For an LTL (Less-than Truckload) carrier, the allocation of doors at a consolidation facility to outbound trailers assigned to various destinations, and to inbound trailers in the continuous stream arriving from various origins, has a significant impact on its operations, and on the nightly man-hours needed for consolidation. In the past literature door allocations to destinations of outbound trailers are determined using deterministic mathematical models based on average volumes of shipments between origin-destination pairs. The online nature of allocation of doors to inbound trailers is either ignored or simple rules like FCFS (First Come First Served) are assumed that do not take advantage of the data on the trailer's actual contents readily available at the time of its arrival. In reality the actual shipment volume between any origin-destination pair varies significantly from day to day. Due to this wide variation destination door allocations that are optimal for the average volume tend to be far from optimum for most nights. Also, very simple on-line policies for door allocation to each inbound truck at the time of its arrival based on its actual contents can significantly reduce the man-hours needed to consolidate its contents. In this paper we develop a new model that uses such an on-line policy for door allocations to inbound trailers, and determines doors to allocate to destinations to minimize the expected man-hours for consolidating freight nightly taking the random variation in freight volumes into account. Computational results on data from an actual facility indicate that the man-hour requirement can be reduced by over 20% compared to current practice.

/pdf/door-allocations-to-origins-and-destinations-at-less-than-3kd7e31qox.pdf

Door allocations to origins and destinations at less-than-truckload trucking terminals

http://sustainability-sa.com/wp-content/uploads/2014/07/DNPV-Case-Study-Solar-Project-Final2.pdf

Using DNPV for valuing investments in the energy sector: A solar project case study

The Department of Defense Engineered Resilient Systems (ERS) goes beyond the concept of resilience as a response to a disruption. The ERS project focuses on improving design agility and cost-effectiveness leading to improvements in systems analysis, development, testing, manufacturing, and fielding of mission-effective and adaptable systems. Point-Based Design (PBD) seeks to select a point as a design baseline and proceed into development. Our research uses Set-Based Design (SBD) for early concept evaluation of engineered systems as an alternative to PBD. SBD seeks to eliminate less cost-effective sets (in the value vs. cost tradespace) and focus on one or more remaining sets during early design. Our research develops a technique called Convergent Set-Based Design (SBD), a procedure for mathematical elimination and improvement of sets of designs. We developed Convergent SBD for the ERS program. Our research includes the capability to measure and quantify mission resilience using probability trees for all system performance measures. Convergent SBD uses dominance identification equations to compare statistical means for value and cost. The demonstration illustrates the effect of adding mission resilience in the design tradespace. Convergent SBD provides a foundational mathematical technique for eliminating less promising design sets and identifying the most promising design sets.

Convergent set-based design for complex resilient systems

We consider a problem commonly faced in the nuclear power industry, involving annual selection of plant capital investments under the constraints of a limited and uncertain budget. When the budget is assumed known, a typical approach to such problems is built on a multi-dimensional knapsack model. This model takes as input the available budget in each year, the stream of liabilities induced by selecting each project, and the profit, i.e., net present value (NPV), of each project. The goal is to select the portfolio of projects with the highest total NPV, while observing the budget constraint for each year, as well as any additional constraints. We show that a portfolio selected in this manner can fail to hedge against uncertainties in the budget. While the budget may be known at the beginning of the planning period, external events can cause this to change as time unfolds, and hence the funds that will actually be allocated over time are typically uncertain. So, we propose a model that forms an optimal priority list of projects, incorporating multiple budget scenarios. The model is applied to example projects from the South Texas Project Nuclear Operating Company (STPNOC).

Optimizing Project Prioritization Under Budget Uncertainty

This thesis examines and analyzes the use of data mining techniques and simulations as a forecasting tool. Decision making process for business can be risky. Corporate decision makers have ...

/pdf/investment-and-financial-forecasting-a-data-mining-approach-1stp0yptdr.pdf

Investment and Financial Forecasting: A Data Mining Approach on Port Industry

This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.

http://ieeexplore.ieee.org/iel5/46/5235437/05235452.pdf

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