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Multi-variant products to be assembled on mixed-model assembly lines at locations within a production network need to be scheduled locally. Scheduling is a highly complex task especially if it simultaneously covers the assignment of orders, which are product variants to be assembled within a production period, to assembly lines as well as their sequencing on the lines. However, this is required if workers can flexibly fulfill tasks across stations of several lines and, thus, capacity of workers is shared among the lines. As this is the case for final assembly of the Airbus A320 Family, this paper introduces an optimization model for local order scheduling for mixed-model assembly lines covering both assignment to lines as well as sequencing. The model integrates the planning approaches mixed-model sequencing and level scheduling in order to minimize work overload in final assembly and to level material demand with regard to suppliers. The presented model is validated in the industrial application of the final assembly of the Airbus A320 Family. The results demonstrate significant improvement in terms of less work overload and a more even material demand compared to current planning.

Production of multi-variant products in a network requires the assignment of customer orders to locations and periods. This is a highly complex planning task, as requirements of procurement, production, distribution, and sales have to be considered. Providing customers with the flexibility of configuring their ordered products after order assignment further increases the complexity of the planning task by taking uncertainty into account. Therefore, a robust optimisation model, using scenarios representing potential customer-specific order configurations, is introduced. By providing enough flexibility to handle maximum work overload caused by the potential order configurations at locations, a robust assignment of orders can be guaranteed in order to avoid undesirable situations causing delays and additional costs. Therefore, the mid-term adjustments of the flexibility limits are enabled by the changeability of workforce supply by making use of external workers. An industrial application of the model in manufacturing of the Airbus A320 Family of aircrafts is presented. The costs for offering configuration flexibility to customers are quantified by the expected value of perfect information. The explicit consideration of configuration uncertainty through the use of scenarios is discussed based on the value of the stochastic solution in comparison to the results attained by simplistically using the expected value.

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Modern traceability technologies promise to improve supply chain management by simplifying recalls, increasing demand visibility, or ascertaining sustainable supplier practices. Managers in the traceability initiatives developing such technologies face a difficult question: which companies should they target as early adopters to ensure that their technology is broadly employed? To answer this question, managers must consider that supply chains are interlinked in complex networks and that a supply chain effect is inherent to traceability technologies. More specifically, the benefits obtained from traceability are conditional on technology adoption throughout a product's supply chain.We introduce a model of the dynamics of traceability technology adoption in supply chain networks to tackle the problem of selecting the smallest set of early adopters guaranteeing broad dissemination. Our model builds on extant diffusion models while incorporating that a firm's adoption decision depends on previous adoption decisions throughout its supply chains. We show that the problem is NP-hard and that no approximation within a polylogarithmic factor can be guaranteed for any polynomial-time algorithm.Nevertheless, we introduce an algorithm that identifies an exact solution in polynomial time under certain assumptions on the network structure and provide evidence that it is tractable for real-world supply chain networks. We further propose a random generative model that outputs networks consistent with real-world supply chain networks. The networks obtained display, whp, structures that allow us to find the optimal seed set in subexponential time using our algorithm. Our generative model also provides approximate seed sets when information on the network is limited.