The Inventory Routing Problem (IRP) has been highlighted as a valuable strategy for tackling routing and inventory problems. This paper addresses the IRP but considers the forward delivery and the use of Returnable Transport Items (RTIs) in the distribution strategy. We develop an optimization model by considering inventory routing decisions with RTIs collection (backhaul customers) of a Closed-Loop Supply Chain (CLSC) within a short-term planning horizon. RTIs consider reusable packing materials such as trays, pallets, recyclable boxes, or crates. The RTIs represent an essential asset for many industries worldwide. The solution of the model allows concluding that if RTIs are considered for the distribution process, the relationship between the inventory handling costs of both the final goods and RTIs highly determines the overall performance of the logistics system under study. The obtained results show the efficiency of the proposed optimization scheme for solving the combined IRP with RTIs, which could be applied to different real industrial cases.

Digitalization of BSR (buyer supplier relationship) is generally one of the effective tools to strengthen the supply chain. This study aims to establish the correlations between the perceived importance of BSR, investment in BSR digitalization and ER (economic result) change. Data collecting was realized in the form of a questionnaire survey. This survey was carried out in Czech enterprises of different focuses and sizes. Hypotheses are tested using Pearson's Chi-squared test. The study confirmed that correlation between the perceived importance of BSR and investment to BSR digitalization is stronger for producers than for service providers. The investment in BSR digitalization brings about ER development in the short term only for services providers. Enterprises do not associate the development of ER and the importance of BSR. BSR is considered an important area as part of the SCM whole, but ER development is not associated with this area, nor does investment in this area have a clear economic effect in a recession. Thus, the logical triad of "importance of the process- investment in process development – process effect" is unprovable in the case of BSR digitalization in a recession.

Smart robotic wheelchairs have emerged as promising assistive devices to enhance mobility and independence for individuals with mobility impairments. The successful integration of advanced sensor systems plays a critical role in improving the functionality and safety of these wheelchairs. This paper presents a comprehensive review of the design and implementation of advanced sensor systems for smart robotic wheelchairs. Through an extensive literature review, the limitations of existing sensor technologies are identified, and the potential of advanced sensors is explored. Vision-based sensors, range and proximity sensors, force and pressure sensors, inertial sensors, and environmental sensors are discussed in detail. Furthermore, this review highlights the design considerations, hardware components, software development, and calibration procedures involved in implementing advanced sensor systems. Evaluation and performance analysis metrics are discussed to assess the effectiveness of the sensor systems. The research findings indicate that advanced sensor systems have the potential to significantly enhance the functionality and safety of smart robotic wheelchairs. However, challenges such as sensor integration, data fusion, and user feedback must be addressed. This review paper concludes by discussing the implications of advanced sensor systems in improving wheelchair functionality and user experience, and proposes future directions for research in this domain.

The previous research relating to CSR and mindfulness leadership, for the purposes of this study, provides an intersection where both backgrounds come together to invite an opportunity for collaboration. The most critical factor in the implementation of CSR is the development of leaders for a sustainable global society as mindfulness should be relevant to the achievement of organizational and leadership objectives. It is here that we find the inspiration for the current study and the relevance of the research query endeavoring to discover if the framework of mindfulness leadership could prove an effective driver for CSR? For this reason, 20 in-depth interviews were conducted from the middle level managers working in an emerging economy - Pakistan. The conceptual framework of leadership theory guided this study’s investigation as it brought to the forefront certain challenges facing organizational leaders endeavoring to facilitate CSR objectives; and explored if the application of mindfulness leadership could prove an effective leadership framework for successful CSR engagement. This supporting data coupled with the study’s interview of executive leaders, across industry lines, familiar with CSR initiatives, lent to certain analytical insight that identified mindfulness leadership as a framework for organizations to facilitate the accomplishment of CSR objectives.

Additive Manufacturing (AM) is an automated process of fabricating three-dimensional (3D) physical objects from a 3D-CAD data by adding layers of materials one upon another through a print head or nozzle without using any tooling components or machining environments. Due to freedom in design, any complex shape can be produced using this process. Fused Deposition Modeling (FDM) is one such AM technology that is commonly used for its simplicity, environment friendliness and low requirement for process monitoring. However, this technology is limited only to small-scale production due to high cost and high build time. The present work focuses on the development of a framework for parametric optimization of the FDM process using multi-objective optimization based on ratio analysis (MOORA). A CAD model of the cam follower mechanism has been prepared in the Solidworks platform and used in this experiment for optimization of build time and cost which have been considered as response variables of the experiment. The experiment has been conducted following the full factorial design of experiment (DoE) method.

Suppliers can use different strategies to distribute their products, Among the most common complex optimization problems related to the transportation of products is the traveling salesman problem. In the traveling-salesman problem, a route is chosen that visits each node exactly once, taking into account the shortest travel time, and finally returns to the original node. In this problem, all nodes must be visited. If we consider the application of this problem in logistics, we can study the necessity of this problem in transportation means such as trucks or drones. The upcoming paper is thoroughly studied and researched considering the related articles published in the last three decades, and bibliometric analysis is used for the details of this problem. This paper aims to statistically evaluate the influence and importance of the traveling salesman on logistics without considering financial risk by presenting an analysis of the works published between 1983 and 2023. As part of our comprehensive literature review table with analysis of export, we will conduct a comprehensive review of the most relevant articles in the field from 2020 to 2023 to better understand the trend in the subject in the last few years. Data were obtained from the Web of Science and focused on metrics such as the total number of publications, citations, average citations per publication, and trending countries. Graphical and statistical analysis was performed using Excel and R-Studio. China, the USA, and Germany are the countries with the most publications. Laporte is the most prolific author with 8 publications. Much research has been done on this topic, especially in the Journal of transportation research part E-logistic with 43 articles, and the main application areas are logistics, vehicles, and drones. These data may prove useful to researchers seeking an overview of the traveling salesman problem to determine future research directions.

Production plans based on Material Requirement Planning (MRP) frequently fall short in reflecting actual customer demand and coping with demand fluctuations, mainly due to the rising complexity of the production environment and the challenge of making precise predictions. At the same time, MRP is deficient in effective adjustment strategies and has inadequate operability in plan optimization. To address material management challenges in a volatile supply-demand environment, this paper creates a make-to-stock (MTS) material production planning model that is based on customer demand and the demand-driven production planning and control framework. The objective of the model is to optimize material planning output under resource constraints (capacity and storage space constraints) to meet the fluctuating demand of customers. To solve constrained optimization problems, the demand-driven material requirements planning (DDMRP) management concept is integrated with the grey wolf optimization (GWO) algorithm and proposed the DDMRP-GWO algorithm. The proposed DDMRP-GWO algorithm is used to optimize the inventory levels, shortage rates, and production line capacity utilization simultaneously. To validate the effectiveness of the proposed approach, two sets of customer demand data with different levels of volatility are used in experiments. The results demonstrate that the DDMRP-GWO algorithm can optimize the production capacity allocation of different types of parts under the resource constraints, enhance the material supply level, reduce the shortage rate, and maintain a stable production process.

This paper explores the impact of retailers' corporate social responsibility (CSR) and manufacturers' overconfidence on manufacturers' carbon reduction in sustainable supply chains. We analyze the profits of manufacturers and retailers under different scenarios and explore the social welfare and environmental impacts under CSR. Our results suggest that retailers' CSR and manufacturers' overconfidence contribute positively to promoting carbon mitigation and reducing environmental impacts under certain conditions. However, with increasing CSR and manufacturer overconfidence levels, manufacturers are more likely to lead to worse environmental impacts and carbon emission reduction. In addition, we show that when the manufacturer's overconfidence level is high, manufacturers and retailers are more profitable and contribute to carbon emission reductions in the manufacturer without overconfidence (retailer without CSR) scenario. Moreover, we find that firms have the higher potential to capture optimal overall social welfare in the presence of retailers with CSR and manufacturer overconfidence.

With the implementation of the Belt and Road Initiative, the inland ports planning is receiving more and more attention. In this work, we aim to determine the scale and function of different potential inland ports in a certain region while considering the cargo flow allocation schemes for the inland ports and seaports in cross-border trade. Unlike previous studies, we consider the dynamic interaction between local government and manufacturing enterprises in the inland port planning process. Based on this, we formulate a bilevel programming model for the considered inland port planning problem, where the upper-level focuses on the local government and the lower-level concentrates on the manufacturing enterprise. To solve the proposed model, we develop a hybrid heuristic algorithm by combining a genetic algorithm and an exact solution method. Furthermore, we conduct a case study of the inland ports planning for the Huaihai Economic Zone in China to verify the applicability of the proposed model and algorithm. The computational results demonstrate that the proposed optimization approach can effectively increase the cross-border transportation market share of inland ports within a limited investment amount and reduce the competition among these inland ports. Our case study also provides valuable management insights on inland port planning in terms of manufacturing enterprises weights, investment limit amount, scale effect, and cargo value weights.

There have been many applications for machine learning algorithms in different fields. The importance of hyperparameters for machine learning algorithms is their control over the behaviors of training algorithms and their crucial impact on the performance of machine learning models. Tuning hyperparameters crucially affects the performance of machine learning algorithms, and future advances in this area mainly depend on well-tuned hyperparameters. Nevertheless, the high computational cost involved in evaluating the algorithms in large datasets or complicated models is a significant limitation that causes inefficiency of the tuning process. Besides, increased online applications of machine learning approaches have led to the requirement of producing good answers in less time. The present study first presents a novel classification of hyperparameter types based on their types to create high-quality solutions quickly. Then, based on this classification and using the hypergradient technique, some hyperparameters of deep learning algorithms are adjusted during the training process to decrease the search space and discover the optimal values of the hyperparameters. This method just needs only the parameters of the previous two steps and the gradient of the previous step. Finally, the proposed method is combined with other techniques in hyperparameter optimization, and the results are reviewed in two case studies. As confirmed by experimental results, the performance of the algorithms with the proposed method have been increased 36.62% and 23.16% (based on the best average accuracy) for Cifar10 and Cifar100 dataset respectively in early stages while the final produced answers with this method are equal to or better than the algorithms without it. Therefore, this method can be combined with hyperparameter optimization algorithms in order to improve their performance and make them more appropriate for online use by just using the parameters of the previous two steps and the gradient of the previous step.