All vital operations of a supply chain network (SCN) are inherently affected by uncertainties and risks in the economic environment. In this study, we extend an existing SCN model by elaborating on stochastic price and demand, and integrating economic uncertainty. Financial performance and credit solvency are of utmost importance when analysing the financial status of an SCN. Each of these components focuses on a different aspect of investment attractiveness under various economic conditions, and in turn, facilitates access to capital for the companies involved. We extend the SCN model with financial performance optimisation approach to a novel model to include the trade-offs between two objective functions: (1) the total equity as the financial performance of the SC, and (2) the Altman’s Z-score as the credit solvency factor of the company. The model ascertains the future profitability, leverage, and liquidity of the SC by determining the optimal levels of production and inventory in each facility of the new network design. We introduce a weighted sum method and a novel genetic algorithm (GA) solution approach to solve the resultant bi-objective mixed integer nonlinear programming problem. We illustrate the representation scheme required for the GA in this paper.
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