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Automated storage and Retrieval (AS/R) systems have had a significant impact on storage and retrieval of finished goods, work-in-process, and raw materials and supplies. A microcomputer-based simulation model was developed to evaluate different unit load AS/R systems serving multiple input sources and output destinations. The simulation results were statistically analyzed on different performance measures including throughput, mean waiting times maximum waiting times and rejects. The results showed that for single-dock, square-in-time layouts, the class based arrangement produced significantly higher throughput for all scheduling policies. Among the scheduling policies, the relief nearest neighbor produced consistently higher throughput. Comparing square-in-time versus non-square-in-time layouts, the square-in-time layout performance was better; the performance deteriorated as deviations from square-in-time increased. For the two dual-dock layouts, at lower arrival rates the dedicated layout produced higher throughput; there was no significant difference between the two layouts at higher arrival rates. / Graduation date: 1992

This thesis proposes a computationally effective analytical approach to automated material handling system (AMHS) performance modeling for a simple closed loop AMHS, such as is typical in supporting a 300mm wafer fab bay. Discrete-event simulation can produce accurate assessments of the production performance, including the contribution by the AMHS. However, the corresponding simulation models are both expensive and time-consuming to construct, and require long execution times to produce statistically valid estimates. These attributes render simulation ineffective as a decision support tool in the early phase of system design, where requirements and configurations are likely to change often. We propose an alternative model that estimates the AMHS performance considering the possibility of vehicle-blocking. A probabilistic model is developed, based on a detailed description of AMHS operations, and the system is analyzed as an extended Markov chain. The model tracks the operations of all the vehicles on the closed-loop considering the possibility of vehicle-blocking. The resulting large-scale model provided reasonably accurate performance estimates; however, it presented some computational challenges. These computational challenges motivated the development of a second model that also analyzes the system as an extended Markov chain but with a much reduced state space because the model tracks the movement of a single vehicle in the system with additional assumptions on vehicle-blocking. Neither model is a conventional Markov Chain because they combine the conventional Markov Chain analysis of the AMHS operations with additional constraints on AMHS stability and vehicle-blocking that are necessary to provide a unique solution to the steady-state behavior of the AMHS. Based on the throughput capacity model, an approach is developed to approximate the expected response time of the AMHS to move requests. The expected response times are important to measure the performance of the AMHS and for estimating the required queue capacity at each pick-up station. The derivation is not straightforward and especially complicated for multi-vehicle systems. The approximation relies on the assumption that the response time is a function of the distribution of the vehicles along the tracks and the expected length of the path from every possible location to the move request location.

The nature of how an Automatic Storage & Retrieval System stacker crane moves through the aisle, between picking and storing operations, is analyzed through a simulation model. A continuous SLAM model simulates the performance of a single aisle Automatic Storage & Retrieval System stacker crane under various control conditions. Crane speed, acceleration rate, job arrival rate, and discrete or continuous interrupt sensing are the variables examined within the model. The performance level of the system is observed while altering the control. / M.S.

This research identifies the attributes to be considered in the selection of an automated guided vehicle (AGV). A distinction is made between automated guided vehicles (AGVs) and an automated guided vehicle system (AGVS). This research is concerned only with the selection of automated guided vehicles (AGVs). A selection model is developed which forces the user to evaluate attributes. his requirements and preferences for AGV The first step of the model allows the user to enter his specifications for AGV attributes which are applicable to his production environment. The second step in the selection model is for the user to determine 8-15 attributes to use as selection criteria. In the third phase, the user inputs his preferences and priorities with respect to the attributes chosen as selection criteria in the second step. model ranks the Based on this information, the selection AGV models in the feasible set. A description of the model and a numerical example are included. Steps 1 and 2, described above, are implemented using an R:BASE™ program. The BASIC computer language was used to perform the interrogation of the user with respect to his priorities and preferences among attributes in Step 3. The IBM PC™ is the hardware chosen for running the selection model. / M.S.

This dissertation focuses on the material transport system design problem (MTSDP), integrating decisions of technology selection and flow network design. This research is motivated by the design of material transport systems (MTS) in manufacturing plants. The objective is to design a MTS with minimum lifetime costs, subject to service requirements, flow network restrictions, and limited resources. We characterize the MTSDP from the perspectives of task requirements, transport technology, and space utilization. A classification is proposed for transport technologies such that instances in the same class share the same properties, and a decision framework is proposed to emphasize the inter-relationships of three major decisions: task clustering, network connecting, and technology selection. We consider fixed and variable costs, arc capacities, and empty travel in our formulations. We propose two solution approaches for the MTSDP. The first is the compact formulation (CF) approach where the three major decisions are handled by a mixed integer non-linear programming (MINLP) formulation. Relaxation techniques are applied to linearize the model. The solution of the resulting linear formulation (MILP) provides a lower bound to that of MINLP. A tightened formulation reduces the computational time by a factor of 3.85. The experiment also shows that when control system costs are significant, designs with multiple-task clusters are more economical than those restricted to single-task clusters. The other approach is clustering/set partition (CSP), where the three decisions are decomposed and solved sequentially. In an example MTS design problem, three methods are compared: CSP, a GREEDY approach from the literature, and enumeration. CSP finds the optimal solution, while GREEDY results in 31% greater costs. A similar comparison with another example is made for the CF and CSP approaches. We apply the CSP approach in a case problem, using data from an auto parts manufacturer. We include flow path crossing constraints and perform experiments to determine solution quality over a range of small problem sizes. The largest difference from optimality is 3.34%, and the average is 0.98%. More importantly, based on these experiments, it seems there is no evidence that the difference percentage grows with an increase in the number of tasks.