Knowledge that Transforms

The people living and working around the roads used for hazardous material (hazmat) shipments face the risk of suffering undesirable consequences of an accident. The main responsibility to mitigate the hazmat transport risk at a population zone belongs to the government agency with jurisdiction over that region. One of the common policy tools is to close certain road links to vehicles carrying hazmats. In effect, the road network available to dangerous goods carriers can be determined by the regulator. The transport risk in the region, however, is determined by the carriers' routing decisions over the available road network. Thus, the regulator needs to make the road closure decisions so that the total risk resulting from the carriers' route choices is minimized. We provide a path-based formulation for this network design problem. Alternative solutions can be generated by varying the routing options included in the model for each shipment. Each solution corresponds to a certain compromise between the two parties in terms of transport risk and economic viability. The proposed framework can be used for identifying mutually agreeable hazmat transport policies. We present two applications of the methodology to illustrate the insights that can be gained through its use: The first application focuses on hazmat shipments through the highway network of Western Ontario, Canada, whereas the second application studies the problem in a much larger geographical region that covers the provinces of Ontario and Quebec.

This paper derives the properties of the discount rate that should be applied to a public-sector project when the affected population has heterogeneous degrees of impatience. We show that, for any distribution of discount rates, the social discount rate has the following properties: it decreases over time, it is lower than the average of the discount rates in the population, and it converges to the discount rate of the most patient individual in the economy. These properties hold for both constant and decreasing individual discount rates. Finally, we evaluate how changes in the distribution of individual discount rates affect the social discount rate.

Classical reasons for carrying inventory include fixed (nonlinear) production or procurement costs, lead times, nonstationary or uncertain supply/demand, and capacity constraints. The last decade has seen active research in supply chain coordination focusing on the role of incentive contracts to achieve first-best levels of inventory. An extensive literature in industrial organization that studies incentives for vertical controls largely ignores the effect of inventories. Does the ability to carry inventory influence the problem of vertical control? Conversely, can inventories arise purely due to incentive effects? This paper explicitly considers both incentives and inventories, and their interplay, in a dynamic model of an upstream firm (supplier) and a downstream firm (buyer) who can carry inventories. In our model, none of the classical reasons for carrying inventory exists. However, as we prove, the buyer's optimal strategy in equilibrium is to carry inventories, and the supplier is unable to prevent this. These inventories arise out of purely strategic considerations not yet identified in the literature, and have a significant impact on the equilibrium solution as well as supplier, buyer, and channel profits. We prove that strategic inventories play a pivotal role under arbitrary contractual structures, general (arbitrary) demand functions and general (finite or infinite) horizon lengths. As one example, two-part tariff contracts do not lead to optimal channel performance, nor can the supplier extract away all of the channel profits, in our dynamic model. Our results imply that firms can and must carry inventories strategically, and that optimal vertical contracts must take the possibility of inventories into account.

Practitioners and academics hypothesize that when there is high diversity in resource consumption patterns, costing systems are more sensitive to errors. Given that firms' resources to enhance costing accuracy are typically constrained, it is argued that costing system refinement efforts should be focused on such cases, where they are likely to be most effective. However, little guidance is available on how to identify those situations where costing system refinement efforts (such as introducing an activity-based costing system) are likely to pay off most in terms of increased accuracy. Further, to our knowledge, the existing guidance provided by this high diversity rule of thumb has never been empirically tested. Using a simulation method, we address these issues in this paper. Specifically, we model various aspects, and degrees, of diversity in the resource consumption patterns to be reflected by the costing system and find that more diversity in resource consumption patterns only leads to increased costing system sensitivity to errors for some of the aspects of diversity studied. We also identify situations in which allocating costing system refinement resources to cases characterized by high diversity in resource consumption patterns is detrimental to improved accuracy.

This paper considers network supply chains with price dependent demand by modelling them as large acyclic networks. Such large networks are common in the automobile and apparel industries. We develop a model to analyze the effect of these large-scale problems involving long sequences of contracts, and show that contract leadership, as well as leader position in the network, affect the performance of the entire supply chain. We generalize Spengler (Spengler, J. 1950. Vertical integration and anti-trust policy. J. Political Econom. 58 347–352) to a game on a “contract tree” for a particular supply chain and extend the concept of double marginalization so that it can be applied in the form of a transformation to each contract that is offered by one member to another in the “contract tree.” We construct an algorithm to find the equilibrium solution, and derive the optimal location of the leader (“optimal” being the leader location that maximizes total supply chain profits). Our work formalizes many intuitive insights; for example, member profits are determined by systemwide rather than individual costs. Finally, we model Cournot competition between competing supply chains (both two heterogeneous trees and multiple identical trees) and show the effect of changes in leader position as well as cost structure on the equilibrium.

We investigate the benefits of migrating from a call center, where all agents are pooled and customers are treated indifferently by any agent, toward a call center where customers are grouped into clusters with dedicated teams of agents. Each cluster is referred to as a portfolio. Customers of the same portfolio are always served by an agent of the corresponding team. There is no specialization involved in this organization in the sense that all customer portfolios as well as all agent teams have (statistically) identical behaviors. The reason for moving to this organization is that dealing with teams of limited size allows a much better workforce management than the situation usually encountered in large call centers. The purpose of this paper is to examine how the benefits of moving to this new organization can outweigh the drawbacks. The drawbacks come from the fact that there is less of a pooling effect in the new organization than in the original one. The benefits come from the better human resource management that results in a higher efficiency of the agents, both in terms of speed and quality of the answers they provide to customers. Our analysis is supported by the use of some simple queueing models and provides some interesting insights. In particular, it appears that for some reasonable ranges of parameters, the new organization can outperform the original organization. We then extend the analysis to the case where, in addition to the identified customer portfolios, there is an additional flow of calls called out-portfolio flow. It is shown that this feature makes the new organization even more efficient.

Can investors with incorrect beliefs survive in financial markets and have a significant impact on asset prices? My paper addresses this issue by analyzing a dynamic general equilibrium model where some investors have rational expectations, whereas others have incorrect beliefs concerning the mean growth rate of the economy. The main result is that an investor can survive if and only if he has the lowest survival index, which is a function of his belief accuracy, patience parameter, and relative risk aversion coefficient. If preferences are held constant across all investors, then those with incorrect beliefs cannot survive in the limit, although calibrations reveal that the selection process is excessively slow. However, if preferences vary across investors, even slightly, it becomes possible for an irrational investor to dominate the market even if his beliefs persistently and substantially deviate from the truth.

Prediction markets now cover many important political events. The 2004 presidential election featured an active online prediction market at Intrade.com, where securities addressing many different election-related outcomes were traded. Using the 2004 data from this market, we examined three alternative models for these security prices, with special focus on the electoral college rules that govern U.S. presidential elections to see which models are more (or less) consistent with the data. The data reveal dependencies in the evolution of the security prices across states over time. We show that a simple diffusion model provides a good description of the overall probability distribution of electoral college votes, and an even simpler ranking model provides excellent predictions of the probability of winning the presidency. Ignoring dependencies in the evolution of security prices across states leads to considerable underestimation of the variance of the number of electoral college votes received by a candidate, which in turn leads to overconfidence in predicting whether that candidate will win the election. Overall, the security prices in the Intrade presidential election prediction market appear jointly consistent with probability models that satisfy the rules of the electoral college.

To remedy challenges resulting from a high number of objectives in multiobjective programming and multicriteria decision making, this paper chooses to decompose the vector objective function and characterizes the relationships between solutions for the original problem and the collection of decomposed subproblems. In particular, it is shown how solutions that are found using this decomposition approach relate to solutions found by traditional scalarization techniques. For the selection of a final solution, two interactive coordination methods are proposed that allow to find any solution for the original problem by merely solving the smaller-sized subproblems, while integrating both preferences of the decision maker and trade-off information obtained from a sensitivity analysis. A theoretical foundation for the procedures is established, and their application is illustrated for portfolio optimization and a design selection problem.

We study an iterative cutting-plane algorithm on an integer program for minimizing the staffing costs of a multiskill call center subject to service-level requirements that are estimated by simulation. We solve a sample average version of the problem, where the service levels are expressed as functions of the staffing for a fixed sequence of random numbers driving the simulation. An optimal solution of this sample problem is also an optimal solution to the original problem when the sample size is large enough. Several difficulties are encountered when solving the sample problem, especially for large problem instances, and we propose practical heuristics to deal with these difficulties. We report numerical experiments with examples of different sizes. The largest example corresponds to a real-life call center with 65 types of calls and 89 types of agents (skill groups).