Generating Compliant Motion of Objects with an Articulated Hand

The flexibility of the robot is the key to its success as a viable aid to production. Flexibility of a robot can be explained in two directions. The first is to increase the physical generality of the robot such that it can be easily reconfigured to handle a wide variety of tasks. The second direction is to increase the ability of the robot to interact with its environment such that tasks can still be successfully completed in the presence of uncertainties. The use of articulated hands are capable of adapting to a wide variety of grasp shapes, hence reducing the need for special tooling. The availability of low mass, high bandwidth points close to the manipulated object also offers significant improvements I the control of fine motions. This thesis provides a framework for using articulated hands to perform local manipulation of objects. N particular, it addresses the issues in effecting compliant motions of objects in Cartesian space. The Stanford/JPL hand is used as an example to illustrate a number of concepts. The examples provide a unified methodology for controlling articulated hands grasping with point contacts. We also present a high-level hand programming system based on the methodologies developed in this thesis. Compliant motion of grasped objects and dexterous manipulations can be easily described in the LISP-based hand programming language.

The Benefits of Re-Evaluating Real-Time Fulfillment Decisions

At the time of a customer order, the e-tailer assigns the order to one or more of its order fulfillment centers, and/or to drop shippers, so as to minimize procurement and transportation costs, based on the available current information. However this assignment is necessarily myopic as it cannot account for all future events, such as subsequent customer orders or inventory replenishments. We examine the potential benefits from periodically re-evaluating these real-time order-assignment decisions. We construct near-optimal heuristics for the re-assignment for a large set of customer orders with the objective to minimize the total number of shipments. We investigate how best to implement these heuristics for a rolling horizon, and discuss the effect of demand correlation, customer order size, and the number of customer orders on the nature of the heuristics. Finally, we present potential saving opportunities by testing the heuristics on sets of order data from a major e-tailer.

An Extension to the Tactical Planning Model for a Job Shop: Continuous-Time Control

We develop an extension to the tactical planning model (TPM) for a job shop by the third author. The TPM is a discrete-time model in which all transitions occur at the start of each time period. The time period must be defined appropriately in order for the model to be meaningful. Each period must be short enough so that a job is unlikely to travel through more than one station in one period. At the same time, the time period needs to be long enough to justify the assumptions of continuous workflow and Markovian job movements. We build an extension to the TPM that overcomes this restriction of period sizing by permitting production control over shorter time intervals. We achieve this by deriving a continuous-time linear control rule for a single station. We then determine the first two moments of the production level and queue length for the workstation.

Logistics Network Design with Differentiated Delivery Lead-Time: Benefits and Insights

Most logistics network design models assume exogenous customer demand that is independent of the service time or level. This paper examines the benefits of segmenting demand according to lead-time sensitivity of customers. To capture lead-time sensitivity in the network design model, we use a facility grouping method to ensure that the different demand classes are satisfied on time. In addition, we perform a series of computational experiments to develop a set of managerial insights for the network design decision making process.

The Complexity of Safety Stock Placement in General-Network Supply Chains

We consider the optimization problem of safety stock placement in a supply chain, as formulated in [1]. We prove that this problem is NP-Hard for supply chains modeled as general acyclic networks. Thus, we do not expect to find a polynomial-time algorithm for safety stock placement for a general-network supply chain.