Quantitative supply chain resilience (SCR) measurement has accumulated a substantial methodological base, but that base is unevenly distributed across the units of analysis at which resilience is measured. This paper maps the as-is literature landscape of selected SCR measurement studies by unit of analysis and by measurement stream, and diagnoses what that distribution reveals. The mapping shows that methodological depth concentrates at the firm, network, and system levels, with sector-level composite benchmarking as the largest aggregate unit, while country-level SCR measurement emerges as weakly represented and conceptually underdeveloped. Read across the classification, this country-level gap appears as a structural feature of the literature rather than a temporary data shortage. On the basis of this diagnosis, the paper proposes a roadmap framework for the future development of country-level SCR metrics and illustrates its application through a case on Qatar, leaving empirical validation to subsequent research.

The resilience of a supply chain reflects its capability to withstand disruptions, adapt to changing conditions and recover its operational performance within an uncertain and dynamic environment. This capability has become essential for modern global supply chains that are continuously exposed to major risks such as natural disasters, geopolitical conflicts and pandemics. Resilience requires the implementation of cost-effective strategies to mitigate the impact of disruptions and support recovery processes. However, because resilience primarily focuses on maintaining or restoring performance following individual disruptions within a limited time horizon and a closed-system setting, it may be insufficient to address recurrent, long-lasting, and progressively escalating disruptions such as the Covid-19 pandemic. Consequently, supply chain viability has emerged as a new research paradigm that extends resilience by emphasizing long-term survivability as a strategic objective alongside profitability. In this context, the present study focuses on the design of viable supply chain networks that jointly optimize profitability and viability through the implementation of multiple resilience strategies including backup supply, supplier fortification and emergency inventory. Specifically, we propose a novel viability metric that captures long-term survivability by limiting the duration during which service performance remains below a specified target level across all disruption scenarios. To incorporate this metric into the decision-making process, we develop a bi-objective, stochastic, multi-period supply chain network design model that determines supplier selection and factory location decisions, while balancing profitability and viability objectives. A real-life pharmaceutical industry case study is then used to validate the proposed model and investigate the trade-off between profitability and viability.

Ensuring food security is crucial to safeguarding national stability and the well-being of citizens. Diversifying food imports improves a country's resilience against sudden supply disruptions and mitigates risks associated with geopolitical uncertainties. In this paper, we address the problem of food import diversification, one of the very important aspects of food security. We build a mathematical model of import diversification and devise an optimization algorithm to determine the optimal strategy. Our methodology aims to maximize diversification while minimizing both costs and risks. The experimental results demonstrate that our approach generates a diverse set of policies, each optimized for different combinations of objectives, providing adaptable solutions for sustainable food security.

Food import diversification is an important strategy for improving the resilience of import-dependent food systems. This paper studies the allocation of food imports across multiple suppliers while considering three conflicting objectives: reducing supplier risk, reducing import cost, and limiting dependence on dominant suppliers. The problem is formulated as a constrained multi-objective optimization model and solved using two evolutionary approaches: NSGA II and MOEA/D. A projection step is used to ensure that all generated allocations satisfy demand and supplier capacity constraints. Computational experiments compare the two algorithms using hypervolume, inverted generational distance, runtime, and non-dominated solution quality. The results provide insight into the behavior of dominance-based and decomposition-based evolutionary methods for constrained food import allocation.