Search results

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

Passage of the 1996 Farm Bill marked a dramatic departure in federal farm policy as the longstanding deficiency payment program was replaced with non‐risk responsive transition payments. In light of the departure, subsidized savings has been proposed as a mechanism to provide risk protection to agricultural producers. Using Canada’s National Income Stabilization Account (NISA) program as an example of a subsidized savings program, a stochastic programming model of income stabilization is developed. The model is then used to investigate the optimizing behavior of a typical Midwestern crop producer. The results suggest a fair amount of program design flexibility exists, and that the government can use this flexibility to stimulate initial and continual participation while minimizing capital outlays.

From the perspectives of the probable replacement of the national calamity funds by multi-peril grassland insurance, the purpose of this paper is to estimate demand for grassland production insurance.

A discrete stochastic programming model with a three-year planning horizon was used to run simulations for farms raising suckler cows primarily with grasslands. In this model, the annual area insured and some production decisions are optimized under grasland yield uncertainty, with possible ex post production-system adjustments. The effects of insurance loading cost (14 levels), insurance coverage level (three levels), risk aversion (two levels) and stock levels (forage and animal stocks vary according to grassland yields and to farm management of the previous years) were analyzed.

The results show that grassland insurance could be used as a flexible risk management tool, when farm becomes vulnerable to fodder shortfall. According to previous years’ grassland yields and to the subsequent states of hay stock and animal liveweight, the area insured could vary between nearly the none and full. Farmers with low-average stocking rate and important hay storage capacity have less incentive to buy grassland insurance. The author also demonstrates that for a given loading cost, more insurance is purchased at a coverage level of 70 percent of average yield than at higher coverage levels. The cost of self-insurance increases for important and rare losses while multi-peril grassland insurance premium decreases. Higher levels of risk aversion also raise the quantity of insurance subscribed. Eventually, insurance price is a key factor. Almost no insurance is bought for loading costs greater than 1.1 under low-risk aversion and for loading costs greater than 1.3 under moderate risk aversion.

The willingness to pay for insurance could have been overestimated for different reasons. First, basis risks have not been introduced in the simulation framework. Although the Forage Production Index performed quite well, basis risks are high enough to trigger inappropriate indemnifications in some cases. Consequences of these risks should be estimated in further research. Second, other self-insurance options and public emergency measures such as subsidized loan or reduction in social security contributions should also be considered to assess and reduce farmers vulnerability to risks.

The launching of the multi-peril grassland insurance is likely to be successful thanks to the 65 percent of public subsidies on insurance premiuml. However, considering that the loading cost is likely to be high and that demand for grassland production insurance is rather low, multi-peril grassland production insurance may struggle to continue unsubsidized.

This paper provides a framework that enables to estimate demand for grassland production insurance factoring in substitution with self-insurance and taking into account successive risks.

Online ride-hailing platforms match drivers with passengers by receiving ride requests from passengers and forwarding them to the nearest driver. In this context, the low acceptance rate of offers by drivers leads to friction in the process of driver and passenger matching. What policies by the platform may increase the acceptance rate and by how much? What factors influence drivers' decisions to accept or reject offers and how much? Are drivers more likely to turn down a ride offer because they know that by rejecting it, they can quickly receive another offer, or do they reject offers due to the availability of outside options? This paper aims to answer such questions using a novel dataset from Tapsi, a ride-hailing platform located in Iran.

The authors specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that increase the acceptance rate. In this model, drivers compare the value of each ride offer with the value of outside options and the value of waiting for better offers before making a decision. The authors use the simulated method of moments (SMM) method to match the dynamic model with the data from Tapsi and estimate the model's parameters.

The authors find that the low driver acceptance rate is mainly due to the availability of a variety of outside options. Therefore, even hiding information from or imposing fines on drivers who reject ride offers cannot motivate drivers to accept more offers and does not affect drivers' welfare by a large amount. The results show that by hiding the information, the average acceptance rate increases by about 1.81 percentage point; while, it is 4.5 percentage points if there were no outside options. Moreover, results show that the imposition of a 10-min delay penalty increases acceptance rate by only 0.07 percentage points.

To answer the questions of the paper, the authors use a novel and new dataset from a ride-hailing company, Tapsi, located in a Middle East country, Iran and specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that could potentially increase the acceptance rate.

As global markets become more customer oriented, rapid response rates are now often among the most important metrics in business. To achieve the required agility, many companies are forced to take decisions of whether to vertically integrate a value chain or to outsource some of its operations. The purpose of this paper is to develop a sequential decision modeling process to enable determination of optimal outsourcing policy decisions with respect to the variables such as warehouse inventory, in‐house manufacturing capacity and the ordering cost to the outsource supplier.

In this paper, a discrete dynamic programming‐based modeling framework is developed for analyzing outsourcing policies for supply chain management problems. Specifically, the assumed situation entails a dynamic decision between in‐house production vis‐à‐vis outsourcing, which is contingent upon several factors such as demand during the period under consideration, available inventory, available production capacity of the firm, ordering cost to the outsourced supplier and the fixed capital cost of machine capacity enhancement.

The framework enables the determination of a time‐based outsourcing policy, which is a prescription regarding: the optimum quantities to be produced in‐house vs those to be outsourced, and the level of capacity to be set in each period.

The problem investigates useful managerial decisions that are relevant to a real life dynamic situation within a manufacturing industry when effecting outsourcing decisions.

Condition‐based maintenance (CBM) has increasingly drawn attention in industry because of its many benefits. The CBM problem is a kind of state‐dependent scheduling problem, and is very hard to solve within the conventional Markov decision process framework. The purpose of this paper is to present an intelligent CBM scheduling model for which incremental decision tree learning as an evolutionary system identification model and dynamic programming as a control model are developed.

To fully exploit the merits of CBM, this paper models CBM scheduling as a state‐dependent, sequential decision‐making problem. The objective function is formulated as the minimization of the total maintenance cost. Instead of interpreting the problem within the widely used Markovian framework, this paper proposes an intelligent maintenance scheduling approach that integrates an incremental decision tree learning method and deterministic dynamic programming techniques.

Although the intelligent maintenance scheduling approach proposed in this paper does not guarantee an optimal scheduling policy from a mathematical viewpoint, it is verified through a simulation‐based experiment that the intelligent maintenance scheduler is capable of providing a good scheduling policy that can be used in practice.

This paper presents an intelligent maintenance scheduler. As a system identification model, we devise a new incremental decision tree learning method by which interaction patterns among attributes and machine condition are disclosed in an evolutionary manner. A deterministic dynamic programming technique is then applied to select the best safe state in terms of the total maintenance cost.

A loyalty program might influence buyer behavior in several ways. Prior research offers evidence that buyers might increase the frequency of purchases and volume per occasion in a loyalty program; however, the effect on buyers' price tolerance has not been studied before. The aim of this paper is to examine buyers' willingness to pay a price premium for a firm offering a loyalty program reward.

An analytical model of dynamic consumer choice is developed, where one of the two selling firms offers a reward for a certain number of purchases. The maximum price premium that a normatively rational buyer should be willing to pay at each level of accumulated purchases is obtained. A price tolerance in controlled settings is obtained and these are compared with normative solutions.

Analytically, it is shown that the maximum price premium increases as purchases are accumulated; and the exact solutions can be found, given the price distributions and program design parameters. In the empirical studies it is found that individuals' maximum premiums are less than the normative levels. On the other hand, as buyers accumulate purchases from the reward offering firm, and get closer to the reward, maximum premiums paid increase – particularly when the reward is immediate.

This paper contributes to the loyalty programs literature by examining the price premium, or switching barrier, aspect of buyer response. Furthermore, the paper not only models and solves the normative strategy, but also obtains actual price tolerance in laboratory settings.

This paper aims to provide a “biography” of sorts on Agricultural Finance Review. The paper tracks the evolution of Agricultural Finance Review from its introduction in 1938 to its current status.

The paper is based on a complete review of every paper and every issue. Not all papers were read by the author, but key papers of interest that in one way or another made significant contributions to the study of agricultural finance were reviewed.

The paper shows the evolution of agricultural finance from the early days of reporting financial data in the 1930s and 1940s, to its emergence as a major and significant sub discipline of the general field of agricultural economics.

As indicated, not all papers were fully reviewed or read. It is possible that papers identified as “firsts” may have been preceded by other papers. Nonetheless the paper identifies the basic evolutionary path of the journal and defines key points in time when a paradigm shift emerged to change the direction of this discipline.

As Agricultural Finance Review transitions from the Department of Applied Economics and Management at Cornell University to Emerald Group Publishing Limited, this “biography” provides readers with a general overview of the journal's and the discipline's historical development.

This paper is simply a review of the existing literature found in Agricultural Finance Review.