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The Use of a Genetic Algorithm for Sorting Warehouse Optimisation processes Article The Use of a Genetic Algorithm for Sorting Warehouse Optimisation Patrik Grznár 1 , Martin Krajˇcoviˇc 1 , Arkadiusz Gola 2,* , L’uboslavDulina 1 , Beáta Furmannová 1, Štefan Mozol 1, Dariusz Plinta 3 , Natália Burganová 1, Wojciech Danilczuk 2 and Radovan Svitek 1 1 Department of Industrial Engineering, Faculty of Mechanical Engineering, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia; [email protected] (P.G.); [email protected] (M.K.); [email protected] (L’.D.); [email protected] (B.F.); [email protected] (Š.M.); [email protected] (N.B.); [email protected] (R.S.) 2 Department of Production Computerization and Robotization, Faculty of Mechanical Engineering, Lublin University of Technology, ul. Nadbystrzycka 36, 20-618 Lublin, Poland; [email protected] 3 Department of Production Engineering, Faculty of Mechanical Engineering and Computer Science, University of Bielsko-Biala, ul. Willowa 2, 43-309 Bielsko-Biała, Poland; [email protected] * Correspondence: [email protected]; Tel.: +48-81-538-45-35 Abstract: In the last decade, simulation software as a tool for managing and controlling business processes has received a lot of attention. Many of the new software features allow businesses to achieve better quality results using optimisation, such as genetic algorithms. This article describes the use of modelling and simulation in shipment and sorting processes that are optimised by a genetic algorithm’s involvement. The designed algorithm and simulation model focuses on optimising the Citation: Grznár, P.; Krajˇcoviˇc,M.; duration of shipment processing times and numbers of workers. The commercially available software Gola, A.; Dulina, L’.;Furmannová, B.; Tecnomatix Plant Simulation, paired with a genetic algorithm, was used for optimisation, decreasing Mozol, Š.; Plinta, D.; Burganová, N.; Danilczuk, W.; Svitek, R. The Use of a time durations, and thus selecting the most suitable solution for defined inputs. This method has Genetic Algorithm for Sorting produced better results in comparison to the classical heuristic methods and, furthermore, is not Warehouse Optimisation. Processes as time consuming. This article, at its core, describes the algorithm used to determine the optimal 2021, 9, 1197. https://doi.org/ number of workers in sorting warehouses with the results of its application. The final part of this 10.3390/pr9071197 article contains an evaluation of this proposal compared to the original methods, and highlights what benefits result from such changes. The major purpose of this research is to determine the number of Academic Editors: José Barbosa and workers needed to speed up the departure of shipments and optimise the workload of workers. Luis Puigjaner Keywords: computer simulation; genetic algorithm; shipment; sorting; optimisation; logistic Received: 2 June 2021 Accepted: 7 July 2021 Published: 10 July 2021 1. Introduction Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in The shipping industry is still figuring out how to manage its processes in a cost- published maps and institutional affil- effective and efficient manner. As a result, several delivery companies seek out cutting-edge iations. solutions to improve customer experience and revenues [1,2]. Whether it is a company with a large or small volume of shipments, it is always necessary to adhere to specific transport standards of, and solutions to, the sorting process. A delivery company often stands as a link between a product’s manufacturer and customers [3]. Such a company will ensure customer satisfaction mainly by fast delivery, suitable packaging of the consignment Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. and, last but not least, the lowest price. This is why it is crucial to continually improve said This article is an open access article company’s logistics processes [4,5]. In a delivery company, every process is closely linked; distributed under the terms and it must therefore respond flexibly to every change and error in the process [6]. This means conditions of the Creative Commons that the delay of one element of a shipment and sorting processes will be reflected in the Attribution (CC BY) license (https:// next process [7,8]. creativecommons.org/licenses/by/ Today, modelling and simulations are commonly used for all production or logistics 4.0/). companies [9,10]. Due to the sensitivity of delivery processes, finding the optimal solution Processes 2021, 9, 1197. https://doi.org/10.3390/pr9071197 https://www.mdpi.com/journal/processes Processes 2021, 9, x FOR PEER REVIEW 2 of 14 Processes 2021, 9, 1197 2 of 13 Today, modelling and simulations are commonly used for all production or logistics companies [9,10]. Due to the sensitivity of delivery processes, finding the optimal solution withwith the the highest highest possible possible quality quality is is necessary necessary [11,12]. [11,12]. Simulation Simulation can can be be especially especially useful useful inin cases cases involving involving a acomplex complex system system with with dynamic dynamic inputs inputs and and conditions conditions [13,14]. [13,14 It]. can It canbe usedbe used not notonly only for forthe thedetermination determination of ofa future a future object’s object’s state state but but also also to to determine determine the the optimaloptimal path path to to achieve achieve this this state state [15,16]. [15,16]. Genetic Genetic algorithms algorithms (GA) (GA) are are commonly commonly used used to to generategenerate high-quality high-quality solutions, solutions, as as well well as as to to optimise optimise and and search search for for problems problems by by relying relying onon biologically biologically inspired inspired operators operators such such as as mu mutations,tations, crossover, crossover, and and selection selection [17,18]. [17,18]. The The advantagesadvantages of of such such algorithms algorithms include, include, for for example, example, the the ability ability to toimplement implement GAs GAs as asa “universala “universal optimiser” optimiser” that that can canbe used be used to optimise to optimise any anytype typeof problem of problem belonging belonging to dif- to ferentdifferent areas areas [19,20]. [19, 20Genetic]. Genetic algorithms algorithms in the in field the field of logistics of logistics are an are area an areaof interest of interest for manyfor many researchers. researchers. Figure Figure 1 presents1 presents the thenumb numberer of publications of publications on genetic on genetic algorithm algorithm is- suesissues in the in the context context of logistics of logistics (based (based on ondata data from from the the Scopus Scopus database). database). FigureFigure 1. 1. NumberNumber of of publications publications on on genetic genetic algorithms algorithms and and logistics. logistics. AsAs will will be be demonstrated demonstrated further, further, this technique cancan solvesolve practical practical problems problems that that occur oc- curin logisticsin logistics companies. companies. This This technique technique has ha tremendouss tremendous efficiency efficiency and and performs performs well well in creating complex adaptations. A GA can find elegant, multifactorial solutions to problems in creating complex adaptations. A GA can find elegant, multifactorial solutions to prob- in hidden ways, problems that a researcher may be unaware of and that are unlikely to be lems in hidden ways, problems that a researcher may be unaware of and that are unlikely discovered by traditional engineering approaches [21,22]. to be discovered by traditional engineering approaches [21,22]. Based on [23], the selection of model structure and model parameter identification are Based on [23], the selection of model structure and model parameter identification two main problems in system identification. There is currently a growing number of uses of are two main problems in system identification. There is currently a growing number of genetic algorithms to solve various tasks because of its advantages, such as wide adaptabil- uses of genetic algorithms to solve various tasks because of its advantages, such as wide ity, stable calculation, high identification accuracy, and simultaneous determination of the adaptability, stable calculation, high identification accuracy, and simultaneous determi- model structure and parameters. A linear system, system order, time lag, and parameters nation of the model structure and parameters. A linear system, system order, time lag, can be constituted into a chromosome. The value of fitness function includes the modelling and parameters can be constituted into a chromosome. The value of fitness function in- error thanks to the system identification problem becoming an optimal problem that a GA cludes the modelling error thanks to the system identification problem becoming an opti- can solve. mal problemThere are that many a GA areas can wheresolve. genetic algorithms find application in optimisation, such as artificialThere are intelligence many areas [24 where], robotics genetic [25 algorithms], services find [26], application automatic controlin optimisation, [27], manufac- such asturing artificial [28], intelligence and warehouses. [24], robotics Genetic [25], algorithms services are [26], used automatic in warehouses control in[27], various manufac- ways, turingsuch as[28], solving and warehouses. problems related Genetic
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