scholarly journals Discrete self-organizing migration algorithm and p-location problems

2020 ◽  
Vol 11 (2) ◽  
pp. 241-248
Author(s):  
Jaroslav Janacek ◽  
Marek Kvet
Keyword(s):  
Optimization Problems ◽  
Computational Study ◽  
Human Life ◽  
Optimal Solution ◽  
Location Problems ◽  
Computational Time ◽  
Exact Methods ◽  
Practical Applications ◽  
Special Adaptation ◽  
Self Organizing

Mathematical modelling, and integer programming generally, has many practical applications in different areas of human life. Effective and fast solving approaches for various optimization problems play an important role in the decision-making process and therefore, big attention is paid to the development of many exact and approximate algorithms. This paper deals only with a special class of location problems in which given number of facilities are to be chosen to minimize the objective function value. Since the exact methods are not suitable for their unpredictable computational time or memory demands, we focus here on possible usage of a special type of a particle swarm optimization algorithm transformed by discretization and meme usage into so-called discrete self-organizing migrating algorithm. In the paper, there is confirmed that it is possible to suggest a sophisticated heuristic for zero-one programming problem, which can produce near-to-optimal solution in much smaller time than the time demanded by exact methods. We introduce a special adaptation of the discrete self-organizing migration algorithm to the $p$-location problem making use of the path-relinking method. In the theoretical part of this paper, we introduce several strategies of the migration process. To verify their features and effectiveness, a computational study with real-sized benchmarks was performed. The main goal of the experiments was to find the most efficient version of the suggested solving tool.

scholarly journals Application of self organizing map in construction, geology and petroleum industry

2017 ◽  
Vol 20 (K4) ◽  
pp. 30-38
Author(s):  
Tung Son Pham ◽  
Huy Minh Truong ◽  
Tuan Ba Pham
Keyword(s):  
Industrial Revolution ◽  
Human Life ◽  
Petroleum Industry ◽  
Self Organizing Map ◽  
Practical Applications ◽  
Fourth Industrial Revolution ◽  
Som Algorithm ◽  
Clustering Data ◽  
Self Organizing

In recent years, Artificial Intelligence (AI) has become an emerging subject and been recognized as the flagship of the Fourth Industrial Revolution. AI is subtly growing and becoming vital in our daily life. Particularly, Self-Organizing Map (SOM), one of the major branches of AI, is a useful tool for clustering data and has been applied successfully and widespread in various aspects of human life such as psychology, economic, medical and technical fields like mechanical, construction and geology. In this paper, the primary purpose of the authors is to introduce SOM algorithm and its practical applications in geology and construction. The results are classification of rock facies versus depth in geology and clustering two sets of construction prices indices and building material costs indice.

A Non-Permutation Flow Shop Manufacturing Cell Scheduling Problem with Part's Sequence Dependent Family Setup Times

2014 ◽  
Vol 5 (4) ◽  
pp. 70-86 ◽  
Author(s):  
Behzad Nikjo ◽  
Yaser Zarook
Keyword(s):  
Flow Shop ◽  
Optimal Solution ◽  
Setup Times ◽  
Computational Time ◽  
Scheduling Problem ◽  
Dynamic Flow ◽  
Exact Methods ◽  
Release Date ◽  
Manufacturing Cell ◽  
Cell Scheduling

This article presents a new mathematical model for a dynamic flow shop manufacturing cell scheduling problem (DFMCSP) with agreeable job release date for each part family where family setup times are dependent on sequence of parts within families. It means this article considers non-permutation schedules for both sequence of families and sequence of parts within families. The objective is minimizing the Makespan (Cmax). Since, this problem belongs to NP-Hard class. Therefore, reaching an optimal solution in a reasonable computational time by using exact methods is extremely difficult. Thus, this article proposes meta-heuristic methods such as Genetic Algorithms (GA) and Tabu Search (TS). Finally, the computational results compare efficiency of the proposed algorithms under the performance measures.

scholarly journals Optimization of Traveling Salesman Problem Using Affinity Propagation Clustering and Genetic Algorithm

2015 ◽  
Vol 5 (4) ◽  
pp. 239-245 ◽  
Author(s):  
Ahmad Fouad El-Samak ◽  
Wesam Ashour
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Solution Space ◽  
Affinity Propagation ◽  
Computational Time ◽  
Combinatorial Optimization Problems ◽  
Affinity Propagation Clustering ◽  
Core Idea ◽  
Computation Algorithms

Abstract Combinatorial optimization problems, such as travel salesman problem, are usually NP-hard and the solution space of this problem is very large. Therefore the set of feasible solutions cannot be evaluated one by one. The simple genetic algorithm is one of the most used evolutionary computation algorithms, that give a good solution for TSP, however, it takes much computational time. In this paper, Affinity Propagation Clustering Technique (AP) is used to optimize the performance of the Genetic Algorithm (GA) for solving TSP. The core idea, which is clustering cities into smaller clusters and solving each cluster using GA separately, thus the access to the optimal solution will be in less computational time. Numerical experiments show that the proposed algorithm can give a good results for TSP problem more than the simple GA.

Modeling Defender-Attacker Problems as Robust Linear Programs with Mixed-Integer Uncertainty Sets

2021 ◽  
Author(s):  
Juan S. Borrero ◽  
Leonardo Lozano
Keyword(s):  
Robust Optimization ◽  
Convex Sets ◽  
Optimization Problems ◽  
Computational Study ◽  
Mixed Integer ◽  
Computational Time ◽  
Point Problem ◽  
Solution Algorithms ◽  
Uncertainty Sets ◽  
Uncertainty Set

We study a class of sequential defender-attacker optimization problems where the defender’s objective is uncertain and depends on the operations of the attacker, which are represented by a mixed-integer uncertainty set. The defender seeks to hedge against the worst possible data realization, resulting in a robust optimization problem with a mixed-integer uncertainty set, which requires the solution of a challenging mixed-integer problem, which can be seen as a saddle-point problem over a nonconvex domain. We study two exact solution algorithms and present two feature applications for which the uncertainty is naturally modeled as a mixed-integer set. Our computational experiments show that the considered algorithms greatly outperform standard algorithms both in terms of computational time and solution quality. Moreover, our results show that modeling uncertainty with mixed-integer sets, instead of approximating the data using convex sets, results in less conservative solutions, which translates to a lower cost for the defender to protect from uncertainty. Summary of Contribution: We consider a class of defender-attacker problems where the defender has to make operational decisions that depend on uncertain actions from an adversarial attacker. Due to the type of information available to the defender, neither probabilistic modeling, nor robust optimization methods with convex uncertainty sets, are well suited to address the defender’s decision-making problem. Consequently, we frame the defender’s problem as a class of robust optimization problems with a mixed-integer uncertainty sets, and devise two exact algorithms that solve this class of problems. A comprehensive computational study shows that for the considered applications, our algorithms improves the performance of existing robust optimization approaches that can be adapted to solve this class of problems. Moreover, we show how mixed-integer uncertainty sets can reduce the level of over-conservatism that is a known issue of robust optimization approaches.

Harmony Search Algorithm

2018 ◽  
pp. 1-30 ◽  
Author(s):  
Alireza Askarzadeh ◽  
Esmat Rashedi
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Computational Time ◽  
Engineering Applications ◽  
Heuristic Search Algorithm ◽  
And Performance ◽  
Significant Attention

Harmony search (HS) is a meta-heuristic search algorithm which tries to mimic the improvisation process of musicians in finding a pleasing harmony. In recent years, due to some advantages, HS has received a significant attention. HS is easy to implement, converges quickly to the optimal solution and finds a good enough solution in a reasonable amount of computational time. The merits of HS algorithm have led to its application to optimization problems of different engineering areas. In this chapter, the concepts and performance of HS algorithm are shown and some engineering applications are reviewed. It is observed that HS has shown promising performance in solving difficult optimization problems and different versions of this algorithm have been developed. In the next years, it is expected that HS is applied to more real optimization problems.

scholarly journals A heuristic method for solving airline crew pairing problems

2018 ◽  
Vol 204 ◽  
pp. 02006
Author(s):  
Khusnul Novianingsih ◽  
Rieske Hadianti
Keyword(s):  
Optimization Problems ◽  
Heuristic Method ◽  
Optimal Solution ◽  
Exact Methods ◽  
Flight Schedules ◽  
Airline Crew ◽  
Short Period ◽  
Crew Pairing ◽  
Local Optimal Solution ◽  
Airline Schedules

The airline crew pairing problem is one of the optimization problems which classified as a NP-hard problem. Since the number of feasible pairings in flight schedules can be numerous, the exact methods will not efficient to solve the problem. We propose a heuristic method for solving crew pairing problems. Initially, we generate a feasible solution by maximizing the covered flights. Then, we improve the solution by constructing a procedure to avoid the local optimal solution. We test our method to an airline schedules. The computational results show that our method can give the optimal solution in short period of time.

scholarly journals A Branch-and-Bound Algorithm Embedded with DCA for DC Programming

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Meihua Wang ◽  
Fengmin Xu ◽  
Chengxian Xu
Keyword(s):  
Branch And Bound ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Dc Programming ◽  
Computational Time ◽  
Special Importance ◽  
Branch Number ◽  
Dc Algorithm ◽  
Computational Performance

The special importance of Difference of Convex (DC) functions programming has been recognized in recent studies on nonconvex optimization problems. In this work, a class of DC programming derived from the portfolio selection problems is studied. The most popular method applied to solve the problem is the Branch-and-Bound (B&B) algorithm. However, “the curse of dimensionality” will affect the performance of the B&B algorithm. DC Algorithm (DCA) is an efficient method to get a local optimal solution. It has been applied to many practical problems, especially for large-scale problems. A B&B-DCA algorithm is proposed by embedding DCA into the B&B algorithms, the new algorithm improves the computational performance and obtains a global optimal solution. Computational results show that the proposed B&B-DCA algorithm has the superiority of the branch number and computational time than general B&B. The nice features of DCA (inexpensiveness, reliability, robustness, globality of computed solutions, etc.) provide crucial support to the combined B&B-DCA for accelerating the convergence of B&B.

The Locomotive Assignment Problem with Distributed Power at the Canadian National Railway Company

2021 ◽  
Vol 55 (2) ◽  
pp. 510-531
Author(s):  
Camilo Ortiz-Astorquiza ◽  
Jean-François Cordeau ◽  
Emma Frejinger
Keyword(s):  
Assignment Problem ◽  
Benders Decomposition ◽  
Optimization Problems ◽  
Computational Study ◽  
Computational Time ◽  
Fixed Cost ◽  
Modeling Framework ◽  
Distributed Power ◽  
Railway Company ◽  
Locomotive Assignment

Some of the most important optimization problems faced by railway operators arise from the management of their locomotive fleet. In this paper, we study a general version of the locomotive assignment problem encountered at the tactical level by one of the largest railroads in North America: the Canadian National (CN) Railway Company. We present a modeling framework with two integer linear programming formulations and contribute to the state of the art by allowing decisions on each train’s operating mode (distributed power or not) over the whole (weekly) planning horizon without partitioning it winto smaller time windows. Given the difficulty in solving the problem, one of the formulations is enhanced through various refinements, such as constraint relaxations, preprocessing, and fixed cost approximations. We thus achieve a significant reduction in the required computational time to solve instances of realistic size. We also present two versions of a Benders decomposition–based algorithm to obtain feasible solutions. On average, it allows a reduction of the associated computational time by two hours. Results from an extensive computational study and a case study with data provided by CN confirm the potential benefits of the model and solution approach.

Harmony Search Algorithm

2017 ◽  
pp. 1-36 ◽  
Author(s):  
Alireza Askarzadeh ◽  
Esmat Rashedi
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Computational Time ◽  
Engineering Applications ◽  
Heuristic Search Algorithm ◽  
And Performance ◽  
Significant Attention

Harmony search (HS) is a meta-heuristic search algorithm which tries to mimic the improvisation process of musicians in finding a pleasing harmony. In recent years, due to some advantages, HS has received a significant attention. HS is easy to implement, converges quickly to the optimal solution and finds a good enough solution in a reasonable amount of computational time. The merits of HS algorithm have led to its application to optimization problems of different engineering areas. In this chapter, the concepts and performance of HS algorithm are shown and some engineering applications are reviewed. It is observed that HS has shown promising performance in solving difficult optimization problems and different versions of this algorithm have been developed. In the next years, it is expected that HS is applied to more real optimization problems.

scholarly journals A Decomposition Heuristic for the Maximal Covering Location Problem

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Edson Luiz França Senne ◽  
Marcos Antonio Pereira ◽  
Luiz Antonio Nogueira Lorena
Keyword(s):  
Location Problem ◽  
Optimal Solution ◽  
Real Data ◽  
Upper Bounds ◽  
Location Problems ◽  
Exact Methods ◽  
Maximal Covering ◽  
Facility Locations ◽  
Coupling Constraints ◽  
Partitioning Technique

This paper proposes a cluster partitioning technique to calculate improved upper bounds to the optimal solution of maximal covering location problems. Given a covering distance, a graph is built considering as vertices the potential facility locations, and with an edge connecting each pair of facilities that attend a same client. Coupling constraints, corresponding to some edges of this graph, are identified and relaxed in the Lagrangean way, resulting in disconnected subgraphs representing smaller subproblems that are computationally easier to solve by exact methods. The proposed technique is compared to the classical approach, using real data and instances from the available literature.

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