Autonomous vehicle-based storage and retrieval system (AVS/RS) offers considerable flexibility with respect to throughput capacity in the transfer of unit loads in high density storage areas. AVS/RS relies on autonomous vehicles to provide horizontal movement within a tier and uses lifts to provide vertical movement between tiers. In these systems, vehicle blocking delays in the aisles and cross-aisles could significantly impact system throughput and transaction cycle times. In this research, protocols are developed to address vehicle blocking, and a semi-open queueing network model is proposed to analyze system performance and evaluate design trade-offs. A decomposition-based method is used to solve the queueing network and quantify the effect of blocking. This model is adopted to analyze the effect of varying tier configuration parameters such as number of storage locations, depth/width ratio, number of vehicles, and vehicle utilization on blocking delays. These insights are useful for design conceptualization using AVS/RS.

We present an analytical model for an autonomous vehicle storage and retrieval system (AVS/RS). The system is modeled as a semi-open queueing network (SOQN). An SOQN consists of customers, a secondary resource and servers. Each arriving customer is paired with the secondary resource. The two visit the set of servers required by the customer in the specified sequence. In the context of an AVS/RS, storage/retrieval (S/R) transactions are customers and the autonomous vehicles are the secondary resources. If an S/R transaction requires a vertical movement, it uses a lift. The lifts and horizontal travel times to and from a storage space are modeled as servers. First, we define all possible scenarios for storage and retrieval transactions and their occurrence probabilities. Second, we derive general travel times of vehicles and lifts by considering all possible locations of the two devices based on the predefined storage and retrieval scenarios, defined in step 1. Third, each scenario is modeled as a customer type and these customer classes are aggregated into a single class. Thus, we model the system as a single-class, multiple-server, SOQN. Finally , we solve the SOQN using an approximate method and obtain the performance measures. We apply the method to analyze a warehouse in France that utilizes AVS/RS.

We address the problem of optimal node activation in a sensor network, where the optimization objective is represented as a global time-average utility function over the deployment area of the network. Each sensor node is rechargeable, and can hold up toKquanta of energy. When the recharge and/or discharge processes in the network are random, the problem of optimal sensor activation is a complex stochastic decision question. For the case of identical sensor coverages, we show the existence of a simplethreshold policywhich isasymptotically optimalwith respect to the energy bucket sizeK, that is, the performance of this threshold policy approaches the optimal performance asKbecomes large. We also show that the performance of the optimal threshold policy is robust to the degree of spatial correlation in the discharge and/or recharge processes. We then extend this approach to a general sensor network where coverage areas of different sensors could have complete, partial or no overlap with each other. We demonstrate through simulations that a local information based threshold policy, with an appropriately chosen threshold, achieves a performance which is very close to the global optimum.

We consider a network of rechargeable sensors, deployed redundantly in a random sensing environment, and address the problem of how sensor nodes should be activated dynamically so as to maximize a generalized system performance objective. The optimal sensor activation problem is a very difficult decision question, and under Markovian assumptions on the sensor discharge/recharge periods, it represents a complex semi-Markov decision problem. With the goal of developing a practical, distributed but efficient solution to this complex, global optimization problem, we first consider the activation question for a set of sensor nodes whose coverage areas overlap completely. For this scenario, we show analytically that there exists a simple threshold activation policy that achieves a performance within a factor of 3/4 of the optimum over all possible policies. We extend this threshold policy to a general network setting where the coverage areas of different sensors could have partial or no overlap with each other, and show by simulations that the performance of our policy is very close to that of the globally optimal policy. Our policy is fully distributed, and requires the sensor nodes to only keep track of the node activation states in its immediate neighborhood. We also consider the effects of spatial correlation on the performance of the threshold activation policy, and the choice of the optimal threshold.