Green Manufacturing Systems Research Articles

Modeling and optimization algorithm for energy-efficient distributed assembly hybrid flowshop scheduling problem considering worker resources

Considering increasingly serious environmental issues, sustainable development and green manufacturing have received much attention. Meanwhile, with the development of economic globalization and requirement of customization production, distributed hybrid flowshop scheduling problem (DHFSP) and assembly shop problem (ASP) have widely existed in realistic manufacturing systems. In addition to machine resources, worker resources are a key element affecting production efficiency. However, previous studies have not considered the integration mode of DHFSP, ASP, and worker resources in green manufacturing systems. Therefore, this paper focuses on an energy-efficient distributed assembly hybrid flowshop scheduling problem considering worker resources (EDAHFSPW) for the first time. To solve this problem, a mixed-integer linear programming (MILP) model and a multi-objective memetic algorithm (MOMA) are proposed with minimization the total tardiness (TTD) and total energy consumption (TEC) objectives. In MOMA, a speed-related decoding method is developed to improve the quality of solutions. To generate excellent initial solutions, an initialization strategy is proposed based on problem characteristics. A local search strategy is presented to improve the exploitation capability. An energy-saving strategy is designed to further optimize TEC. Additionally, to validate the proposed MILP model, we implement CPLEX to solve it on 12 small-sized instances. To verify the effectiveness of the proposed MOMA, extensive experiments are conducted to compare with other 5 comparison algorithms on 90 large-sized instances. Experimental results illustrate that MOMA is superior to its competitors.

On developing a green and intelligent manufacturing system

This paper aims to present an autonomous intelligent system that improves the performance of conventional manufacturing processes to make them friendly to the environment. This improvement is made considering objectives that involve a reduction of supplies, use of tools, and low energy consumption; but maintaining desirable characteristics of machined parts in quality, mechanical properties, and dimensions. We present an architecture that integrates modeling with neural networks, validation using metrical statistics, and multi-objective optimization based on MOEA/D which takes experimental or historical data of the process and generates a set of parameters to satisfy all three criteria of sustainability, efficiency, and quality. Two cases for welding and machining process are presented to illustrate the use of the proposed approach which results in evidence of the feasibility of having green and intelligent manufacturing systems. In the welding process, the user can select a low amperage but increase lightly the cycles of operation to reduce energy consumption but satisfy the quality objective. In machining, the user can select a solution to avoid the use of cooling maintaining quality and efficiency in acceptable levels of operation.

A double-adaptive general variable neighborhood search for an unmanned electric vehicle routing and scheduling problem in green manufacturing systems

In recent years, more and more manufacturing factories have employed unmanned electric vehicles (UEVs) to perform the materials delivery, and efficient routing and scheduling approaches are required to improve the delivery efficiency. However, most of existing papers studied the routing problem of fuel vehicles or the drones. This paper investigates the optimization of UEVs delivery routes and charging strategies. Due to the limited payload capacity and battery capacity, the UEV must return to the depot multiple times to load the materials and go to the recharging station during the route to charge, and thus the problem is modeled as a multi-trip UEV routing and scheduling problem, which assigns the recharging station to the UEV’s route and optimizes a set of UEV’s trips. The investigated problem is firstly formulated as a mixed integer programming model. Then, considering the routing and scheduling challenges, we develop an efficient double-adaptive variable neighborhood search (DA-GVNS), which incorporates the general variable neighborhood search with a new adaptive mechanism, for this problem. The experimental results on the test and benchmark instances highlight that the DA-GVNS outperforms other existing algorithms. Furthermore, sensitivity analyses are conducted to study the impact of model’s characteristics on the optimality and complexity of solutions. Finally, a case study is proposed to present how the developed approach works for a manufacturing factory. This paper can offer decision-makers a valuable approach to construct a high-quality routing and scheduling scheme for the UEV delivery while providing potential managerial insights for implementing efficient green manufacturing systems.

Integration of multi-criteria decision-making approaches adapted for quality function deployment: an analytical literature review and future research agenda

PurposeThis paper aims to classify journal papers in the context of hybrid quality function deployment QFD and multi-criteria decision-making (MCDM) methods published during 2004–2021.Design/methodology/approachA conceptual classification scheme is presented to analyze the hybrid QFD-MCDM methods. Then some recommendations are given to introduce directions for future research.FindingsThe results show that among all related areas, the manufacturing application has the most frequency of published papers regarding hybrid QFD-MCDM methods. Moreover, using uncertainty to establish a hybrid QFD-MCDM the relevant papers have been considered during the time interval 2004–2021.Originality/valueThere are various shortcomings in conventional QFD which limit its efficiency and potential applications. Since 2004, when MCDM methods were frequently adopted in the quality management context, increasing attention has been drawn from both practical and academic perspectives. Recently, the integration of MCDM techniques into the QFD model has played an important role in designing new products and services, supplier selection, green manufacturing systems and sustainability topics. Hence, this survey reviewed hybrid QFD-MCDM methods during 2004–2021.

Analysis of supporting factors of the successful implementation of green manufacturing for Indonesian Manufacturing SMEs

Many manufacturing SMEs experience difficulties in finding an effective framework for building green manufacturing system for production and operations activities. The study purpose to identify the elements support the successful implementation of green manufacturing systems in SMEs, develop a conceptual hierarchical model of elements based on the ISM approach, and identify and analyze the dominant elements. This study applies a survey method to obtain data that is relevant to the research objectives. The respondents involved were 32 managers/owners of manufacturing SMEs located in Tegal Regency, Central Java Province of Indonesia. The study results show the existence of 39 supporting elements for the successful implementation of green manufacturing in SMEs. Factor analysis shows that these elements can be simplified into 12 common elements. MICMAC analysis shows that five elements can be categorized as dependent elements; lower production costs, environmental knowledge, Improved waste management, investing in innovation and technology, and new market opportunities; seven elements can be categorized as independent elements, namely green supply chain, government promotion and regulation, pressure from the market, organizational culture, involvement of various business stakeholders, top management commitment, and environmental specialist assistance. ISM analysis shows there are four elements that can be categorized as dominant elements; namely green supply chain, involvement of various business stakeholders, top management commitment, and government promotion and regulation.

Identification of Key Brittleness Factors for the Lean–Green Manufacturing System in a Manufacturing Company in the Context of Industry 4.0, Based on the DEMATEL-ISM-MICMAC Method

In the context of Industry 4.0, the lean–green manufacturing system has brought many advantages and challenges to industrial participants. Security is one of the main challenges encountered in the new industrial environment, because smart factory applications can easily expose the vulnerability of manufacturing and threaten the operational security of the whole system. It is difficult to address the problem of the brittleness factor in manufacturing systems. Therefore, building on vulnerability theory, this study proposes a vulnerability index system for lean–green manufacturing systems in a manufacturing company in the context of Industry 4.0. The index has four dimensions: human factors, equipment factors, environmental factors, and other factors. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach was used to calculate the degree of influence, the degree of being influenced, and the centrality and causes of the factors. The causal relationships and key influences between the factors were identified. Then, the dependence and hierarchy of each of the key influencing factors were analyzed using the Matrix-Based Cross-Impact Multiplication Applied to Classification (MICMAC) and Interpretative Structural Model (ISM) methods, and a hierarchical structural model of the factors was constructed. Finally, an intelligent manufacturing system that produces a micro-acoustic material and device was used as an example to verify the accuracy of the proposed method. The results show that the method not only identifies the key brittleness factors in a lean–green manufacturing system but can also provide a guarantee for the safe operation of a manufacturing system. This study provides theoretical guidance for the effective management of intelligent manufacturing systems; moreover, it lays a foundation and provides a new methodology for assessing the vulnerability of manufacturing systems.

Environmental Management Systems and Manufacturing Companies' Performance in Nigeria

Purpose: The purpose of this study was to investigate the environmental management systems and environmental performance of manufacturing firms in Abuja City, Nigeria.
 Methodology: The study adopted a longitudinal research design that was descriptive in nature. Stratified sampling technique was used to select a sample size of 88 respondents who were employees of the manufacturing firms in Abuja City, Nigeria. Structured questionnaires with open and closed ended questions were used as instruments of data collection.
 Findings: The findings of this study revealed that there was a positive statistical correlation between green manufacturing systems, pollution prevention systems, management systems and pollution control systems and environmental performance of manufacturing firms in Abuja City, Nigeria.
 Conclusion: The study concluded that environmental management systems have become drivers of competitive organizations in the changing business environment.
 Recommendation: The study recommended that manufacturing companies should ensure that employees are trained on ISO 14001 regulations, appropriate technology is adopted and industry partnership is based on best environmental practices rather than on the sole revenues gain objective.

Coupling and coordinated development of urban land use economic efficiency and green manufacturing systems in the Chengdu-Chongqing Economic Circle

• Measure the CCD of land use economic efficiency and green manufacturing system level • Construct a comprehensive and multi-dimensional green manufacturing system • Combine SBM model, the weighting method, the CCD model and convergence model • Spatially, the urban belt aggregation pattern exists in the Chengdu-Chongqing UA • In temporal distribution, the CCD begins to shift to the direction of convergence Industrial activity has been a powerful driver of urbanization and economic growth in China, which has often been at the expense of the natural environment. However, sustainable urban development requires improvements in the coupling and coordination of urban land use economic efficiency and green manufacturing systems. Based on statistical data from the Chengdu-Chongqing urban agglomeration, two index systems: urban land economic efficiency and green manufacturing systems; were developed to reveal the agglomeration's spatio-temporal sustainable development evolution from 2007 to 2019. A global SBM-DEA model was combined with a weighting method, a coupling coordination degree (CCD), and a convergence model, from which it was found that the CCD between the urban land use economic efficiency and green manufacturing system level in the Chengdu-Chongqing Economic Circle was in a slow growth stage. The government should formulate a scientific regional urban development strategy that condenses the special relationship between the two core cities and strengthens their driving effects to decrease the siphoning effect.

Beyond “Leanear” production: A multi-level approach for achieving circularity in a lean manufacturing context

Despite the growth of the research on lean and green manufacturing systems, consideration of how circular economy principles complement lean has been virtually absent from the literature due to a mismatch between research on lean green, which has focused on the internal manufacturing operations (product and process levels), and research on the circular economy, which takes a broader, more holistic view of environmental impact (the system level). Only a few researchers have considered the lean and the circular together. We argue that there is a need to understand the complementarities and conflicts between “lean” and “circularity”. By combining the system level with process and product levels, this paper highlights which potentials in a lean manufacturing context are identified from a circular perspective and how to realize them. In this paper, we present a critical case study conducted at a Swedish manufacturing firm/original equipment manufacturer within the heavy-duty and off-road (HDOR) industry. Our results show that the company is locked into a lean and linear production, where material waste is not seen as a resource. We present how the three levels interplay and argue that they be combined when trying to integrate circular measures into this context.

Innovative framework for lean and green complex manufacturing systems using value stream mapping

Innovative framework for lean and green complex manufacturing systems using value stream mapping

Innovative framework for lean and green complex manufacturing systems using value stream mapping

Innovative framework for lean and green complex manufacturing systems using value stream mapping

Policy incentives, R&D investment, and the energy intensity of China’s manufacturing sector

The Chinese strategic plan “Made in China (2025)” aims at improving the independent innovation capacity of the manufacturing industry. It also commits to constructing green manufacturing systems through cleaner production and green technologies. The Chinese government provides research and development (R&D) preferential policies to promote the realization of these aims. Using the two-stage least square (2SLS) method, this paper tests the impact of R&D capital and its spillover on the energy intensity of the Chinese manufacturing industry. It also evaluates the effectiveness of R&D policy in promoting R&D investment at the industry level. The findings show that R&D capital is positively linked to the reduction of energy intensity, and heterogeneity exists in the relationship between energy intensity and R&D capital across industries. This study finds that in general, R&D subsidies and tax relief do not significantly promote R&D investment, but R&D subsidies generate a significantly positive effect on the R&D intensity in the high-tech industries.

Modelling and analysis of barriers affecting the implementation of lean green agile manufacturing system (LGAMS)

Purpose Many types of research have already investigated the lean, green or agile manufacturing systems in a discrete manner or as combinations of two of them. In today’s competitive scenario, if industry wants to perpetuate its name in the market, then it has to supervene proper thinking and smart approach. Therefore, the combination of lean, green and agile manufacturing systems can provide better and beneficial results. The purpose of this paper is to discern the barriers to the combined lean green agile manufacturing system (LGAMS), understand their interdependence and develop a framework to enhance LGAMS by using total interpretive structural modeling (TISM) and MICMAC (Matriced’ Impacts Croise’s Multiplication Appliquée a UN Classement) Analysis. Design/methodology/approach This paper uses TISM methodology and MICMAC analysis to deduce the interrelationships between the barriers and rank them accordingly. A total of 13 barriers have been identified through extensive literature review and discussion with experts. Findings An integrated LGAMS has been presented that balances the lean, green and agile paradigms and can help supply chains become more efficient, streamlined and sustainable. Barriers are identified while referring to all three strategies to showcase the clear relevance. TISM models the barriers in different levels showcasing direct and important transitive relations. Further, MICMAC analysis distributes the barriers in four clusters in accordance with their driving and dependence power. Research limitations/implications The inferences have been drawn from a model developed on the basis of inputs from a small fraction of the industry and academia and may show variations when considering the whole industry. Practical implications The outcome of this research can contribute to bringing the change to the manufacturing systems used in most developing nations. Also, top managers considering adoption of LGAMS can be cautious of the most influential barriers. Originality/value A TISM-based model of the barriers to an integrated LGAMS has been proposed with evaluation of the influence of the barriers.

Toward dynamic energy management for green manufacturing systems

The manufacturing industry is responsible for significant energy consumption, particularly in the form of electricity. From the perspective of the energy management system in manufacturing, reducing this consumption is not only a matter of exhibiting environmental responsibility, but also of substantially reducing the production cost. We discuss how dynamic energy management in manufacturing systems can not only solve the current technical issues in manufacturing, but can also aid in the integration of additional energy equipment into energy systems. We quantitatively estimate these potential savings through analysis of a simple manufacturing process. We also address a future research direction, wherein advanced manufacturing systems such as Industry 4.0 are deployed.

Simulation Based Assessment of Lean and Green Strategies in Manufacturing Systems

The increase of resource (energy and material) efficiency by eliminating unnecessary consumption represents the logical continuation from lean manufacturing to lean and green manufacturing. However, economic efficiency remains the primary decision criterion for the implementation of corresponding strategies. This paper presents a simulation based approach for monetary assessment of lean and green manufacturing systems considering non-monetary green limits. Inclusion of material and energy consumption as well as resulting greenhouse gas emissions enables planners to predict the overall economic performance of a factory. Furthermore, product variant specific footprints of material and energy demands as well as resulting emissions support in-depth analysis of value streams in manufacturing.

Using activity-based costing to evaluate capital investments for green manufacturing systems

The purpose of this study is to assess how an activity-based costing (ABC) system can assist the justification of capital investments for green manufacturing systems (GMSs). We provide an investment decision model and a numerical example for the justification of GMS based on the ABC system to include not only the direct performances but also the intangible benefits that are generated by the adoption of GMS. Our findings provide insight into the value of capital investments for a GMS based ABC system and demonstrate the opportunity to reduce pollution while simultaneously improving the value of investment.

A system model for green manufacturing

Manufacturing systems evolution is afunction in multiple external and internal factors. With today’s global awareness of environmental risks as well as the pressing needs to compete through efficiency, manufacturing systems are evolving into a new paradigm. This paper presents a system model for the new green manufacturing paradigm. The model captures various planning activities to migrate from a less green into a greener and more eco-efficient manufacturing. The various planning stages are accompanied by the required control metrics as well as various green tools in an open mixed architecture. The system model is demonstrated by an industrial case study. The proposed model is a comprehensive qualitative answer to the question of how to design and/or improve green manufacturing systems as well as a roadmap for future quantitative research to better evaluate this new paradigm.

Development of a multi criteria decision model for justification of green manufacturing systems

Rapid industrial growth in the last century has led to many economic, environmental and social problems from global warming to local waste disposal. The industry has come under increasing pressure to reduce the environmental impacts involved in the production of goods and services. In recent years green manufacturing systems (GMS) have been widely considered for implementation to reduce environmental and social impacts as well as to improve economical competitiveness. However, the implementation of such systems is expensive and relative investments tend to be irreversible, thus, necessarily requiring careful consideration before a decision can be made. However, the managers find it difficult to justify implementation of GMS to top management as traditional economic approaches do not account for the significant intangible benefits offered by GMS. In this paper, an attempt has been made to overcome the deficiencies of traditional economic justification approaches by presenting a multi criteria decision model – analytic hierarchy process (AHP) – for the justification of green manufacturing systems. The usefulness of the model is demonstrated through an illustrative example.

PERFORMANCE VALUE ANALYSIS FOR JUSTIFICATION OF GREEN MANUFACTURING SYSTEMS

Green Manufacturing Systems (GMS) has attracted the attention of organizations all over the world. There is a general challenge offered to the field of engineering economics by the introduction of green manufacturing systems as, typically, the traditional approaches do not account for the significant intangible benefits offered by GMS. In this paper, an attempt has been made to overcome the above-mentioned deficiencies by presenting a multi attribute decision model — Performance Value Analysis (PVA) — for the justification of green manufacturing systems. The usefulness of the model is demonstrated through an illustrative example. The results clearly show that the implementation of green manufacturing systems offers far better competitive advantages over the non-green manufacturing systems.