A review of design and control of automated guided vehicle systems <s> Single-load capacity vehicles <s> SUMMARY One of the important issues in the design of an automated guided vehicles system (AGVS) is the determination of the number of vehicles needed to operate the system in an efficient and economical way. In this paper a multi-criteria optimization model is developed using two goals, cost and throughput performance. By using a trade-off ratio between the goals the number of AGVs needed in the systems is determined. Use of management decision tables to enhance the solution procedure is introduced <s> BIB001 </s> A review of design and control of automated guided vehicle systems <s> Single-load capacity vehicles <s> Abstract The estimation of the number of vehicles is of paramount importance in AGV-based systems and requires a careful and detailed study, especially when AGVs are selected as the primary material handling system. This paper describes the development of an analytical model for estimation of the number of AGVs. The ability of the model to estimate the vehicle requirements under various conditions have been tested. The results indicate the consistent performance of the model and confirm the utility. <s> BIB002 </s> A review of design and control of automated guided vehicle systems <s> Single-load capacity vehicles <s> Abstract In this paper we present a general-purpose analytical model to compute die approximate throughput capacity of a trip-based material handling system used in a manufacturing setting. A wide variety of handling systems, including freight elevators, cranes, microload automated storage/retrieval (AS/R) systems, industrial lift trucks, and automated guided vehicle (AGV) systems can be modeled as trip-based handling systems. To our knowledge, this model is one of the few analytical models that explicitly considers an empty device dispatching rule. The model is first developed for a single-device system (such as a crane) and subsequently, with a simple modification, it is extended to multiple-device systems (such as lift trucks and AGVs). Using this model one can rapidly evaluate a wide range of handling and layout alternatives for given flow data. Hence, die model would be most effective when used early in the design phase to narrow down die set of alternative handling systems and configurations prior t... <s> BIB003 </s> A review of design and control of automated guided vehicle systems <s> Single-load capacity vehicles <s> This research finds the minimum number of vehicles needed in an automated guided vehicle system (AGVS), considering the impact of vehicle dispatching rules, such that the chance of a vehicle-initiated situation occurring is less than a given very small threshold specified by the system designer. Under these conditions, the probability of load requests waiting will be very small and the impact of the vehicle-initiated dispatching rule on the system performance will be negligible. As a result, this research considers only the workcentre-initiated dispatching rule. A model using queuing theory and Markovian processes to represent the relationships among empty-vehicle traveltime, number of vehicles, and the various types of workcentre-initiated dispatching rules is used to formulate a dispatching-rule-based algorithm (DRBA) minimum number of vehicles. Also, given the number of vehicles required in the system, this research investigates the to determine the impact of the nearest-vehicle and farthest-vehicle di... <s> BIB004 </s> A review of design and control of automated guided vehicle systems <s> Single-load capacity vehicles <s> interest in automated material handling systems has been increasing in manufacturing and assembly facilities. Automated guided vehicle system (AGVS) is one such material handling system. One of the decisions in implementing an AGVS is to determine the number of vehicles required for a facility at the early planning stage. This paper undertakes the development of an analytical model using a regression technique to estimate the number of vehicles required by a facility. The model attempts to use characteristics of a facility for which an automated guided vehicle system is under consideration to determine the number of vehicles required. Although several facility characteristics were investigated, only a few were finally included in the model based on the statistical significance of their contributions to predicting vehicle requirements. The predictive abilities of several models hypothesized were compared with results obtained through simulation on some test facilities. Based on the results of the compariso... <s> BIB005 </s> A review of design and control of automated guided vehicle systems <s> Single-load capacity vehicles <s> This paper describes the development of a minimum flow algorithm to determine the number of automated guided vehicles (AGVs) required at a semi-automated container terminal. At such a terminal the containers are transported by AGVs from the quay cranes to the automated stacking cranes and vice versa. A model and a strongly polynomial time algorithm are developed to solve the case in which containers are available for transport at known time instants. <s> BIB006
The number of vehicles heavily influences the performance of AGV systems  . AGVs are usually expensive, so determining types and the appropriate number of vehicles is important. For a tandem configuration, the required number of vehicles is equal to the number of zones, but for other guide-path systems this number has to be estimated. According to  there are three main factors affecting the required number of vehicles: (1) the guide path layout, (2) location of load transfer points and (3) vehicle dispatching strategies. He proposes four analytical models to calculate the number of vehicles based on several information sources such as the expected number of loaded trips between stations and the number of workstations in the facility. Using one of these models (model 4)  estimates the number of vehicles as: with n-number of workstations; f ij -expected number of loaded trips required between workstation i and workstation j during the period or shift; D ij -the estimated empty and loaded travel distance between stations i and j; T-length of the period of shift during which the f ij exchange occur; V-average vehicle travel speed; t lmean time to load a vehicle; t u -mean time to unload a vehicle; t-expected lost time by each vehicle during a time period of T due to battery change.  indicates that the first three models in his paper are normally over optimistic estimating the number of vehicles. The fourth model (described above) provides a reasonably good estimation in most cases except when the shortest-travel-distance first vehicle dispatching rule is used. The main factors that lead to difference results between models is the method of estimating the empty travel and time lost caused by blocking. Actually,  mentions the important role of dispatching rules in estimating the required number of vehicles, but he does not take it into account explicitly. Similar approaches are proposed by  , BIB002 . Since the dispatching rule has an important role in estimating the required number of vehicles, methods explicitly considering dispatching rules in the estimation model normally provide better results. BIB003 analyze a system using only one vehicle traveling under the modified-FCFS rule. They extend the result for the multi-vehicle case, by approximating K-vehicles by a single vehicle traveling K-times faster. They gain a good approximation for vehicle statistics such as the fraction of time a vehicle travels empty and also for the required number of vehicles. The quality of their model deteriorates when the system requires a large number of vehicles. BIB004 propose another analytical model to estimate the number of vehicles in light-traffic systems. Other analytical approaches estimating the required number of vehicles include: queuing models  ; statistical approach BIB005 ; multi-criteria decision modeling BIB001 ) and network-flow modeling BIB006 ). The queuing model of  estimates the number of vehicles to achieve a desired fill rate (the probability to have a vehicle available at a station to satisfy a request). The quality of estimation deteriorates in light-traffic systems, as it tends to overestimate the number of vehicles. BIB006 propose a network-flow formulation for determining the number of AGVs required at a semi-automated container terminal in which a job is a node and an arc(i,j) with capacity of one corresponds to a vehicle that can execute both jobs (i, j) in sequence, satisfying certain time-window restrictions. The minimum number of vehicles equals the minimum number of directed paths such that each node in the network is included in exactly one path. They also develop a polynomial time minimum-flow algorithm to solve the problem.