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Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (20 January 2022) | Viewed by 24463

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Prof. Dr. Vladimir Modrak

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Faculty of Manufacturing Technologies, Technical University of Košice, 040 01 Presov, Slovakia
Interests: industrial engineering; production planning; manufacturing management; optimization algorithms; production engineering; mass customization; optimization methods; logistics
Special Issues, Collections and Topics in MDPI journals

Dr. Zuzana Soltysova

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Guest Editor

Department of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies, Technical University of Košice, Presov, Slovakia
Interests: production planning and schedulin; manufacturing management; simulation; mass customization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart manufacturing practice is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy or equivalent national policies and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but as an important practice within organizations since it significantly increases the productivity of manufacturing processes and brings other benefits for companies and their customers. The introduction of smart manufacturing systems is associated with the adaptation of the Internet of Things, cyberphysical systems, artificial intelligence, advanced robotics, cloud technology, and so forth. Moreover, the implementation of these technologies is paving the way for the digital evolution, which is impacting almost all industries and sectors worldwide. However, recent studies have shown that pre-existing managerial methods and philosophies such as lean manufacturing, reconfigurable manufacturing systems or cellular manufacturing systems are of utter importance for the concept of smart manufacturing. In this context, manufacturing system design and scheduling methods should be further improved. As a prime example of co-existence traditional, existing manufacturing methods and I4.0 technologies are efforts to develop integrative models supporting both lean manufacturing tools and I4.0 technologies. This Special Issue aims to collect original contributions related to designing and scheduling smart manufacturing systems.

Potential topics include but are not limited to the following:

Modern methods and techniques for designing layout manufacturing systems;
Innovative approaches for solving manufacturing cell formation problems;
Modelling and designing flexible and reconfigurable manufacturing systems;
Heuristics and metaheuristics for solving facility layout design problems;
Heuristics and metaheuristics for solving scheduling problems;
Multiobjective methods and techniques for solving design problems;
Modeling manufacturing processes for smart cyberphysical environments;
Modeling assembly processes for mass customized manufacturing;
Design of architecture for human–robot collaborative assembly systems;
Multiobjective optimization of mixed-model assembly line balancing problems.

Prof. Vladimir Modrak
Dr. Zuzana Soltysova
Guest Editors

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Keywords

optimization
facilities’ layout design
assembly line balancing
mass customization
scheduling problem

Published Papers (11 papers)

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Editorial

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4 pages, 170 KiB

Open AccessEditorial

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

by Vladimir Modrak and Zuzana Soltysova

Appl. Sci. 2022, 12(6), 3011; https://doi.org/10.3390/app12063011 - 16 Mar 2022

Viewed by 1343

Abstract

This Special Issue is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems [...] Full article

(This article belongs to the Special Issue Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems)

Research

Jump to: Editorial

23 pages, 7006 KiB

Open AccessArticle

Meta-Heuristic Technique-Based Parametric Optimization for Electrochemical Machining of Monel 400 Alloys to Investigate the Material Removal Rate and the Sludge

by Vengatajalapathi Nagarajan, Ayyappan Solaiyappan, Siva Kumar Mahalingam, Lenin Nagarajan, Sachin Salunkhe, Emad Abouel Nasr, Ragavanantham Shanmugam and Hussein Mohammed Abdel Moneam Hussein

Appl. Sci. 2022, 12(6), 2793; https://doi.org/10.3390/app12062793 - 09 Mar 2022

Cited by 11 | Viewed by 2047

Abstract

Electrochemical machining (ECM) is a preferred advanced machining process for machining Monel 400 alloys. During the machining, the toxic nickel hydroxides in the sludge are formed. Therefore, it becomes necessary to determine the optimum ECM process parameters that minimize the nickel presence (NP) emission in the sludge while maximizing the material removal rate (MRR). In this investigation, the predominant ECM process parameters, such as the applied voltage, flow rate, and electrolyte concentration, were controlled to study their effect on the performance measures (i.e., MRR and NP). A meta-heuristic algorithm, the grey wolf optimizer (GWO), was used for the multi-objective optimization of the process parameters for ECM, and its results were compared with the moth-flame optimization (MFO) and particle swarm optimization (PSO) algorithms. It was observed from the surface, main, and interaction plots of this experimentation that all the process variables influenced the objectives significantly. The TOPSIS algorithm was employed to convert multiple objectives into a single objective used in meta-heuristic algorithms. In the convergence plot for the MRR model, the PSO algorithm converged very quickly in 10 iterations, while GWO and MFO took 14 and 64 iterations, respectively. In the case of the NP model, the PSO tool took only 6 iterations to converge, whereas MFO and GWO took 48 and 88 iterations, respectively. However, both MFO and GWO obtained the same solutions of EC = 132.014 g/L, V = 2406 V, and FR = 2.8455 L/min with the best conflicting performances (i.e., MRR = 0.242 g/min and NP = 57.7202 PPM). Hence, it is confirmed that these metaheuristic algorithms of MFO and GWO are more suitable for finding the optimum process parameters for machining Monel 400 alloys with ECM. This work explores a greater scope for the ECM process with better machining performance. Full article

(This article belongs to the Special Issue Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems)

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14 pages, 1922 KiB

Open AccessArticle

A Multi-Criteria Assessment of Manufacturing Cell Performance Using the AHP Method

by Zuzana Soltysova, Vladimir Modrak and Julia Nazarejova

Appl. Sci. 2022, 12(2), 854; https://doi.org/10.3390/app12020854 - 14 Jan 2022

Cited by 5 | Viewed by 1763

Abstract

Research of manufacturing cell design problems is still pertinent today, because new manufacturing strategies, such as mass customization, call for further improvement of the fundamental performance of cellular manufacturing systems. The main scope of this article is to find the optimal cell design(s) from alternative design(s) by multi-criteria evaluation. For this purpose, alternative design solutions are mutually compared by using the selected performance criteria, namely operational complexity, production line balancing rate, and makespan. Then, multi-criteria decision analysis based on the analytic hierarchy process method is used to show that two more-cell solutions better satisfy the determined criteria of manufacturing cell design performance than three less-cell solutions. The novelty of this research approach refers to the use of the modification of Saaty’s scale for the comparison of alternatives in pairs based on the objective assessment of the designs. Its benefit lies in the exactly enumerated values of the selected criteria, according to which the points from the mentioned scale are assigned to the alternatives. Full article

(This article belongs to the Special Issue Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems)

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26 pages, 374 KiB

Open AccessArticle

A Hybrid Bat Algorithm for Solving the Three-Stage Distributed Assembly Permutation Flowshop Scheduling Problem

by Jianguo Zheng and Yilin Wang

Appl. Sci. 2021, 11(21), 10102; https://doi.org/10.3390/app112110102 - 28 Oct 2021

Cited by 8 | Viewed by 1488

Abstract

In this paper, a hybrid bat optimization algorithm based on variable neighbourhood structure and two learning strategies is proposed to solve a three-stage distributed assembly permutation flowshop scheduling problem to minimize the makespan. The algorithm is firstly designed to increase the population diversity by classifying the populations, which solves the difficult trade-off between convergence and diversity of the bat algorithm. Secondly, a selection mechanism is used to update the bat’s velocity and location, solving the difficulty of the algorithm to trade-off exploration and mining capacity. Finally, the Gaussian learning strategy and elite learning strategy assist the whole population to jump out of the local optimal frontier. The simulation results demonstrate that the algorithm proposed in this paper can well solve the DAPFSP. In addition, compared with other metaheuristic algorithms, IHBA has better performance and gives full play to its advantage of finding optimal solutions. Full article

(This article belongs to the Special Issue Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems)

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18 pages, 8825 KiB

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Optimization of Process Parameters for Turning Hastelloy X under Different Machining Environments Using Evolutionary Algorithms: A Comparative Study

by Vinothkumar Sivalingam, Jie Sun, Siva Kumar Mahalingam, Lenin Nagarajan, Yuvaraj Natarajan, Sachin Salunkhe, Emad Abouel Nasr, J. Paulo Davim and Hussein Mohammed Abdel Moneam Hussein

Appl. Sci. 2021, 11(20), 9725; https://doi.org/10.3390/app11209725 - 18 Oct 2021

Cited by 11 | Viewed by 1842

Abstract

In this research work, the machinability of turning Hastelloy X with a PVD Ti-Al-N coated insert tool in dry, wet, and cryogenic machining environments is investigated. The machinability indices namely cutting force (CF), surface roughness (SR), and cutting temperature (CT) are studied for the different set of input process parameters such as cutting speed, feed rate, and machining environment, through the experiments conducted as per L₂₇ orthogonal array. Minitab 17 is used to create quadratic Multiple Linear Regression Models (MLRM) based on the association between turning parameters and machineability indices. The Moth-Flame Optimization (MFO) algorithm is proposed in this work to identify the optimal set of turning parameters through the MLRM models, in view of minimizing the machinability indices. Three case studies by considering individual machinability indices, a combination of dual indices, and a combination of all three indices, are performed. The suggested MFO algorithm’s effectiveness is evaluated in comparison to the findings of Genetic, Grass-Hooper, Grey-Wolf, and Particle Swarm Optimization algorithms. From the results, it is identified that the MFO algorithm outperformed the others. In addition, a confirmation experiment is conducted to verify the results of the MFO algorithm’s optimal combination of turning parameters. Full article

(This article belongs to the Special Issue Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems)

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19 pages, 3167 KiB

Open AccessArticle

Harmony Search Algorithm for Minimizing Assembly Variation in Non-linear Assembly

by Siva Kumar Mahalingam, Lenin Nagarajan, Sachin Salunkhe, Emad Abouel Nasr, Jõao Paulo Davim and Hussein M. A. Hussein

Appl. Sci. 2021, 11(19), 9213; https://doi.org/10.3390/app11199213 - 03 Oct 2021

Cited by 7 | Viewed by 1239

Abstract

The proposed work aims to acquire the maximum number of non-linear assemblies with closer assembly tolerance specifications by mating the different bins’ components. Before that, the components are classified based on the range of tolerance values and grouped into different bins. Further, the manufacturing process of the components is selected from the given and known alternative processes. It is incredibly tedious to obtain the best combinations of bins and the best process together. Hence, a novel approach using the combination of the univariate search method and the harmony search algorithm is proposed in this work. Overrunning clutch assembly is taken as an example. The components of overrunning clutch assembly are manufactured with a wide tolerance value using the best process selected from the given alternatives by the univariate search method. Further, the manufactured components are grouped into three to nine bins. A combination of the best bins is obtained for the various assembly specifications by implementing the harmony search algorithm. The efficacy of the proposed method is demonstrated by showing 24.9% of cost-savings while making overrunning clutch assembly compared with the existing method. The efficacy of the proposed method is demonstrated by showing 24.9% of cost-savings while making overrunning clutch assembly compared with the existing method. The results show that the contribution of the proposed novel methodology is legitimate in solving selective assembly problems. Full article

(This article belongs to the Special Issue Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems)

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20 pages, 5514 KiB

Open AccessArticle

A Novel Methodology for Simultaneous Minimization of Manufacturing Objectives in Tolerance Allocation of Complex Assembly

by Lenin Nagarajan, Siva Kumar Mahalingam, Sachin Salunkhe, Emad Abouel Nasr, Jõao Paulo Davim and Hussein M. A. Hussein

Appl. Sci. 2021, 11(19), 9164; https://doi.org/10.3390/app11199164 - 02 Oct 2021

Cited by 2 | Viewed by 1460

Abstract

Tolerance cost and machining time play crucial roles while performing tolerance allocation in complex assemblies. The aim of the proposed work is to minimize the above-said manufacturing objectives for allocating optimum tolerance to the components of complex assemblies, by considering the proper process and machine selections from the given alternatives. A novel methodology that provides a two-step solution is developed for this work. First, a heuristic approach is applied to determine the best machine for each process, and then a combined whale optimization algorithm with a univariate search method is used to allocate optimum tolerances with the best process selection for each sub-stage/operation. The efficiency of the proposed novel methodology is validated by solving two typical tolerance allocation problems of complex assemblies: a wheel mounting assembly and a knuckle joint assembly. Compared with previous approaches, the proposed methodology showed a considerable reduction in tolerance cost and machining time in relatively less computation time. Full article

(This article belongs to the Special Issue Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems)

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19 pages, 812 KiB

Open AccessArticle

An Algorithm for Arranging Operators to Balance Assembly Lines and Reduce Operator Training Time

by Ming-Liang Li

Appl. Sci. 2021, 11(18), 8544; https://doi.org/10.3390/app11188544 - 14 Sep 2021

Cited by 1 | Viewed by 2582

Abstract

Industry 4.0 is transforming how costs, including labor costs, are managed in manufacturing and remanufacturing systems. Managers must balance assembly lines and reduce the training time of workstation operators to achieve sustainable operations. This study’s originality lies in its use of an algorithm to balance an assembly line by matching operators to workstations so that the line’s workstations achieve the same targeted output rates. First, the maximum output rate of the assembly line is found, and then the number of operators needed at each workstation is determined. Training time is reduced by matching operators’ training and skills to workstations’ skill requirements. The study obtains a robust, cluster algorithm based on the concept of group technology, then forms operator skill cells and determines operator families. Four numerical examples are presented to demonstrate the algorithm’s implementation. The proposed algorithm can solve the problem of arranging operators to balance assembly lines. Managers can also solve the problem of worker absences by assigning more than one operator with the required skillset to each workstation and rearranging them as needed. Full article

(This article belongs to the Special Issue Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems)

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18 pages, 815 KiB

Open AccessArticle

Reflections on the Customer Decision-Making Process in the Digital Insurance Platforms: An Empirical Study of the Baltic Market

by Gedas Baranauskas and Agota Giedrė Raišienė

Appl. Sci. 2021, 11(18), 8524; https://doi.org/10.3390/app11188524 - 14 Sep 2021

Cited by 7 | Viewed by 4020

Abstract

Multifold effects of the COVID-19 global health crisis and economic lockdowns are reflected in the insurance industry, and are predicted to expand to the post-COVID-19 era. It is expected that, within a short period of time, the current worldwide situation, in regards to the coronavirus pandemic, will be reflected in new trends, regarding customer behavior, organizational management, and culture, as well as reveal improved business management models, legacy infrastructure, and service systems in insurance organizations. Here, a focus on end-user preferences, data, and their behavior modeling in digital platforms are major practical drivers within the modern insurance concept, but there is a paucity of researches within the theoretical synthesis of consumer decision-making (CDM) models, information system theories, and behavioral economics concerning modern insurance-specific value chains and digitalized decision-making processes. Therefore, the following research aims to expand upon the existing scientific knowledge of end-user behavioral patterns and process frameworks in the Baltic insurance market, by including and examining a factor group of technological enablers and a digital environment. Research results in digitalization, personalization, and customization levels within the Baltic non-life insurance market are homogenous with a leading position of Estonia and overall evaluations ranging between Satisfied and Rather Good. There are also three major factor groups and process stages identified, which influence insurance purchase decision-making in digital insurance platforms in the Baltic market. Full article

(This article belongs to the Special Issue Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems)

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10 pages, 596 KiB

Open AccessFeature PaperArticle

Calibration of GA Parameters for Layout Design Optimization Problems Using Design of Experiments

by Vladimir Modrak, Ranjitharamasamy Sudhakara Pandian and Pavol Semanco

Appl. Sci. 2021, 11(15), 6940; https://doi.org/10.3390/app11156940 - 28 Jul 2021

Cited by 5 | Viewed by 1857

Abstract

In manufacturing-cell-formation research, a major concern is to make groups of machines into machine cells and parts into part families. Extensive work has been carried out in this area using various models and techniques. Regarding these ideas, in this paper, experiments with varying parameters of the popular metaheuristic algorithm known as the genetic algorithm have been carried out with a bi-criteria objective function: the minimization of intercell moves and cell load variation. The probability of crossover (A), probability of mutation (B), and balance weight factor (C) are considered parameters for this study. The data sets used in this paper are taken from benchmarked literature in this field. The results are promising regarding determining the optimal combination of the genetic parameters for the machine-cell-formation problems considered in this study. Full article

(This article belongs to the Special Issue Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems)

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16 pages, 1205 KiB

Open AccessArticle

Unified Genetic Algorithm Approach for Solving Flexible Job-Shop Scheduling Problem

by Jin-Sung Park, Huey-Yuen Ng, Tay-Jin Chua, Yen-Ting Ng and Jun-Woo Kim

Appl. Sci. 2021, 11(14), 6454; https://doi.org/10.3390/app11146454 - 13 Jul 2021

Cited by 17 | Viewed by 2575

Abstract

This paper proposes a novel genetic algorithm (GA) approach that utilizes a multichromosome to solve the flexible job-shop scheduling problem (FJSP), which involves two kinds of decisions: machine selection and operation sequencing. Typically, the former is represented by a string of categorical values, whereas the latter forms a sequence of operations. Consequently, the chromosome of conventional GAs for solving FJSP consists of a categorical part and a sequential part. Since these two parts are different from each other, different kinds of genetic operators are required to solve the FJSP using conventional GAs. In contrast, this paper proposes a unified GA approach that enables the application of an identical crossover strategy in both the categorical and sequential parts. In order to implement the unified approach, the sequential part is evolved by applying a candidate order-based GA (COGA), which can use traditional crossover strategies such as one-point or two-point crossovers. Such crossover strategies can also be used to evolve the categorical part. Thus, we can handle the categorical and sequential parts in an identical manner if identical crossover points are used for both. In this study, the unified approach was used to extend the existing COGA to a unified COGA (u-COGA), which can be used to solve FJSPs. Numerical experiments reveal that the u-COGA is useful for solving FJSPs with complex structures. Full article

(This article belongs to the Special Issue Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems)

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