Sustainable–resilient supplier selection is becoming increasingly challenging because decision data are often incomplete, vague, or conflicting. Many fuzzy multi-attribute decision making (MADM) methods still face two key limitations: (i) uncertainty may not be represented with sufficient flexibility, and (ii) they use either objective or subjective criteria weights, which can lead to inconsistent decisions. To overcome these issues, this study proposes a hybrid MADM decision-support framework developed in the p,q-quasirung orthopair fuzzy set (p,q-QOFS) environment. First, the essential criteria weights are objectively evaluated by Weights by ENvelope and SLOpe (WENSLO) method and subjectively by Fuzzy Weighted Zero-Inconsistency (FWZIC) within the p,q-QOF environment. Subsequently, the subjective and objective weights are integrated to obtain balanced final criteria weights for decision-making. Finally, the feasible suppliers are evaluated and ranked using a p,q-QOFS-based Mixed Aggregation by Comprehensive Normalization Technique (MACONT) method against sustainable and resilient criteria under uncertainty. The methodology is validated through a real-world case study of a food manufacturing company addressing a sustainable–resilient supplier selection problem with multiple feasible suppliers. The proposed hybrid MADM approach in the p,q-QOFS environment selects A1 as the most suitable sustainable–resilient supplier, underscoring the dominant role of social and resilience criteria and demonstrating stable ranking performance across different evaluation scenarios. Sensitivity analyses confirm that the solutions remain stable under different decision-making scenarios. Thus, the study presents a robust and practical decision-support model to support sustainability- and resilience-focused supplier selection in food supply chains, which are often highly disruption-sensitive. Although the case study is limited to one industry, the proposed framework can be extended to support related supply chain decisions such as order allocation and multi-period sourcing.
Scheduled Service Network Design (SSND) focuses on tactical planning in freight and passenger transportation systems, especially across rail, air, and maritime logistics. The SSND determines the optimal demand itineraries by selecting a set of scheduled services connecting origin, transfer, and destination terminals. This paper presents a unified review of the SSND literature, where different generic types of the SSND in freight and passenger transportation systems are first defined and formulated. Subsequently, 106 relevant journal papers published between January 2008 and June 2026 were systematically collected and analyzed. To gain a deeper understanding of the SSND research landscape, a bibliometric analysis was conducted using VOSviewer software to map key authors, leading countries, and research trends. These studies are classified according to their objectives, underlying assumptions, and network design frameworks, enabling the identification of major trends and research gaps. Particular attention is given to recent advancements, with emphasis on emerging research directions such as demand selection, synchronization, flexible SSND, multi-tier SSND, disruption scenarios, and capacity configurations. Extending beyond traditional optimization-based SSND, this survey investigates the emerging paradigm of intelligent, data-driven, and cyber–physical network design. It reviews how the integration of Internet of Things (IoT), machine learning, reinforcement learning, digital twins, and cyber–physical systems is reshaping SSND through enhanced situational awareness, predictive decision-making, and adaptive network operations. This review serves as a valuable resource for both researchers and practitioners by offering a clear overview of the current state of SSND research and providing actionable guidance for advancing tactical planning in next-generation SSNDs.
Given the intensive consumption of energy and resources in the steel industry, adopting Circular Economy (CE) principles is essential to reduce environmental impacts, improve resource efficiency, and ensure long-term sustainability. To address the existing research gaps, the main novelty of this study lies in integrating industrial symbiosis with CE principles within a comprehensive sustainability framework under epistemic uncertainty. Accordingly, this study develops a novel bi-objective possibilistic model for designing a symbiotic circular sustainable supply chain network in the steel industry by simultaneously considering all sustainability dimensions under epistemic uncertainty of critical parameters. Moreover, in alignment with the principles of the CE, the collection of scrap from customers for reuse is emphasized, alongside identifying and collaborating with customers for by-products’ resue. To reach a final robust solution, the original model is converted to its robust possibilistic programming counterpart. Furthermore, for the trade-off analysis, the final crisp equivalent bi-objective model is solved using the augmented ε-constraint method, resulting in Pareto-optimal solutions. A real-world case study is also provided whose results validate the technical soundness of the model and its practicality.
