Hierarchische Planung für industrielle Logistikdienstleister

In this paper a planning and control system for logistics service providers (called LPS system) is developed under consideration of their specific flexibility potentials. Due to the complexity of such planning decisions (different planning levels and data quality of these levels as well as time horizons etc.), a multi-stage, hierarchical planning system is proposed which minimizes the relevant costs under consideration of the flexibilities. The advantage of a hierarchical planning concept consists in a limited planning complexity because otherwise data collection and solution development would lead to unsolvable problems in practice. The results of using LPS systems for a real-life case show a significant pay-off for logistics service providers due to the remarkable cost savings by applying a hierarchical planning concept.     

Determination of recovery effort for a probabilistic recovery system under various inventory control policies

In this study we investigate the desired level of recovery under various inventory control policies when the success of recovery is probabilistic. All the used and returned items go into a recovery process that is modelled as a single stage operation. The recovery effort is represented by the expected time spent for it. The effect of increasing recovery effort on the success probability together with unit cost of the operation is included by assuming general forms of dependencies. Alternative to recovered items, demand is satisfied by brand-new items. Four inventory control policies that differ in timing of and information used in purchasing decision are proposed. The objective is to find the recovery level together with inventory control parameter that minimize the long-run average total cost. A numerical study covering a wide range of system parameters is carried out. Finally computational results are presented with their managerial implications.     

Computing Optimal Beams in Two and Three Dimensions

The problem of computing an optimal beam among weighted regions (called the optimal beam problem) arises in several applied areas such as radiation therapy, stereotactic brain surgery, medical surgery, geological exploration, manufacturing, and environmental engineering. In this paper, we present computational geometry techniques that enable us to develop efficient algorithms for solving various optimal beam problems among weighted regions in two and three dimensional spaces. In particular, we consider two types of problems: the covering problems (seeking an optimal beam to contain a specified target region), and the piercing problems (seeking an optimal beam of a fixed shape to pierce the target region). We investigate several versions of these problems, with a variety of beam shapes and target region shapes in 2-D and 3-D. Our algorithms are based on interesting combinations of computational geometry techniques and optimization methods, and transform the optimal beam problems to solving a collection of instances of certain special non-linear optimization problems. Our approach makes use of interesting geometric observations, such as utilizing some new features of Minkowski sums.     

Inference for the generalized Rayleigh distribution based on progressively censored data

In this paper, and based on a progressive type-II censored sample from the generalized Rayleigh (GR) distribution, we consider the problem of estimating the model parameters and predicting the unobserved removed data. Maximum likelihood and Bayesian approaches are used to estimate the scale and shape parameters. The Gibbs and Metropolis samplers are used to predict the life lengths of the removed units in multiple stages of the progressively censored sample. Artificial and real data analyses have been performed for illustrative purposes.     

Is regression adjustment supported by the Neyman model for causal inference?

This paper examines both theoretically and empirically whether the common practice of using OLS multivariate regression models to estimate average treatment effects (ATEs) under experimental designs is justified by the Neyman model for causal inference. Using data from eight large U.S. social policy experiments, the paper finds that estimated standard errors and significance levels for ATE estimators are similar under the OLS and Neyman models when baseline covariates are included in the models, even though theory suggests that this may not have been the case. This occurs primarily because treatment effects do not appear to vary substantially across study subjects.     

Bayesian prediction for progressively censored data from the Burr model

Received: August 14, 2000; revised version: April 23, 2001     

The present task of family planning as suggested by the findings of the National Sample Survey of Fertility]

Results from the 1982 census show great progress has been made in the field of family planning because of close cooperation between the people and the government. Under the influence of the high birth rate in the 1950s and 1960s, a large number of youths are reaching the age for marriage each year, and they are bringing heavy pressure on the population growth. As a result of this situation, family planning work is still urgently needed. A great difference exists between cities and rural areas in family planning work. Economic conditions, cultural and educational levels, occupational characteristics, living environments, and concepts of population growth have contributed to this difference. Henceforth, special emphasis should be placed on family planning work in rural areas, so that the large scale population groth in the countryside may be brought under control. In areas of scientific management, propaganda and education, and technical measures for family planning, modern and and progressive methods should be used in order to reach the national goal of controlling population growth.     

The characteristics of population growth of the Dongxiang nationality and the necessity of carrying out family planning]

For a long time, the Dongxiang nationality has lived in the area of Dongxiang, which is a part of Gu Hezhou in Gansu Province. Among the various minorities in China, the Dongxiang people has been noted for their small population. In recent years, however, their population growth rate has been very high. Statistics show their annual population growth rate was 13.5% in 1970 and 32.8% in 1980. This situation is caused mainly by the young age of the population (young persons under the age of 19 constitute 55.7% of the entire population). Because of the popularity of Islam among the people, the custom of early marriage and having children at an early age has long been a tradition. Rapid population growth has created numerous problems. Food production has been unable to keep up with the speed of the population growth, and economic development cannot satisfy the needs of the rapid growing population. The Dongxiang people are physically healthy and strong, and marriages between close relatives is prohibited. Because of the backward condition in education and culture, efforts to promote the quality of the Dongxiang population have remained slow and ineffective. Starting in 1974, the local government in Dongxiang launched extensive birth control projects and positive results have been achieved. At the present time, about 62% of its population have taken birth control measures, and the natural population growth rate dropped to 15.34% in 1982.     

Optimizing multi-stage production with constant lot size and varying numbers of batches

The model presented in this paper assumes that a uniform lot size is produced through a series of manufacturing stages, with a single set-up and without interruption at each stage. Transportation of partial lots, called batches, is allowed between stages after the whole batch is completed. The batch sizes must be equal at any particular stage, but the optimal number of equal-sized batches may differ across stages. Of course, the set-up costs, the inventory-holding costs and the transportation costs influence both the optimal batch-sizes at the various stages and the uniform lot size. An optimization method for this deterministic model is developed and is illustrated by an example.