CASH FLOW OPTIMIZATION IN DELIVERY SCHEDULING
Abstract The paper examines the inventory routing problem from the perspective of the present value of the cash flow associated with the distribution of a commodity such as propane. We analyze this problem for both deterministic and stochastic customer demands and validate our results on data from a real life distribution operation of propane. The analysis based on the present value of the cash flow indicates that optimization of propane deliveries based on efficiency/cost criteria alone will generate inferior solutions and it would be more advantageous for the company to set deliveries for a large percentage of the customers based on the present value of cash flow. In addition, in the case of stochastic demands, deliveries based on the cash flow consideration will tend to reduce the number of stockouts (i.e. improve both profit and service).
Inventory/routing: Reduction from an annual to a short-period problem
The inventory-routing problem is a distribution problem in which each customer maintains a local inventory of a product such as heating oil and consumes a certain amount of that product each day. Given a central supplier, the objective is to minimize the annual delivery costs while attempting to insure that no customer runs out of the commodity at any time. In this article we present a procedure for reducing the long-term version of this problem to a single-period problem, which can be attacked using standard routing algorithms. The reduction procedure involves the definition of single-period costs that reflect long-term costs, the definition of a safety stock level and a specification of the customer subset to be considered during a single period.
Cell-averaging CFAR for multiple-target situations
The performance of a mean-level detector is considered when one or more interfering target returns is present in the set of cells used in estimating the reference level. A serious degradation of detection probability is demonstrated for Swerling target fluctuation models 1 and 3. To alleviate the problem we analyse an alternative procedure. A censoring scheme is proposed whereby samples exceeding an adaptive threshold are excluded from the reference set. An iterative procedure is described where samples exceeding a threshold computed with a predetermined scale constant are excluded from the reference set; then the threshold is recomputed using another predetermined scale constant with the censored sample set. The procedure is repeated and eventually terminated when no reference cell sample exceeds the computed threshold. This then forms the detection threshold. Expressions for false-alarm and detection probabilities are derived for general background distribution. Numerical results are presented for the case of Gaussian white noise. The procedure is shown to maintain acceptable performance in dense target environments, providing a significant improvement in the detection probability.