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Abstract
Mass customization (MC) is one of the key strategies adopted by many organizations to increase their competitive advantage. To implement the MC strategy, an organization must consider and optimize numerous decisions. Given its objective of minimizing total cost or maximizing total profit. Two of the fundamental decisions outlined in the literature are the product line selection, and the supply chain design decisions. These decisions were tackled in the literature first separately and then jointly as it was proven that the integration of these decisions would achieve near optimal solution. In this research, the simultaneous decisions of product line selection and supply chain design is considered as a problem that must be solved and optimized. The purpose of this research is to address the problem presented by Kumar & Chatterjee in [1], with a different algorithm, namely the Simulated Annealing (SA) algorithm. The proposed algorithm is tested to fine-tune its parameters, in addition to ensuring the effectiveness and efficiency of SA algorithm. A comparison is drawn between SA and heuristic (the algorithm used in [1]) using different versions of the problem. Results of the SA significantly outperformed those of the heuristic. Then, the current mathematical model was modified to capture more realistic factors. The enhanced model introduced two main modifications. First, it took into consideration supplier disruptions which will lead to supplier{u2019}s failure to deliver the requested modules. Second, maximizing the quality of the final composed product was added as a second objective function to the model besides the profit maximization function