary location decision and capacity adjustment simultaneously. Finally, a realistic case study contains sixteen patient points with maximum of six open treatment units is tested to validate the availability and applicability of the whole approach. The results demonstrate that the proposed model is suitable to be applied as an extensive planning tool for decision makers (DMs) to generate policies and strategies in healthcare and design other facility projects.

scholarly journals A Shuffle-Based Artificial Bee Colony Algorithm for Solving Integer Programming and Minimax Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1211
Author(s):  
Ivona Brajević
Keyword(s):  
Integer Programming ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Minimax Problems ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Effective Performance ◽  
Complex Optimization ◽  
Overall Performance ◽  
Integer Programming Problems

The artificial bee colony (ABC) algorithm is a prominent swarm intelligence technique due to its simple structure and effective performance. However, the ABC algorithm has a slow convergence rate when it is used to solve complex optimization problems since its solution search equation is more of an exploration than exploitation operator. This paper presents an improved ABC algorithm for solving integer programming and minimax problems. The proposed approach employs a modified ABC search operator, which exploits the useful information of the current best solution in the onlooker phase with the intention of improving its exploitation tendency. Furthermore, the shuffle mutation operator is applied to the created solutions in both bee phases to help the search achieve a better balance between the global exploration and local exploitation abilities and to provide a valuable convergence speed. The experimental results, obtained by testing on seven integer programming problems and ten minimax problems, show that the overall performance of the proposed approach is superior to the ABC. Additionally, it obtains competitive results compared with other state-of-the-art algorithms.

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