| dc.description.abstract | Material Handling (MH) consists in the movement and storage of parts, in a manufacturing or distribution process, from one location
to another. Material Handling Systems (MHSs) are everywhere in production plants, assembly lines, product distribution, logistics, intermodal
activities (railways, road transportation, container ships, etc..). They usually are distributed, sometimes itinerant and often
mixed manned and automated. Although not adding value in the manufacturing process, MH usually influences great part of a company’s operation costs, especially, for example, in the food distribution chain. Due to the increasing demand for a high variety of products, flexibility and efficiency are two important keywords in MHSs. Optimizing MH activities means having shorter response times and an increased throughput of the plant. The importance of this optimization process is very high in today’s companies. Nowadays, the interest in this process is growing rapidly since several new technologies, like the Radio Frequency Identification (RFID) are available which finally allow to introduce an automation level to operating MHSs, almost without stopping operations and at a very low cost. In MHSs control iusses involve the problem of the optimal sequencing and scheduling of short-term activities. The so-called problem of
"Dispatching” consists in defining a procedure to assign resources to missions. This is often made by using heuristic rules called Dispaching rules. For control purposes, a model of the system is necessary. Due to the complex and heterogeneous nature of MHSs, modeling approaches proposed in the literature are typically very specific and context-dependent. Moreover, the strong combinatorial nature of the control problem, and the presence of a great number of constraints to be considered, usually make the design of a control solution very tough. To devise a closed form analytical control action
can require a great computational effort and could result not so convenient. Indeed, turbulence and variations in the input set of the system can suddenly make not more adequate a hardly designed control action. Thus, the choice of Dispatching rules as control actions, despite
producing only local optimum solutions, is very usual for MHSs. Dispatching rules, indeed, result in a more reasonable and robust way to control MHSs since they are effective and computationally inexpensive. In the absence of a closed form control solution, Simulation is fundamental to evaluate the effects of a control action which
cannot be analytically predicted. The outcome of the application of a rule or another can be easily tested via simulation and this is the reason why having a good model assumes a further major importance. In this thesis a unique arcchitecture for the modeling and the control of complex MHSs has been proposed. | en_US |
