Integrating expected coverage and local reliability for emergency medical services location problems

Daskin's MEXCLP model [Daskin M. A maximum expected covering location model: formulation, properties, and heuristic solution. Transportation Science 1983;17:48-70] was one of the first efforts to capture the stochastic nature of emergency medical services (EMS) location problems within a mixed-integer formulation. With their subsequent introduction of MALP, ReVelle and Hogan [The maximum availability location problem. Transportation Science 1989;23:192-200] offered two key advances, local vehicle busyness estimates and the α-reliability objective. While these constructs have influenced many subsequent EMS location models, they have been subjected to relatively little empirical analysis. To address this, we introduce the LR-MEXCLP, a hybrid model combining the local busyness estimates of MALP with the maximum coverage objective of MEXCLP. We then solve a series of problems with all three models and employ simulation to estimate aggregate service levels. We find that LR-MEXCLP leads to modest but consistent service gains over both MALP and MEXCLP. These results support the merits of local busyness estimates, but they also suggest that the α-reliability objective may be inappropriate when seeking to maximize aggregate system response capabilities. More generally, our research underscores the utility of (a) linking modeling assumptions and goals with real-world application contexts, and (b) employing simulation or other techniques to validate theoretical results.     

The Impact of Omitting Promotion Variables on Simulation Experiments

Using store-level scanner data, elasticity matrices are estimated using a twotiered demand system. Two basic models are estimated, one with promotion variables and one without. Differences between the estimates across the two models are statistically significant. However, when the elasticities are used as 'inputs' into several simulation exercises, there are only small differences in merger effects and patent damage estimates.The results suggest that the differences are not 'economically' significant.     

Block recursion and structural vector autoregressions

In applications of structural VAR modeling, finite-sample properties may be difficult to obtain when certain identifying restrictions are imposed on lagged relationships. As a result, even though imposing some lagged restrictions makes economic sense, lagged relationships are often left unrestricted to make statistical inference more convenient. This paper develops block Monte Carlo methods to obtain both maximum likelihood estimates and exact Bayesian inference when certain types of restrictions are imposed on the lag structure. These methods are applied to two examples to illustrate the importance of imposing restrictions on lagged relationships.     

Design of limited-stop bus service with capacity constraint and stochastic travel time

This paper develops a mathematical model for the optimal stopping design of limited-stop bus service, which allows each bus vehicle to skip some stops. To better reflect the reality, this paper considers the vehicle capacity and stochastic travel time. Also, vehicles are all allowed to skip stops whereas any stop is not allowed to be skipped by two consecutive vehicles. A hybrid artificial bee colony (ABC) and Monte Carlo method is developed to solve the optimal stopping strategy. Finally, the model and solution method are validated by a numerical example, and a sensitivity analysis is performed on the passenger demand.     

Assessment of stock size to minimize cutting stock production costs

A method for assessing the optimal stock size for the expected order size for a single-period one-dimensional cutting stock problem is proposed. The stock size is optimal when the expected total costs of trim loss, warehousing, and non-fulfilment are minimum. The stock size is the sum of all bar lengths in the stock, and the order size is the sum of shorter bar lengths in various numbers of pieces. Using simulated test cases, a statistical estimation of optimal stock size is conducted, which in our case is approximately 50% above the expected order. The proposed method can help company choose the appropriate level of stock to minimize their total costs.     

The federal reserve board MPS quarterly econometric model of the US economy

This paper describes the structure and properties of the Federal Reserve Board's (MPS) econometric model of the US economy. The theoretical underpinnings are developed using a small growth model that is a simplified version of the steady-state structure of the full model. Short-run dynamic properties, which are Keynesian, are discussed and quantified with simulation results. The largest part of the paper consists of a discussion of the theoretical basis and empirical properties of the key behavioural equations. A complete list of equations is included.     

Adjoint-based Monte Carlo calibration of financial market models

Adjoint methods have recently gained considerable importance in the finance sector, because they allow to quickly compute option sensitivities with respect to a large number of model parameters. In this paper we investigate how the efficiency of adjoint methods can be exploited to speed up the Monte Carlo-based calibration of financial market models. After analyzing the calibration problem both theoretically and numerically, we derive the associated adjoint equation and propose its application in combination with a multi-layer method, for which we prove convergence to a stationary point of the underlying optimization problem. Detailed numerical examples illustrate the performance of the method. In particular, the proposed algorithm reduces the calibration time for a typical equity market model with time-dependent model parameters from over three hours to less than ten minutes on a usual desktop PC.      

Transportation cost changes with statewide school district consolidation

This article studies the relationship between school district size and bus transportation costs, and estimates the change in such costs when a statewide policy of consolidation is pursued. To explore this relationship, we develop a multiple-objective model and solution procedure that combines a geographic aggregation and bus routing heuristic to generate consolidation scenarios. The heuristic was developed to explicitly consider efficiency, effectiveness, and equity objectives, and can be applied in both urban and rural states. The scenarios will generate average statewide bus transportation costs. As applied to the State of Iowa, within the legislature's proposed range of consolidation of 500-1000 students, it was found that transportation operational and capital cost increases range from 0.6 to 10.6 percent and 0.7 to 7.7 percent, respectively.     

Disruption management in distributed enterprises: A multi-agent modelling and simulation of cooperative recovery behaviours

Disruption management in industrial areas consists in dealing with unanticipated events that get the plans deviate from their intended course. The solution results from the design and the maintenance of an operating mode ensuring a relevant deployment of individual recovery behaviours.The paper proposes an approach to minimize the impact of disrupting events on the whole system. It is based on an analysis of disrupting events and the characterization of the recovery process, and on a cooperative repair method for distributed industrial systems. This method is based on a cooperative distributed problem solving approach supported by a multi-agent system framework.     

A STATE-SPACE PARTITIONING METHOD FOR PRICING HIGH-DIMENSIONAL AMERICAN-STYLE OPTIONS

The pricing of American-style options by simulation-based methods is an important but difficult task primarily due to the feature of early exercise, particularly for high-dimensional derivatives. In this paper, a bundling method based on quasi-Monte Carlo sequences is proposed to price high-dimensional American-style options. The proposed method substantially extends Tilley's bundling algorithm to higher-dimensional situations. By using low-discrepancy points, this approach partitions the state space and forms bundles. A dynamic programming algorithm is then applied to the bundles to estimate the continuation value of an American-style option. A convergence proof of the algorithm is provided. A variety of examples with up to 15 dimensions are investigated numerically and the algorithm is able to produce computationally efficient results with good accuracy.