The accelerated growth of cities, population increase and economic development have leveraged waste generation globally. This trend is expected to continue, with a significant increase projected in the coming years. Therefore, efficient waste management has become a crucial concern for local, national and international authorities. Transportation plays a key role in waste collection and disposal, being directly related to traffic congestion, fuel consumption and environmental pollution. Despite the existing studies on household waste collection, there is a gap in the literature regarding routing for residential waste collection in medium-sized cities, especially in emerging and frontier developing countries. Therefore, this study seeks through the science tree metaphor and PRISMA methodology, to find studies focused on the vehicle routing problem in waste collection operations, considering aspects such as Modeling approaches and solution techniques, applied Vehicle Routing Problems variants, objective functions, decision variables and constraints, applications in real environments, applied algorithms, and studies considering uncertainty and real conditions. A methodological outline of Vehicle Routing Problems in waste collection operations is presented, where central research topics are identified such as processes developed with Geographic Information System and their integration with exact methods, time windows, multi-objective capacitated vehicle routing problems, the application of stochastic models consider the uncertainty in waste collection, which has allowed including future prediction and optimization as prediction models, based on neural networks, to foresee uncertain conditions of the operations. This article analyzes the evolution in the optimization of municipal solid waste collection routes since 1964, highlighting the transition from iterative models to advanced technologies and multi-objective approaches. The importance of tools such as 3D Geographic Information System and heuristic/metaheuristic algorithms in improving planning and efficiency, despite limitations in the face of uncertainty, is emphasized. The systematic review shows a trend towards sustainable and efficient solutions, indicating future directions for research in urban waste management.

In recent years, various issues such as industrial waste and emissions of greenhouse gases have led to serious environmental pollution. Industrial managers nowadays need to regard cutting carbon emissions as one of their principal responsibilities in relation to the environment, as industry is a major source of carbon emissions. Two prominent regulatory approaches to reducing carbon emissions from operations are the carbon tax and the cap-and-trade system. The existing literature on inventory studies has often considered the market-expanding effects of greening efforts. Nevertheless, a number of additional factors exert influence on greening efforts, with the cost reduction effect representing a critical one. This paper develops an inventory system in which each time a lot of items is received, a proportion of items are found to be of imperfect quality; to identify these, the retailer carries out a 100% inspection of goods received. Following this inspection, the saleable items are added to the inventory in the warehouse in batches of equal size, rather than one by one, and the retailer allows backordering to meet demand. Carbon emissions are incurred at every stage, including ordering, purchasing, repairing, transporting, and holding, so advanced green technology is employed to reduce them. Imperfect products can be sold to a second-hand market or sent to a repair shop. The model discussed in this paper calculates, for both options, the most cost-effective lot size for orders, shortage quantity, scale of green investment and number of batches.

This study aims to investigate the key drivers of supply chain complexity in the footwear industry business sector. After conducting extensive literature review and consultation with experts, complexity drivers in the context of the footwear business sector were identified. The content validity of the identified drivers was checked by 13 experts using content validity ratio (CVR), which were used as the basis to select 20 key Supply Chain complexity drivers. Using a multi-criteria decision-making DEMATEL approach the cause and effect drivers were investigated. The findings of the study investigated 12 cause and 8 effect drivers in the footwear sector. Among the cause drivers, four linkage drivers that have strong driving power and dependency were identified. This study is the first in the developing county footwear business sector using DEMATEL approach to identify the key supply chain complexity drivers and their interrelationship. The study identified complexity drivers in the context of the footwear business sector that were not explored before in the existing studies. The outcome of this study will help SC complexity decision-makers in that case sector to control and manage the cause and effect drivers and thus improve the efficiency and effectiveness of their SC performance.

In today's fiercely competitive retail landscape, implementing effective pricing strategies is critical not only for boosting sales but also for securing a larger market share and ensuring long-term business sustainability. The ability to capture a greater share of the market directly influences a retailer's positioning and competitive edge, making pricing decisions pivotal. This paper introduces a hybrid optimization model that strategically combines Everyday Low Pricing (EDLP) and High-Low Pricing (HL) strategies, designed to address the intricacies of dynamic retail markets. The model is initially formulated as a nonlinear optimization problem aimed at maximizing sales to increase market share, all while maintaining profitability within a predefined threshold to ensure the retailer does not incur losses. To enhance the model's practical applicability, particularly in small-scale scenarios, the nonlinear problem is transformed into a Mixed-Integer Programming (MIP) model, facilitating its solvability. However, as retail applications scale up, the computational complexity becomes more challenging, necessitating the use of the Grey Wolf Optimization (GWO) algorithm. The GWO algorithm effectively balances computational efficiency with solution quality, making it a robust approach for large-scale problems. A significant contribution of this research is the linearization of the model under conditions where the products designated for High-Low pricing (referred to as 'Golden' products) are predetermined by the retailer. This linearization simplifies the computational process, enabling the model to scale and be applied in large retail settings. Developed in collaboration with a major Iranian supermarket chain, the model leverages real-world data to optimize discount levels and timing across various product categories. Extensive numerical experiments demonstrate the model's effectiveness in increasing sales, thereby contributing to a larger market share while ensuring that profitability remains within acceptable bounds. By providing actionable insights and strategic recommendations, this research offers a practical, scalable solution for optimizing retail pricing strategies in a data-driven and competitive environment, ultimately supporting retailers in their quest to dominate the market.

Providing K-12 students with proper science, technology, engineering, and math (STEM) education is important to ensuring an innovative and prosperous economy. A highly skilled STEM workforce can lead to increased productivity and competitiveness, which can lead to a host of new ideas being researched and developed. STEM workers make added-value products, build bridges and roads, and conduct lifesaving medical research, among other important activities. The use of virtual reality (VR) technology for both education and workforce training has grown in recent years. VR technology can accelerate these processes at maximum efficacy and minimum costs and can have a significant impact on productivity gains, earnings, new jobs, innovation through research and development, and high-growth industries. This paper presents the development of a series of VR modules using the Unity game engine, the HTC VIVE Pro VR headset, and the Hi5 VR glove for the purposes of K-12 STEM education. Specifically, these developed modules have been designed to instruct K-12 students on topics related to motion and heat, with future goals to expand the modules to cover topics related to light, magnetism, electricity, radioactivity, sound, and waves. This paper will cover the methodology and design considerations that went into developing these modules, with a focus on how these modules relate to various learning strategies as well as with existing research on the use of VR in K-12 education.

This study aims to describe, analyze, and interpret the role of multi-set data consistency in regional development planning in Kebumen Regency, Central Java Province, Indonesia. This study uses qualitative research methods. The selection of informants used purposive and snow-ball sampling. Data collection uses in-depth interviews, observations, and documentation. Data analysis was carried out through open coding, axial coding, and selective coding procedures. The validity of the data is determined through an examination technique based on the criteria of credibility, transferability, dependability, and confirmability. The results of the study show: (1) Regional development planning policies, which regulate the process and stages of the preparation of regional long-term development plans/RPJPD, regional medium-term development plans/RPJMD, and regional development work plans/RKPD were developed in the process of preparing the three regional development planning documents of Kebumen Regency to meet local needs; (2) The process of preparing the 2005–2025 RPJPD, 2006–2010 RPJM, 2010–2015 RPJMD, and 2006 to 2014 RKPD uses a technocratic approach that emphasizes the importance of multi-sector data (3) Factors that affect the process of preparing and documenting regional development planning documents are compliance in the hierarchy of multi-sector existing data administration, the development of regulations for the implementation of regional development planning policies, the existence of existing multi-sector planning data documents national, provincial, and regional development documents related to development planning, as well as the use of technocratic approaches; (4) The 2010–2015 RPJMD is inconsistent with the 2010–2015 RPJPD.

This study provides a comprehensive analysis of supply chain management practices based on survey responses from a sample of enterprises. Through descriptive statistics, hypothesis testing, predictive modeling, advanced analytics techniques such as classification, clustering, and association rule mining, the research offers valuable insights into key areas of collaboration, quality management, technology adoption, agility, risk management, and customer responsiveness within supply chains. The findings highlight the importance of strategic integration, proactive problem-solving, customer-centric practices, and agility in meeting changing demands. The study also identifies distinct profiles of practice adoption and reveals intricate relationships between different supply chain practices. Overall, the research contributes to a deeper understanding of supply chain dynamics and offers actionable insights for improving operational performance and strategic decision-making.

The purpose of the study was to investigate factors affecting the career satisfaction of vendors’ employees who perform project tasks on-site at clients’ owned facilities for several years as part of project delivery in Sri Lanka. The study identified six factors that influence the career satisfaction of these employees, namely, work content, financial rewards and other gains, work relationships, supervisor support, general working conditions, and opportunities experienced due to being stationed at clients’ firms. Overall, the study context is novel and the findings make contributions to the advancement of project management literature and practice.

The objective of this study is to propose an instrument for identifying the level of maturity of purchasing and supply management in relation to Strategic Sourcing. Based on an initial model, a maturity measurement instrument was applied to a case study company in a chemical industry company in Vale dos Sinos, in order to validate the tool. Based on this, a survey was carried out with the aim of capturing the perception of purchasing professionals on the topic. By sending a questionnaire to 250 professionals in the purchasing and supply management area, a return rate of 28% (70 respondents) was obtained from this sample. For the data obtained, structural equation modeling (SEM) was carried out using Smart PLS 3.0® software. With the modeling, a load of 0.428 was obtained for the suppliers construct, 0.555 for the inventory construct and 0.158 for the indicators construct. Overall, the satisfactory constructs for this sample positively impact 67.8% on the Strategic Sourcing construct. Therefore, for this specific sample, the constructs referring to suppliers, inventory and indicators jointly impact Strategic Sourcing. The research in question proposed a model for a better understanding of Strategic Sourcing structures and aims to contribute to practical and organizational contexts regarding the maturity of this topic among purchasing professionals. This understanding aims to provide readers with greater empirical and academic understanding of this emerging and important topic for business and company competitiveness.

This systematic literature review critically examines the application of the Resource-Based View (RBV) theory within Supply Chain Management (SCM) across various industries. Developed by Jay B. Barney in 1991, RBV posits that a firm's sustained competitive advantage is driven by its unique resources and capabilities that are valuable, rare, inimitable, and non-substitutable. RBV suggests that a company’s long-term competitive advantage stems from its distinct resources and capabilities, which are valuable, rare, difficult to imitate, and not easily substitutable. Despite extensive utilization in strategic management, the direct application of RBV in SCM has been less explored, particularly in understanding how specific internal resources enhance SCM capabilities and outcomes. The review adopts a systematic approach, analyzing 97 peer-reviewed articles from diverse journals. This method includes a comprehensive search and evaluation process, ensuring the inclusion of significant studies that discuss the application of RBV in SCM across various industries. The articles were sourced from Scopus databases, with keywords related to RBV and SCM to ensure thorough topic coverage. The findings indicate a pronounced increase in related publications since 2010, reflecting a growing scholarly and practical interest in RBV’s application to SCM. The findings revealed that emerging trends like integrating advanced technologies like Blockchain, Artificial Intelligence and the Internet of Things are identified as strategic resources that redefine competitive landscapes by enhancing transparency, responsiveness and responsiveness within supply chains. The review also highlights the increasing importance of sustainability practices within SCM, aligning with RBV to potentially offer a sustainable competitive advantage. Conclusively, this review contributes to both academic knowledge and guides practitioners toward leveraging internal resources strategically to navigate contemporary challenges, setting a foundation for future inquiries into global supply chain resilience and dynamic capabilities.