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Design and Optimization of Manufacturing Systems

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

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 18310

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Prof. Dr. Zoran Jurković

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Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Interests: modeling and optimization of processes; machine tools; application of evolutionary algorithms and other natural-based algorithms; process efficiency; energy savings in production processes
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Dr. David Ištoković

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Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Interests: production planning and scheduling; simulation of production processes; batch sizing; operations management; optimization algorithms
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Dr. Janez Gotlih

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Guest Editor

Faculty of Mechanical Engineering, University of Maribor, Smetanova ul. 17, 2000 Maribor, Slovenia
Interests: smart production and manufacturing engineering; Industry 4.0; robotics and assembly systems; collaborative systems; simulation modelling; machine learning and optimization in production engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today's market demands require great efforts to ensure the survival and competitiveness of production companies. Production companies are required to produce certain products of sufficient quality with the lowest possible production costs and timely delivery. In this context, production planning activities are of great importance. These activities are primarily related to the design of a suitable manufacturing system and the planning and optimization of the manufacturing process. Production planning problems represent a group of problems with high computational complexity, and finding a suitable solution is extremely challenging. Traditional approaches to manufacturing system design and production planning in general are not capable of capturing information in real-time and responding quickly to changes in the production environment. For this reason, new theoretical and practical solutions are of great importance.

This Special Issue collects research on the design and optimization of manufacturing systems to increase efficiency, reduce production costs, improve sustainability, and other performance measures relevant to today's production environments. Research papers that provide solutions to current real-world problems are also welcome. Therefore, all experts are invited to submit their research contributions for a Special Issue of Applied Sciences, "Design and optimization of manufacturing systems." We look forward to receiving your contributions to this Special Issue.

Prof. Dr. Zoran Jurković
Dr. David Ištoković
Dr. Janez Gotlih
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

manufacturing systems
layout design
industry 4.0
smart factory
production planning
scheduling
optimization
simulation

Related Special Issue

Design and Optimization of Manufacturing Systems, 2nd Edition in Applied Sciences (5 articles)

Published Papers (11 papers)

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Research

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39 pages, 5525 KiB

Open AccessArticle

Designing Sustainable Flexible Manufacturing Cells with Multi-Objective Optimization Models

by Emine Bozoklar and Ebru Yılmaz

Appl. Sci. 2024, 14(1), 203; https://doi.org/10.3390/app14010203 - 25 Dec 2023

Cited by 1 | Viewed by 655

Abstract

Having sustainable and flexible features is crucial for manufacturing companies considering the increasing competition in the globalized world. This study considers three aspects of sustainability, namely economic, social, and environmental factors, in the design of flexible manufacturing cells. Three different multi-objective integer mathematical programming models were developed with the objective of minimizing the costs associated with carbon emissions, inter-cellular movements, machine processing, machine replacement, worker training, and additional salary (bonus). Simultaneously, these models aim to minimize the carbon emission amount of the cells within the environmental dimension scope. The developed models are a goal programming model, an epsilon constraint method, and an augmented epsilon constraint (AUGMECON) method. In these models, alternative routes of parts are considered while assigning parts to machines. The results are obtained using the LINGO 20.0 optimization program with a developed illustrative example. The obtained results are tested and compared by performing sensitivity analyses. The sensitivity analyses include examinations of the effects of changes in part demands, machine capacity values, carbon limit value, and the maximum number of workers in cells. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems)

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30 pages, 4741 KiB

Open AccessArticle

Optimisation of Buffer Allocations in Manufacturing Systems: A Study on Intra and Outbound Logistics Systems Using Finite Queueing Networks

by Mohamed Amjath, Laoucine Kerbache, James MacGregor Smith and Adel Elomri

Appl. Sci. 2023, 13(17), 9525; https://doi.org/10.3390/app13179525 - 23 Aug 2023

Cited by 2 | Viewed by 1697

Abstract

Optimal buffer allocations can significantly improve system throughput by managing variability and disruptions in manufacturing or service operations. Organisations can minimise waiting times and bottlenecks by strategically placing buffers along the flow path, leading to a smoother and more efficient production or service delivery process. Determining the optimal size of buffers poses a challenging dilemma, as it involves balancing the cost of buffer allocation, system throughput, and waiting times at each service station. This paper presents a framework that utilises finite queueing networks for performance analysis and optimisation of topologies, specifically focusing on buffer allocations. The proposed framework incorporates a finite closed queuing network to model the intra-logistics material transfer process and a finite open queueing network to model the outbound logistics process within a manufacturing setup. The generalised expansion method (GEM) is employed to calculate network performance measures of the system, considering the blocking phenomenon. Discrete event simulation (DES) models are constructed using simulation software, integrating optimisation configurations to determine optimal buffer allocations to maximise system throughput. The findings of this study have significant implications for decision-making processes and offer opportunities to enhance the efficiency of manufacturing systems. By leveraging the proposed framework, organisations can gain valuable insights into supply chain performance, identify potential bottlenecks, and optimise buffer allocations to achieve improved operational efficiency and overall system throughput. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems)

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24 pages, 3958 KiB

Open AccessArticle

Production Planning Forecasting System Based on M5P Algorithms and Master Data in Manufacturing Processes

by Hasup Song, Injong Gi, Jihyuk Ryu, Yonghwan Kwon and Jongpil Jeong

Appl. Sci. 2023, 13(13), 7829; https://doi.org/10.3390/app13137829 - 03 Jul 2023

Viewed by 1712

Abstract

With the increasing adoption of smart factories in manufacturing sites, a large amount of raw data is being generated from manufacturers’ sensors and Internet of Things devices. In the manufacturing environment, the collection of reliable data has become an important issue. When utilizing the collected data or establishing production plans based on user-defined data, the actual performance may differ from the established plan. This is particularly so when there are modifications in the physical production line, such as manual processes, newly developed processes, or the addition of new equipment. Hence, the reliability of the current data cannot be ensured. The complex characteristics of manufacturers hinder the prediction of future data based on existing data. To minimize this reliability problem, the M5P algorithm, is used to predict dynamic data using baseline information that can be predicted. It combines linear regression and decision-tree-supervised machine learning algorithms. The algorithm recommends the means to reflect the predicted data in the production plan and provides results that can be compared with the existing baseline information. By comparing the existing production plan with the planning results based on the changed master data, it provides data results that help production management determine the impact of work time and quantity and confirm production plans. This means that forecasting data directly affects production capacity and resources, as well as production times and schedules, to help ensure efficient production planning. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems)

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24 pages, 5930 KiB

Open AccessArticle

Load Balancing of Two-Sided Assembly Line Based on Deep Reinforcement Learning

by Guangpeng Jia, Yahui Zhang, Shuqi Shen, Bozu Liu, Xiaofeng Hu and Chuanxun Wu

Appl. Sci. 2023, 13(13), 7439; https://doi.org/10.3390/app13137439 - 23 Jun 2023

Viewed by 963

Abstract

In the complex and ever-changing manufacturing environment, maintaining the long-term steady and efficient work of the assembly line is the ultimate goal pursued by relevant enterprises, the foundation of which is a balanced load. Therefore, this paper carries out research on the two-sided assembly line balance problem (TALBP) for load balancing. At first, a mathematical programming model is established with the objectives of optimizing the line efficiency, smoothness index, and completion time smoothness index of the two-sided assembly line (TAL). Secondly, a deep reinforcement learning algorithm combining distributed proximal policy optimization (DPPO) and the convolutional neural network (CNN) is proposed. Based on the distributed reinforcement learning agent structure assisted by the marker layer, the task assignment states of the two-sided assembly and decisions of selecting tasks are defined. Task assignment logic and reward function are designed according to the optimization objectives to guide task selection and assignment. Finally, the performance of the proposed algorithm is verified on the benchmark problem. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems)

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25 pages, 8211 KiB

Open AccessArticle

Robustness Optimization of Cloud Manufacturing Process under Various Resource Substitution Strategies

by Xiaodong Zhang, Xin Zheng and Yiqi Wang

Appl. Sci. 2023, 13(13), 7418; https://doi.org/10.3390/app13137418 - 22 Jun 2023

Cited by 2 | Viewed by 912

Abstract

Cloud manufacturing is characterized by large uncertainties and disturbances due to its networked, distributed, and loosely coupled features. To target the problem of frequent cloud resource node failure, this paper proposes (1) three resource substitution strategies based on node redundancy and (2) a new robustness analysis method for cloud manufacturing systems based on a combination of the complex network and multi-agent simulation. First, a multi-agent simulation model is constructed, and simulation evaluation indexes are designed to study the robustness of the dynamic cloud manufacturing process (CMP). Second, a complex network model of cloud manufacturing resources is established to analyze the static topological robustness of the cloud manufacturing network. Four types of node failure modes are defined, based on the initial and recomputed topologies. Further, three resource substitution strategies are proposed (i.e., internal replacement, external replacement, and internal–external integration replacement) to enable the normal operation of the system after resource node failure. Third, a case study is conducted for a cloud manufacturing project of a new energy vehicle. The results show that (1) the proposed robustness of service index is effective at describing the variations in CMP robustness, (2) the two node failure modes based on the recalculated topology are more destructive to the robustness of the CMP than the two based on the initial topology, and (3) under all four failure modes, all three resource substitution strategies can improve the robustness of the dynamic CMP to some extent, with the internal–external integration replacement strategy being most effective, followed by the external replacement strategy, and then the internal replacement strategy. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems)

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10 pages, 1574 KiB

Open AccessCommunication

Density-Based Prioritization Algorithm for Minimizing Surplus Parts in Selective Assembly

by Kanghyeon Shin and Kyohong Jin

Appl. Sci. 2023, 13(11), 6648; https://doi.org/10.3390/app13116648 - 30 May 2023

Viewed by 726

Abstract

Selective assembly is a manufacturing method that matches and assembles pairs of parts in a manner that offsets the machining errors of these parts. In the production of products requiring high precision and efficient mass production, flow production and search-based selective assembly must be combined for market competitiveness; however, this method increases computational costs and generates many surplus parts. Therefore, research should aim to minimize surplus parts in search-based selective assembly at a low computational cost to suit flow production systems. In this paper, we propose the density-based prioritization (DBP) algorithm, which minimizes surplus parts in the search-based selective assembly of flow production systems. In addition, a method of varying the assembly tolerance is developed and incorporated into DBP to increase its process capability. The proposed algorithm requires an assembly facility to prepare parts with as many different sizes as possible. This paper confirms that DBP reduces computational costs and surplus parts while enhancing process capability. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems)

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18 pages, 3320 KiB

Open AccessArticle

Assembly Line Optimization Using MTM Time Standard and Simulation Modeling—A Case Study

by Matic Breznik, Borut Buchmeister and Nataša Vujica Herzog

Appl. Sci. 2023, 13(10), 6265; https://doi.org/10.3390/app13106265 - 20 May 2023

Cited by 3 | Viewed by 2985

Abstract

This study presents an approach to solving the assembly line balancing problem (ALBP) using the Methods-Time Measurement (MTM) time standard and simulation software. ALBP is a common problem in manufacturing where a set of tasks with fixed times must be assigned to a series of sequential workstations in order to minimize the total idle time and reduce the assembly cost per product. This study uses MTM, a widely used production process scheduling method, to create a new time analysis of an assembly process that was previously balanced using the Work-Factor method and time study. This literature review shows that there are a lack of combinations of updated time analyses with newer simulation approaches in the current literature, and this was the motivation for the present work. An assembly line simulation was performed using Simio software to evaluate different design options and operating scenarios. The results show that the use of MTM and simulation can help minimize idle time and improve assembly line performance, thereby reducing costs and increasing efficiency. This study shows that the approach of using MTM and simulation is effective in solving ALBP and is a useful tool for manufacturing companies to improve the performance of their assembly lines and reduce costs. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems)

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21 pages, 868 KiB

Open AccessArticle

Mixed-Integer Linear Programming, Constraint Programming and a Novel Dedicated Heuristic for Production Scheduling in a Packaging Plant

by Soukaina Oujana, Lionel Amodeo, Farouk Yalaoui and David Brodart

Appl. Sci. 2023, 13(10), 6003; https://doi.org/10.3390/app13106003 - 13 May 2023

Cited by 3 | Viewed by 1542

Abstract

In this paper, we are discussing a research project aiming to optimize the scheduling of production orders within a real application in the packaging field. As a first approach, we model the problem as an extended version of the hybrid and flexible flowshop scheduling problem with precedence constraints, parallel machines, and sequence-dependent setups. The optimization objective considered is the minimization of the total tardiness. To tackle this problem, we use two methodologies: mixed-integer linear programming (MILP) and constraint programming (CP). These two models were further extended by adding resource calendar constraints named also availability constraints; this implies that the tasks should be scheduled only when the machine is available. The different proposed models were compared to each other on a set of generated benchmarks that reflect the specific properties of the industrial partner. Finally, as the studied configuration relies on practical real-world application, where thousands of orders are produced monthly, a novel dedicated heuristic was designed to address the need for quick solutions. The latter outperforms the other proposed algorithms for expected total tardiness minimization. The proposed problem can be readily modified to suit a wide range of real-world situations involving the scheduling of activities that share similar characteristics. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems)

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14 pages, 2941 KiB

Open AccessArticle

Switch-Off Policies in Job Shop Controlled by Workload Control Concept

by Paolo Renna

Appl. Sci. 2023, 13(8), 5210; https://doi.org/10.3390/app13085210 - 21 Apr 2023

Viewed by 787

Abstract

The reduction in emissions and the increase in energy costs push companies to identify solutions to reduce energy consumption in production systems. One of the approaches proposed in the literature is the shutdown of machines to reduce energy consumption in the idle state. This solution does not affect production processes and can be applied in various manufacturing fields. This paper proposes switch-off policies in manufacturing systems under a workload control system. The shutdown policies developed consider the number of items in the queue and the calculation derived from the workload control mechanism. Simulation models have been developed to test the proposed policies using the case always on as a benchmark, considering different levels of absorbed power in the inactivity and warm-up states and different warm-up times. The results highlight how the switch policies that include the workload evaluation drastically reduce the number of on/off activities, assuring lower energy consumption. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems)

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12 pages, 839 KiB

Open AccessArticle

Application of Modified Steady-State Genetic Algorithm for Batch Sizing and Scheduling Problem with Limited Buffers

by Gordan Janeš, David Ištoković, Zoran Jurković and Mladen Perinić

Appl. Sci. 2022, 12(22), 11512; https://doi.org/10.3390/app122211512 - 12 Nov 2022

Cited by 4 | Viewed by 1511

Abstract

Batch sizing and scheduling problems are usually tough to solve because they seek solutions in a vast combinatorial space of possible solutions. This research aimed to test and further develop a scheduling method based on a modified steady-state genetic algorithm and test its performance, in both the speed (low computational time) and quality of the final results as low makespan values. This paper explores the problem of determining the order and size of the product batches in a hybrid flow shop with a limited buffer according to the problem that is faced in real-life. Another goal of this research was to develop a new reliable software/computer program tool in c# that can also be used in production, and as result, obtain a flexible software solution for further research. In all of the optimizations, the initial population of the genetic algorithm was randomly generated. The quality of the obtained results, and the short computation time, together with the flexibility of the genetic paradigm prove the effectiveness of the proposed algorithm and method to solve this problem. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems)

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Review

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26 pages, 2882 KiB

Open AccessReview

Task Complexity and the Skills Dilemma in the Programming and Control of Collaborative Robots for Manufacturing

by Peter George, Chi-Tsun Cheng, Toh Yen Pang and Katrina Neville

Appl. Sci. 2023, 13(7), 4635; https://doi.org/10.3390/app13074635 - 06 Apr 2023

Viewed by 2859

Abstract

While traditional industrial robots participate in repetitive manufacturing processes from behind caged safety enclosures, collaborative robots (cobots) offer a highly flexible and human-interactive solution to manufacturing automation. Rather than operating from within cages, safety features such as force and proximity sensors and programmed protection zones allow cobots to work safely, close to human workers. Cobots can be configured to either stop or slow their motion if they come in contact with a human or obstacle or enter a protection zone, which may be a high pedestrian traffic area. In this way, a task can be divided into sub-processes allocated to the cobot or the human based on suitability, capability or human preference. The flexible nature of the cobot makes it ideal for low-volume, ‘just-in-time’ manufacturing; however, this requires frequent reprogramming of the cobot to adapt to the dynamic processes. This paper reviews relevant cobot programming and control methods currently used in the manufacturing industry and alternative solutions proposed in the literature published from 2018 to 2023. The paper aims to (1) study the features and characteristics of existing cobot programming and control methods and those proposed in the literature, (2) compare the complexity of the task that the cobot is to perform with the skills needed to program it, (3) determine who is the ideal person to perform the programming role, and (4) assess whether the cobot programming and control methods are suited to that person’s skillset or if another solution is needed. The study is presented as a guide for potential adopters of cobots for manufacturing and a reference for further research. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems)

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