No abstract

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.

Personalization of products, mix variability and short time to market are the most important factors that have forced companies to a new form of organization during past years. A very common reply to this question is a lean organization based on flexibility of productive lines, reduction of storage and integration among company sections. In this context, quite differently from a traditional system, the maintenance function must work efficiently. Also the maintenance division must contribute to the success of the factory. Aims to introduce a methodology for a soft and tenable application of the principles of total productive maintenance (TPM) in Italian factories. The first step of the study is an explanation of the actual situation, usually based on traditional or on productive maintenance. After a brief introduction, focuses on TPM links with productive maintenance in order to suggest a method for TPM. Concludes with a real application of TPM in a big factory, with a description of a world leader in plant manufacturing for the ceramics industry.

The present research deals with car pooling as a means of making better use of existing infrastructure and as a means of reducing traffic congestion with all its associated induced effects. Car pooling schemes involve several drivers getting together to share a private vehicle simultaneously, in order to reach their destinations points according to a semi-common route rather than each driver using their own vehicle. The Car Pooling Problem belongs to the non-polynomial computational complexity family of operations problems. In the current literature there are only a few studies on this optimization problem: the research group has designed several different new automatic and heuristic data processing routines to support efficient matching in car pool schemes. These are based on savings hnctions and belong to two distinct macro classes of algorithms to give two different modelings of this problem. They offer average savings of more than 50% in traveled distances demonstrating the effectiveness of a trivial matching scheme for real applications.