Resource levelling aims to obtain a feasible schedule to minimise the resource usage fluctuations during project execution. It is of crucial importance in project scheduling to ensure the effective use of scarce and expensive renewable resources, and has been successfully applied to production environments, such as make-to-order and engineering-to-order systems. In real-life projects, general temporal relationships are often needed to model complex time-dependencies among activities. We develop a novel genetic algorithm (GA) for the resource levelling problem with generalised precedence relations. Our design and implementation of GA features an efficient schedule generation scheme, built upon a new encoding mechanism that combines the random key representation and the shift vector representation. A two-pass local search-based improvement procedure is devised and integrated into the GA to enhance the algorithmic performance. Our GA is able to obtain near optimal solutions with less than 2% optimality gap for small instances in fractions of a second. It outperforms or is competitive with the state-of-the-art algorithms for large benchmark instances with size up to 1000 activities.

Configuring a supply chain for a new product is a challenging task due to the lack of historical demand data and the dynamic/uncertain nature of the new product diffusion process. An integrated supply chain configuration (SCC) and new product diffusion (NPD) model is developed to explicitly account for the impact of demand dynamics during a new product's diffusion on an optimal supply chain configuration. Our hybrid NPD-SCC model allows a manufacturer to source from multiple suppliers, vendors or modes for its supply chain entities. Such a multiple-sourcing approach not only helps the manufacturer to diversify its pool of suppliers and maintain bargaining power, but also builds redundancy into the supply chain to hedge against potential demand surge and supply disruption during the new product life cycle. Through a case study and a comprehensive computational study, we find that although the single-sourcing solution is able to achieve lower unit-manufacturing cost (UMC), the multiple-sourcing approach is superior to single-sourcing on the overall supply chain performance in the environment with random supply disruptions. By building-in redundancy as multiple suppliers and modes, the resultant supply chain has less chance of being disrupted and achieves higher overall profit on average. We also draw several other managerial insights closing the gap between some supply chain operations and marketing strategies.