Improved managerial decisions for fuzzy stochastic bi-objective linear programming models: a trade-off ratio-based autonomised approach

Industry-derived fuzzy stochastic bi-objective linear programming (FSBOLP) models typically focus on a single major objective function. However, mathematical analysis based classical minmax method fails to distinguish between two competing objectives. This sometimes causes Pareto optimal solutions failing to meet with managerial goals. Additionally, to assume that both objectives in a FSBOLP shall attain the same highest standard is unrealistic. To overcome these limitations, the proposed amended minmax model uses the absolute difference operator. This pushes intermediate non-dominated points closer to predefined reference levels, generating more desirable Pareto optimal solutions. Then this study frames one trade-off ratio-based autonomised optimisation algorithm, updating reference levels throughout iterations and reducing the need for frequent manager interactions. A numerical case study validates the effectiveness of proposed approach. This way, this study provides an improved framework for FSBOLP models, addressing the shortcomings of the traditional method and providing better decision-making support for industry managers.