Traditional Inventory Models in an E-Retailing Setting: A Two-Stage Serial System with Space Constraints

In an e-retailing setting, the efficient utilization of inventory, storage space, and labor is paramount to achieving high levels of customer service and company profits. To optimize the storage space and labor, a retailer will split the warehouse into two storage regions with different densities. One region is for picking customer orders and the other to hold reserve stock. As a consequence, the inventory system for the warehouse is a multi-item two-stage, serial system. We investigate the problem when demand is stochastic and the objective is to minimize the total expected average cost under some space constraints. We generate an approximate formulation and solution procedure for a periodic review, nested ordering policy, and provide managerial insights on the trade-offs. In addition, we extend the formulation to account for shipping delays and advanced order information.Singapore-MIT Alliance (SMA

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.Singapore-MIT Alliance (SMA

Modeling and computational issues in the development of batch processes

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1997.Includes bibliographical references (p. 385-401).by Russell John Allgor.Ph.D

Benefits of Reevaluating Real-Time Order Fulfillment Decisions

When a customer orders online, an online retailer assigns the order to one or more of its warehouses and/or drop-shippers to minimize procurement and transportation costs based on the available current information. However, this assignment is necessarily myopic because it cannot account for any subsequent customer orders or future inventory replenishment. We examine the benefits of periodically reevaluating these real-time assignments. We construct near-optimal heuristics for the reassignment for a large set of customer orders to minimize the total number of shipments. Finally, we present evidence of significant saving opportunities by testing the heuristics on order data from a major online retailer.online retailing, online order fulfillment, local search heuristics

DSL48S -- A Large-Scale Differential-Algebraic and Parametric Sensitivity Solver

DSL48S is a package of FORTRAN 77 subroutines that can be called by a user provided main program to obtain numerical solutions to Initial Value Problems (IVPs) in Differential-Algebraic Equations (DAEs). The code works with DAEs in the general nonlinear form: f(ẏ, y, t) = 0 (1) where y ∈ Rn are the state variables, y ̇ ∈ Rn are the first time derivatives of y with respect to the independent variable time t, and f: Rn ×Rn ×R → Rn. DAEs are distinguished from Ordinary Differential Equations by the fact that the Jacobian matrix ∂f/∂y ̇ is singular everywhere. Given consistent initial values, y(0) and ẏ(0), the code will calculate a numerical approximation to the solution y(t) of (1) over a specified time interval (0, tf]. This approximation will satisfy a user specified truncation error tolerance. DSL48S is particularly suitable for large-scale problems (e.g., n = 1,000–100,000+). In order to set up a problem, the user must provide: • a main program that calls DSL48S and co-ordinates solution of the problem the user wants to solve. • a subroutine that will calculate the vector of values for f given values for y, y ̇ and t (often known as a residual evaluator). • the pattern of nonzero elements in the Jacobian matrices ∂f/∂y and ∂f/∂ẏ. • an optional subroutine that can return analytical values for some or all of the nonzero elements of the Jacobian matrices ∂f/∂y and ∂f/∂y ̇ given values for y, y ̇ and t (often known as a Jacobian evaluator). In addition, if the DAEs are stated in terms of a vector of time invariant parameters v ∈ Rp: f(ẏ, y, v, t) = 0 (2) it is possible to calculate the parametric sensitivity trajectories simultaneously with the state tra-jectories. The parametric sensitivities are defined by