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 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.

Optimizing Product Line Designs: Efficient Methods and Comparisons

We compare a broad range of optimal product line design methods. The comparisons take advantage of recent advances that make it possible to identify the optimal solution to problems that are too large for complete enumeration. Several of the methods perform surprisingly well, including Simulated Annealing, Product-Swapping and Genetic Algorithms. The Product-Swapping heuristic is remarkable for its simplicity. The performance of this heuristic suggests that the optimal product line design problem may be far easier to solve in practice than indicated by complexity theory.

Electrical Design: A Problem for Artificial Intelligence Research

This report outlines the problem of intelligent failure recovery in a problem-solver for electrical design. We want our problem solver to learn as much as it can from its mistakes. Thus we cast the engineering design process on terms of Problem Solving by Debugging Almost-Right Plans, a paradigm for automatic problem solving based on the belief that creation and removal of "bugs" is an unavoidable part of the process of solving a complex problem. The process of localization and removal of bugs called for by the PSBDARP theory requires an approach to engineering analysis in which every result has a justification which describes the exact set of assumptions it depends upon. We have developed a program based on Analysis by Propagation of Constraints which can explain the basis of its deductions. In addition to being useful to a PSBDARP designer, these justifications are used in Dependency-Directed Backtracking to limit the combinatorial search in the analysis routines. Although the research we will describe is explicitly about electrical circuits, we believe that similar principles and methods are employed by other kinds of engineers, including computer programmers.