A supply chain redesign problem considering facility functionality transformation

The increasing complexity of supply chains and the demand for adaptability in competitive business environments have necessitated innovative network planning approaches. This study investigates a new supply chain redesign problem that incorporates the transformation of facility functionalities under demand uncertainty. Unlike traditional models that focus solely on opening or closing facilities, the current research introduces, for the first time, a new redesign strategy that enables the transformation of facility roles based on supply chain requirements. A novel graph-based mathematical model is developed to consider the problem’s unique characteristics and support the key features of the transformation process, including its feasibility, time, and cost. To address demand uncertainty, a stochastic programming approach is employed. Given the problem’s complexity, four metaheuristic algorithms, two population-based (genetic algorithm and differential evolution) and two single-solution-based (simulated annealing and adaptive large neighborhood search), are designed to efficiently solve the problem for large-scale networks. The algorithms employ a specialized encoding scheme aligned with the problem structure, and their parameters are tuned using the Taguchi design of experiments. Numerical experiments are conducted to evaluate the performance of the developed model and proposed methodologies. Results demonstrate the effectiveness of the model in solving the problem while accounting for facility transformation. The performance evaluation of the metaheuristics shows that the differential evolution algorithm demonstrates superior results compared to others, especially in larger instances. For smaller examples, it also produces competitive solutions, achieving reasonable optimality gaps compared to CPLEX. The investigated problem is also evaluated through a case study of a carpet supply chain. Managerial insights are given for decision-makers in the field.