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Number of results: 26
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Abstract

The problem that this paper investigates, namely, optimization of overlay computing systems, follows naturally from growing need for effective processing and consequently, fast development of various distributed systems. We consider an overlay-based computing system, i.e., a virtual computing system is deployed on the top of an existing physical network (e.g., Internet) providing connectivity between computing nodes. The main motivation behind the overlay concept is simple provision of network functionalities (e.g., diversity, flexibility, manageability) in a relatively cost-effective way as well as regardless of physical and logical structure of underlying networks. The workflow of tasks processed in the computing system assumes that there are many sources of input data and many destinations of output data, i.e., many-to-many transmissions are used in the system. The addressed optimization problem is formulatedin the form of an ILP (Integer Linear Programing) model. Since the model is computationally demanding and NP-complete, besides the branch-and-bound algorithm included in the CPLEX solver, we propose additional cut inequalities. Moreover, we present and test two effective heuristic algorithms: tabu search and greedy. Both methods yield satisfactory results close to optimal.
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Authors and Affiliations

Krzysztof Walkowiak
Andrzej Kasprzak
Karol Andrusieczko
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Abstract

Training a neural network can be a challenging task, particularly when working with complex models and large amounts of training data, as it consumes significant time and resources. This research proposes a hybrid model that combines population-based heuristic algorithms with traditional gradient-based techniques to enhance the training process. The proposed approach involves using a dynamic population-based heuristic algorithm to identify good initial values for the neural network weight vector. This is done as an alternative to the traditional technique of starting with random weights. After several cycles of distributing search agents across the search domain, the training process continues using a gradient-based technique that starts with the best initial weight vector identified by the heuristic algorithm. Experimental analysis confirms that exploring the search domain during the training process decreases the number of cycles needed for gradient descent to train a neural network. Furthermore, a dynamic population strategy is applied during the heuristic search, with objects added and removed dynamically based on their progress. This approach yields better results compared to traditional heuristic algorithms that use the same population members throughout the search process.
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Authors and Affiliations

Amer Mirkhan
1
Numan Çelebi
2
ORCID: ORCID

  1. Sakarya University, Computer Engineering Department
  2. Sakarya University, Information Systems Engineering Department
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Abstract

This paper explores selected heuristics methods, namely CDS, Palmer’s slope index, Gupta’s

algorithm, and concurrent heuristic algorithm for minimizing the makespan in permutation

flow shop scheduling problem. Its main scope is to explore how different instances sizes

impact on performance variability. The computational experiment includes 12 of available

benchmark data sets of 10 problems proposed by Taillard. The results are computed and

presented in the form of relative percentage deviation, while outputs of the NEH algorithm

were used as reference solutions for comparison purposes. Finally, pertinent findings are

commented.

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Authors and Affiliations

Zuzana Soltysova
Pavol Semanco
Jan Modrak
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Abstract

In the article, the authors propose a typology of political knowledge from online learning activities and test its validity in an empirical qualitative study. The essence of their proposal is that meaningful study of the process of acquiring knowledge (rational analysis of factors modifying attitudes) must take into account both the perspective of the citizen (the demand for information) and an analysis of the publicly available knowledge (the supply of information). The authors distinguish three main methods of acquiring information: heuristic, reflective, and by-product learning. They note the importance of generational factors in shaping the cognitive activity of Internet users. There has been a gradual increase in the importance of source management, with simultaneous alienation and skepticism towards information obtained on the Internet. While the authors’ analysis is restricted to the Internet, their approach is not reductionist in that they consider the internet to be a medium for traditional media and its influence on civic attitudes.

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Authors and Affiliations

Michał Wenzel
Maciej Kryszczuk
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Abstract

A method of creating production schedules regarding production lines with parallel machines is presented. The production line setup provides for intermediate buffers located between individual stages. The method mostly concerns situations when part of the production machines is unavailable for performance of operations and it becomes necessary to modify the original schedule, the consequence of which is the need to build a new schedule. The cost criterion was taken into account, as the schedule is created with the lowest possible costs regarding untimely completion of products (e.g. fines for delayed product completion). The proposed method is relaxing heuristics, thanks to which scheduling is performed in a relatively short time. This was confirmed by the presented results of computational experiments. These experiments were carried out for the rescheduling of machine parts production.

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Authors and Affiliations

Marek Magiera
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Abstract

In this paper,we proposed a modified meta-heuristic algorithm based on the blind naked mole-rat (BNMR) algorithm to solve the multiple standard benchmark problems. We then apply the proposed algorithm to solve an engineering inverse problem in the electromagnetic field to validate the results. The main objective is to modify the BNMR algorithm by employing two different types of distribution processes to improve the search strategy. Furthermore, we proposed an improvement scheme for the objective function and we have changed some parameters in the implementation of the BNMR algorithm. The performance of the BNMR algorithm was improved by introducing several new parameters to find the better target resources in the implementation of a modified BNMR algorithm. The results demonstrate that the changed candidate solutions fall into the neighborhood of the real solution. The results show the superiority of the propose method over other methods in solving various mathematical and electromagnetic problems.
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Bibliography

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Authors and Affiliations

Mohammad Taherdangkoo
1
ORCID: ORCID

  1. Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Abstract

The paper presents a novel heuristic procedure (further called the AH Method) to investigate function shape in the direct vicinity of the found optimum solution. The survey is conducted using only the space sampling collected during the optimization process with an evolutionary algorithm. For this purpose the finite model of point-set is considered. The statistical analysis of the sampling quality based upon the coverage of the points in question over the entire attraction region is exploited. The tolerance boundaries of the parameters are determined for the user-specified increase of the objective function value above the found minimum. The presented test-case data prove that the proposed approach is comparable to other optimum neighborhood examination algorithms. Also, the AH Method requires noticeably shorter computational time than its counterparts. This is achieved by a repeated, second use of points from optimization without additional objective function calls, as well as significant repository size reduction during preprocessing.
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Authors and Affiliations

Daniel Andrzej Piętak
1
Piotr Bilski
1
ORCID: ORCID
Paweł Jan Napiórkowski
2

  1. Institute of Radioelectronics and Multimedia Technology, Warsaw University of Technology, Poland
  2. Heavy Ion Laboratory, University of Warsaw, Poland
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Abstract

Despite many years of development in the field of rotor dynamics, many issues still need to be resolved. This is due to the fact that turbomachines, even those with low output power, have a very complex design. The author of this article would like to signal these issues in the form of several questions, to which there are no precise answers. The questions are as follows: How can we build a coherent dynamic model of a turbomachine whose some subsystems have non-linear characteristics? How can we consider the so-called prehistory in our analysis, namely, the relation between future dynamic states and previous ones? Is heuristic modelling the future of rotor dynamics? What phenomena may occur when the stability limit of the system is exceeded? The attempt to find answers to these questions constitutes the subject of this article. There are obviously more similar questions, which encourage researchers from all over the world to further their research.
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Bibliography

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  6.  S. Dousti and P. Allaire, “A thermohydrodynamic approach for single-film and double-film floating disk fixed thrust bearings verified with experiment,” Tribol. Int., vol. 140, p. 105858, Dec 2019.
  7.  H. Engel, “Berechung der Strömung, der Drücke und Temperaturen in Radial-Axialbund-Gleitlagern mit Hilfe eines Finite-Elemente-Programms,” Ph.D. thesis, Universität Stuttgart, 1992.
  8.  T. Hagemann, H. Blumenthal, C. Kraft, and H. Schwarze, “A study on energetic and hydraulic interaction of combined journal and thrust bearings,” in Proceedings of ASME Turbo Expo 2015: Turbine Technical Conference and Exposition, no. GT2015‒43460, 2015, pp. 1–11.
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Authors and Affiliations

Jan Kiciński
1

  1. Institute of Fluid-Flow Machinery, Polish Academy of Sciences, ul. Fiszera 14, Gdańsk 80-231, Poland
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Abstract

The paper presents a novel Iterated Local Search (ILS) algorithm to solve multi-item multi-family capacitated lot-sizing problem with setup costs independent of the family sequence. The model has a direct application to real production planning in foundry industry, where the goal is to create the batches of manufactured castings and the sequence of the melted metal loads to prevent delays in delivery of goods to clients. We extended existing models by introducing minimal utilization of furnace capacity during preparing melted alloy. We developed simple and fast ILS algorithm with problem-specific operators that are responsible for the local search procedure. The computational experiments on ten instances of the problem showed that the presence of minimum furnace utilization constraint has great impact on economic and technological conditions of castings production. For all test instances the proposed heuristic is able to provide the results that are comparable to state-of-the art commercial solver.

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Authors and Affiliations

A. Stawowy
J. Duda
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Abstract

In the paper, we present a coordinated production planning and scheduling problem for three major shops in a typical alloy casting

foundry, i.e. a melting shop, molding shop with automatic line and a core shop. The castings, prepared from different metal, have different

weight and different number of cores. Although core preparation does not required as strict coordination with molding plan as metal

preparation in furnaces, some cores may have limited shelf life, depending on the material used, or at least it is usually not the best

organizational practice to prepare them long in advance. Core shop have limited capacity, so the cores for castings that require multiple

cores should be prepared earlier. We present a mixed integer programming model for the coordinated production planning and scheduling

problem of the shops. Then we propose a simple Lagrangian relaxation heuristic and evolutionary based heuristic to solve the coordinated

problem. The applicability of the proposed solution in industrial practice is verified on large instances of the problem with the data

simulating actual production parameters in one of the medium size foundry.

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Authors and Affiliations

A. Stawowy
J. Duda
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Abstract

The paper presents a production scheduling problem in a foundry equipped with two furnaces and one casting line, where the line is a bottleneck and furnaces, of the same capacity, work in parallel. The amount of produced castings may not exceed the capacity of the line and the furnaces, and their loads determine metal type from which the products are manufactured on the casting line. The purpose of planning is to create the processing order of metal production to prevent delays in the delivery of the ordered products to the customers. The problem is a mix of a lot-sizing and scheduling problems on two machines (the furnaces) run in parallel. The article gives a mathematical model that defines the optimization problem, and its relaxed version based on the concept of a rolling-horizon planning. The proposed approaches, i.e. commercial solver and Iterated Local Search (ILS) heuristic, were tested on a sample data and different problem sizes. The tests have shown that rolling horizon approach gives the best results for most problems, however, developed ILS algorithm gives better results for the largest problem instances with tight furnace capacity.

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Authors and Affiliations

A. Stawowy
J. Duda
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Abstract

Cross-docking is a strategy that distributes products directly from a supplier or manufacturing plant to a customer or retail chain, reducing handling or storage time. This study focuses on the truck scheduling problem, which consists of assigning each truck to a door at the dock and determining the sequences for the trucks at each door considering the time-window aspect. The study presents a mathematical model for door assignment and truck scheduling with time windows at multi-door cross-docking centers. The objective of the model is to minimize the overall earliness and tardiness for outbound trucks. Simulated annealing (SA) and tabu search (TS) algorithms are proposed to solve large-sized problems. The results of the mathematical model and of meta-heuristic algorithms are compared by generating test problems for different sizes. A decision support system (DSS) is also designed for the truck scheduling problem for multi-door cross-docking centers. Computational results show that TS and SA algorithms are efficient in solving large-sized problems in a reasonable time.

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Authors and Affiliations

G. Ozden
I. Saricicek
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Abstract

The results presented here are twofold. First, a heuristic algorithm is proposed which, through removing some unnecessary arcs from a digraph, tends to reduce it into an adjoint and thus simplifies the search for a Hamiltonian cycle. Second, a heuristic algorithm for DNA sequence assembly is proposed, which uses a graph model of the problem instance, and incorporates two independent procedures of reducing the set of arcs - one of them being the former algorithm. Finally, results of tests of the assembly algorithm on parts of chromosome arm 2R of Drosophila melanogaster are presented.

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Authors and Affiliations

J. Błazewicz
M. Kasprzak
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Abstract

The problem of sequencing jobs on a single machine to minimize total cost (earliness and

tardiness) is nowadays not just important due to traditional concerns but also due to its

importance in the context of Collaborative Networked Organizations and Virtual Enterprises,

where precision about promptly responses to customers’ requests, along with other

important requirements, assume a crucial role. In order to provide a contribution in this

direction, in this paper the authors contribute with an applied constructive heuristics that

tries to find appropriate solutions for single machine scheduling problems under different

processing times and due dates, and without preemption allowed. In this paper, two different

approaches for single-machine scheduling problems, based on external and internal

performance measures are applied to the problem and a comparative analysis is performed.

Computational results are presented for the problem under Just-in-Time and agile conditions

on which each job has a due date, and the objective is to minimize the sum of holding costs

for jobs completed before their due date and tardiness costs for jobs completed after their

due date. Additional computational tests were developed based on different customer and

enterprise oriented performance criteria, although preference is given to customer-oriented

measures, namely the total number of tardy jobs and the maximum tardiness.

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Authors and Affiliations

Maria L.R. Varela
Justyna Trojanowska
Ana M. Madureira
Joana D. Dantas
André S. Santos
Goran D. Putnik
José Machado
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Abstract

The demand for energy on a global scale increases day by day. Unlike renewable energy sources, fossil fuels have limited reserves and meet most of the world’s energy needs despite their adverse environmental effects. This study presents a new forecast strategy, including an optimization-based S-curve approach for coal consumption in Turkey. For this approach, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) are among the meta-heuristic optimization techniques used to determine the optimum parameters of the S-curve. In addition, these algorithms and Artificial Neural Network (ANN) have also been used to estimate coal consumption. In evaluating coal consumption with ANN, energy and economic parameters such as installed capacity, gross generation, net electric consumption, import, export, and population energy are used for input parameters. In ANN modeling, the Feed Forward Multilayer Perceptron Network structure was used, and Levenberg-Marquardt Back Propagation has used to perform network training. S-curves have been calculated using optimization, and their performance in predicting coal consumption has been evaluated statistically. The findings reveal that the optimization-based S-curve approach gives higher accuracy than ANN in solving the presented problem. The statistical results calculated by the GWO have higher accuracy than the PSO, WOA, and GA with R 2 = 0.9881, RE = 0.011, RMSE = 1.079, MAE = 1.3584, and STD = 1.5187. The novelty of this study, the presented methodology does not need more input parameters for analysis. Therefore, it can be easily used with high accuracy to estimate coal consumption within other countries with an increasing trend in coal consumption, such as Turkey.
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Authors and Affiliations

Mustafa Seker
1
ORCID: ORCID
Neslihan Unal Kartal
2
Selin Karadirek
3
Cevdet Bertan Gulludag
3

  1. Sivas Cumhuriyet University, Turkey
  2. Burdur Mehmet Akif Ersoy University, Turkey
  3. Akdeniz University, Antalya, Turkey
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Abstract

The effective implementation of new market strategies presents the mining enterprises with new challenges which require precise assessment instruments of the carried out business to be met at the level of mines, preparation plants, coking plants, and steelworks. These instruments include deposit, technological, and economic parameters, which together with a safety margin, determining the percentage reserve level of each parameter, shape the profitability of undertaken projects. The paper raises the issue of designing an IT architecture of the system for deposit modelling and mining production scheduling, implemented in the JSW SA. The development and application of the system was important with regard to the overriding objective of the Quality ProgramProgram of the JSW Capital Group, which is increasing the effectiveness of deposit and commercial product quality management. The paper also presents the required specification of the technical architecture necessary to implement systems and the actions required to integrate them with other IT systems of the JSW Group. The heuristic technical architecture of the JSW SA production line management system presented in the paper enables an analysis of the production process profitability in a carried account system in the area of mines, preparation plants, and coking plants of the mining group of the biggest European coal producer for metallurgical purposes.
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Authors and Affiliations

Artur Dyczko
1
ORCID: ORCID

  1. Mineral and Energy Economy Research Institute, Polish Academy of Sciences, Kraków, Poland
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Abstract

Economic Load Dispatch (ELD) is utilized in finding the optimal combination of the real power generation that minimizes total generation cost, yet satisfying all equality and inequality constraints. It plays a significant role in planning and operating power systems with several generating stations. For simplicity, the cost function of each generating unit has been approximated by a single quadratic function. ELD is a subproblem of unit commitment and a nonlinear optimization problem. Many soft computing optimization methods have been developed in the recent past to solve ELD problems. In this paper, the most recently developed population-based optimization called the Salp Swarm Algorithm (SSA) has been utilized to solve the ELD problem. The results for the ELD problem have been verified by applying it to a standard 6-generator system with and without due consideration of transmission losses. The finally obtained results using the SSA are compared to that with the Particle Swarm Optimization (PSO) algorithm. It has been observed that the obtained results using the SSA are quite encouraging.
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Authors and Affiliations

Ramesh Devarapalli
1
ORCID: ORCID
Nikhil Kumar Sinha
1
ORCID: ORCID
Bathina Venkateswara Rao
2
ORCID: ORCID
Łukasz Knypinski
3
ORCID: ORCID
Naraharisetti Jaya Naga Lakshmi
4
ORCID: ORCID
Fausto Pedro García Márquez
5
ORCID: ORCID

  1. Department of EE, B. I. T. Sindri, Dhanbad, Jharkhand – 828123, India
  2. Department of EEE, V R Siddhartha Engineering College (Autonomous), Vijayawada-520007, A.P., India
  3. Poznan University of Technology, Poland
  4. SR Engineering College: Warangal, Telangana, India
  5. Ingenium Research Group, University of Castilla-La Mancha, Spain
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Abstract

The paper deals with the issue of production scheduling for various types of employees in a large manufacturing company where the decision-making process was based on a human factor and the foreman’s know-how, which was error-prone. Modern production processes are getting more and more complex. A company that wants to be competitive on the market must consider many factors. Relying only on human factors is not efficient at all. The presented work has the objective of developing a new employee scheduling system that might be considered a particular case of the job shop problem from the set of the employee scheduling problems. The Neuro-Tabu Search algorithm and the data gathered by manufacturing sensors and process controls are used to remotely inspect machine condition and sustainability as well as for preventive maintenance. They were used to build production schedules. The construction of the Neuro-Tabu Search algorithm combines the Tabu Search algorithm, one of the most effective methods of constructing heuristic algorithms for scheduling problems, and a self-organizing neural network that further improves the prohibition mechanism of the Tabu Search algorithm. Additionally, in the paper, sustainability with the use of Industry 4.0 is considered. That would make it possible to minimize the costs of employees’ work and the cost of the overall production process. Solving the optimization problem offered by Neuro-Tabu Search algorithm and real-time data shows a new way of production management.
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Authors and Affiliations

Anna Burduk
1
ORCID: ORCID
Kamil Musiał
1
Artem Balashov
1
Andre Batako
2
Andrii Safonyk
3
ORCID: ORCID

  1. Wroclaw University of Science and Technology, Faculty of Mechanical Engineering, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
  2. Liverpool John Moores University, Faculty of Engineering and Technology,70 Mount Pleasant Liverpool L3 3AF, UK
  3. National University of Water and Environmental Engineering, Department of Automation, Electrical Engineering and Computer-Integrated Technologies, Rivne 33000, Ukraine
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Abstract

The paper presents a novel hybrid cuckoo search (CS) algorithm for the optimization of the line-start permanent magnet synchronous motor (LSPMSM). The hybrid optimization algorithm developed is a merger of the heuristic algorithm with the deterministic Hooke–Jeeves method. The hybrid optimization procedure developed was tested on analytical benchmark functions and the results were compared with the classical cuckoo search algorithm, genetic algorithm, particle swarm algorithm and bat algorithm. The optimization script containing a hybrid algorithm was developed in Delphi Tiburón. The results presented show that the modified method is characterized by better accuracy. The optimization procedure developed is related to a mathematical model of the LSPMSM. The multi-objective compromise function was applied as an optimality criterion. Selected results were presented and discussed.
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Authors and Affiliations

Łukasz Knypiński
1
ORCID: ORCID

  1. Poznan University of Technology, Institute of Electrical Engineering and Electronics, Piotrowo 3a, 60-965 Poznan, Poland
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Abstract

Improving production processes includes not only activities concerning manufacturing itself, but also all the activities that are necessary to achieve the main objectives. One such activity is transport, which, although a source of waste in terms of adding value to the product, is essential to the realization of the production process. Over the years, many methods have been developed to help manage supply and transport in such a way as to reduce it to the necessary minimum. In the paper, the problem of delivering components to a production area using trains and appropriately laid-out carriages was described. It is a milk run stop locations problem (MRSLP), whose proposed solution is based on the use of heuristic algorithms. Intelligent solutions are getting more and more popular in the industry because of the possible advantages they offer, especially those that include the possibility of finding an optimum local solution in a relatively short time and the prevention of human errors. In this paper, the applicability of three algorithms – tabu search, genetic algorithm, and simulated annealing – was explored.
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Authors and Affiliations

Joanna Kochańska
1
Anna Burduk
1
ORCID: ORCID
Dagmara Łapczyńska
1
Kamil Musiał
1

  1. Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
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Abstract

Appropriate product categorization in distribution centres is important for business success because of the possibility of intuitive product finding by the picker and increased product movement. Both of these factors result in the operational efficiency of the distribution centre. The goal of this paper is to explore a model of shelf space dimensioning of storage location on a rack with vertical and horizontal product categorization in a distribution centre, where the aim is to increase total product movement/profit from all shelves of the rack. This is controlled by a packer who must complete orders by getting the goods from shelves and picking them to the container. In this problem, we develop two heuristics and compare the archived results to the CPLEX solver. The average profit ratios of both heuristics are high and approximately equal to 99%. In 10 cases, optimal solutions have been found by heuristics.
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Authors and Affiliations

Kateryna Czerniachowska
1
ORCID: ORCID
Radosław Wichniarek
2
ORCID: ORCID
Krzysztof Żywicki
2
ORCID: ORCID

  1. Wroclaw University of Economics and Business, Wroclaw, Poland
  2. Poznan University of Technology, Poznan, Poland
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Abstract

Distribution centres are the important elements of modern supply chains. A distribution centre stores and ships products. In this paper, we investigate the model of the dimensioning of shelf space on the rack with vertical and horizontal product categorisation in a distribution centre, where the objective is to maximise the total product movement/profit from all shelves of the rack which is being managed by a packer who needs to complete orders selecting the products from the shelves and picking them to the container. We apply two newly developed heuristics to this problem and compare the results to the optimal solution found by the CPLEX solver. There are 8 steering parameters that allow for reducing the search space implemented in heuristics. Among them are parameters that decrease the number of products on the shelves, the category with a range for assigning most space for the most profitable products within the category; two versions of steering parameters for the number of generated product allocations, the step parameters for the intensity of solution diversification, and the movement/profit below which the solutions are not generated. The computational results are presented and indicate that higher-quality solutions can be obtained using the new heuristics. In 10 from 15 tests, both heuristics can find optimal solutions without exploring the whole solution space. For the rest test sets, the solutions received by heuristics are not less than 92.58%.
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Authors and Affiliations

Kateryna Czerniachowska
1
ORCID: ORCID
Radosław Wichniarek
2
ORCID: ORCID
Krzysztof Żywicki
2
ORCID: ORCID

  1. Wroclaw University of Economics and Business, Wroclaw, Poland
  2. Poznan University of Technology, Poznan, Poland
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Abstract

The present paper describes a methodological framework developed to select a multi-label dataset transformation method in the context of supervised machine learning techniques. We explore the rectangular 2D strip-packing problem (2D-SPP), widely applied in industrial processes to cut sheet metals and paper rolls, where high-quality solutions can be found for more than one improvement heuristic, generating instances with multi-label behavior. To obtain single-label datasets, a total of five multi-label transformation methods are explored. 1000 instances were generated to represent different 2D-SPP variations found in real-world applications, labels for each instance represented by improvement heuristics were calculated, along with 19 predictors provided by problem characteristics. Finally, classification models were fitted to verify the accuracy of each multi-label transformation method. For the 2D-SPP, the single-label obtained using the exclusion method fit more accurate classification models compared to the other four multi-label transformation methods adopted.
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Authors and Affiliations

Neuenfeldt Júnior Alvaro
Matheus Francescatto
Gabriel Stieler
David Disconzi
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Abstract

In this paper, we study the constrained exact and approximate controllability of traveling wave solutions of Korteweg-de Vries (third order) and Boussinesq (fourth order) semi-linear equations using the Green’s function approach. Control is carried out by a moving external source. Representing the general solution of those equations in terms of the Frasca’s short time expansion, system of constraints on the distributed control is derived for both types of controllability. Due to the possibility of explicit solution provided by the heuristic method, the controllability analysis becomes straightforward. Numerical analysis confirms theoretical derivations.

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Authors and Affiliations

Jerzy Klamka
ORCID: ORCID
Ara S. Avetisyan
Asatur Zh. Khurshudyan

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