Today's distribution warehouses often need to process a far higher volume of smaller orders of multiple products which considerably increases logistics costs. They use the so-called order picking (OP) systems where products have to be picked from a set of specific storage locations by an OP process usually driven by production batches or customer orders. The OP is often very labour-intensive and its efficiency largely depends on the distance the order pickers have to travel, which therefore needs to be minimised. Minimising this distance is affected by several factors e.g. facility layout, shape of storage area, and especially the storage assignment strategy. Products that are frequently ordered together in multi-item, less than unit load customer orders should be stored near each other: this is the correlated storage assignment strategy. This study develops, tests and compares a set of different storage allocation rules based on the application of original similarity coefficients and clustering techniques. Lastly, a case study demonstrates the effectiveness of the proposed rules in minimising logistic costs.

International audienceIndustrial organisations must supply a variety of products and services, meet the needs of fragmented customer expectations, and cope with the consequences of the globalisation of world markets, all of which are producing significant levels of complexity. This study develops a new quantitative measurement of complexity for supply network based on Network Analysis, which is often used to study natural ecosystems, focussing in particular on the concept of entropy of information. The research reports advances in both theory on Supply Network Analysis problem and on its application to industrial contexts. Eight indexes based on entropy are presented. These measures provide a meaningful analysis of the level of complexity in the whole supply network mapping the exchanges of goods between the different actors in the network. The impact of possible modifications of the structure can simply be evaluated using these tools, providing a simple evaluation of the different scenarios. The proposed method takes an holistic point of view to tackle the problem of supply network optimisation. A real world application of the developed new methodology is presented

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.