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The recent Industry 4.0 trend, followed by the technological advancement of collaborative robots, has urged many industries to shift towards new types of assembly lines with human-robot collaboration (HRC). This type of manufacturing line, in which human skill is supported by robot agility, demands an integrated balancing and scheduling of tasks and operators among the stations. This study attempts to deal with these joint problems in the straight and U-shaped assembly lines while considering different objectives, namely, the number of stations (Type-1), the cycle time (Type-2), and the cost of stations, operators, and robot energy consumption (Type-rw). The latter type often arises in the real world, where multiple types of humans and robots with different skills and energy levels can perform the assembly tasks collaboratively or in parallel at stations. Additionally, practical constraints, namely robot tool changes, zoning, and technological requirements, are considered in Type-rw. Accordingly, different mixed-integer linear programming (MILP) models for straight and U-shaped layouts are proposed with efficient lower and upper bounds for each objective. The computational results validate the efficiency of the proposed MILP model with bounded objectives while addressing an application case and different test problem sizes. In addition, the analysis of results shows that the U-shaped layout offers greater flexibility than the straight line, leading to more efficient solutions for JIT production, particularly in objective Type-2 followed by Type-rw and Type-1. Moreover, the U-shaped lines featuring a high HRC level can further enhance the achievement of desired objectives compared to the straight lines with no or limited HRC.

Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput.

Assembly-line balancing is a significant issue in production systems. Employing industrial robots as the main production resource was a milestone in developing assembly lines, and emerging Industry 4.0 led industries to build collaborative assembly lines by combining robots and human operator skills. Recently, the majority of research on assembly line balancing has contributed to addressing aspects of utilizing robots in assembly lines and how they can increase line performance. Various models and methods are developed, considering different objectives and performance indicators. Despite the increasing number of studies in this area, a thorough literature review is lacking in identifying gaps, shedding light on research directions, and facilitating future development. This study systematically reviews assembly-line balancing studies targeted at assembly lines with industrial and collaborative robots. Studies are classified based on their objectives and reviewed for their solution method, line layout, and other essential specifications. A descriptive analysis is provided to assist researchers and practitioners in linking different properties of assembly lines to the objectives and applied methodologies. The results show that most studies developed models and solution methods that focused on simultaneously optimizing more than one objective. The review reveals that minimizing the cycle time is the most popular objective, and meta-heuristic algorithms are the dominant solution approaches. It is also observed that balancing assembly lines with collaborative robots has received more attention in the last five years with the emergence of Industry 4.0. The review also highlights gaps in the related literature and provides promising insights for future research.

Purpose: This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach. Design/methodology/approach: The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0. Findings: Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations. Practical implications: While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits. Originality/value: This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

Simulation-based learning (SBL) presents a wide variety of opportunities to practice complex computer and networking skills in higher education, employing various platforms to enhance educational outcomes. The integration of SBL tools in teaching computer networking courses is useful for both instructors and learners. Furthermore, the increasing importance of SBL in higher education highlights the necessity to further explore the factors that affect the adoption of SBL technologies, particularly in the field of computer networking courses. Despite these advantages, minimal effort has been made to examine the factors that impact instructors' intentions to use SBL tools for computers and networking courses. The main objective of this article is to examine the factors that affect instructors' intentions to utilize SBL tools in computer networking courses offered by higher education institutions. By employing interpretive structural modeling and Matriced' Impacts Croise's Multiplication Appliquee a UN Classement analysis, the article attempts to provide an in-depth understanding of the interdependencies and hierarchical associations among 12 identified factors. Results showed that system quality, self-efficacy, technological knowledge, and information quality have high driving power. This article offers valuable perspectives for higher education institutions and for upcoming empirical studies and aids in comprehending the advantages of using SBL tools in teaching and higher education.

In line with Industry 5.0, ergonomic factors have recently received more attention in balancing assembly lines to enhance the human-centric aspect. Meanwhile, today's mass-customized trend yields manufacturers to offset the assembly lines for different product variants. Thus, this study addresses the mixed -model assembly line balancing problem (MMALBP) by considering worker posture. Digital human modeling and posture assessment technologies are utilized to assess the risks of work-related musculoskeletal disorders using a method known as rapid entire body analysis (REBA). The resulting MMALBP is formulated as a mixed-integer linear programming (MILP) model while considering three objectives: cycle time, maximum ergonomic risk of workstations, and total ergonomic risks. An enhanced non-dominated sorting genetic algorithm (E-NSGA-II) is developed by incorporating a local search procedure that generates neighborhood solutions and a multi-criteria decision-making mechanism that ensures the selection of promising solutions. The E-NSGA-II is benchmarked against Epsilon-constraint, MOGA, and NSGA-II while solving a case study and also test problems taken from the literature. The computational results show that E-NSGA-II can find promising Pareto front solutions while dominating the considered methods in terms of performance metrics. The robustness of E-NSGA-II results is evaluated through one-way ANOVA statistical tests. The analysis of results shows that a smooth distribution of time and ergonomic loads among the workstations can be achieved when all three objectives are simultaneously considered.

Healthcare waste disposal center location (HCWDCL) impacts the environment and the health of living beings. Different and sometimes contradictory criteria in determining the appropriate site location for disposing of healthcare waste (HCW) complicate the decision-making process. This research presents a hybrid multi-criteria decision-making (MCDM) method, named PROMSIS, to determine the appropriate HCWDCL in a real case. The PROMSIS is the combination of two well-known MCDM methods, namely TOPSIS and PROMETHEE. Moreover, fuzzy theory is used to describe the uncertainties of the problem parameters. To provide a reliable decision on selecting the best HCWDCL, a comprehensive list of criteria is identified through a literature review and experts' opinions obtained from the case study. In total, 40 criteria are identified and classified into five major criteria, namely economic, environmental, social, technical, and geological. The weight of the considered criteria is determined by the Analytical Hierarchy Process (AHP) method. Then, the score of the alternative HCWDCLs in each considered criterion is obtained. Finally, the candidate locations for disposing of HCWs are ranked by the proposed fuzzy PROMSIS method. The results show that the most important criteria in ranking the alternatives in the studied case are economic, environmental, and social, respectively. Moreover, the sub-criteria of operating cost, transportation cost, and pollution are identified as the most important sub-criteria, respectively.

The effective distribution of relief goods is critical in mitigating the impact of natural disasters and preserving human life. This study addresses a relief goods distribution problem, assuming the existence of multiple relief orders that must be delivered to various disaster-stricken regions from a network of warehouses using a fleet of diverse vehicles. The objective is to identify the most suitable warehouse for each relief order, allocate relief orders to vehicles, batch the orders in the designated vehicles, and devise routing plans to minimize the total delivery time. A mixed-integer linear programming model is formulated to tackle this problem. Owing to the problem's NP-hard nature, a metaheuristic algorithm, known as the Multiple League Championship Algorithm, is developed. Furthermore, two innovative variants of the MLCA , namely the League Base Multiple League Championship Algorithm (L- MLCA) and the Playoff Multiple League Championship Algorithm (P-MLCA), are introduced.Experimental results indicate that the P-MLCA outperforms the other two algorithms. The solutions derived from the P-MLCA are compared with the optimal solutions obtained by a commercial solver for small-scale problems. This comparative analysis demonstrates the promising performance of the P-MLCA in finding the optimal distribution of relief goods.

Preventing energy waste in residential and office buildings has emerged as a critical issue in both developed and developing countries over recent decades. The growing demand for oil and energy reserves has amplified the urgency of this concern. The deployment of building energy management systems (BEMSs) can lead to timely responses to changes in environmental conditions, the prevention of energy wastage, a reduction in CO2 emissions, and an increase in the longevity of building equipment. Despite the undeniable benefits of BEMSs, their market size remains small, creating challenges for providers in reaching potential customers. This research seeks to identify and prioritize the marketing strategies for BEMSs. A case study was conducted, employing the “Strengths, Weaknesses, Opportunities, and Threats” analysis as a tool for identifying marketing strategies related to BEMSs. This method resulted in the identification of 18 distinct marketing strategies. These strategies were subsequently prioritized using a novel fuzzy multicriteria decision-making technique, VIkor-topSIS, considering six specific criteria. The findings of the study suggested a hierarchical influence of six criteria on the BEMS market, arranged in the following order of significance: effectiveness, cost, attainability, complexity, timing, and popularity. Furthermore, the top three marketing strategies for BEMSs were found to be internet advertising strategies, discounts to consumers, and online sales. The analysis of the results has also offered valuable insights into the strengths and weaknesses of the studied BEMS provider, as well as the opportunities and threats present within the BEMS market.

The Healthcare Waste Disposal Method Selection (HCWDMS) is a complicated problem due to multiple and often contradictory criteria with different importance degrees. Thus, decision-makers are restored to multi-criteria decision-making (MCDM) methods to prioritize and select the best HCW disposal methods. This study introduces an enhanced MCDM method to deal with the HCWDMS problem. To address the problem, a comprehensive list of criteria and HCW disposal methods are identified. All the criteria are categorized into four main criteria, and Fuzzy Analysis Hierarchy Process is used to determine the weights of considered criteria and sub-criteria. The study results show that environmental, economic, technical, and social criteria are the most important in selecting disposal methods, respectively. Moreover, the sub-criteria of 'Health Risk', 'Release with health effects', and 'Capital cost' have the highest importance, respectively. Additionally, the methods of 'Microwave', 'Sterilization by autoclave', and 'Reverse polymerization' have the highest priority, respectively.

This study aims at reducing energy consumption in supply chain networks by providing optimal integrated production and transportation scheduling. The considered supply chain consists of one main manufacturing center, multiple production units (i.e., suppliers), and multiple heterogeneous vehicles as the transportation fleet. To schedule this complex supply chain network in an energy-efficient way, several decisions should be made concerning the assignment of orders to suppliers and determining their production sequence, splitting orders, assigning orders to vehicles, and assigning delivery priority to orders. To cope with the problem, a mixed-integer linear programming model is presented. Due to the complexity of the problem, a novel development of the genetic algorithm named the Multiple Reference Group Genetic Algorithm (MRGGA) is also proposed. Four objectives are considered to be optimized to meet both suitability and energy-efficiency aspects in the supply chain network. These optimization objectives are to minimize the total orders' delivery times to the manufacturing center, fuel consumption by the vehicles, energy consumption at supplies, and maximize orders' quality. To analyze the performance of the proposed algorithm, a real case and a set of generated instances are solved. The results obtained by the proposed algorithm are compared with an existing genetic algorithm in the literature. Moreover, the results are also compared with the optimal solutions obtained from the mathematical model for small-size problems. The results of the comparisons show the efficiency of the proposed MRGGA in finding energy-efficient solutions for the considered supply chain network.

This study aims to introduce a method to integrate and coordinate departments' activities to enhance the service quality of organizations using Quality Function Deployment (QFD). To this purpose, the classical two-dimensional House Of Quality (HOQ) matrix is changed to a three-dimensional form (3D-HOQ). The 3D-HOQ is applied to the marketing and Human Resources (HR) departments of a bank to determine customers' and employees' demands, respectively. The 3D-HOQ is also employed to provide a unique list of technical requirements to satisfy the identified demands. Obtaining a unique list of technical requirements with the cooperation of both departments reduces the inconsistency between departments, saves cost and time by preventing reworks and parallel works, and increases the organization's efficiency. Moreover, 3D-HOQ is combined with the SERVQUAL technique and fuzzy theory to determine the weight of obtained technical requirements. The study is conducted in four main steps: 1) identifying the customers' and employees' demands; 2) identifying the technical requirements for simultaneous satisfaction of both customers' and employees' demands; 3) determining the relationships between the technical requirements and the identified demands; and 4) prioritizing technical requirements. Applying the 3D-HOQ resulted in identifying 30 customers' demands, 30 employees' demands, and 50 technical requirements. The study results show that "using new banking technologies" has the highest weight among the customers' demands, and "job security" has been found to have the highest weight among employees' demands. Moreover, "Intraorganizational processes automation" has been identified as the technical requirement with the highest weight.

This study aims to develop appropriate models for income distribution in Iran using the econophysics approach for the 2006-2018 period. For this purpose, the three improved distributions of the Pareto, Lognormal, and Gibbs-Boltzmann distributions are analyzed with the data extracted from the target household income expansion plan of the statistical centers in Iran. The research results indicate that the income distribution in Iran does not follow the Pareto and Lognormal distributions in most of the study years but follows the generalized Gibbs-Boltzmann distribution function in all study years. According to the results, the generalized Gibbs-Boltzmann distribution also properly fits the actual data distribution and could clearly explain the income distribution in Iran. The generalized Gibbs-Boltzmann distribution also fits the actual income data better than both Pareto and Lognormal distributions.

The supplier selection problem (SSP) is a significant issue in renewable supply chain management (RSCM). Selecting a strategic green supplier can not only discover the sustainable development of supply chains but also optimize the consumption rate of resources and decrease the negative environmental effects, which adopts to the green development context. As a multiple criteria group decision-making (MCGDM) problem, choosing a strategic green supplier is important to renewable supply chains. However, how to choose a strategic green supplier for supply chains is a great effort. Hence, In the present work, evaluating a set of strategic suppliers is primarily based on green capabilities by using an integrated fuzzy Best Worst Method (FBWM) with the other two techniques, namely COPRAS (Complex Proportional Assessment of Alternatives) and WASPAS (Weighted Aggregated Sum-Product Assessment). Initially, nine strategic supplier selection criteria have been identified through literature review and a real-world case study of Iran's renewable energy supply chain is deliberated to exhibit the proposed framework's applicability. The applied methodology and its analysis will provide insight to decision-makers of strategic supplier selection. It may aid decision-makers and the procurement department in differentiating the significant strategic green supplier selection criteria and assess the strategic green supplier in the local and global market supply chain. Finally, the strengths and limitation of the framework are discussed by using comparative analysis with other methods.

In this paper, we have considered that ranked set sampling is able to estimate the parameters of exponentiated Pareto distribution. The method with which the maximum likelihood estimators for the parameters of exponentiated Pareto distribution is studied is numerical since there is no presence or possibility of a closed-form at the hands of estimators or any other intellectual. The numerical approach is a well-suited one for this study as there has been struggles in achieving it with any other technique. In order to compare the different sampling methods, simulation studies are performed as the main technique. As for the illustrative purposes, analysis of a simulated dataset is desired for the objective of the presentation. The conclusion that we can reach based on these is that the estimators based on the ranked set sample have far better efficiency than the simple random sample at the same sample size.

Industry 4.0 has been associated with the rise of disruptive intelligence and information technologies. These cutting-edge technologies have the potential to increase productivity while simultaneously having a significant impact on social and environmental sustainability. As a result, manufacturers must evaluate the role of these innovative technologies in sustainable development, as these technologies have the potential to address prevalent sustainability issues. A content-centric study of the implementation of these Industry 4.0 cutting-edge technologies in sustainable manufacturing is currently absent. A systematic literature study was conducted to explain the potential contribution of these novel technologies to the economic, social, and environmental dimensions of manufacturing industries. This study describes how these cutting-edge technologies are used in sustainable manufacturing. The findings of this study are particularly beneficial to practitioners who seek to apply one or more digital technologies to sustainable development.

Globalization, market turbulence, and global crisis such as the COVID-19 pandemic push the manufacturing industry to rush towards automation, data-centricity, and greater interconnectivity under the ongoing digital industrial transformation known as Industry 4.0. Smaller manufactures worldwide strive to keep up with the digitalization race and secure sustainable competitiveness. Fierce competition and market turbulence force Middle Eastern manufacturers to make the most of the digitalization race. The present study reports on the Industry 4.0 digital transformation and development of small and medium-sized manufacturing enterprises in Iran, one of the most economically influential countries within the Middle East. Smaller manufacturers in Iran consider Industry 4.0 to improve productivity, operate in the data-driven economy, and increase business resilience. Although the implementation rate of generic digital technologies has been somewhat promising among Iranian manufacturers, the present report identifies significant barriers slowing the implementation rate of more advanced Industry 4.0 technologies such as predictive analytics and blockchain technology. Overall, Industry 4.0 transformation has provided Iranian manufacturing enterprises with meaningful opportunities and benefits. Nonetheless, smaller manufacturers cannot generally develop an all-inclusive strategic plan for navigating digitalization efforts. This issue, along with many other barriers such as resource scarcity, digitalization immaturity, and cybersecurity risks, has slowed down the digital transformation of the Iranian manufacturing industry.

Bottleneck analysis, known as one of the essential lean manufacturing concepts, has been extensively researched in the literature. Recently, there has been a move towards using new Industry 4.0-based concepts and technologies in the development of bottleneck analysis. However, the interrelations between bottleneck analysis and Industry 4.0 have not been studied thoroughly. The present study addresses this gap and performs a systematic literature review on articles available in major scientific databases (i.e., Web of Science and Scopus) to investigate the impact of Industry 4.0 on the advancement of bottleneck analysis in production and manufacturing. Bibliometric analysis and content review were performed to extract the quantitative and qualitative data. Results revealed that only five out of 15 design principles and five out of eleven technologies of Industry 4.0 were addressed previously in developing bottleneck analysis methods. In addition to highlighting the existing gaps in the literature and proposing topics for future research, several potential development streams are proposed by studying the design principles and technologies of Industry 4.0, which have not been considered in bottleneck analysis before.

Facility layout design is becoming more challenging as manufacturing moves from traditionally emphasised mass production to mass customisation. The increasing demand for customised products and services is driving the need to increase flexibility and adaptability of both production processes and their material handling systems. A holistic approach for designing facility layouts with optimised flows considering production and logistics systems constraints seems to be missing in the literature. Several tools, including traditional methods, analytic hierarchy process, multiple-attribute decision making, simulation, and optimisation methods, can support such a process. Among these, simulation-based optimisation is the most promising. This paper aims to develop a facility layout design methodology supported by simulation-based optimisation while considering both production and logistics constraints. A literature review of facility layout design with simulation and optimisation and the theoretical and empirical challenges are presented. The integration of simulation-based optimisation in the proposed methodology serves to overcome the identified challenges, providing managers and stakeholders with a decision support system that handles the complex task of facility layout design.

Supply chain optimization concerns the improvement of the performance and efficiency of the manufacturing and distribution supply chain by making the best use of resources. In the context of supply chain optimization, scheduling has always been a challenging task for experts, especially when considering a distributed manufacturing system (DMS). The present study aims to tackle the supply chain scheduling problem in a DMS while considering two essential sustainability aspects, namely environmental and economic. The economic aspect is addressed by optimizing the total delivery time of order, transportation cost, and production cost while optimizing environmental pollution and the quality of products contribute to the environmental aspect. To cope with the problem, it is mathematically formulated as a mixed-integer linear programming (MILP) model. Due to the complexity of the problem, an improved genetic algorithm (GA) named GA-TOPKOR is proposed. The algorithm is a combination of GA and TOPKOR, which is one of the multi-criteria decision-making techniques. To assess the efficiency of GA-TOPKOR, it is applied to a real-life case study and a set of test problems. The solutions obtained by the algorithm are compared against the traditional GA and the optimum solutions obtained from the MILP model. The results of comparisons collectively show the efficiency of the GA-TOPKOR. Analysis of results also revealed that using the TOPKOR technique in the selection operator of GA significantly improves its performance.

Developing sustainable products and processes is essential for the survival of manufacturers in the current competitive market and the industry 4.0 era. The activities of manufacturers and their supply chain partners should be aligned with sustainable development goals. Manufacturers have faced many barriers and challenges in implementing sustainable practices along the entire supply chain due to globalisation, outsourcing, and offshoring. Blockchain technology has the potential to address the challenges of sustainability. This study aims to explain the applications of blockchain technology to sustainable manufacturing. We conducted a systematic literature review and explained the potential contributions of blockchain technology to the economic, environmental, and social performances of manufacturers and their supply chains. The findings of the study extend our understanding of the blockchain applications in sustainable manufacturing and sustainable supply chains. Furthermore, the study explains how blockchain can influence the sustainable performance of manufacturers by creating transparency, traceability, real-time information sharing, and security of the data capabilities.

The fourth industrial revolution, known as Industry 4.0, and the underlying digital transformation, is a cutting-edge research topic across various disciplines. Industry 4.0 literature is growing exponentially, overexpanding the current understanding of the digital industrial revolution through thousands of academic publications. This unprecedented growth calls for a systematic review of the concept, scope, definition, and functionality of Industry 4.0. Such systematic review should address the existing ambiguities and deliver a clear, comprehensive, and up-to-date overview of this phenomenon, including the possible implications for sustainability. Consistently, the present study carried out a systematic literature review of related articles, published online within the Industry 4.0 discipline until November 2020. The systematic literature review identified 745 eligible articles and applied extensive qualitative and quantitative data analysis methodically. The study provides a descriptive assessment of eligible articles' properties and offers a unified conceptualization of Industry 4.0 and the underlying building blocks. The study explains how the implications of Industry 4.0 for value creation expand beyond the manufacturing industry. The study further describes the sustainability value drivers of the fourth industrial revolution and identifies the conditions on which digital industrial transformation success lays. Overall, findings reveal that Industry 4.0 transformation could address pressing issues of sustainable development goals, particularly concerning the manufacturing-economic development. The study also draws on the findings and offers important theoretical and practical implications, highlights the existing gaps within the literature, and discusses the possible future research dir

Understanding the interactions of Industry 4.0 and sustainability is a cutting-edge research topic. The present study aims to contribute to this research topic by explaining how Industry 4.0 may contribute to energy sustainability. The present study performs a content-centric qualitative review of the extant digitalization literature to identify the primary energy sustainability functions of Industry 4.0. The interpretive structural modeling technique is further used for mapping the interrelationships among various energy sustainability functions identified. The interpretive model developed, and the Matrice d'Impacts Croisés Multiplication Appliquée àun Classement analysis offered exciting insights into the Industry 4.0-energy sustainability interactions. Findings show that Industry 4.0 promotes energy sustainability via a very complex mechanism that involves ten interrelated functions. Contrary to the general opinion, production efficiency offered by the digitalization of the manufacturing industry is not the immediate and most essential energy efficiency outcome of the digital industrial transformation. Industry 4.0 primarily contributes to energy sustainability by enabling the energy industry to reshape its operating landscape and enjoy more advanced, intelligent, and complicated energy production and distribution equipment. The digitalization of the energy demand sector, digitalization of the manufacturing industry, and the introduction of smarter and more sustainable products are among the main opportunities of Industry 4.0 for energy sustainability. Overall, the study and the ISM model of energy sustainability developed explains how Industry 4.0 contributes to energy sustainability via different functions and how each function is placed within the structural model based on its driving and dependence powers.

Optimizing production systems is urgent and indispensable if companies are to cope with global competition and a move from mass production to mass customization. The urgency of this need is more obvious in old production plants with a history of modifications, expansions, and adaptations in their production facilities. It is common to find complex, intricate and inefficient systems of material and product flows as a result of poor production facility layout. Several approaches can be used to support the design of optimal facility layouts. However, there is a lack of a suitable generic methodology for designing such layouts. Additionally, there has been little focus on the data and resources required, or on how simulation and optimization can support the design of optimal facilities. To overcome these deficiencies, this paper studies the integration of simulation and optimization for the design and improvement of facility layouts taking into account production and logistics constraints. The paper includes a generic perspective and a detailed implementation. The proposed methodology is evaluated in two case studies and by drawing on the principles and tools of the functional resonance analysis method. This method analyzes the implementation order and variability of a group of processes that can lead to unwanted outcomes. The results can provide managers and other stakeholders with a methodology that adequately considers production and logistics constraints when seeking an optimized facility layout design.

Purpose: The purpose of this study is to first investigate the efficiency of the most commonly used performance measures for minimizing the number of workstations (NWs) in approaches addressing simple assembly line balancing problem (SALBP) for both straight and U-shaped line, and second to provide a comparative evaluation of 20 constructive heuristics to find solutions to the SALBP-1. Design/methodology/approach: A total of 200 problems are solved by 20 different constructive heuristics for both straight and U-shaped assembly line. Moreover, several comparisons have been made to evaluate the performance of constructive heuristics. Findings: Minimizing the smoothness index is not necessarily equivalent to minimizing the NWs; therefore, it should not be used as the fitness function in approaches addressing the SALBP-1. Line efficiency and the idle time are indeed reliable performance measures for minimizing the NWs. The most promising heuristics for straight and U-shaped line configurations for SALBP-1 are also ranked and introduced. Practical implications: Results are expected to help scholars and industrial practitioners to better design effective solution methods for having the most balanced assembly line. This study will further help with choosing the most proper heuristic with regard to the problem specifications and line configuration. Originality/value: There is limited research assessing the efficiency of the common objectives for SALBP-1. This study is among the first to prove that minimizing the workload smoothness is not equivalent to minimizing the NWs in SALBP-1 studies. This work is also one of the first attempts for evaluating the constructive heuristics for both straight and U-shaped line configurations.