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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.

For successful management of construction projects, a precise analysis of the balance between time and cost is imperative to attain the most effective results. The aim of this study is to present an innovative approach tailored to tackle the challenges posed by time-cost trade-off (TCTO) problems. This objective is achieved through the integration of the multi-verse optimizer (MVO) with opposition-based learning (OBL), thereby introducing a groundbreaking methodology in the field.

The paper aims to develop a new hybrid meta-heuristic algorithm. This is achieved by integrating the MVO with OBL, thereby forming the iMVO algorithm. The integration enhances the optimization capabilities of the algorithm, notably in terms of exploration and exploitation. Consequently, this results in expedited convergence and yields more accurate solutions. The efficacy of the iMVO algorithm will be evaluated through its application to four different TCTO problems. These problems vary in scale – small, medium and large – and include real-life case studies that possess complex relationships.

The efficacy of the proposed methodology is evaluated by examining TCTO problems, encompassing 18, 29, 69 and 290 activities, respectively. Results indicate that the iMVO provides competitive solutions for TCTO problems in construction projects. It is observed that the algorithm surpasses previous algorithms in terms of both mean deviation percentage (MD) and average running time (ART).

This research represents a significant advancement in the field of meta-heuristic algorithms, particularly in their application to managing TCTO in construction projects. It is noteworthy for being among the few studies that integrate the MVO with OBL for the management of TCTO in construction projects characterized by complex relationships.

Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

Although extensive research has been conducted on precast production, irregular component loading constraints have received little attention, resulting in limitations for transportation cost optimization. Traditional irregular component loading methods are based on past performance, which frequently wastes vehicle space. Additionally, real-time road conditions, precast component assembly times, and delivery vehicle waiting times due to equipment constraints at the construction site affect transportation time and overall transportation costs. Therefore, this paper aims to provide an optimization model for Just-In-Time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time.

In order to propose a JIT (just-in-time) delivery optimization model, the effects of the sizes of irregular precast components, the assembly time, and the loading methods are considered in the 3D loading constraint model. In addition, for JIT delivery, incorporating real-time road conditions in the transportation process is essential to mitigate delays in the delivery of precast components. The 3D precast component loading problem is solved by using a hybrid genetic algorithm which mixes the genetic algorithm and the simulated annealing algorithm.

A real case study was used to validate the JIT delivery optimization model. The results indicated this study contributes to the optimization of strategies for loading irregular precast components and the reduction of transportation costs by 5.38%.

This study establishes a JIT delivery optimization model with the aim of reducing transportation costs by considering 3D loading constraints, real-time road conditions and assembly time. The irregular precast component is simplified into 3D bounding box and loaded with three-space division heuristic packing algorithm. In addition, the hybrid algorithm mixing the genetic algorithm and the simulated annealing algorithm is to solve the 3D container loading problem, which provides both global search capability and the ability to perform local searching. The JIT delivery optimization model can provide decision-makers with a more comprehensive and economical strategy for loading and transporting irregular precast components.

The coal-fired power plants have been confronted with new operation challenge since the unified carbon trading market was launched in China. To make the optimal decision for the carbon emissions and power production has already been an important subject for the plants. Most of the previous studies only considered the market prices of electricity and coal to optimize the generation plan. However, with the opening of the carbon trading market, carbon emission has become a restrictive factor for power generation. By introducing the carbon-reduction target in the production decision, this study aims to achieve both the environmental and economic benefits for the coal-fired power plants to positively deal with the operational pressure.

A dynamic optimization approach with both long- and short-term decisions was proposed in this study to control the carbon emissions and power production. First, the operation rules of carbon, electricity and coal markets are analyzed, and a two-step decision-making algorithm for annual and weekly production is presented. Second, a production profit model based on engineering constraints is established, and a greedy heuristics algorithm is applied in the Gurobi solver to obtain the amounts of weekly carbon emission, power generation and coal purchasing. Finally, an example analysis is carried out with five generators of a coal-fired power plant for illustration.

The results show that the joint information of the multiple markets of carbon, electricity and coal determines the real profitability of power production, which can assist the plants to optimize their production and increase the profits. The case analyses demonstrate that the carbon emission is reduced by 2.89% according to the authors’ method, while the annual profit is improved by 1.55%.

As an important power producer and high carbon emitter, coal-fired power plants should actively participate in the carbon market. Rather than trade blindly at the end of the agreement period, they should deeply associate the prices of carbon, electricity and coal together and realize optimal management of carbon emission and production decision efficiently.

This paper offers an effective method for the coal-fired power plant, which is struggling to survive, to manage its carbon emission and power production optimally.

Exact solution of time–cost trade-off problem (TCTP) by the state-of-the-art meta-heuristic algorithms can be obtained for small- and medium-scale problems, while satisfactory results cannot be obtained for large construction projects. In this study, a hybrid heuristic meta-heuristic algorithm that adapts the search domain is developed to solve the large-scale discrete TCTP more efficiently.

Minimum cost slope–based heuristic network analysis algorithm (NAA), which eliminates the unfeasible search domain, is embedded into differential evolution meta-heuristic algorithm. Heuristic NAA narrows the search domain at the initial phase of the optimization. Moreover, activities with float durations higher than the predetermined threshold value are eliminated and then the meta-heuristic algorithm starts and searches the global optimum through the narrowed search space. However, narrowing the search space may increase the probability of obtaining a local optimum. Therefore, adaptive search domain approach is employed to make reintroduction of the eliminated activities to the design variable set possible, which reduces the possibility of converging into local minima.

The developed algorithm is compared with plain meta-heuristic algorithm with two separate analyses. In the first analysis, both algorithms have the same computational demand, and in the latter analysis, the meta-heuristic algorithm has fivefold computational demand. The tests on case study problems reveal that the developed algorithm presents lower total project costs according to the dependent t-test for paired samples with α = 0.0005.

In this study, TCTP is solved without considering quality or restrictions on the resources.

The proposed method enables to adapt the number of parameters, that is, the search domain and provides the opportunity of obtaining significant improvements on the meta-heuristic algorithms for other engineering optimization problems, which is the theoretical contribution of this study. The proposed approach reduces the total construction cost of the large-scale projects, which can be the practical benefit of this study.

Companies should enhance their market position and competitiveness by improving staff effectiveness, skills, resource commitment, and applying relevant managerial methods. This study aims to examine the impact of knowledge management (KM) and total quality management (TQM) on employee effectiveness (EE) and supply chain performance (SCP) in emerging economies.

The used methodology consists on conducting a survey within Tunisian companies, where the authors gathered 206 responses. Collected data was analyzed using statistical package for the social sciences (SPSS) software, enabling the authors to establish a conceptual model. This model was further examined through structural equation modeling, using analysis of moment structures (AMOS) software for hypothesis validation. Additionally, the authors’ research aimed to enhance SCP and boost EE while minimizing costs through a nonlinear mathematical model and the quality function deployment method.

The results indicate that TQM and KM positively impact EE, and KM and EE positively impact SCP. However, the significance of employee performance on SCP varies depending on company location and industry sector studied.

This work emphasized the involvement of small- and medium-sized enterprise managers from emerging economies in the studied concepts and confirmed the effects of KM and TQM practices on EE and SCP.

Mobile robots with independent wheel control face challenges in steering precision, motion stability and robustness across various wheel and steering system types. This paper aims to propose a coordinated torque distribution control approach that compensates for tracking deviations using the longitudinal moment generated by active steering.

Building upon a two-degree-of-freedom robot model, an adaptive robust controller is used to compute the total longitudinal moment, while the robot actuator is regulated based on the difference between autonomous steering and the longitudinal moment. An adaptive robust control scheme is developed to achieve accurate and stable generation of the desired total moment value. Furthermore, quadratic programming is used for torque allocation, optimizing maneuverability and tracking precision by considering the robot’s dynamic model, tire load rate and maximum motor torque output.

Comparative evaluations with autonomous steering Ackermann speed control and the average torque method validate the superior performance of the proposed control strategy, demonstrating improved tracking accuracy and robot stability under diverse driving conditions.

When designing adaptive algorithms, using models with higher degrees of freedom can enhance accuracy. Furthermore, incorporating additional objective functions in moment distribution can be explored to enhance adaptability, particularly in extreme environments.

By combining this method with the path-tracking algorithm, the robot’s structural path-tracking capabilities and ability to navigate a variety of difficult terrains can be optimized and improved.

The use of quality management (QM) to achieve the United Nations Sustainable Development Goals (UNSDGs) is a topic of growing interest in academia and industry. The IAQ (International Academy for Quality) established Quality Sustainability Award in 2020, a testament to this growing interest. This study aims to investigate how QM philosophies, methodologies and tools can be used to achieve sustainable development in organizations.

Five large manufacturing organizations – three from India and two from China – who reported their achievements about using QM in achieving Sustainable Development Goals (SDGs) were studied using multiple sources of data collection. A detailed within-case and cross-case analysis were conducted to unearth this linkage's practical and theoretical aspects.

The study finds that QM methodologies effectively met the five organizations' UNSDGs. These organizations successfully used OPEX (Operational Excellence) methodologies such as Lean, Kaizen and Six Sigma to meet UNSDGs 7, 11, 12 and 13. Moreover, UNSG 12 (Responsible Consumption and Production) is the most targeted goal across the case studies. A cross-case analysis revealed that the most frequently used quality tools were Design of Experiments (DoE), Measurement Systems Analysis (MSA), C&E analysis and Inferential statistics, among other essential tools.

The study's sample size was limited to large-scale manufacturing organizations in the two most populous countries in the world. This may limit the study's generalizability to other countries, continents, or micro-, small- and medium-sized enterprises (SMEs). Additionally, the study's conclusions would be strengthened if tested as hypotheses in a follow-up survey.

This practical paper provides case studies on how to use QM to impact SDGs. It offers both descriptive and prescriptive solutions for practitioners. The study highlights the importance of using essential QM tools in a structured and systematic manner, with effective teams, to meet the SDGs of organizations.

The study shows how QM can be used to impact UNSDGs, and this is very important because the UNSDGs are a set of global objectives that aim to address a wide range of social and environmental issues. This study could motivate organizations to achieve the UNSDGs using essential QM tools and make the world a better place for the present and future generations.

This case study is the first to investigate at a micro-level how QM can impact UNSDGs using live examples. It uses data from the IAQ to demonstrate how QM can be integrated into UNSDGs to ensure sustainable manufacturing.

This article aims to reveal the factors influencing the sustainable development of mobile e-commerce from both user and operational perspectives. It fills the gap in qualitative research on the sustainable development of artificial intelligence (AI) technology in mobile e-commerce based on the grounded theory. This study provides valuable insights and inspiration for sustainable development in this field and lays the theoretical foundation and research reference for future studies.

Based on the grounded theory (GT), interview method was used to conduct the study.

The impact of AI applications on mobile e-commerce is mainly reflected in three stages of the customer shopping process. They are pre-shopping, mid-shopping and after-shopping AI services and each of the three stages has its own separate dimensions that need attention. The study and its persistence aspects are discussed.

The results of this study can provide forward-looking suggestions and paths for the construction and optimization of future e-commerce platforms, contribute to the sustainable development of e-commerce and contribute to the sustainable and healthy growth of the social economy.

This study proposes sustainable development measures for the application of AI in mobile e-commerce, from operation to supervision, which is an important reference for promoting coordinated and rapid socio-economic development.