When making a batch production decision for a buyer-vendor coordination system, the management must simultaneously consider the operating expenses incurred in in-house manufacturing and inventory, finished goods’ shipping, and stock holding at the retailer end. Achieving the operational goals of desirable quality, minimal production disruption, and shortening fabrication time help minimize overall in-house operating costs and maximize customer satisfaction. This work builds an operating cost minimization model for buyer-vendor coordination batch system with scrap, breakdowns, overtime, multi-shipment, and an external source to assist the management in optimizing their production-delivery plan. Removing inevitable scrap items ensures product quality, and correction action on stochastic equipment breakdown prevents unacceptable production delays. Implementing partial overtime and adopting an external source expedites in-house manufacturing time. Model construction and cost analysis enable us to decide the operating expense function. Then, we verify the function’s convexity and decide our model’s best manufacturing runtime with the differential calculus and a proposed algorithm. Furthermore, the numerical demonstrations are used to exhibit our work’s applicability and show what kinds of crucial in-depth information can be disclosed and made accessible to the production planners for their decision-making.

The planning of charging infrastructure is crucial to developing electric vehicles. Planning for charging stations requires considering several variables, including building costs, charging demand, and coverage levels. It might be advantageous to use a multi-objective optimization method based on the NSGA-II. We need to address the current problems in choosing the location of electric vehicle charging stations. Firstly, urban land use is divided into five functional areas, and the TF-IDF algorithm is applied to the division of functional areas. A combined clustering algorithm is proposed to cluster POIs in functional areas into several clusters and determine the cluster centers as charging demand points. We Analyze charging practices and travel patterns of electric car users, fit the charging likelihood of various functional regions, and calculate the charging demand of each charging demand point in the study area. Introduce the NSGA-II algorithm and consider the charging station's progressive coverage to fit the actual area covered by the charging station.Taking the maximization of system benefits and the maximization of the minimum coverage level as the optimization objectives to carry out multi-objective optimization. Finally, we take the charging station planning in the urban area of Hohhot as an example and provide different site selection planning schemes. The planning schemes for different numbers of charging stations are analyzed to obtain a charging station planning scheme that takes into account both objectives.

Timely and cost-effective supply of spare parts is the main purpose of spare parts inventory management and substitution is an effective way to fulfill demand on time. However, direct substitution of spare parts is not suitable for the high-tech industries due to the ever-changing nature of the product structures. Hence, parts should be transformed to be used as substitutes. This paper provides a novel spare parts inventory control model for the high-tech industries. In the proposed model, part transformation-based substitution is considered and the near-optimal values of spare part inventory levels (s, S) that minimize total cost are determined by using a simulated annealing-based simulation optimization approach. Computational analyses are performed for a hypothetical inventory system by considering transformation and no-transformation cases. The results reveal that transformation is very useful for the companies who endure long production lead times and high penalty costs associated with backorders.

From the perspective of carbon quota policy, we consider integrating carbon quota sharing contracts into the collaborative decision-making model of the photovoltaic industry chain and compare and explore the collaborative decision-making of the photovoltaic industry chain under decentralized decision-making and considering carbon quota sharing contracts. Research has found that carbon quota sharing contracts can effectively promote green innovation among the main body of the photovoltaic industry chain; In the case of carbon quota sharing contracts, the pricing between photovoltaic system suppliers and photovoltaic power plant enterprises is lower; Under the carbon quota sharing contract, the profits, and overall profits of each entity in the photovoltaic industry chain are better than the optimal profits under decentralized conditions.

Efficiency in logistics is often affected by the fair distribution of the customers along the routes and the available depots for goods delivery. From this perspective, in this study, the Multi-depot Vehicle Routing Problem (MDVRP), by considering two objectives, is addressed. The two objectives in conflict for MDVRP are the distance traveled by vehicles and the standard deviation of the routes’ length. A significant standard deviation value provides a small distance traveled by vehicles, translated into unbalanced routes. We have used a weighted average objective function involving the two objectives. A Variable Neighborhood Search algorithm within a Chu-Beasley Genetic Algorithm has been proposed to solve the problem. For decision-making purposes, several values are chosen for the weight factors multiplying the terms at the objective function to build up a non-dominated front of solutions. The methodology is tested in large-size instances for the MDVRP, reporting noticeable results for managerial insights.

The escalating demand for items and their consumption has exerted immense pressure on industrial production and supply chain systems, leading to adverse environmental and societal consequences. The rise in pollution rates and ecological disasters resulting from industrial production has prompted numerous scholars and industry specialists to focus on Sustainable Production and Consumption matters within the framework of Sustainable Supply Chain Management (SSCM). The interest in sustainable supply chain management has significantly increased in recent years, spanning both business and academic sectors. This phenomenon is seen in the growing prevalence of papers, conferences, specialized periodicals, and websites exclusively focused on the subject matter. Nevertheless, the importance of sustainable development in developing economies has only just started to be acknowledged. This literature review aims to assess existing research on sustainable supply chain management (SSCM) in developing nations while considering a worldwide perspective. This paper thoroughly examines the rapid expansion of the subject from an evolutionary perspective; aimed at comprehending the progression of sustainability concerns by examining patterns across different industries, and economies, and employing diverse approaches. An extensive thematic analysis was conducted on 56 selected publications published between 2010 and 2023, emphasizing the growth and significance of the knowledge base. The analysis is conducted utilizing a descriptive and content-oriented methodology. Subsequently, the results are presented, demonstrating an increasing interest in Sustainable Supply Chain Management (SSCM). Nevertheless, there is a conspicuous discrepancy in the extent of research carried out on this subject in emerging economies as opposed to industrialized ones. The findings indicate that the context in less developed countries plays a crucial role in carrying out empirical or case study research. Moreover, it is crucial to analyze how the integration of the three dimensions of sustainability impacts the efficiency of the supply chain, especially when considering the perspective of an emerging economy. Hence, the limitations of this investigation are delineated. Ultimately, it is crucial to do further research from multiple angles within the supply chain, encompassing collaboration, sustainable practices, innovation, sourcing, and supplier growth, with a special emphasis on the position and background of rising countries.

Automated Teller Machine (ATM) transactions are now regarded as secure, dependable, and unavoidable for meeting our financial obligations. The conventional method of utilizing an ATM requires the use of a debit card. But occasionally users run out of money in their accounts or forget their cards, which makes it difficult to execute a purchase. Mobile phone use has been an unavoidable development, similar to ATM usage. By connecting these electronic devices, it has become possible to make cash withdrawals that are both quick and secure without the use of a debit card, or “card-less cash withdrawals”. For user authentication, face detection, OTP and fingerprint scanning are used. Three tiers of security are constructed by this. If all three parameters are authenticated then and then only the user is allowed to the banking transaction.

The lean manufacturing approach is employed across several sectors to minimise waste and extend its ideas to institutional settings. The objective of this work is to provide instruction to aspiring engineers regarding the implementation and utilisation of lean tools. The machine shop of an engineering college utilised several tools, including 5S, standard work, and machine maintenance. The students were educated on the difficulties and achievements associated with the implementation of lean ideas across several tiers within the machine shop. The findings indicate that it is imperative to remove non-value-added operations, commonly referred to as Muda, in any manifestation inside the shop. A sequence of 5S audits was carried out to facilitate the enhancement of KAIZEN initiatives and uphold the shop's commitment to lean principles. The determination and assessment of the influence of lean tools on the Machine Shop were conducted through the utilisation of a questionnaire employing a five-point Likert scale. Following the implementation of lean principles, there has been a notable enhancement in several aspects. Specifically, there has been a 6.6% improvement in space utilisation, a substantial 95.12% increase in safety measures, a significant reduction of 83.3% in machine failure occurrences, and a noteworthy decrease of 80.2% in the time required for tool search.

The economic landscape of Saudi Arabia has undergone significant changes in recent years, largely due to various administrative law reforms. The aim of this research paper is to examine the administrative law reforms in Saudi Arabia and their impact on the country's business environment. Based on the findings, the law reforms made in Saudi Arabia include initiating businesses, safeguarding the interests of minority investors, facilitating trade, ensuring contract enforcement, providing access to electricity, facilitating credit acquisition, granting construction permits, and resolving insolvency. Saudi Arabia should take steps to raise public awareness about corruption and establish more robust laws to safeguard whistleblowers. The country should also allocate resources to training programs and establish partnerships with advanced, specialized courts. Creating entrepreneurship programs and workshops to promote an entrepreneurial mindset in schools and the community is crucial. Saudi Arabia can create a favorable environment for economic growth and development by implementing these measures.

Climate change demands clean energy solutions, and renewable sources such as solar and wind are prime candidates. However, their variability poses challenges for their integration into large-scale power systems. This paper addresses this issue by proposing a novel hybrid mathematical model. The proposal integrates both fossil and renewable sources, considering real-world constraints such as system demand, reserves, and transmission dynamics. The model combines several approaches. By using a novel block composition technique, the computational complexity is reduced, making the model applicable to large-scale systems. A neural network is also developed to improve the forecasting of renewable energy production, which is crucial for managing its intermittency. The effectiveness of the proposed model is tested by considering the large Argentinean electricity system, demonstrating its practical applicability. The results show that acceptable forecasts can be obtained for the generation and transmission scheduling of the whole system.