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Architectural programming, as a critical phase in construction projects, has been widely recognized for its importance and advantages throughout the construction process. With the rapid development of the socioeconomic landscape, architectural programming has garnered increasing attention from various other disciplines, becoming a key trend in interdisciplinary collaboration. This study aims to provide a comprehensive understanding of the current status and future directions of architectural programming from an interdisciplinary perspective through scientometric analysis and systematic review.

This study first collected English journal articles on architectural programming published between 1975 and 2024 from the Web of Science and Scopus databases. After an initial screening of titles and abstracts, 515 articles were selected for scientometric analysis to reveal the current state and advantages of architectural programming research in multidisciplinary fields. Subsequently, a second screening of full-text articles identified 75 journal articles for systematic review, focusing on research topics and challenges in interdisciplinary applications.

The study reveals an exponential increase in the number of papers related to architectural programming between 1975 and 2024, particularly in the last decade. Six key research topics of architectural programming in multidisciplinary fields were identified: (1) performance optimization and evaluation, (2) digitalization and automation development, (3) project management and decision support, (4) improvement of human and social welfare, (5) sustainable resources and environment and (6) educational practices of architectural programming. Additionally, the study identified the main challenges in the interdisciplinary application of architectural programming, including (1) incompatibility among disciplines, (2) limitations of data and methodologies and (3) insufficient social engagement. To address these challenges, three potential future directions were proposed: (1) establishing interdisciplinary teams and platforms, (2) enhancing multi-source data integration and digital transformation and (3) improving governance mechanisms and educational training.

By combining quantitative and qualitative methods, this study provides a comprehensive review of architectural programming research and applications in multidisciplinary fields, offering theoretical foundations and practical references for the future development of architectural programming. This review not only aids in understanding the overall status of current architectural programming research but also offers valuable insights and recommendations for future research directions and practical applications.

The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology.

Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques.

Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate.

Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.

The purpose of this paper is mainly to develop the adjoint method within the method of magnetic moment (MMM) and thus, to provide an efficient new way to solve topology optimization problems in magnetostatic to design 3D-magnetic circuits.

First, the MMM is recalled and the optimization design problem is reformulated as a partial derivative equation-constrained optimization problem where the constraint is the Maxwell equation in magnetostatic. From the Karush–Khun–Tucker optimality conditions, a new problem is derived which depends on a Lagrangian parameter. This problem is called the adjoint problem and the Lagrangian parameter is called the adjoint parameter. Thus, solving the direct and the adjoint problems, the values of the objective function as well as its gradient can be efficiently obtained. To obtain a topology optimization code, a semi isotropic material with penalization (SIMP) relaxed-penalization approach associated with an optimization based on gradient descent steps has been developed and used.

In this paper, the authors provide theoretical results which make it possible to compute the gradient via the continuous adjoint of the MMMs. A code was developed and it was validated by comparing it with a finite difference method. Thus, a topology optimization code associating this adjoint based gradient computations and SIMP penalization technique was developed and its efficiency was shown by solving a 3D design problem in magnetostatic.

This research is limited to the design of systems in magnetostatic using the linearity of the materials. The simple examples, the authors provided, are just done to validate our theoretical results and some extensions of our topology optimization code have to be done to solve more interesting design cases.

The problem of design is a 3D magnetic circuit. The 2D optimization problems are well known and several methods of resolution have been introduced, but rare are the problems using the adjoint method in 3D. Moreover, the association with the MMMs has never been treated yet. The authors show in this paper that this association could provide gains in CPU time.

Prefabricated columns connected by grouted sleeves are increasingly used in practical projects. However, seismic fragility analyses of such structures are rarely conducted. Seismic fragility analysis has an important role in seismic hazard evaluation. In this paper, the seismic fragility of sleeve connected prefabricated column is analyzed.

A model for predicting the seismic demand on sleeve connected prefabricated columns has been created by incorporating engineering demand parameters (EDP) and probabilities of seismic failure. The incremental dynamics analysis (IDA) curve clusters of this type of column were obtained using finite element analysis. The seismic fragility curve is obtained by regression of Exponential and Logical Function Model.

The IDA curve cluster gradually increased the dispersion after a peak ground acceleration (PGA) of 0.3 g was reached. For both columns, the relative displacement of the top of the column significantly changed after reaching 50 mm. The seismic fragility of the prefabricated column with the sleeve placed in the cap (SPCA) was inadequate.

The sleeve was placed in the column to overcome the seismic fragility of prefabricated columns effectively. In practical engineering, it is advisable to utilize these columns in regions susceptible to earthquakes and characterized by high seismic intensity levels in order to mitigate the risk of structural damage resulting from ground motion.

Organisations and industries are often looking for technologies that can accomplish multiple tasks, providing economic benefits and an edge over their competitors. In this context, drones have the potential to change many industries by making operations more efficient, safer and more economic. Therefore, this study investigates the use of drones as the next step in smart/digital warehouse management to determine their socio-economic benefits.

The study identifies various enablers impacting drone applications to improve inventory management, intra-logistics, inspections and surveillance in smart warehouses through a literature review, a test of concordance and the fuzzy Delphi method. Further, the graph theory matrix approach (GTMA) method was applied to ranking the enablers of drone application in smart/digital warehouses. In the subsequent phase, researchers investigated the relation between the drone application's performance and the enablers of drone adoption using logistic regression analysis under the TOE framework.

This study identifies inventory man agement, intra-logistics, inspections and surveillance are three major applications of drones in the smart warehousing. Further, nine enablers are identified for the adoption of drone in warehouse management. The findings suggest that operational effectiveness, compatibility of drone integration and quality/value offered are the most impactful enablers of drone adoption in warehouses. The logistic regression findings are useful for warehouse managers who are planning to adopt drones in a warehouse for efficient operations.

This study identifies the enablers of drone adoption in the smart and digital warehouse through the literature review and fuzzy Delphi. Therefore, some enablers may be overlooked during the identification process. In addition to this, the analysis is based on the opinion of the expert which might be influenced by their field of expertise.

By considering technology-organisation-environment (TOE) framework warehousing companies identify the opportunities and challenges associated with using drones in a smart warehouse and develop strategies to integrate drones into their operations effectively.

This study proposes a TOE-based framework for the adoption of drones in warehouse management to improve the three prominent warehouse functions inventory management, intra-logistics, inspections and surveillance using the mixed-method.

This work presents the results of a case study aimed at revitalizing an agricultural equipment manufacturing consortium facing prolonged losses. The purpose of this paper is to enhance productivity and profitability by identifying and eliminating waste within the manufacturing processes. The study uses lean principles and tools to achieve this objective.

The study begins with the creation of a questionnaire, administered to the consortium to gather insights. The questionnaire responses serve as a foundation for pinpointing critical areas in need of immediate attention. To tackle the challenge of demand forecasting without customer data, a demand forecasting model is introduced. Value stream mapping (VSM) is used to identify and highlight process inefficiencies and waste. The findings are further analyzed using a Pareto chart to prioritize waste reduction efforts. Based on these insights, the study proposes alternative manufacturing methods and waste elimination strategies. A multiphase lean framework is developed as a step-by-step roadmap for implementing lean manufacturing.

The study identifies a broken process flow within the consortium’s manufacturing processes and highlights areas of waste through VSM. The Pareto chart analysis reveals the most significant waste areas requiring immediate intervention. Recommendations for process improvements and waste reduction strategies are provided to the consortium.

This study contributes to the field by applying lean principles and tools to address the unique challenges faced by an agricultural equipment manufacturing consortium. The integration of a demand forecasting model and the development of a multiphase lean framework offer innovative approaches to enhancing productivity and profitability in this context.

This study aims to evaluate linear mixed data sampling (MIDAS), nonlinear artificial neural networks (ANNs) and a hybrid approach for exploiting high-frequency information to improve low-frequency gross domestic product (GDP) forecasting. Their capabilities are assessed through direct forecasting comparisons.

This study compares quarterly GDP forecasts from unrestricted MIDAS (UMIDAS), standalone ANN and ANN-enhanced MIDAS models using five monthly predictors. Rigorous empirical analysis of recent US data is supplemented by Monte Carlo simulations to validate findings.

The empirical results and simulations demonstrate that the hybrid ANN-MIDAS performs best for short-term predictions, whereas UMIDAS is more robust for long-term forecasts. The integration of ANNs into MIDAS provides modeling flexibility and accuracy gains for near-term forecasts.

The model comparisons are limited to five selected monthly indicators. Expanding the variables and alternative data processing techniques may reveal further insights. Longer analysis horizons could identify structural breaks in relationships.

The findings guide researchers and policymakers in leveraging mixed frequencies amidst data complexity. Appropriate modeling choices based on context and forecast horizon can maximize accuracy.

Enhanced GDP forecasting supports improved policy and business decisions, benefiting economic performance and societal welfare. More accurate predictions build stakeholder confidence and trust in statistics underlying critical choices.

This direct forecasting comparison offers unique large-scale simulation evidence on harnessing mixed frequencies with leading statistical and machine learning techniques. The results elucidate their complementarity for short-term versus long-term modeling.

This study aims to develop an optimal tolerance allocation strategy involves integrating the unique transfer (UT) approach and the difficulty coefficient evaluation (DCE) routine in an interactive hybrid method. This method combines the strengths of both UT and DCE, ensuring simultaneous utilization for enhanced performance. The proposed tolerancing model manifests an integrated computer-aided design (CAD) tool.

By combining UT and DCE based on failure mode, effects and criticality analysis (FMECA) tool and the Ishikawa diagram, the proposed collaborative hybrid tool ensures an efficient and optimal tolerance allocation approach. The integration of these methodologies not only addresses specific transfer challenges through UT but also conducts a thorough evaluation of difficulty coefficients via DCE routine using reliability analysis tools as FMECA tool and the Ishikawa diagram. This comprehensive framework contributes to a robust and informed decision-making process in tolerance allocation, ultimately optimizing the design and manufacturing processes.

The presented methodology is implemented with the aim of generating allocated tolerances that align with specific difficulty requirements, facilitating the creation of a mechanical assembly characterized by high quality and low cost. To substantiate and validate the conceptual framework and methods, an integrated tool has been developed, featuring a graphical user interface (GUI) designed in MATLAB. This interface serves as a platform to showcase various interactive and integrated tolerance allocation approaches that adhere to both functional and manufacturing prerequisites. The proposed integrated tool, designed with a GUI in MATLAB, offers the capability to execute various examples that effectively demonstrate the benefits of the developed tolerance transfer and allocation methodology.

The originality of the proposed approach is the twining between the UT and DCE simultaneous in an integrated and concurrent tolerance transfer and allocation model. Therefore, the proposed approach is named an integrated CAD/tolerance model based on the manufacturing difficulty tool. The obtained results underscore the tangible advantages stemming from the integration of this innovative tolerance transfer and allocation approach. These benefits include a notable reduction in total cost and a concurrent enhancement in product quality.

This study aims to examine the effect of the board of directors (BoD) structure on environmental, social and governance (ESG) performance in publicly traded non-financial firms from the perspective of agency theory, with investors as the principal, the management team as the agent, the BoD as an information system that reduces information asymmetries between them and ESG performance as a shareholder’s expectation.

Sample data is cross-sectional as of January 2023 and includes 1,695 non-financial firms listed in 59 stock markets across 54 countries. Data were sourced from the FactSet Research Systems database. The generalized least squares method was used to run quadratic and exponential models to assess the research hypotheses.

Results revealed that board size, independence, age, gender diversity and participation on other corporate boards have a nonlinear relationship with ESG performance. Board tenure is the only BoD attribute for which a nonlinear association is not found. This study found that firms with larger boards and more female board members tend to exhibit a stronger commitment to ESG performance. In contrast, companies with a board of directors consisting of independent members, advanced age, service on other corporate boards and CEO duality may struggle to prioritize positive ESG outcomes.

This study contributes to the academic discussion on BoD–ESG by examining nonlinear relationships among a large sample of publicly traded firms; providing results that could be applied internationally; using ESG data that is based on the Sustainability Accounting Standards Board's materiality framework, which identifies key ESG factors for investors; emphasizing the significance of diversity and inclusion within the decision-making bodies of public companies, thereby improving their ESG performance; and supporting the agency theory perspective and suggesting that the effect of board structure on ESG may reflect the board's focus on investors’ best interests.

Significant interest in Integrated Management Systems (IMS), as a key area within ISO-related Management System Standards (MSS) literature, has been evident from both academia and industry over the past three decades. This study aims to map the evolution and current state of IMS research and propose possible directions for future studies.

A comprehensive content and bibliometric analysis of 846 documents from the Scopus database across the period 1995 to 2023 was conducted. This included performance analysis to track publication trends and identify key contributors, and content analysis to specify dominant research methodologies and the MSS most commonly integrated. Furthermore, science mapping techniques—such as co-authorship networks, keyword co-occurrence analysis, and bibliographic coupling—were utilized to outline the collaborative networks and the conceptual and intellectual structure of the field.

The study identifies three principal IMS research themes: the practical implementation of IMS, their role in promoting sustainability and social responsibility, and their impact on continuous performance improvement. It also highlights the field’s evolution and key research constituents—including influential works, prolific authors, leading academic institutions and countries, and top publishing journals. It further reveals that IMS research exhibits strong collaboration across authors and countries, and a rich methodological plurality, notably with a marked increase in empirical surveys in recent years. Additionally, it identifies the most frequently referenced MSS for integration, prominently featuring ISO 9001, ISO 14001, and ISO 45001/OHSAS 18001.

This study is original in its application of a dual analytical approach—bibliometric and content analysis—to provide a holistic overview of IMS research. It offers new insights into the integration of diverse MSS and proposes several promising paths for future research. Among the most prominent are standardizing IMS fundamental specifications, conducting more empirical research with advanced methods to evaluate the effects of MSS integration, providing practical support for organizations in IMS implementation through tailored methodologies and tools, and exploring the potential of Industry 4.0 and 5.0 technologies to advance IMS practices.