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This study aims to systematically examine 69 secondary studies to understand trends and implications in sustainability in higher education (SHE), addressing gaps in secondary studies, which often lack comprehensive thematic overviews that hinder future directions.

This tertiary study uses meticulous thematic coding to unveil overarching themes and future research directions across seven SHE areas: institutional frameworks, campus operations, education, research, outreach and collaborations, campus experiences and assessment and reporting.

The analysis reveals challenges higher education institutions (HEIs) face in integrating sustainability, highlighting the need for a holistic approach. Barriers such as awareness deficits and resistance to change emphasize the importance of interdisciplinary research. Emphasizing holistic integration and innovation is vital for seamlessly embedding sustainability within HEIs.

Key future research themes include holistic approaches to sustainability integration, leadership for sustainable change and innovative pedagogical approaches. Additionally, it is important to explore transdisciplinary approaches in sustainability research and the role of research centers and funding in addressing sustainability challenges.

This study provides a comprehensive overview of SHE, offering insights for researchers and practitioners, and serves as a resource for advancing sustainable educational practices.

Due to mitigate against natural disasters like earthquake and to distribute relief items, designing humanitarian relief chain networks is an attentional issue. Agile and efficient distribution of relief items after occurring a disaster is significant, especially when some of the relief items are perishable. Therefore, the purpose of this paper is to create a resilient and integrated decision-making structure to distribute relief items at demand points, considering two dimensions of sustainability, under disruption.

This study developed a mixed-integer nonlinear mathematical model to handle the pre- and post-disaster planning when a disaster occurs. The represented model has two objective functions: minimizing weighted unmet demand and total costs. Therefore, to convert this multi-objective problem into a single objective one, the e-constraint method was applied.

The main results showed that considering some resilience strategies has a significant effect in reducing the weighted amount of unmet demand and saves the total costs. More precisely, considering resilience strategies results in a 60% reduction in total unmet demand and 11% reduction in total pre-positioning costs. On the other hand, reducing the maximum response time with applying resilience strategies is another achievement of the present study. For these reasons, the use of these strategies can reduce people’s pain and suffer from natural disasters. In general, the application and effectiveness of sustainability dimensions and resilience strategies in the introduced humanitarian relief chain network were analyzed.

To verify the applicability of this study, this model is applied on a probable real-life case study in Tehran. Finally, some managerial insights are discussed to help humanitarian organizations, managers and stakeholders to make better decisions to reduce negative effects of natural disasters.

This paper introduced a two-stage stochastic mathematical model for designing a resilient humanitarian relief chain network under disruption, at pre- and post-disaster stages. Also, economic and social dimensions of sustainability are considered in this study. Moreover, assembling perishable and im-perishable relief items as relief kits, dynamically is a main contribution of this research.

Unfortunately, the occurrence of natural disasters is inevitable all over the world. Hence, this paper aims to analyze a scenario-based collaborative problem in a relief supply chain (RSC), where nongovernmental organizations can participate in relief activities with governmental organizations. This study focuses on location-allocation, inventory management and distribution planning under uncertain demand, budget, transportation and holding costs where government and private distribution centers receive relief items from suppliers then send them to affected areas. The performance of the proposed model is surveyed in a real case study in Dorud.

This paper develops a nonlinear mixed-integer programming model that seeks to maximize the coverage of demand points and minimize operating costs and traveled distance. The linear programming-metric technique and grasshopper optimization algorithm are applied to survey the model's applicability and efficiency.

This study compares noncollaborative and collaborative cases in terms of the number of applied distribution centers and RSC's goals, then demonstrates that the collaborative model not only improves the coverage of demand points but also minimizes cost and traveled distance. In fact, the presented approach helps governments efficiently surmount problems created after a disaster, notwithstanding existing uncertainties, by determining a strategic plan for collaboration with nongovernmental organizations for relief activities.

Relief strategies considered in previous research have not been sufficiently examined from the perspective of collaboration of governmental and nongovernmental organizations and provided an approach to develop the coverage of affected areas and reducing costs and traveled distance despite various uncertainties. Hence, the authors aim to manage RSCs better by offering a mathematical model whose performance has been proved in a real case study.

This paper aims to address a location-distribution-routing problem for distributing relief commodities during a disaster under uncertainty by creating a multi-stage model that can consider information updates during the disaster. This model aims to create a relief network that chooses distribution centers with the highest value while maximizing equity and minimizing response time.

A hybrid algorithm of adaptive large neighborhood search (ALNS) and multi-dimensional local search (MDLS) is introduced to solve the problem. Its results are compared to ALNS and an augmented epsilon constraint (AUGMECON) method.

The results show that the hybrid algorithm can obtain high-quality solutions within reasonable computation time compared to the exact solution. However, while it yields better solutions compared to ALNS, the solution is obtained in a little longer amount of time.

In this paper, the uncertain nature of some key features of the relief operations problem is not discussed. Moreover, some assumptions assumed to simplify the proposed model should be verified in future studies.

In order to verify the effectiveness of the designed model, a case study of the Sarpol Zahab earthquake in 2017 is illustrated and based on the results and the sensitivity analyses, some managerial insights are listed to help disaster managers make better decisions during disasters.

A novel robust multi-stage linear programming model is designed to address the location-distribution-routing problem during a disaster and to solve this model an efficient hybrid meta-heuristic model is developed.

Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief.

A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations.

A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained.

Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.

Motivated by the huge potential of biosimilars in the near future and rapid growth of Iranian biosimilar producing firms in recent two decades, this paper aims to explore the positioning of these firms in biopharmaceutical value chain and their path of technological capability building to extract policy-relevant advice.

As part of a two-year research project, an online questionnaire was designed and sent to biopharmaceutical experts in Iran between May and October 2016. Respondents came from biopharmaceutical firms. Also, 12 semi-structured interviews were conducted to analyze the path of capability building in Iranian biosimilar-producing firms.

The findings show that Iranian biopharmaceutical firms (BPFs) are mostly concentrated on “pharmaceutical development,”, “drug manufacturing” and “ after-sales services’ activities.” The study also demonstrates that most BPFs in Iran are at the “assimilative” level of capability and a few of them have recently moved toward the “adaptive” level.

The findings show that Iranian BPFs are mostly concentrated on “pharmaceutical development,” “drug manufacturing” and “after-sales services” activities. The study also demonstrates that most BPFs in Iran are at the “assimilative” level of capability and a few of them have recently moved toward the “adaptive” level.

This paper aims to consider a multi-site production planning problem with failure in rework and breakdown subject to demand uncertainty.

In this new mathematical model, at first, a feasible range for production time is found, and then the model is rewritten considering the demand uncertainty and robust optimization techniques. Here, three evolutionary methods are presented: robust particle swarm optimization, robust genetic algorithm (RGA) and robust simulated annealing with the ability of handling uncertainties. Firstly, the proposed mathematical model is validated by solving a problem in the LINGO environment. Afterwards, to compare and find the efficiency of the proposed evolutionary methods, some large-size test problems are solved.

The results show that the proposed models can prepare a promising approach to fulfill an efficient production planning in multi-site production planning. Results obtained by comparing the three proposed algorithms demonstrate that the presented RGA has better and more efficient solutions.

Considering the robust optimization approach to production system with failure in rework and breakdown under uncertainty.