<p>The flower industry, as a key sector of agriculture, not only plays an important role in beautifying the living environment, but also, as an economic resource with export potential and job creation, can contribute to the development of the country. This research focuses on designing an effective logistics network for the flower industry integrating perishability, resilience, and circular economy (CE) principles. This work advances the existing body of knowledge by introducing a novel multi-phase decision framework that incorporates circular economy (CE) principles, resilience considerations, and product perishability into the design of floriculture supply chain networks under uncertain conditions. For the first time, a combined supply-waste chain network model is proposed, which is assessed using fuzzy BWM-VIKOR and optimized for both profitability and sustainability, thereby addressing notable gaps in the current literature. To achieve this, an efficient multi-stage decision tool is developed to configure an integrated supply-waste chain network (ISWCN), where logistics operations and waste management systems are designed in parallel. In the initial phase, candidate retailers are assessed against CE and resilience criteria through a hybrid fuzzy best-worst method (BWM) and fuzzy VIKOR approach. Subsequently, a mathematical programming formulation is constructed to design a resilience-aware ISWCN. Finally, a flexible robust fuzzy technique is employed to handle uncertainty. Finally, the proposed model is solved using the Normalized Weighted Sum (NWS) method. As far as we are aware, this study represents the first attempt to jointly design an ISWCN that incorporates both circular economy and resilience dimensions within a multi-stage decision-making framework. The practical viability of the proposed model is illustrated through a real-world application in Iran’s flower sector. The findings indicate a 15.7% rise in overall profit and a 22.4% decrease in waste across the flower supply chain, along with notable improvements in resilience indicators. Furthermore, multiple sensitivity analyses were performed to examine how the research problem responds to variations in key parameters. The effectiveness of the adopted methods is also evaluated by comparing their outputs with those generated by alternative approaches. Lastly, several practical implications for management are provided.</p>