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.

Spare parts supply chains in asset-intensive industries are highly exposed to disruption because demand is intermittent, suppliers are distant, and lead times are long. Integrating on-site additive manufacturing alongside conventional suppliers is increasingly viewed as a proactive resilience capability, yet existing sourcing models rarely capture the process-specific economics of metal additive manufacturing. This paper develops a mixed-integer linear programming model for a hedged spare parts supply chain in which conventional suppliers and an on-site bound metal deposition facility are jointly optimized. The model embeds the full bound metal deposition cost structure, covering design, feedstock procurement and inventory, printing, debinding, sintering, post-processing, and part qualification, together with supplier capacities, minimum order quantities, and multimodal transportation. A twelve-month numerical study of three industrial spare parts shows the hedged design meets all demand at minimum cost, qualifies every part for additive production, deploys additive capacity exactly where conventional sourcing binds, and retains substantial reserve capacity for disruption response. A demand sensitivity analysis reveals the demand thresholds at which the additive share expands, clarifying the conditions under which the capability is most valuable. The study is thus positioned as a decision-support demonstration for spare parts sourcing, offering managerial guidance on when embedding on-site AM capacity constitutes a cost-effective resilience investment.

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.