Inventory constraints, costs of lost production, safety and environmental objectives, strategies of maintenance adopted, logistics aspects of spare parts are some of the criteria taken into account, and spare parts classification is thus defined with respect to multiple attributes. In virtue of the large number of the potential operational characteristics to be considered, the decision diagram is integrated with a set of analytic hierarchy process models used to solve the various multi‐attribute decision sub‐problems at the different levels/nodes of the decision tree. An inventory policy matrix is defined to link the different classes of spare parts with the possible inventory management policies so as to identify the “best” control strategy for the spare stocks. The principles of the theory and an actual application in a company operating in the paper industry are reported in the paper.

Assembly line rebalancing is a problem companies are frequently confronted with as continuous changes in product features and volume demand caused by the volatility of modern markets result in re-definition of assembly tasks and line cycle time fluctuations. Consequently, managers are forced to adjust the balancing of their lines in order to adapt to the new conditions while trying to minimise both increases in completion costs and costs related to changes in task assignment. In particular, when modifications are made to line balancing, costs are incurred for operator training, equipment switching and moving, and quality assurance. The stochastic assembly line rebalancing problem is essentially composed of a multi-objective problem in which two joint objectives, total expected completion cost of the new line and similarity between the new and the existing line, must be optimised. Consequently, this paper presents a multiple single-pass heuristic algorithm developed for the purpose of finding the most complete set of dominant solutions representing the Pareto front of the problem. The operative parameters of the heuristic are set as a result of a great deal of experimentation. Moreover, a multi-objective genetic algorithm is developed and then compared with the proposed heuristic in order to demonstrate its effectiveness. Finally, an illustrative case study is presented.

Consignment stock (CS) is an innovative approach to supply and stock management, based on a strong and continuous collaboration between vendor and buyer to create a ‘win-win’ situation, where both partners have equal gains. An analytic formulation of CS policy with obsolescence has been proposed in Persona et al. ( 2005 Persona, A , Grassi, A and Catena, M . 2005. Consignment stock of inventories in the presence of obsolescence. International Journal of Production Research, 43(23): 4969–4988. [Taylor & Francis Online], [Web of Science ®] [Google Scholar] ). This article considers new critical factors (present in several industrial environments) providing a logical extension of the above mentioned study. The proposed methodology addresses, in particular, the design of a new stock policy in an industrial environment with demand variability, stock-out risk and limited warehouses space. The analytical model presented demonstrates that a traditional stock policy, such as the economic order quantity approach is always more expensive than the consignment stock approach. In addition, the benefits of the CS policy to the supply chain are consistently high including when applied to realities with high demand variation and space limitations.