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This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.

Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.

Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.

In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.

Delay and disruption claims involve a complex process that often result in disputes, unnecessary expenses and time loss on construction projects. This study aims to review and synthesize the contributions of previous research undertaken in this area and propose future directions for improving the process of delay and disruption claims.

This study adopted a holistic systematic review of literature following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. A total of 230 articles were shortlisted related to delay and disruption claims in construction using Scopus and Web of Science databases.

Six research themes were identified and critically reviewed including delay analysis, disruption analysis, claim management, contract administration, dispute resolution and delay and disruption information and records. The systematic review showed that there is a dearth of research on managing the wide-ranging information required for delay and disruption claims, ensuring the transparency and uniformity in delay and disruption claims’ information and adopting an end-user’s centred research approach for resolving the problems in the process of delay and disruption claims.

Complexities in delay and disruption claims are real-world problems faced by industry practitioners. The findings will help the research community and industry practitioners to prioritize their energies toward information management of delay and disruption claims.

This study contributes to the body of knowledge in delay and disruption claims by identifying the need for conducting more research on its information requirements and management. Subsequently, it provides an insight on the use of modern technologies such as drones, building information modeling, radio frequency identifiers, blockchain, Bigdata and machine learning, as tools for more structured and efficient attainment of required information in a transparent and consistent manner. It also recommends greater use of design science research approach for delay and disruption claims. This will help to ensure delay and disruption claims are the least complex and less dispute-prone process.

The purpose of this paper is to explore a new approach to solve the problem of positive and negative offset in the calculation process of integral elements, then propose a series of new grey relational degree model for cross sequences.

The definitions of cross sequences and area elements have been proposed at first. Then the concept of difference degree between sequences has been put forward. Based on the definition of difference degree between sequences, various modified grey relational degree models for cross sequences have been proposed to solve the measurement problem of cross sequence correlation relationships.

(1) The new definition of cross sequences; (2) The area element; (3) Various modified grey relational degree models for cross sequences based on the definition of difference degree between sequences.

The grey relational analysis model of cross sequences is a difficult problem in grey relational analysis. The new model proposed in this article can effectively avoid the calculation deviation of grey relational analysis model for cross sequences, and reasonably measure the correlation between cross sequences. The new model was used to analyse the food consumer price index in Shaanxi Province, clarifying the relationship between different types of food consumer price indices, some interesting results that are not completely consistent with general economic theory were obtained.

The new definition of cross sequences, the area element and various modified grey relational degree models for cross sequences were proposed.

This study aims to explore how network visualization provides opportunities for learners to explore data literacy concepts using locally and personally relevant data.

The researchers designed six locally relevant network visualization activities to support students’ data reasoning practices toward understanding aggregate patterns in data. Cultural historical activity theory (Engeström, 1999) guides the analysis to identify how network visualization activities mediate students’ emerging understanding of aggregate data sets.

Pre/posttest findings indicate that this implementation positively impacted students’ understanding of network visualization concepts, as they were able to identify and interpret key relationships from novel networks. Interaction analysis (Jordan and Henderson, 1995) of video data revealed nuances of how activities mediated students’ improved ability to interpret network data. Some challenges noted in other studies, such as students’ tendency to focus on familiar concepts, are also noted as teachers supported conversations to help students move beyond them.

To the best of the authors’ knowledge, this is the first study the authors are aware of that supported elementary students in exploring data literacy through network visualization. The authors discuss how network visualizations and locally/personally meaningful data provide opportunities for learning data literacy concepts across the curriculum.

In a packaging olives manufacturing system, the drained weight of the product plays a decisive role in customer’s satisfaction as well as in financial saving for the organization. The purpose of this study is to minimize the variation of the drained weight of olives in the production system to avoid the negative consequences.

The research develops a practical implementation step-by-step of Six Sigma define, measure, analyze, improve and control (DMAIC) in reducing the variation of the drained weight of olives.

Data analysis was used at various phases of the project to identify the root causes of rejection and rework. As a result of the necessary interventions and actions to optimize the manufacturing process, the standard deviation of drained weight was significantly reduced by 51.02%, with a 99.97% decrease in the number of parts per million defectives. Thus, the yield of the production process was improved by 8.24%. The estimated annual savings from this project were US$ 228,000 resulting from reduced rejection and rework.

This research may be used in packaging olives production systems as a tool for managers and engineers planning to increase productivity and efficiency while also improving product quality. The study also provided the organization with helpful actions that will be used to guide future Six Sigma operations management on the system. Thus, practical guidelines and solutions are provided.

In this project, for the first time, the Six Sigma methodology has been applied to solve a real-world problem in the packaging olives manufacturing system and to show that the DMAIC approach may assist to improve the efficiency of their operations and hence contribute to their quest toward continuous improvement.

This study aims to mitigate the inherent challenges associated with implementing project-based learning (PjBL) by integrating it with the service engineering methodology (SEEM). The study demonstrates that combining PjBL with a methodological approach provides a step-by-step procedure that facilitates the practical application of PjBL and preserves the development of competencies inherent to PjBL.

Action research methodology was used to assess the effectiveness of combining PjBL learning strategy with SEEM. Data was collected through observations, questionnaires and focus group discussions to evaluate students’ expectations and perceptions of this combination.

The results show that PjBL implementation combined with SEEM enabled the organization of class dynamics, helping to mitigate difficulties encountered in the application of PjBL. Students conveyed that the integration of PjBL with SEEM afforded them a guiding structure without compromising their autonomy in decision-making for proposed solutions. It proved efficacious in honing skills pertinent to service design and analysis, teamwork, solution formulation, creativity and innovation stimulation.

This research has been limited to four classes in one university in Brazil. Besides, PjBL was combined with only one methodology (SEEM). Therefore, this needs to be tested in broader settings and contexts.

The article highlights the potential benefits of PjBL in bridging the gap between academia and the professional world while acknowledging the challenges involved in its implementation, combining PjBL with a methodology that provides a sequence of steps to be followed.

The social implications of implementing PjBL in higher education in the Brazilian and international contexts are multifaceted. The adoption of PjBL encourages instructors to adapt their learning strategies and align them with the evolving needs of worldwide society. Through PjBL, Brazilian and international higher education institutions may contribute to the development of individuals who are not only knowledgeable but also capable of applying their knowledge effectively in practical situations around the world.

The theoretical contribution lies in suggesting that combining PjBL with a methodology that provides a sequence of steps to be followed (such as that exemplified through SEEM) can address intrinsic issues that consider the complexity of PjBL implementation, preserving the development of competencies inherent to PjBL.

Design-based research (DBR) involves multiple iterations, and innovations are needed in analytical methods for understanding how learners experience a learning experience in ways that both embrace the complexity of learning and allow for data-driven changes to the design of the learning experience between iterations. The purpose of this paper is to propose a method of crafting design moves in DBR using network analysis.

This paper introduces learning experience network analysis (LENA) to allow researchers to investigate the multiple interdependencies between aspects of learner experiences, and to craft design moves that leverage the relationships between struggles, what worked and experiences aligned with principles from theory.

The use of network analysis is a promising method of crafting data-driven design changes between iterations in DBR. The LENA process developed by the authors may serve as inspiration for other researchers to develop even more powerful methodological innovations.

LENA may provide design-based researchers with a new approach to analyzing learner experiences and crafting data-driven design moves in a way that honors the complexity of learning.

LENA may provide novice design-based researchers with a structured and easy-to-use method of crafting design moves informed by patterns emergent in the data.

To the best of the authors’ knowledge, this paper is the first to propose a method for using network analysis of qualitative learning experience data for DBR.

Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

The purpose of this paper is to describe this new evaluation experience with portfolio in economics, not only from the teacher’s point of view but from the student perspective, and all the learning from its implementation; to provide ideas of evaluation practices in virtual and face-to-face modality in international business education; to motivate the rethinking of assessment practices in higher education to combine the best of each modality in the future.

The present work is a case study based on a qualitative description of the implementation of a portfolio as an assessment practice, supported by a reflection questionnaire with students’ perceptions and some elements of metacognition. The first section summarizes the literature used as a theoretical framework of this work. The second section describes the portfolio implementation by analyzing teachers and students reflections with a qualitative approach. The third section presents the findings. The fourth section is a discussion of findings, practical implications, limitations and future research directions. Finally, the conclusions of the work are shared.

Because the portfolio has had overwhelming results to assess what students have learned during the pandemic, it has become the learning and assessment tool after the pandemic, as it transforms the classes experience by shifting the focus from traditional examinations to more comprehensive, personalized and reflective ones. It also empowers students to take ownership of their learning, develop essential skills and cultivate a deeper understanding. Among other benefits, the portfolio means the creation of a safe and supportive environment for honest reflection, the development and design of strategic directions to improve learning and lead students toward metacognitive autonomy. Reflection pieces, a critical component of the portfolio, are a vital tool in the proactive learning process, as through reflection students learn to examine their own performance and discuss strategies to enhance their success in future work.

This work began as an educational experience per se, not for research purposes, which caused it to be systematized and reconstructed in a descriptive way, not to measure quantitative results. In this way, the present work describes that the portfolio helps to achieve better results on students’ learning than traditional examinations but, as another limitation, it does not measure them nor the process. One more limitation of this work is that it was written in a postpandemic context but was implemented during the pandemic; therefore, the circumstances of writing are not the same as those of implementation, and this could also entail a certain margin of decontextualization. At the same time, this is an experience that is still in process and continually being adapted to this changed and changing educational postpandemic context.

One of the main implications of the portfolio experience, transferable to all educational contexts, is that it transforms the final exam into a metacognitive one, letting students be aware of their own process of learning and results – objectives and competences – acquired. In this way, it lets teachers witness a part of the learning process that is not so evident in the traditional assessment practices – focused on some aspect of the learning – as it makes visible the way in which students receive, process and apply content, that is to say, how they make it their own.

The portfolio promotes reflective learning and metacognition, vital skills that benefit students beyond the classroom. This can have a positive impact on societal attitudes toward education and the quality of learning. Of the students, 82% felt the portfolio creation was helpful in their personal and professional lives, suggesting a broader societal impact. The paper’s findings contribute to the body of knowledge about the effectiveness of portfolio-based assessment in higher education, especially in the worldwide transition from online education to postpandemic education. This could guide future studies in similar educational contexts or with different pedagogical innovative tools.

In light of the 2020 pandemic lockdown, this work delves into the pressing need for educators to adapt and modify their teaching approaches. The relevance of this study is accentuated by the worldwide transition from online education to postpandemic education. This paper bridges the gap between theory and practice because the research can be applied to the educational practice of any international business education context, as well as lay the foundations for future research in the field that contributes to increasing evidence of the effectiveness of the use of the portfolio to achieve significant and deep learning in higher education.