This study introduces the problem of minimizing average relative percentage of imbalance (ARPI) with sequence-dependent setup times in a parallel-machine environment. A mathematical model that minimizes ARPI is proposed. Some heuristics, and two metaheuristics, an ant colony optimization algorithm and a genetic algorithm are developed and tested on various random data. The proposed ant colony optimization method outperforms heuristics and genetic algorithm. On the other hand, heuristics using the cumulative processing time obtain better results than heuristics using setup avoidance and a hybrid rule in assignment. Abstract: This study introduces the problem of minimizing average relative percentage of imbalance (ARPI) with sequence-dependent setup times in a parallel-machine environment. KeywordsA mathematical model that minimizes ARPI is proposed. Some heuristics, and two metaheuristics, an ant colony optimization algorithm and a genetic algorithm are developed and tested on various random data. The proposed ant colony optimization method outperforms heuristics and genetic algorithm. On the other hand, heuristics using the cumulative processing time obtain better results than heuristics using setup avoidance and a hybrid rule in assignment.

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Given the lengthy product development lifecycle process, high cost, and low success rate, many firms avoid considering discontinuous innovation strategies, in spite of their increase in frequency and importance in many markets. Even with advances in automation and technology, many of the techniques being utilised in product development are relatively unchanged, and the definition of discontinuous innovation itself lacks a structural component. To address this problem, the authors developed a methodology for generative customisation to implement discontinuous innovation. Using the emerging technologies of generative design and agent-based modelling, the authors developed a methodology to create product inventions and measure product innovations using a complex adaptive system (CAS) model. This appears to be the first model that represents a complex adaptive system environment to measure the success of discontinuous innovation in the development of a market equilibrium agent model.