A prescriptive analytics framework for service and carbon tradeoffs in disrupted supply chains

Ashish Chandra; Ashish Chandra

doi:10.3934/jimo.2026184

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 10: 5351-5381. doi: 10.3934/jimo.2026184

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A prescriptive analytics framework for service and carbon tradeoffs in disrupted supply chains

Ashish Chandra ^,

College of Business, Illinois State University, Normal, 61761, Illinois, USA

Received: 04 July 2026 Revised: 01 September 2026 Accepted: 18 September 2026 Published: 23 September 2026
90B06, 90B20, 90C29

Supply chain networks (SCNs) are increasingly required to balance high service performance with low carbon emissions, while also remaining resilient to disruptions. Although both sustainability and disruption management have been widely studied, limited attention has been given to how disruptions affect the tradeoff between service and environmental performance. This paper examines how link failures reshape the service-carbon tradeoff in multi-echelon SCNs. We model the network as a capacitated flow system, where disruptions eliminate the capacity of failed links, and the service performance is measured by the total lost demand (TLD). Transportation-related carbon emissions are modeled as flow-dependent quantities, and a lexicographic $ \epsilon $-constraint approach is used to generate efficient service–carbon frontiers between the TLD and the total transportation-related carbon emissions (TCE). To capture severe operational conditions, the principal analysis selects a fixed adverse disruption scenario that maximizes the minimum TLD across all failure scenarios involving a given number of link failures, while additional experiments examine alternative fixed-scenario selection rules. Computational experiments on synthetically generated networks reveal changes in the marginal service recovery along the service–carbon tradeoff under disruption. Under the principal max–min adverse-scenario analysis, larger failure budgets are associated with worse service outcomes and generally earlier declines toward lower marginal-improvement levels. Additional experiments using critical-link, random, and representative-average random fixed disruption scenarios show that the magnitude of service degradation depends on the failed-link configuration, while the underlying service–carbon tradeoff and changing marginal effectiveness of additional emissions persist across the alternative scenario-selection rules. From a managerial perspective, these findings highlight the importance of both the network structure and the link criticality when planning sustainability-constrained recovery.
- supply chain disruptions,
- carbon emissions modeling,
- service level analysis,
- disruption modeling,
- multi-echelon networks,
- pareto optimization
Citation: Ashish Chandra. A prescriptive analytics framework for service and carbon tradeoffs in disrupted supply chains[J]. Journal of Industrial and Management Optimization, 2026, 22(10): 5351-5381. doi: 10.3934/jimo.2026184

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Abstract

Supply chain networks (SCNs) are increasingly required to balance high service performance with low carbon emissions, while also remaining resilient to disruptions. Although both sustainability and disruption management have been widely studied, limited attention has been given to how disruptions affect the tradeoff between service and environmental performance. This paper examines how link failures reshape the service-carbon tradeoff in multi-echelon SCNs. We model the network as a capacitated flow system, where disruptions eliminate the capacity of failed links, and the service performance is measured by the total lost demand (TLD). Transportation-related carbon emissions are modeled as flow-dependent quantities, and a lexicographic $ \epsilon $-constraint approach is used to generate efficient service–carbon frontiers between the TLD and the total transportation-related carbon emissions (TCE). To capture severe operational conditions, the principal analysis selects a fixed adverse disruption scenario that maximizes the minimum TLD across all failure scenarios involving a given number of link failures, while additional experiments examine alternative fixed-scenario selection rules. Computational experiments on synthetically generated networks reveal changes in the marginal service recovery along the service–carbon tradeoff under disruption. Under the principal max–min adverse-scenario analysis, larger failure budgets are associated with worse service outcomes and generally earlier declines toward lower marginal-improvement levels. Additional experiments using critical-link, random, and representative-average random fixed disruption scenarios show that the magnitude of service degradation depends on the failed-link configuration, while the underlying service–carbon tradeoff and changing marginal effectiveness of additional emissions persist across the alternative scenario-selection rules. From a managerial perspective, these findings highlight the importance of both the network structure and the link criticality when planning sustainability-constrained recovery.

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