Heavy equipment performance is an important factor that can affect productivity factors in the Indonesian coal mining sector. A major reason behind the high downtime is because they follow time-based maintenance (TBM) methods on their heavy equipment. This has led to the search of new and much better and effective methods such as Condition-Based Maintenance (CBM) method. The use of CBM as a predictive maintenance methodology by coal industry players to minimise equipment downtime is proliferating but, at present, little evidence exists regarding how operational environmental conditions and human resource competencies impact the effectiveness of CBM. This study investigates the impact of Predictive Data Analysis (PDA), Maintenance Actions Proactive (MAP) and Supporting Technology (STE) on heavy equipment performance (HEP) and which include Operational Environment Conditions (OEC) and Human Resource Competency (HRC) as moderating variables. This study collected data from 207 operational and maintenance personnel in Indonesian coal mining companies that have adopted CBM. Analysis of data was conducted through Partial Least Squares Structural Equation Modeling (PLS-SEM) using Smart-PLS 4.0 software. The results showed that PDA, MAP, and STE were positively related to heavy equipment performance, with MAP showing the strongest relationship among the three CBM dimensions. OEC and HRC also had a significant direct effect on performance. However, among the proposed moderating relationships, only the interaction between HRC and MAP was statistically significant. These findings suggest that CBM effectiveness is influenced not only by maintenance-related practices but also by workforce capabilities. This study provides empirical evidence on the relationship between CBM implementation, operational conditions, human resource competencies, and heavy equipment performance in the context of coal mining operations.
Hand grip strength (HGS) is a well-known parameter of physical capability, clinical health status, and functional work performance. However, the scarcity of standardized, region-specific HGS data for Southeast Asian populations restricts the accuracy of health screening practices and the development of ergonomics and occupational safety guidelines. The aim of this study was to measure the dominant-hand HGS for healthy young adult women of Malaysia and Thailand, to compare the HGS of these two groups of women, and to investigate the relationship between age and anthropometric variables and the HGS. Researchers conducted a cross-sectional study involving 166 healthy women aged 20 to 39 years. This study recruited 92 participants from Malaysia and 74 from Thailand, primarily from university students and staff populations. Dominant-hand HGS was measured using a Jamar dynamometer (Sammons Preston, USA) while participants adopted a standardized standing position with the forearm in a neutral posture. Thai women demonstrated significantly greater mean HGS than Malaysian women (27.31 ± 6.96 kg vs. 23.64 ± 4.67 kg; p < 0.001), corresponding to an approximately 16% difference and a medium-to-large effect size (Cohen’s d = 0.63). Among Thai participants, HGS was significantly associated with palm circumference (r = 0.544), height, weight, and age. These variables collectively explained 44.8% of the variation in HGS. In contrast, only height showed a modest association with HGS among Malaysian participants. Meanwhile, the corresponding regression model demonstrated limited explanatory capability. These findings reveal population-specific differences in both HGS and its anthropometric correlates when assessed under standardized testing conditions. The study provides protocol-specific, preliminary reference data for healthy young adult Malaysian and Thai women, which may inform future development of validated, population-specific reference standards. By generating population-specific reference data, this study contributes to improving the accuracy and equity of health monitoring practices, in line with the objectives of United Nations SDG 3 (Good Health and Well-being).
The high variability of consumer demand makes the development of inventory strategies crucial, especially regarding operational inventory resilience. Combining defect prediction with inventory strategies is crucial amidst uncertainty related to quality. Conventional Demand-Driven Material Requirements Planning (DDMRP) strategies are sensitive to shifts in consumer demand based on buffers for replenishment. However, this strategy has the disadvantage of not considering losses due to defective production output quality. This study develops a hybrid inventory model by combining DDMRP with Order-Up-To-Level (OUTL) replenishment management, and defect rate prediction. Production output is assessed from estimated defect rates converted into yield factors. OUTL is used for conditional quantity setting by determining the amount of excess replenishment. Defect rate prediction uses a manufacturing defect dataset along with production volume, supplier quality, maintenance hours, time percentage, and worker productivity. The initial predictive element in inventory simulation using a random forest regressor configuration achieved an R² value of 0.7208. Numerical experiments to evaluate the inventory model used 24 scenarios over a 200-day daily review period. Scenarios were conducted by integrating various demand patterns, production process conditions, and production capacity limitations. The DDMRP-OUTL hybrid strategy model can reduce the Bullwhip Effect Ratio (Ratio of Echelon Logistics - REL) compared to conventional DDMRP for various scenarios, and the most significant reduction is close to 24% under intermittent demand. Furthermore, it demonstrates a higher average inventory increase as a trade-off between replenishment stability and inventory load. Stockout events are not consistently reduced across all scenarios, although the integration of defect rate prediction and the DDMRP-OUTL hybrid model leads to replenishment stability, and inventory load and service reliability must be balanced when implementing this policy.
Salt remains an important commodity for households and food-related businesses in Indonesia. In Padang City, the availability of salt can be affected when problems occur in raw material procurement, processing, or distribution. Such problems may also make daily activities more difficult for the actors in the chain. Although these risks are often encountered in practice, their priority in the local salt industry has not been systematically studied. This study applies the House of Risk (HOR) method to analyze supply chain risks in the salt industry of Padang City. The data were collected using questionnaires distributed to actors involved in procurement, processing, and distribution. These respondents were included because they handle the activities directly and understand the problems that appear in day-to-day operations. The analysis considers the severity of risk events, the occurrence of risk agents, and the relationship between risk events and their causes. These values were then used to calculate the Aggregate Risk Potential (ARP), which served as the basis for prioritizing risks. The findings show that risk exposure is mainly concentrated in procurement and production activities. This means that mitigation efforts should give more attention to these two stages. Preventive actions were then compared by looking at two practical considerations: how useful each action was expected to be and how difficult it would be to apply. The analysis shows that a small number of risk agents make a major contribution to the overall risk exposure. For this reason, these agents should be handled first. This order of action can help the actors reduce the most important risks without making the supply chain too rigid when conditions change.
The rapid development of IoT research in various fields has promoted the evolution of manufacturing in the Industry 4.0 context. However, the growing and dispersed literature makes it difficult to see the dominant trends and open challenges. The aim of the study is to synthesize the existing IoT research in the manufacturing, by analyzing the sectoral adoption, enabling technologies and implementation objectives. The review develops a systematic understanding of the links between manufacturing sectors, IoT technologies and operational priorities to identify dominant research directions and gaps for future research. A systematic literature review was conducted according to the PRISMA guidelines, screening and analysing peer-reviewed studies along three analytical dimensions: distribution by manufacturing sector, typologies of IoT technologies and strategic objectives of implementation. The analysis identified shared adoption patterns in some manufacturing sectors, common use of sensor-based and cloud-enabled technologies, and a high emphasis on productivity, monitoring and efficiency of operations. The results reveal a significant concentration of IoT research in discrete manufacturing, as well as noticeable attention in process manufacturing, healthcare and general manufacturing, while other sectors remain less explored, indicating an uneven research focus across industries. In terms of technology, Industrial IoT and smart manufacturing solutions are the most common, followed by IoT-enabled digital twin technologies, while the combination of IoT with artificial intelligence, machine learning, and computer vision indicates a growing shift towards more adaptive and intelligent systems. A smaller portion of IoT implementations are related to sensors and monitoring applications, blockchain enabled IoT solutions and distributed architectures, while middleware and system integration appear least often. Regarding implementation objectives, efficiency enhancement is the main driver, followed by predictive maintenance, quality control and productivity enhancement, and real-time monitoring, showing a strong orientation toward improving operational performance. In summary, the synthesis implies that the IoT research in manufacturing is mainly focused on discrete manufacturing applications, operational efficiency objectives, and intelligent automation technologies. The concentration indicates a continued research focus on production optimization, while broader contexts of industrial integration are relatively underexplored.
In a flexographic printing machine, color changeover actually takes up excessive time and serves as a bottleneck in the corrugated cardboard packaging manufacturer. This causes inefficiency and reduces the output. The objective of this study is to optimize production scheduling using a Genetic Algorithm (GA) to minimize sequence-dependent setup times (SDST). The current manual scheduling method used in the studied flexographic printing environment does not explicitly account for the sequence-dependent color changeover structure considered in this study, creating opportunities for improved scheduling performance through optimized production sequencing. To address this problem, the SDST problem was first formulated as a Mixed-Integer Linear Programming (MILP) model based on systematic observation and historical production data. A customized GA was then developed to generate high-quality scheduling solutions, and a systematic parameter tuning process was conducted, identifying an effective configuration of population size 500, 100 generations, and mutation rate 0.7 to ensure stable convergence. Results show that the proposed GA framework reduced setups times by over 70.78% (equivalent to 5.04 hours per shift) compared to the facility’s existing manual scheduling baseline and also outperformed the greedy heuristic benchmark, consistently achieving an average setup time of 8,466.63 seconds across multiple runs with low variability, demonstrating reliable performance. This study shows that GA can serve as a practical approach for optimizing scheduling in flexographic printing and closely related sequence-dependent color changeover production contexts, although the current model is based on deterministic conditions with fixed job sequences, which may limit responsiveness to dynamic production uncertainties such as machine breakdowns or rush orders, suggesting the need for future enhancements using simulation and multi-objective optimization approaches.
Getting a fully digital infrastructure set up for fisheries has become difficult, yet so has understanding how to develop the infrastructure to improve both sustainability and operability. Fish products are especially troubling because they are so perishable. Coordinating the large number of small fishing businesses (which are the main focus of this paper) is troublesome and causes a lack of ability to trace products throughout the supply chain, and a whole host of other inefficiencies. In an attempt to better understand the challenges of technology integration in small-scale fisheries, this paper presents several case studies of how artificial intelligence, machine learning, blockchain, and enterprise resource planning tools have been implemented in fisheries supply chains. In line with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) methodology, the review captures the state of technology in the field by reviewing 111 articles published in the last five years via a systematic search of Scopus and the Web of Science. The bibliometric tool in R was used to process the bibliographic data and ensure a systematic review of the published works. From this analysis, it was found that AI and machine learning tools were primarily used for demand and supply forecasting, whereas blockchain was used to ensure traceability and transparency in the supply chain. ERP tools were used to integrate the logistics, financial, and inventory management systems. While these tools have been implemented, there still appear to be significant barriers to integrating these technologies in a digital ecosystem. The barriers to implementation include high costs, very little digital infrastructure, and a complete unwillingness to adopt the technologies. The review indicates that the use of technology needs to be integrated, and the best way to improve small-scale fisheries is through the use of technology that is flexible, scalable, and can be easily integrated. These solutions lay the groundwork for better sustainable fisheries management. Further down the line, research can focus on creating interconnected systems that make it possible to implement affordable traceability and data-sharing systems, as well as provide real-time decision support across the various stages of the fisheries value chain.
Rising environmental pressures from population growth and industrial expansion in Indonesia necessitate sustainable business practices. Yet, adoption among Small and Medium-sized Enterprises (SMEs) remains obstructed by low energy efficiency, inadequate waste management, limited technological and financial access, and managerial shortcomings. Using two theories, resource based view (RBV) and institutional theory (IT), this study has mapped the relationships between internal and external factors and the triple-bottom-line performance of SMEs. This study investigates and tests these relationships amongst the factors that influence environmental, social, and economic sustainability performance by Partial Least Squares Structural Equation Modeling (PLS-SEM). This study designs the approach as a cross-sectional survey, gathering primary data from 110 SMEs to assess the proposed relationships among internal, external, and sustainability performance. Internal factors consist of green entrepreneurial orientation (GEO), green innovation (GI), and leadership commitment (LC), while market orientation (MO) and stakeholder pressure (SP) represent the external factors. Findings indicate that market orientation (MO) has a positive and significant effect across the entire triple-bottom-line (financial, social, environmental), whereas stakeholders pressure (SP) exerts a significant positive effect only on the financial and social dimensions. On the other hand, the internal factor comprising green entrepreneurial orientation (GEO), green innovation (GI), and leadership commitment (LC) does not significantly impact sustainability performance. These results indicate that among the external drivers examined, market orientation is the most comprehensive predictor of sustainability performance, whereas stakeholder pressure has a significant effect only on the financial and social dimensions. Practically, Indonesian SMEs are advised to constantly interpret and react to market signals and stakeholder expectations, whereas, policymakers are recommended to pair the tool of stakeholder pressure with capability-enhancement programs in terms of the operational system.
The growing demand for sustainable alternatives to fossil fuels has positioned bioethanol as a promising renewable energy source. However, few studies integrate factorial and regression-based process optimization with scalable financial analysis to valorize underutilized Chrysophyllum albidum (African star apple) for bioethanol production, limiting comprehensive frameworks that link process efficiency to economic feasibility. This study re-analysed an existing experimental dataset on bioethanol production from C. albidum to evaluate strategies for improving decision-making through integrated statistical modelling and scalable financial analysis. Four key process factors quantified for their effects on the ethanol yield were pH, yeast dosage (YD), fermentation time (FT), and incubation temperature (IT). A full factorial design coupled with regression modelling revealed that pH was the dominant factor, followed by YD and FT, while IT had a minimal effect. IT was excluded to refine the model, which subsequently demonstrated high predictive power within the specified design space (R² = 0.972, Adj. R² = 0.948). Informed by the statistical trade-off between FT and yield, a financial impact assessment compared two runs of optimized condition (pH 5.0, YD 4.5% wt/v, IT 35°C, FT 72 h) with three runs of an alternative scenario (pH 5.0, YD 4.5% wt/v, IT 35°C, FT 24 h) revealed by the statistical analysis. Crucially, the financial analysis demonstrated that the technically optimized condition was not the most economical; the alternative scenario delivered a lower unit cost. The findings underscore the importance of integrating process optimization with cost analysis to identify conditions that balance technical yield with financial sustainability for scalable bioethanol production, demonstrated here through a scenario-based financial comparison framework applied to underutilized African star apple.
Industrial disasters in high-risk sectors such as the petrochemical industry continue to occur, despite significant advancements in process safety and technological controls over the years. This suggests ongoing operational challenges and a lack of consistent academic understanding across both technical and regulatory dimensions. While earlier bibliometric studies have focused on specific thematic areas such as domino effects and risk analysis, a thorough synthesis of the thematic evolution and collaboration structures in industrial disaster research remains limited. To address this gap, this study provides a systematic bibliometric mapping of research focused on industrial disasters, analyzing the themes, impact, and collaboration trends over the past thirty years. The analysis assessed publication trends, co-authorship networks, citation dynamics, bibliographic coupling and keyword co-occurrence through the evaluation of 357 Scopus-indexed publications from 1995 to 2025. The findings indicate a gradual transition from technical risk assessment to more comprehensive integrative perspectives. While domino effects and process safety remain the primary focus of study, recent publication trends demonstrate a growing interest in safety culture and organizational factors. European nations, especially Italy, the Netherlands, and Belgium, have become significant players in global research collaborations, suggesting a wider European focus on industrial safety management and regulatory structures such as the Seveso Directive. Thematic clustering revealed three main themes: technical risk assessment, human and organizational factors, and the environmental and societal impacts of disasters, highlighting a growing integration of technical and socio-regulatory issues. Through a comprehensive longitudinal and network-based synthesis that addresses various themes, this study highlights significant works, overlooked correlations, and new research domains such as resilience-oriented risk governance, establishing a basis for more integrated industrial safety research, policy formulation and organizational risk management strategies.
Sustainable transport plays a key role in the fight against climate change, particularly in developing countries where reliance on conventional vehicles is high. Motorcycles account for the majority of the fleet of motor vehicles in Indonesia and contribute significantly to emissions. In order to achieve its Paris target of a 29 percent reduction of carbon emissions, the government is encouraging electric cars with various incentives. This study develops a willingness to consider (WTC) model for electric motorcycles in Indonesia based on the powertrain technology transition market agent model (PTTMAM) and utilizes Vensim software to simulate outcomes. The WTC model is built on the assumption that consumers' willingness to consider electric motorcycles is influenced by factors such as costs, marketing, and exposures. The system dynamics model consists of four modules: the conventional motorcycle, the electric motorcycle, the marketing module, and the willingness to consider module. The simulation results show an increasing trend in consumers’ willingness to consider electric motorcycles from 2017-2035, with the WTC value reaching 0.3209 in 2035. While this indicates a positive shift toward greater consumer interest in electric motorcycles, the growth remains modest and slow, reflecting the challenges of widespread adoption. Additionally, this study evaluates three government incentive and subsidy policy scenarios. The scenario results indicate that government subsidies and incentives can increase the consumers’ willingness to consider electric motorcycles in Indonesia, thereby increasing their market share. Among the scenarios, the purchase price subsidy is the most effective, as it directly reduces the financial barrier, encouraging more consumers to make the switch to electric motorcycles.
In the context of rapid technological advancement and the global rise of entrepreneurship, business incubators have become essential mechanisms for supporting technology-based startups, particularly in emerging economies. These incubators play a strategic role in bridging resource gaps, fostering innovation, and enhancing the survival and growth of early-stage ventures. Despite their increasing importance, there remains a limited understanding of how incubator performance directly influences startup outcomes. This study addresses that gap through a comprehensive bibliometric analysis of 920 scholarly articles published between 2010 and 2022, sourced from Scopus and Google Scholar. Using VOSviewer, the analysis identifies key research trends, influential publications, and thematic clusters related to incubator performance. The findings reveal a significant increase in research activity over the past decade, with a peak in 2018, and a strong concentration of publications in journals focused on technology transfer and innovation management. Prominent themes include academic entrepreneurship, incubator performance, technology transfer offices, and the role of innovation ecosystems involving academia, industry, and government. These themes highlight the multifaceted nature of incubator success and the importance of cross-sector collaboration. The study also emphasizes the need for integrated evaluation frameworks to enhance incubator effectiveness and guide institutional and policy-level strategies. The novelty of this research lies in its synthesis of bibliometric insights to propose future research directions and methodological improvements for assessing incubator performance. By mapping the intellectual landscape of incubator research, this study contributes to a deeper understanding of how incubators can be optimized to support sustainable startup development and economic growth in emerging markets.
Employment is crucial for economic sustainability and social inclusion, yet individuals with disabilities face significant barriers. Globally, only 44% of disabled individuals are employed compared to 75% of those without disabilities. Manual material handling (MMH) relies heavily on stability and control in demanding industries such as manufacturing and logistics. Such demands create challenges for individuals with above-knee prostheses, as most current designs focus on walking and do not adequately support the postural and load-bearing requirements of MMH tasks. This study aims to evaluate the performance of transfemoral prosthesis designs during MMH, analyzing the effects of container type, load mass, and their interaction on gait efficiency, discomfort, and stability. Eight male unilateral above-knee amputees (24–39 y) carried handled and handle-less boxes loaded from 4 to 10 kg in a randomised within-subject trial. Gait deviation, perceived discomfort, and steadiness were captured with self-report measures. Two-way analysis of variance analyses showed a significant container × load interaction: handle-less 10 kg loads produced the greatest lateral trunk lean toward the prosthetic side, whereas lighter handled loads minimised deviation. Increasing load also elevated discomfort in the back, waist, stump and contralateral arm and reduced perceived stability. Observed lateral lean and impact-related knee extension suggest three priority modifications: (1) add socket adduction within an ischial-containment design to improve femoral stabilisation, (2) increase knee-swing friction to soften terminal impact, and (3) fit dual-keel feet to cushion heel strike. Implementing these changes may reduce gait errors and fatigue, raising safe lifting capacity for transfemoral prosthesis users in MMH task. Nonetheless, the male-only sample may not capture gender-specificgait strategies; future trials should include female participants and a larger cohort to verify generalisability. These preliminary findings still offer insights into improving prosthetic designs to enhance safety, functionality, and inclusion in industrial MMH tasks.
Sustainable Small and medium-sized enterprises (SMEs) are crucial to economic growth but face significant challenges in occupational safety and health (OSH). SMEs often lack the resources, expertise, and institutional support needed to manage OSH effectively, leading to higher rates of workplace accidents. This study addresses the lack of thematic synthesis and trend forecasting by offering a structured overview of the field's intellectual landscape. Using bibliographic coupling and co-word analysis, we identified key research themes, emerging trends, and influential studies. Data were collected from the Web of Science (WoS) Core Collection (1990 to 2024) yielding 552 initial records. After screening, 393 journal articles were analysed, with a total of 6,408 citations (6,181 excluding self-citations), an average of 16.31 citations per item and an H-index of 39. Applying a 30-citation threshold, 54 highly cited papers were subjected to bibliographic coupling, revealing six thematic clusters. The analysis indicates four prominent clusters: (1) the role of human resources in OSH programs, (2) health certifications and safety management systems, (3) employee perceptions of OSH efforts, and (4) the development of OSH models tailored to SMEs. Consequently, our findings demonstrate that OSH research in SMEs is steadily evolving toward more integrated, systematic management approaches. These insights suggest that enhancing OSH outcomes requires targeted strategies including strengthening human resource roles, adopting formal safety frameworks, emphasizing risk assessment and staff training, and implementing standardized practices fit for the SME context.
This study explicates the impact of consumer perceptions of greenwashing on the purchase intentions of single-use plastic products, specifically bottled still water and soft drinks, within the context of growing sustainability concerns. The objective is to understand how these perceptions influence consumer decisions and how the insights can inform the optimization of industrial practices related to packaging and marketing. Using a quantitative explanatory design, data was collected from no less than one hundred and sixty-eight respondents in Padang City through direct and online surveys. The sampling method is the Non-Probability Sampling method with a Purposive Sampling approach and it is directed to individuals who meet the following criteria: (1) minimum age of 17 years old; (2) domicile in Padang City; and (3) individuals or households who know bottled water and soft drinks brand. The data analysis, conducted via Structural Equation Modelling (SEM) using SmartPLS, reveals that negative perceptions of greenwashing significantly reduce purchase intentions. However, positive word-of-mouth can mitigate these effects, leading to a higher likelihood of purchase. The findings highlight the critical role of environmental awareness in shaping consumer behavior and suggest that companies should prioritize authentic sustainability practices which could be in terms of third-party certification to maintain consumer trust and optimize their product strategies. Companies are expected to be able to do business responsibly, by paying attention to the end-to-end production process that has minimal waste and minimal impact on the environment, through the development of more environmentally friendly products and optimization of waste management programs. Third-party certifications may be useful to support this effort.
Personal hearing protectors (PHPs) used by industrial workers have already been a preferred measure in various industrial sectors that have issues with excessive noise exposure. Although personal hearing protectors (PHPs) are widely provided across industrial workplaces, actual worker compliance with their consistent and correct use remains notably low. Therefore, this study aimed to develop a valid measure to evaluate factors affecting PHP use among industrial workers in Malaysia. A questionnaire was developed on the factors affecting PHP use among industrial workers in Malaysia. The questionnaire comprised several items and was created using a systematic, thorough process consisting of three stages: (i) formulating items, (ii) translating them back-to-back, and (iii) subjecting them to expert content assessment by six (6) panels of experts. The questionnaire constructs and items were evaluated for content validity and reliability. The content validity score for each item was considered satisfactory. The Cronbach’s alpha was 0.940, indicating high overall internal consistency. The domain coefficients were as follows: interpersonal influence, 0.899; perceived severity, 0.902; perceived benefit, 0.868; perceived barrier, 0.893; perceived self-efficacy, 0.879; cues to action, 0.815; and use of PHP, 0.840. The domain coefficients demonstrated good to high internal consistency, ranging from 0.815 (cues to action) to 0.902 (perceived severity). This study shows that the questionnaire on factors affecting PHP use among industrial workers is valid and well-structured. Therefore, this study provides a valid and reliable tool for assessing factors influencing PHP use, which can inform the planning of targeted noise management programs.
Flatfoot (pes planus), characterized by a reduced or absent medial arch, cause biomechanical disorders, pain and a risk of injury. Customized insoles are a key intervention, with the emergence of 3D printing fused deposition modelling (FDM) based on flexible materials such as thermoplastic polyurethane (TPU) and thermoplastic elastomer (THE). This systematic literature review, based on PRISMA guidelines and analysis of six Scopus studies, assesses the biomechanical and ergonomic properties of these insoles. The results show that flexible 3D printed inserts significantly improve biomechanics by increasing the height of the navicular arch, reducing excessive ankle joint eversion, increasing dorsiflexion and improving the distribution of plantar plate pressure. Regarding perceived comfort, evaluations using the Visual Analog Scale (VAS), the Likert scale and the American Orthopaedic Foot and Ankle Society (AOFAS) questionnaire consistently indicate improved user comfort over no insole or conventional option. Despite these advantages, challenges include limited material options, inconsistent print quality and technical fabrication problems. Further research is needed, especially large-scale studies, to resolve these problems and to improve the clinical use of the product. In conclusion, flexible inserts printed with FDM have the potential to improve both the biomechanical function and the perceived comfort of the footwear use.
This study addresses an enhanced version of the Double Row Layout Problem (DRLP) by incorporating two critical constraints: minimum safety distances between machines and geometric limitations on row lengths. A bi-objective mixed-integer non-linear programming (MINLP) model is formulated to simultaneously minimize material handling costs and penalties for violating safety distance requirements. To solve the problem efficiently, a novel metaheuristic called Improved Multi-Objective Variable Neighborhood Search (IMOVNS) is proposed. IMOVNS extends the standard MOVNS by integrating an adaptive archive update strategy and a probabilistic acceptance mechanism inspired by AMOSA, thereby improving both convergence and diversity in Pareto front generation. This study contributes to the layout optimization literature by proposing a tailored MOVNS variant explicitly designed for safety-aware and geometry-constrained DRLP, a challenging problem variant that has received limited attention in prior research. Extensive experiments on 27 DRLP instances show that IMOVNS demonstrates strong performance, significantly outperforming NSGA-II and showing competitive or superior results compared to AMOSA and MOVNS in terms of convergence and solution diversity. Statistical tests further confirm the significant superiority of IMOVNS, particularly over NSGA-II. Additionally, a key managerial insight reveals that layouts with unbalanced row lengths favour safety compliance, while balanced layouts minimize material handling costs. The Pareto-optimal solutions generated by IMOVNS enable decision-makers to select layout configurations that align with specific operational priorities. These findings highlight the practical relevance and robustness of IMOVNS in solving real-world multi-objective facility layout problems under complex spatial and safety constraints.
Although the physical ergonomics of seat design have been extensively studied, emotional comfort is still largely overlooked, especially in public transport. This study addresses this gap by incorporating passengers' emotional perceptions into the design of luxury train seats, in response to documented user discomfort which transcends physical dimensions. The aim was to design seats based on the emotional needs of users and the principles of Kansei engineering, incorporating elements of Javanese cultural values as a form of local wisdom. Emotional responses were captured using Kansei words derived from user interviews, online reviews, and from the expertise of local practitioners. The designs included batik and Javanese decorations. A statistical analysis using Quantitative Theory of Type I (QTT1) identified design elements corresponding to semantic differences between Kansei words. Analysis revealed that the dominant emotional dimension is creative, as indicated by the highest multiple of R-squared 0.9785. This dimension has been operationalized in 14 concrete design elements of the proposed seating concept. The innovative use of batik motifs on the seat backrests was a distinctive feature and underlined the fact that users perceived cultural integration as central to the creative dimension. The study concluded that the integration of emotional perception, represented by the creative dimension, and local wisdom, represented by the batik elements, is a viable strategy for the design of culturally distinct and emotionally attractive luxury train seats. It shows that the culturally rooted approach of Kansei Engineering contributes to the welfare of users.
Resource efficiency lies at the heart of logistics performance, with unloading operations in storage facilities serving as a critical determinant of overall productivity. In less developed regions, the widespread reliance on basic rules-based systems such as FIFO often proves inadequate for handling operational complexities, leading to bottlenecks and inefficiencies. Small and medium-sized enterprises (SMEs), constrained by limited resources, are compelled to optimize existing infrastructure rather than invest in costly upgrades. To address this challenge, the present study introduces a goal-oriented programming model designed to assign trucks to loading docks within specific time slots, thereby enhancing time efficiency. The model evaluates performance across four key metrics: waiting time, loading time, overtime, and equity. By leveraging goal programming, numerical prioritization of these objectives becomes possible, enabling flexible adjustments to meet operational needs. Furthermore, Monte Carlo simulation (MCS) is employed to incorporate variability into the dataset and assess model robustness under real-world uncertainty. Experimental results reveal that the proposed approach consistently outperforms traditional systems, delivering significant improvements in time efficiency. These findings highlight the potential of goal programming as a practical solution for planning in resource-constrained environments. The resulting model offers an adaptive, reliable framework that warehouse managers can implement without incurring substantial infrastructure costs.
