Endogenous information quality: A job-assignment application

This paper investigates the optimal acquisition of information in a model of job assignment within a firm. We consider a firm with two types of jobs, skilled and unskilled. The firm draws workers randomly from the general population, and a worker is either talented or untalented. Initially, a worker's productivity in the firm is unknown to the worker and the firm. Workers are equally productive in the unskilled job, but talented workers are more productive in the skilled job than in the unskilled job, and untalented workers are more productive in the unskilled job than in the skilled job. Before assigning a worker to a job, the firm can test whether the employee is talented, and the firm is able to choose the accuracy of this test. We assume that the cost of a test is increasing and convex in test accuracy. We show that (1) the accuracy of the firm's test increases with the cost of a mismatched worker; (2) increased optimism about the worker's ability need not lead to less rigorous testing; (3) the probability that a worker is assigned to the skilled job need not increase as the gain from assigning a talented worker to a skilled job increases, or the loss from assigning an untalented worker to a skilled job decreases, or the fraction of the population that is skilled increases; and (4) a longer testing period, allowing as many as two tests of workers, leads the firm to use a less expensive, and less accurate, test initially than when there is only one opportunity to gather information.     

An inventory-based simulation model for annual-to-daily temporal freight assignment

In the aggregate freight demand modeling literature, temporal assignment (annual to daily flows) is often oversimplified or neglected altogether. Unlike passenger flows, freight flows over the course of a year are not uniform and can vary significantly as the result of trade-offs between inventory and transportation cost management. We introduce the first temporal assignment model that explicitly considers these trade-offs for aggregate freight forecasting. A two-stage model is proposed that first decomposes aggregate annual zonal flows to firm group annual flows using a supply chain network model, which are then temporally assigned by simulating purchase order transactions throughout supply chains. Lot sizes are estimated with an Economic Order Quantity (EOQ) model and calibrated with monthly inventory data. The result is an aggregate-disaggregate-aggregate model that fits into aggregate freight forecasting models but makes use of more disaggregate logistical data. The model is illustrated with a simple replicable example, followed by a case study conducted with California statewide data to break out the distributed zonal flows into average daily volumes for network assignment. Calibration results using 2007 IMPLAN data showed a median percentage difference of simulated annual flows from FAF³ data of 2.38%, and a median percentage difference of simulated inventories from IMPLAN data of 4.85%, which suggests an excellent fit. Empirical validation results showed the model outperforms fixed factor approaches in mean value accuracy by 15–31%.     

A robust optimization approach to the integrated berth allocation and quay crane assignment problem

This paper investigates the integrated berth allocation and quay crane assignment problem in container terminals. A deterministic model is formulated by considering the setup time of quay cranes. However, data uncertainties widely exist, and it may cause the deterministic solution to be infeasible. To handle the uncertainties, a robust optimization model is established. Furthermore, to control the level of conservativeness, another robust optimization model with the price constraints is proposed. A genetic algorithm and an insertion heuristic algorithm are suggested to obtain near optimal solutions. Computational experiments indicate that the presented models and algorithms are effective to solve the problems.     

The empirical relevance of the competitive storage model

The empirical relevance of models of competitive storage arbitrage in explaining commodity price behavior has been seriously challenged in a series of pathbreaking papers by [Deaton and Laroque, 1992], [Deaton and Laroque, 1995] and [Deaton and Laroque, 1996]. Here we address their major criticism, that the model is in general unable to explain the degree of serial correlation observed in the prices of twelve major commodities. First, we present a simple numerical version of their model which, contrary to Deaton and Laroque (1992), can generate the high levels of serial correlation observed in commodity prices, if it is parameterized to generate realistic levels of price variation. Then, after estimating the [Deaton and Laroque, 1995] and [Deaton and Laroque, 1996] model using their data set, model specification and econometric approach, we show that the use of a much finer grid to approximate the equilibrium price function yields quite different estimates for most commodities. Results are obtained for coffee, copper, jute, maize, palm oil, sugar and tin that support the specifications of the storage model with positive constant marginal storage cost and no deterioration as in Gustafson (1958a). Consumption demand has a low response to price and, except for sugar, stockouts are infrequent. The observed magnitudes of serial correlation of price match those implied by the estimated model.     

论未足额出资股权转让中的责任归属——兼评司法解释的相关规定

未足额出资股权转让中责任归属的判断基础为转让合同效力的认定,而转让合同效力认定应遵循民法中的合同效力规则。转让合同确认有效或因未撤销、被追认而有效时,应由受让股东单独承担对公司的补缴责任和对公司债权人的赔偿责任;转让合同确认无效或因被撤销、未追认而归于无效时,对公司的补缴责任除恶意串通情形下由合同双方当事人承担外,一般应由出让股东承担,但对债权人的赔偿责任则应由合同双方当事人承担。     

区间指派问题的研究

在分析传统意义的指派问题存在不足的基础上,文章定义了区间指派问题,证明了区间指派问题存在区间最优指派的充分必要条件,并给出了存在区间最优指派的几种情况;另外,通过定义一般区间指派问题的区间悲观最优指派和区间乐观最优指派,从一个方面解决了没有区间最优指派的区间指派问题.     

保单转让的理论与其市场发展

本文探讨了保单转让的基础理论,并从保单质押贷款、保单贴现和保单证券化等方面详细分析了保单转让市场的内涵和外延,通过调研国外保单转让市场的发展概况、市场运作机制和市场规模,分析国内保单转让市场的现状,阐明了我国未来保单转让市场的发展前景.     

Social distancing in airplane seat assignments

This paper addresses the airplane passengers’ seat assignment problem while practicing social distancing among passengers. We proposed a mixed integer programming model to assign passengers to seats on an airplane in a manner that will respect two types of social distancing. One type of social distancing refers to passengers being seated far enough away from each other. The metric for this type of social distancing is how many passengers are seated so close to each other as to increase the risk of infection. The other type of social distancing refers to the distance between seat assignments and the aisle. That distance influences the health risk involved in passengers and crew members walking down the aisle. Corresponding metrics for both health risks are included in the objective function. To conduct simulation experiments, we define different scenarios distinguishing between the relative level of significance of each type of social distancing. The results suggest the seating assignments that best serve the intention of the scenarios. We also reformulate the initial model to determine seat assignments that maximize the number of passengers boarding an airplane while practicing social distancing among passengers. In the last part of this study, we compare the proposed scenarios with the recommended middle-seat blocking policy presently used by some airlines to keep social distancing among passengers. The results show that the proposed scenarios can provide social distancing among seated passengers similar to the middle-seat blocking policy, while reducing the number of passengers seated close to the aisle of an airplane.     

Why does on-farm storage fail to mitigate price volatility?

We analyze the role of farm stock management on price volatility under liquidity constraints and heterogeneous price expectations. In commodity markets, speculative behaviors by stockholders tend to reduce price volatility, but this is not the case in certain agricultural markets, where speculation by farmers regarding decisions to sell or store grain is subject to liquidity constraints and heterogeneous price expectations. Like stockholders, most farmers sell grain if they expect a price drop in the near future, but unlike stockholders, they are not necessarily able to purchase grain if they expect a price increase in the next period. Heterogeneous price expectations can also lead to suboptimal storage decisions, further increasing price volatility. For these reasons, the storage management behavior of farmers often fails to mitigate price drops in the way that speculation by stockholders does. We merge historical data on maize prices and household storage collected in Burkina Faso in order to build a dynamic panel over the 2005–2012 period. We show that carryover from one season to the next is associated with unexpected price drops during the preceding lean season and that carryover is associated with more frequent unexpected price drops following the subsequent post‐harvest season.     

Storage losses,liquidity constraints,and maize storage decisions in Benin

This article estimates how storage losses from mold, insects, and other pests, combined with liquidity constraints, influence a smallholder farm household's decision to store maize on farm after harvest. We analyze panel data from 309 smallholders in Benin covering the 2011 and 2013 harvest seasons. Results suggest that smallholders are driven to sell at harvest time for different reasons, depending on their motivation for storing. In households that report direct consumption as their primary goal for storing maize, liquidity constraints, not storage losses, reduce the amount they store. In contrast, households that store maize with the intention of selling it later in the year appear unaffected by liquidity constraints. Instead, these households store less when they expect to lose more during storage. These results suggest that policies to provide liquidity will be more helpful in motivating storage among consumption‐oriented households. Households motivated to store for later sale will benefit from modern storage technologies that mitigate the operational costs associated with storage losses.