Designing a regenerative pomegranate supply chain network under uncertainty: A Lagrangian relaxation approach

Regeneration is a fundamental concept at the heart of the circular economy. This concept emphasizes increasing positive environmental impacts in addition to achieving zero waste. Inspired by this concept, in this research, a mixed-integer linear programming model is developed to design a regenerative supply chain (SC) network in the pomegranate industry under uncertainty. In the investigated network, all pomegranate waste is transported to composting centers. Then, the compost produced by the composting centers is made available to farmers, thus integrating the concept of regeneration with SC operations. A stochastic scenario-based approach is used to deal with the uncertainty of the demand parameter. In addition, a Lagrangian relaxation algorithm is applied in order to solve the problem in large sizes. The validity of the developed algorithm is examined by comparing its results with GAMS results on nine small-sized instances. The results show that the proposed algorithm provides solutions close to GAMS solutions while being faster. Finally, the applicability of the presented model is confirmed through its implementation in a pomegranate industry in Iran. • Using regeneration concept to achieve zero-waste and positive environmental impacts. • Formulating a MILP model to structure a regenerative supply chain for pomegranate. • Forming a CLSC for effective use of agriculture waste. • Developing a Lagrangian relaxation algorithm to solve large-sized problems. • Validating the proposed model using data of a pomegranate industry in Iran.