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Special Issue "Sustainable Intelligent Manufacturing Systems"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (15 May 2019)

Special Issue Editors

Guest Editor
Prof. Dr. Miguel A. Salido

Institute of Control Systems and Industrial Computing, Universitat Politècnica de València, Camino de Vera s/n, 46071, Valencia, Spain
Website | E-Mail
Interests: constraint satisfaction problem; scheduling; robust scheduling; distributed constraints; railway scheduling; green/sustainable manufacturing; metaheuristics
Guest Editor
Prof. Dr. Adriana Giret

Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, Spain
Website | E-Mail
Interests: multi agent systems; intelligent manufacturing systems; agent-supported simulation for manufacturing systems; applications of multi agent systems; sustainable intelligent manufacturing systems

Special Issue Information

Dear Colleagues,

The urgent need for sustainable development is imposing radical changes in the way manufacturing systems are designed and implemented. This urgent requirement has arisen due to several established and emerging causes: Environmental concerns, diminishing non-renewable resources, stricter legislation and inflated energy costs, increasing consumer preference for environmentally friendly products, etc. Moreover, the overall sustainability in industrial activities of manufacturing companies must be achieved at the same time that they face unprecedented levels of global competition.

Many research works have been reported in the Intelligent Manufacturing Systems literature, proposing different approaches to tackle sustainability at different levels of the whole manufacturing system. Nevertheless, there are open problems that still remain unsolved and require urgent attention from academia and industry practitioners.

The objective of this Special Issue is to provide a snapshot of the status, potential, challenges, and recent developments of intelligent solutions for sustainable manufacturing systems. We invite researchers to contribute original research articles as well as review articles that will stimulate the continuing efforts to improve the current state-of-the-art within the field.

Prof. Dr. Miguel A. Salido
Dr. Adriana Giret
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 papers will be 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. Sustainability 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 1700 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

  • intelligent approaches for sustainable manufacturing systems and circular economy
  • sustainable optimization techniques for manufacturing system operations
  • intelligent approaches for green supply chains
  • Cyber Physical Systems and IoT
  • developments for sustainable intelligent manufacturing systems
  • intelligent theoretical models for sustainable manufacturing systems
  • application of distributed intelligent models and solutions for sustainable manufacturing systems

Published Papers (13 papers)

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Research

Open AccessArticle
Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption
Sustainability 2019, 11(11), 3127; https://doi.org/10.3390/su11113127
Received: 15 May 2019 / Revised: 28 May 2019 / Accepted: 29 May 2019 / Published: 3 June 2019
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Abstract
In the context of supply chain sustainability, Physical Internet (PI or π) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and [...] Read more.
In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail–Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail–Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed. Full article
(This article belongs to the Special Issue Sustainable Intelligent Manufacturing Systems)
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Open AccessArticle
An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints
Sustainability 2019, 11(11), 3085; https://doi.org/10.3390/su11113085
Received: 30 April 2019 / Revised: 29 May 2019 / Accepted: 29 May 2019 / Published: 31 May 2019
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Abstract
Nowadays, the manufacturing industry faces the challenge of reducing energy consumption and the associated environmental impacts. Production scheduling is an effective approach for energy-savings management. During the entire workshop production process, both the processing and transportation operations consume large amounts of energy. To [...] Read more.
Nowadays, the manufacturing industry faces the challenge of reducing energy consumption and the associated environmental impacts. Production scheduling is an effective approach for energy-savings management. During the entire workshop production process, both the processing and transportation operations consume large amounts of energy. To reduce energy consumption, an energy-efficient job-shop scheduling problem (EJSP) with transportation constraints was proposed in this paper. First, a mixed-integer programming model was established to minimize both the comprehensive energy consumption and makespan in the EJSP. Then, an enhanced estimation of distribution algorithm (EEDA) was developed to solve the problem. In the proposed algorithm, an estimation of distribution algorithm was employed to perform the global search and an improved simulated annealing algorithm was designed to perform the local search. Finally, numerical experiments were implemented to analyze the performance of the EEDA. The results showed that the EEDA is a promising approach and that it can solve EJSP effectively and efficiently. Full article
(This article belongs to the Special Issue Sustainable Intelligent Manufacturing Systems)
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Open AccessArticle
A Novel Reverse Logistics Network Design Considering Multi-Level Investments for Facility Reconstruction with Environmental Considerations
Sustainability 2019, 11(9), 2710; https://doi.org/10.3390/su11092710
Received: 15 March 2019 / Revised: 6 May 2019 / Accepted: 8 May 2019 / Published: 13 May 2019
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Abstract
Reverse logistics is convincingly one of the most efficient solutions to reduce environmental pollution and waste of resources by capturing and recovering the values of the used products. Many studies have been developed for decision-making at tactical, practical, and operational levels of the [...] Read more.
Reverse logistics is convincingly one of the most efficient solutions to reduce environmental pollution and waste of resources by capturing and recovering the values of the used products. Many studies have been developed for decision-making at tactical, practical, and operational levels of the reverse supply chain. However, many enterprises face a challenge that is how to design the reverse logistics networks into their existing forward logistics networks to account for both economic and environmental sustainability. In this case, it is necessary to design a novel reverse logistics network by reconstructing the facilities based on the existing forward logistics network. Multi-level investments are considered for facility reconstruction because more investment and more advanced remanufacturing technologies need to be applied to reduce the carbon emissions and improve facility capacities. Besides, uncertain elements include the demand for new products and return quantity of used products, making this problem challenging. To handle those uncertain elements, a bi-objective stochastic integer nonlinear programming model is proposed to facilitate this novel reverse logistics network design problem with economic and environmental objectives, where tactical decisions of facility locations, investment level choices, item flows, and vehicle assignments are involved. To show the applicability and computational efficiency of the proposed model, several numerical experiments with sensitivity analysis are provided. Finally, the trade-off between the profit and carbon emissions is presented and the sensitive analysis of changing several key input parameters is also discussed. Full article
(This article belongs to the Special Issue Sustainable Intelligent Manufacturing Systems)
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Open AccessArticle
Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing
Sustainability 2019, 11(9), 2619; https://doi.org/10.3390/su11092619
Received: 1 April 2019 / Revised: 26 April 2019 / Accepted: 30 April 2019 / Published: 7 May 2019
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Abstract
Service management in cloud manufacturing (CMfg), especially the service selection and scheduling (SSS) problem has aroused general attention due to its broad industrial application prospects. Due to the diversity of CMfg services, SSS usually need to take into account multiple objectives in terms [...] Read more.
Service management in cloud manufacturing (CMfg), especially the service selection and scheduling (SSS) problem has aroused general attention due to its broad industrial application prospects. Due to the diversity of CMfg services, SSS usually need to take into account multiple objectives in terms of time, cost, quality, and environment. As one of the keys to solving multi-objective problems, the preference information of decision maker (DM) is less considered in current research. In this paper, linguistic preference is considered, and a novel two-phase model based on a desirable satisfying degree is proposed for solving the multi-objective SSS problem with linguistic preference. In the first phase, the maximum comprehensive satisfying degree is calculated. In the second phase, the satisfying solution is obtained by repeatedly solving the model and interaction with DM. Compared with the traditional model, the two-phase is more effective, which is verified in the calculation experiment. The proposed method could offer useful insights which help DM balance multiple objectives with linguistic preference and promote sustainable development of CMfg. Full article
(This article belongs to the Special Issue Sustainable Intelligent Manufacturing Systems)
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Open AccessArticle
Validation of Sustainability Benchmarking Tool in the Context of Value-Added Wood Products Manufacturing Activities
Sustainability 2019, 11(8), 2361; https://doi.org/10.3390/su11082361
Received: 6 March 2019 / Revised: 8 April 2019 / Accepted: 15 April 2019 / Published: 19 April 2019
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Abstract
The primary objective of this study was to validate the sustainability benchmarking tool (SBT) framework proposed by the authors in a previous study. The SBT framework is focused on benchmarking triple bottom line (TBL) sustainability through exhaustive use of lean, six-sigma, and life [...] Read more.
The primary objective of this study was to validate the sustainability benchmarking tool (SBT) framework proposed by the authors in a previous study. The SBT framework is focused on benchmarking triple bottom line (TBL) sustainability through exhaustive use of lean, six-sigma, and life cycle assessment (LCA). During the validation, sustainability performance of a value-added wood products’ production line was assessed and improved through deployment of the SBT framework. Strengths and weaknesses of the system were identified within the scope of the bronze frontier maturity level of the framework and tackled through a six-step analytical and quantitative reasoning methodology. The secondary objective of the study was to document how value-added wood products industries can take advantage of natural properties of wood to become frontiers of sustainability innovation. In the end, true sustainability performance of the target facility was improved by 2.37 base points, while economic and environmental performance was increased from being a system weakness to achieving an acceptable index score benchmark of 8.41 and system strength level of 9.31, respectively. The social sustainability score increased by 2.02 base points as a function of a better gender bias ratio. The financial performance of the system improved from a 33% loss to 46.23% profit in the post-improvement state. Reductions in CO2 emissions (55.16%), energy consumption (50.31%), solid waste generation (72.03%), non-value-added-time (89.30%), and cost performance (64.77%) were other significant achievements of the study. In the end, the SBT framework was successfully validated at the facility level, and the target facility evolved into a leaner, cleaner, and more responsible version of itself. This study empirically documents how synergies between lean, sustainability, six-sigma and life cycle assessment concepts outweigh their divergences and demonstrates the viability of the SBT framework. Full article
(This article belongs to the Special Issue Sustainable Intelligent Manufacturing Systems)
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Open AccessArticle
A Balancing Method of Mixed-model Disassembly Line in Random Working Environment
Sustainability 2019, 11(8), 2304; https://doi.org/10.3390/su11082304
Received: 29 March 2019 / Revised: 10 April 2019 / Accepted: 12 April 2019 / Published: 17 April 2019
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Abstract
Disassembly is a necessary link in reverse supply chain and plays a significant role in green manufacturing and sustainable development. However, the mixed-model disassembly of multiple types of retired mechanical products is hard to be implemented by random influence factors such as service [...] Read more.
Disassembly is a necessary link in reverse supply chain and plays a significant role in green manufacturing and sustainable development. However, the mixed-model disassembly of multiple types of retired mechanical products is hard to be implemented by random influence factors such as service time of retired products, degree of wear and tear, proficiency level of workers and structural differences between products in the actual production process. Therefore, this paper presented a balancing method of mixed-model disassembly line in a random working environment. The random influence of structure similarity of multiple products on the disassembly line balance was considered and the workstation number, load balancing index, prior disassembly of high demand parts and cost minimization of invalid operations were taken as targets for the balancing model establishment of the mixed-model disassembly line. An improved algorithm, adaptive simulated annealing genetic algorithm (ASAGA), was adopted to solve the balancing model and the local and global optimization ability were enhanced obviously. Finally, we took the mixed-model disassembly of multi-engine products as an example and verified the practicability and effectiveness of the proposed model and algorithm through comparison with genetic algorithm (GA) and simulated annealing algorithm (SA). Full article
(This article belongs to the Special Issue Sustainable Intelligent Manufacturing Systems)
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Open AccessArticle
Sustainable Scheduling of an Automatic Pallet Changer System by Multi-Objective Evolutionary Algorithm with First Piece Inspection
Sustainability 2019, 11(5), 1498; https://doi.org/10.3390/su11051498
Received: 30 January 2019 / Revised: 7 March 2019 / Accepted: 7 March 2019 / Published: 12 March 2019
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Abstract
In this study, the machining center with the Automated Pallet Changer (APC) scheduling problem considering the disturbance of the first piece inspection is presented. The APC is frequently used in industry practice; it is useful in terms of sustainability and robustness because it [...] Read more.
In this study, the machining center with the Automated Pallet Changer (APC) scheduling problem considering the disturbance of the first piece inspection is presented. The APC is frequently used in industry practice; it is useful in terms of sustainability and robustness because it increases the machine utilization rate and enhances the responsiveness to uncertainties in dynamic environments. An enhanced evolutionary algorithm for APC scheduling (APCEA) is developed by combining the multi-objective evolutionary algorithm with APC simulation. The dynamic factors in the simulation model include the pass rate of the first piece inspection (FPI) and the adjusted time when the FPI is unpassed. The proposed APCEA defines the non-robust gene based on the risk combination of the first piece inspection, and screens the non-robust gene in the genetic operation, thus improving the solution quality under the same computation times. Compared with the other three multi-objective evolutionary algorithms (MOEAs), it is demonstrated that the proposed APCEA produces the best result among the four methods. The proposed APCEA has been embedded into the manufacturing execution system (MES) and successfully applied in a manufacturing plant. The application value of the proposed method is verified by a practical example. Full article
(This article belongs to the Special Issue Sustainable Intelligent Manufacturing Systems)
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Open AccessArticle
Real-Time Early Warning System for Sustainable and Intelligent Plastic Film Manufacturing
Sustainability 2019, 11(5), 1490; https://doi.org/10.3390/su11051490
Received: 1 January 2019 / Revised: 5 March 2019 / Accepted: 7 March 2019 / Published: 12 March 2019
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Abstract
In this study, real-time preventive measures were formulated for a crusher process that is impossible to automate, due to the impossibility of installing sensors during the production of plastic films, and a real-time early warning system for semi-automated processes subsequently developed. First, the [...] Read more.
In this study, real-time preventive measures were formulated for a crusher process that is impossible to automate, due to the impossibility of installing sensors during the production of plastic films, and a real-time early warning system for semi-automated processes subsequently developed. First, the flow of a typical film process was ascertained. Second, a sustainable plan for real-time forecasting in a process that cannot be automated was developed using the semi-automation method flexible structure production control (FSPC). Third, statistical early selection of the process variables that are most probably responsible for failure was performed during data preprocessing. Then, a new, unified dataset was created using the link reordering method to transform the time sequence of the continuous process into one time zone. Fourth, a sustainable prediction algorithm was developed using the association rule method along with traditional statistical techniques, and verified using actual data. Finally, the overall developed logic was applied to new production process data to verify its prediction accuracy. The developed real-time early warning system for semi-automated processes contributes significantly to the smart manufacturing process both theoretically and practically. Full article
(This article belongs to the Special Issue Sustainable Intelligent Manufacturing Systems)
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Open AccessArticle
A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode
Sustainability 2019, 11(5), 1329; https://doi.org/10.3390/su11051329
Received: 22 January 2019 / Revised: 21 February 2019 / Accepted: 22 February 2019 / Published: 4 March 2019
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Abstract
Aimed at the problem of the green scheduling problem with automated guided vehicles (AGVs) in flexible manufacturing systems (FMS), the multi-objective and multi-dimensional optimal scheduling process is defined while considering energy consumption and multi-function of machines. The process is a complex and combinational [...] Read more.
Aimed at the problem of the green scheduling problem with automated guided vehicles (AGVs) in flexible manufacturing systems (FMS), the multi-objective and multi-dimensional optimal scheduling process is defined while considering energy consumption and multi-function of machines. The process is a complex and combinational process, considering this characteristic, a mathematical model was developed and integrated with evolutionary algorithms (EAs), which includes a sectional encoding genetic algorithm (SE-GA), sectional encoding discrete particle swarm optimization (SE-DPSO) and hybrid sectional encoding genetic algorithm and discrete particle swarm optimization (H-SE-GA-DPSO). In the model, the encoding of the algorithms was divided into three segments for different optimization dimensions with the objective of minimizing the makespan and energy consumption of machines and the number of AGVs. The sectional encoding described the sequence of operations of related jobs, the matching relation between transfer tasks and AGVs (AGV-task), and the matching relation between operations and machines (operation-machine) respectively for multi-dimensional optimization scheduling. The effectiveness of the proposed three EAs was verified by a typical experiment. Besides, in the experiment, a comparison among SE-GA, SE-DPSO, H-SE-GA-DPSO, hybrid genetic algorithm and particle swarm optimization (H-GA-PSO) and a tabu search algorithm (TSA) was performed. In H-GA-PSO and TSA, the former just takes the sequence of operations into account, and the latter takes both the sequence of operations and the AGV-task into account. According to the result of the comparison, the superiority of H-SE-GA-DPSO over the other algorithms was proved. Full article
(This article belongs to the Special Issue Sustainable Intelligent Manufacturing Systems)
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Open AccessArticle
Simulation Study for Semiconductor Manufacturing System: Dispatching Policies for a Wafer Test Facility
Sustainability 2019, 11(4), 1119; https://doi.org/10.3390/su11041119
Received: 19 December 2018 / Revised: 30 January 2019 / Accepted: 14 February 2019 / Published: 20 February 2019
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Abstract
The manufacture of semiconductor products requires many dedicated steps, and these steps can be grouped into several major phases. One of the major steps found at the end of the wafer fabrication process is the electrical die sorting (EDS) test operation. This paper [...] Read more.
The manufacture of semiconductor products requires many dedicated steps, and these steps can be grouped into several major phases. One of the major steps found at the end of the wafer fabrication process is the electrical die sorting (EDS) test operation. This paper focuses on dispatching policies in an EDS test facility to reduce unnecessary work for the system. This allows the semiconductor manufacturing facility to achieve better overall efficiency, thereby contributing to sustainable manufacturing by reducing material movements, the use of testing machines, energy consumption, and so on. In the facility, wafer lots are processed on a series of workstations (cells), and the facility holds identical parallel machines. The wafers are moved by an automatic material handling system from cell to cell as well as within cells. We propose several scheduling policies consisting of intercell and intracell material movements for efficient system operation. For this, four intercell scheduling policies and two intracell scheduling policies are introduced, and the effects of combinations are tested and evaluated through simulation experiments to obtain performance measures such as cycle time and work in process. The most efficient results among the combinations are presented as a proposed scheduling policy for a given EDS test facility. Full article
(This article belongs to the Special Issue Sustainable Intelligent Manufacturing Systems)
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Open AccessArticle
An Optimization Approach for the Coordinated Low-Carbon Design of Product Family and Remanufactured Products
Sustainability 2019, 11(2), 460; https://doi.org/10.3390/su11020460
Received: 19 November 2018 / Revised: 27 December 2018 / Accepted: 15 January 2019 / Published: 16 January 2019
Cited by 2 | PDF Full-text (1445 KB) | HTML Full-text | XML Full-text
Abstract
With increasingly stringent environmental regulations on emission standards, enterprises and investigators are looking for effective ways to decrease GHG emission from products. As an important method for reducing GHG emission of products, low-carbon product family design has attracted more and more attention. Existing [...] Read more.
With increasingly stringent environmental regulations on emission standards, enterprises and investigators are looking for effective ways to decrease GHG emission from products. As an important method for reducing GHG emission of products, low-carbon product family design has attracted more and more attention. Existing research, related to low-carbon product family design, did not take into account remanufactured products. Nowadays, it is popular to launch remanufactured products for environmental benefit and meeting customer needs. On the one hand, the design of remanufactured products is influenced by product family design. On the other hand, the launch of remanufactured products may cannibalize the sale of new products. Thus, the design of remanufactured products should be considered together with the product family design for obtaining the maximum profit and reducing the GHG emission as soon as possible. The purpose of this paper is to present an optimization model to concurrently determine product family design, remanufactured products planning and remanufacturing parameters selection with consideration of the customer preference, the total profit of a company and the total GHG emission from production. A genetic algorithm is applied to solve the optimization problem. The proposed method can help decision-makers to simultaneously determine the design of a product family and remanufactured products with a better trade-off between profit and environmental impact. Finally, a case study is performed to demonstrate the effectiveness of the presented approach. Full article
(This article belongs to the Special Issue Sustainable Intelligent Manufacturing Systems)
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Open AccessArticle
Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review
Sustainability 2018, 10(12), 4779; https://doi.org/10.3390/su10124779
Received: 24 November 2018 / Revised: 10 December 2018 / Accepted: 11 December 2018 / Published: 14 December 2018
Cited by 5 | PDF Full-text (1193 KB) | HTML Full-text | XML Full-text
Abstract
With the rapid development of sensing, communication, computing technologies, and analytics techniques, today’s manufacturing is marching towards a new generation of sustainability, digitalization, and intelligence. Even though the significance of both sustainability and intelligence is well recognized by academia, industry, as well as [...] Read more.
With the rapid development of sensing, communication, computing technologies, and analytics techniques, today’s manufacturing is marching towards a new generation of sustainability, digitalization, and intelligence. Even though the significance of both sustainability and intelligence is well recognized by academia, industry, as well as governments, and substantial efforts are devoted to both areas, the intersection of the two has not been fully exploited. Conventionally, studies in sustainable manufacturing and smart manufacturing have different objectives and employ different tools. Nevertheless, in the design and implementation of smart factories, sustainability, and energy efficiency are supposed to be important goals. Moreover, big data based decision-making techniques that are developed and applied for smart manufacturing have great potential in promoting the sustainability of manufacturing. In this paper, the state-of-the-art of sustainable and smart manufacturing is first reviewed based on the PRISMA framework, with a focus on how they interact and benefit each other. Key problems in both fields are then identified and discussed. Specially, different technologies emerging in the 4th industrial revolution and their dedications on sustainability are discussed. In addition, the impacts of smart manufacturing technologies on sustainable energy industry are analyzed. Finally, opportunities and challenges in the intersection of the two are identified for future investigation. The scope examined in this paper will be interesting to researchers, engineers, business owners, and policymakers in the manufacturing community, and could serve as a fundamental guideline for future studies in these areas. Full article
(This article belongs to the Special Issue Sustainable Intelligent Manufacturing Systems)
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Open AccessArticle
A Sustainable Decision-Making Framework for Transitioning to Robotic Welding for Small and Medium Manufacturers
Sustainability 2018, 10(10), 3651; https://doi.org/10.3390/su10103651
Received: 17 September 2018 / Revised: 8 October 2018 / Accepted: 10 October 2018 / Published: 12 October 2018
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Abstract
Small and medium-sized enterprises (SMEs) face challenges in implementing industrial robotics in their manufacturing due to limited resources and expertise. There is still good economic potential in using industrial robotics, however, due to manufacturers leaning toward newer technology and automated processes. The research [...] Read more.
Small and medium-sized enterprises (SMEs) face challenges in implementing industrial robotics in their manufacturing due to limited resources and expertise. There is still good economic potential in using industrial robotics, however, due to manufacturers leaning toward newer technology and automated processes. The research on sustainability decision-making for transitioning a traditional process to a robotic process is limited for SMEs. This study presents a systemic framework for assessing the sustainability of implementing robotic techniques in key processes that would benefit SMEs. The framework identifies several key economic, technical, and managerial decision-making factors during the transition phase. Sustainability assessments, including cost, environmental impact, and social impact, are used in the framework for engineers and managers to evaluate the technical and sustainability trade-offs of the transition. A case study was conducted on a typical US metal fabrication SME focusing on transitioning a shielded metal arc welding (SMAW) process to a robotic gas metal arc welding (GMAW) process. A sustainability assessment was conducted following the framework. The results suggest that the transition phase involves numerous factors for engineers and managers to consider and the proposed framework will benefit SMEs by providing an analytical method for industrial robotics implementation decision-making. Full article
(This article belongs to the Special Issue Sustainable Intelligent Manufacturing Systems)
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