This study examines how circular value is created under conditions of structural fragility, disruption, and resource scarcity, where prevailing circular supply chain theories offer limited explanatory power. While prior research emphasizes efficiency and optimization, it provides limited insight into how circular supply chains function when stable infrastructure, predictable flows, and formal coordination mechanisms are absent. Drawing on dynamic capabilities theory and insights from humanitarian logistics, this study proposes a conceptual framework that reconceptualizes circular value creation as an adaptive and capability-mediated process. The framework posits that digital circular capabilities, sensing, seizing, and reconfiguring, enable organizations to develop adaptive reverse logistics systems, which serve as the primary mechanism through which circular value is realized under constrained conditions. Structural fragility and disruption intensity are introduced as boundary conditions shaping the effectiveness of these relationships. The study contributes by reframing circular supply chain theory beyond efficiency-based assumptions, extending dynamic capabilities to disrupted and resource-constrained environments, and positioning reverse logistics as a central adaptive mechanism for circular value recovery. By integrating insights from humanitarian logistics and circular supply chain research, the study advances a more context-sensitive understanding of supply chain functioning under disruption.

The purpose of this paper is to present the relief operations (RO), responding to a sudden, natural, national disaster (SNND) as a multi-mode resource-constrain multi-project scheduling problem (MRCMPSP). A conceptual framework at a strategic level is constructed and the Colombian RO for an earthquake response is shown as an illustrative case. We concluded that RO can be addressed as a MRCMPSP and that for Colombian case, it is a convenient way to board it. Addressing RO as a MRCMPSP allows managers to implement different project scheduling tools successful in other contexts.

In this paper, a multi-period model for blood supply chain in emergency situation is presented to optimize decisions related to locate blood facilities and distribute blood products after natural disasters. In disastrous situations, uncertainty is an inseparable part of humanitarian logistics and blood supply chain as well. This paper proposes a robust network to capture the uncertain nature of blood supply chain during and after disasters. This study considers donor points, blood facilities, processing and testing labs, and hospitals as the components of blood supply chain. In addition, this paper makes location and allocation decisions for multiple post disaster periods through real data. The study compares the performances of “p-robust optimization” approach and “robust optimization” approach and the results are discussed.

In this paper, a robust bi-level model is proposed to optimize decisions related to distribution and evacuation aid after earthquake. Usually in disastrous situation foreign countries help the affected country by sending relief commodities. In this problem, the foreign countries try to minimize their shipping costs and the affected country seeks to minimize its total costs which include inventory, operation, and transportation expenses. This situation is a game between different decision makers after a catastrophic disaster. To deal with this situation, a bi-level model is proposed in which the affected country is the leader and suppliers are the followers. To validate the proposed robust model, we consider Tehran probable earthquake in region 1 as a case study. Then the advantages of using bi-level modeling against considering just one player's point of view is provided. The sensitivity analysis of the experiments are presented to explore the effects of various parameters to show managerial insights that can guide DMs under a variety of conditions.

Humanitarian assistance by foreign organizations in general and foreign military forces in particular, is typically provided in a non-neutral political environment. Local politics that range from national pride, through strained relations with the country offering military logistic support, to blatant aversion to the population in need, affect the ability to provide effective humanitarian aid. The current paper presents the use of mathematical modeling and robustness approach when the government of the affected area declines offers of aid from international organizations because of political constraints. The multi-objective model seeks to minimize unsatisfied demands and total costs of the government and suppliers. To explore the effects of various parameters and show managerial insights that can guide DMs under a variety of conditions, the sensitivity analysis of the experiments are presented.

Most of the time, the government of the affected country cannot handle the entire relief operations. Under such conditions, international organizations act independently but are obliged to obey the law of the country. In this paper, we propose a dynamic multi-level programming where the affected country and international organizations dynamically change their roles, being leader or follower, according to a game. The application of the proposed model is investigated for a case problem where real data are utilized to design a network for humanitarian logistics during potential earthquakes. The advantages of using multi-level modeling against considering just one player's point of view are provided to guide decision-makers under a variety of conditions. The results show for the first three periods, the gov-ernment of the affected area cannot handle the demand for the rescue operation. Thus, the international suppliers act as the leader for the first three periods. However, by decreasing the demand for the rescue operation in the last three periods, the government can manage the evacuation operation properly.