Optimising processes in a supply chain can benefit enormously all participating companies and the entire supply chain, e.g. for cost-cutting or profit raising. Traditionally, simulation-based technologies are used for such purposes. However, such methods can be expensive and under-performing when the solution space is too large to be adequately explored. Biologically inspired computing approaches such as swarm intelligence algorithms are uniquely suited to solve complex, exponential, vectorial problems, such as those posed by multi-product supply chains connected with a large and diverse customer base and transportation methods. Although swarm intelligence algorithms have been used to optimise supply chains before, there has been little work on formalising and optimising the layer-egg supply chain, or the supply chain of a perishable product—where same/similar products can be packaged to form different product offerings to seek optimised configurations for different buyers based on different pricing and cost structures. In this paper, we introduce two new Swarm Intelligence algorithms and use them to optimise the profits of participating suppliers in a real-world layer-egg supply chain using its operational data, trade network, and demand & supply models. Several swarm intelligence algorithms were discussed and their performance was compared. Through this, we aim to understand how the complex domain of real-life layer-egg supply chain may be suitably formalised and optimised in a bid to help improve and sustain layer-egg supply chain’s financial well-beings.