This paper develops an optimization model for a sustainable closed-loop supply chain network with two conflicting objectives, namely, the minimization of the total logistic costs and the total amount of carbon emissions. The first objective relates to financial benefits, whereas the second represents the wider goal of guaranteeing cleaner air and hence a greener and healthier planet. The problem is first modelled as a mixed integer linear programming based-model. The aim is to determine the location of distribution centres and recycling centres, their respective numbers and the type of vehicles assigned to each facility. Vehicle type consideration, not commonly used in the literature, adds another dimension to this practical and challenging logistic problem. A matheuristic using compromise programming is put forward to tackle the problem. The proposed matheuristic is evaluated using a variety of newly generated datasets which produces compromise solutions that demonstrate the importance of an appropriate balance of both objective functions. The robustness analysis considering fluctuations in customer demand is assessed using Monte Carlo simulation. The results show that if the standard deviation of the demand falls within 10% of its average, the unsatisfied demand is insignificant, thus demonstrating the stability of supply chain configuration. This invaluable information is key towards helping senior management make relevant operational and strategic decisions that could impact on both the sustainability and the resilience of their supply chain networks.

This paper presents a new variant of the capacitated multi-source Weber problem that introduces fixed costs for opening facilities. Three types of fixed costs are considered and experimented upon. A guided constructive heuristic scheme based on the concept of restricted regions and a greedy randomized adaptive search procedure (GRASP) are proposed. The four known data sets in the literature, typically used for the uncapacitated multi-source Weber problem, are adapted by adding capacities and facility fixed costs and used as a platform to assess the performance of our proposed approaches. Computational results are provided and some research avenues highlighted.

The construction industry is undergoing a significant transformation due to the increasing influence of digital technology, sustainability requirements, and diverse stakeholder expectations, which highlights the need to update the existing service quality models accordingly. However, the traditional service quality models often fail to address these evolving demands comprehensively. This study systematically reviews 44 peer-reviewed articles to identify the key service quality dimensions and offer clear guidance for future research that can address the complexities of modern construction. The findings reveal that reliability, tangibles, and communication remain the most emphasized dimensions across the reviewed literature, whereas critical areas, such as digital integration, sustainability indicators, and service recovery, are significantly underexplored. This contrast explicitly links the limitations of the classic frameworks to these emerging demands, highlighting their difficulty in accommodating the industry’s growing reliance on real-time data, an environmentally friendly performance, and multi-stakeholder collaboration. Because the construction industry typically contributes 6–10 per cent of the national GDP and underpins wider economic development, inadequate service quality models can propagate cost overruns, productivity losses, and reputational damage across the economy; conversely, improved models enhance project efficiency, and thus support sustained economic growth. This review is limited by its reliance on the Scopus and Web of Science databases, which may exclude relevant regional or non-English studies. Furthermore, many reviewed articles are context-specific, potentially reducing the generalizability of the findings. Despite these limitations, this review offers an evidence-based framework that integrates advanced digital tools, sustainability measures, and diverse stakeholder perspectives. Future studies should demonstrate this framework’s efficacy and applicability in different circumstances.