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Every year volunteers play a crucial role in disaster responses around the world. Volunteer management is known to be more complex than managing a paid workforce, and this is only made worse by the uncertainty of rapidly changing conditions of crisis scenarios. The purpose of this paper is to address the critical problem of assigning tasks to volunteers and other renewable and non-renewable resources simultaneously, particularly under high-load conditions. These conditions are described by a significant mismatch between available volunteer resources and demands or by frequent changes in requirements.

Through a combination of literature reviews and interviews with managers from several major volunteer organizations, six key characteristics of crisis volunteer resource allocation problems are identified. These characteristics are then used to develop a general mixed integer programming framework for modeling these problems. Rather than relying on probabilistic resource or demand characterizations, this framework addresses the constantly changing conditions inherent to this class of problems through a dynamic resource reallocation-based approach that minimizes the undesirable impacts of changes while meeting the desired and changing objectives. The viability of this approach for solving problems of realistic size and scale is demonstrated through a large set of computational experiments.

Using a common commercial solver, optimal solutions to the allocation and reallocation problems were consistently obtained in short timespans for a wide variety of problems that have realistic sizes and characteristics.

The proposed approach has not been previously addressed in the literature and represents a computationally tractable method to allocate volunteer, renewable and non-renewable resources to tasks in highly volatile crisis scenarios without requiring probabilistic resource or demand characterizations.

The rise of remote work increasingly requires organizations to coordinate a single large, consolidated talent pool into ad-hoc, short-term project teams on demand. This problem involves many simultaneous considerations including project revenues and rejection costs, conflicting projects and roles, worker assignment costs, worker utilization preferences and limits, worker reassignment costs, and arbitrary role start and end times. Moreover, plans must be continuously updated in response to changing circumstances. This paper addresses the problem of dynamic virtual team planning and coordination.

We show this problem is NP-hard and provide a dynamic mixed integer linear programming (MILP) formulation for both optimal initial plan generation as well as continuous plan adjustment and re-optimization. We utilized a factorial experiment design to generate benchmark problems spanning a wide range of characteristics and conducted extensive computational experimentation using a common MILP solver.

Exactly optimal solutions to large, realistically sized problems were consistently obtained in short amounts of time. All observed solution times were sufficient to support the operational decision-making requirements of real-world virtual team coordination, demonstrating the viability of this approach.

The approach developed in this research can enable organizations to optimally coordinate virtual teams on a large scale and continually adjust plans in response to changing circumstances, all in an automated manner.

This paper addresses a new and complex problem of increasing importance to organizations due to the rise in remote work. We provide a problem formulation and exact approach for optimally solving both the planning and re-planning aspects of this problem.

Organizations rely on social outreach campaigns to raise financial support, recruit volunteers, and increase public awareness. In order to maximize response rates, organizations face the challenging problem of designing appropriately tailored interactions for each user. An interaction consists of a specific combination of message, media channel, sender, tone, and possibly many other attributes. The purpose of this paper is to address the problem of how to design tailored interactions for each user to maximize the probability of a desired response.

A nearest-neighbor (NN) algorithm is developed for interaction design. Simulation-based experiments are then conducted to compare positive response rates obtained by two forms of this algorithm against that of several control interaction design strategies. A factorial experimental design is employed which varies three user population factors in a combinatorial manner, allowing the methods to be compared across eight distinct scenarios.

The NN algorithms significantly outperformed all three controls in seven out of the eight scenarios. Increases in response rates ranging from approximately 20 to 400 percent were observed.

This work proposes a data-oriented method for designing tailored interactions for individual users in social outreach campaigns which can enable significant increases in positive response rates. Additionally, the proposed algorithm is relatively easy to implement.

The problem of optimal interaction design in social outreach campaigns is scarcely addressed in the literature. This work proposes an effective and easy to implement solution approach for this problem.

The purpose of this paper is to provide an effective solution for a complex planning problem encountered in heavy industry. The problem entails selecting a set of projects to produce from a larger set of solicited projects and simultaneously scheduling their production to maximize profit. Each project has a due window inside of which, if accepted, it must be shipped. Additionally, there is a limited inventory buffer where lots produced early are stored. Because scheduling affects which projects may be selected and vice-versa, this is a particularly difficult combinatorial optimization problem.

The authors develop an algorithm based on the Metaheuristic for Randomized Priority Search (Meta-RaPS) as well as a greedy heuristic and an integer programming (IP) model. The authors then perform computational experiments on a large set of benchmark problems over a wide range of characteristics to compare the performance of each method in terms of solution quality and time required.

The paper shows that this problem is very difficult to solve using IP, with even small instances unable to be solved optimally. The paper then shows that both proposed algorithms will in seconds often outperform IP by a large margin. Meta-RaPS is particularly robust, consistently producing the best or very near-best solutions.

The Meta-RaPS algorithm developed enables companies facing this problem to achieve higher profits through improved decision making. Moreover, this algorithm is relatively easy to implement.

This research provides an effective solution for a difficult combinatorial optimization problem encountered in heavy industry which has not been previously addressed in the literature.

This chapter details a cultural relevance of text project in-service K-12 teachers engage in during their graduate literacy methods class. Through this project, teachers, who work with emergent bilinguals, learn how to select culturally relevant texts using a rubric. They read their selection with a student and reflect on the experience. Four important conclusions came from an analysis of the projects teachers carried out. Teachers found that their students made connections and were more engaged when reading culturally relevant books. Participants found the rubric helpful in identifying what they should look for in a culturally relevant text. In addition, through this project, the teachers discovered that their libraries tended to lack culturally relevant literature. Finally, participant teachers learned more about their emergent bilingual students through the process of finding and reading culturally relevant stories. While this project was carried out by a limited number of teachers in urban environments, it has implications for teachers of emergent bilingual students in other contexts.

Relative to in-service aero-engines, the bypass ratio of future civil architectures may increase further. If traditional design rules are applied to these new configurations and the housing components are scaled, then it is expected that the overall weight, nacelle drag and the effects of aircraft integration will increase. For this reason, the next generation of civil turbofan engines may use compact nacelles to maximise the benefits from the new engine cycles. The purpose of this paper is to present a multi-level design and optimisation process for future civil aero-engines.

An initial set of multi-point, multi-objective optimisations for axisymmetric configurations are carried out to identify the trade-off between intake and fancowl bulk parameters of highlight radius and nacelle length on nacelle drag. Having identified the likely optimal part of the design space, a set of computationally expensive optimisations for three-dimensional non-axisymmetric configurations is performed. The process includes cruise- and windmilling-type operating conditions to ensure aerodynamic robustness of the downselected configurations.

Relative to a conventional aero-engine nacelle, the developed process yielded a compact aero-engine configuration with mid-cruise drag reduction of approximately 1.6% of the nominal standard net thrust.

The multi-point, multi-objective optimisation is carried out with a mixture of regression and classification functions to ensure aerodynamic robustness of the downselected configurations. The developed computational approach enables the optimisation of future civil aero-engine nacelles that target a reduction of the overall fuel consumption.

This is a selective annotated bibliography of the literature on Christopher Columbus from 1970 to 1989. The subject is particularly relevant considering the approach of the Quincentenary of the “discovery” of America in 1992. For that same reason, there has been an outpouring of literature on the subject since 1990, a significant subset of which contributes to are interpretation of Columbus the man, his voyages, and their impact on the new world. It is hoped that this more recent literature will be part of a subsequent annotated bibliography.

The chapter builds on the literature of Agency and Signalling Theories to analyse the corporate governance factors associated with the voluntary decision to prepare an Integrated Report according to the <IR> International Framework promoted by the IIRC.

The chapter is based on the results of a probit regression run with regard to a sample of 35 companies that joined the Pilot Programme in 2011 and 137 similar companies that did not.

The analysis of two samples of European companies reveals that adhesion to the IR Pilot Programme is positively related to the gender diversity and size of the board.

Further research is required in order to study the differences between listed and non-listed companies in terms of variables affecting the adoption of the <IR> Framework and to increase the time range of our study. In addition, it would be interesting to include other variables capturing different aspects other than corporate governance, since the decision to join the Programme, as the results of our analysis have shown, may also be influenced by other factors, such as strategy decisions and communication policies.

The chapter adds to the existing literature by showing the main governance characteristics that impact the decision to adhere to the IR Pilot Programme. It is also important to the existing literature regarding the role played by gender diversity in corporate governance mechanisms and CSR policies.

Due to the autonomous nature of e-learning, learner control options should be provided to allow students to be more selective of content based on learning needs. Maintaining students’ situational interest should also be a goal in e-learning, as research shows that doing so leads to a greater chance of success. The purpose of this paper is to investigate the relationship between learner control and maintained situational interest in hopes of promoting better engagement through content selection based on learning needs.

This study analyzed survey responses from university students (n = 2,173) participating in a variety of massive online courses in South Korea to investigate the correlation between system-provided learner control and maintained situational interest. Additionally, the correlation between learner control and maintained situational interest was examined among demographic subsets including gender, age and grade level.

Results show a positive relationship between learner control and maintained situational interest, as well as a positive relationship between learner control and maintained situational interest among each demographic subset.

These results add to the current literature by promoting a practical way of maintaining students’ situational interest by providing them with the ability to control aspects of their own learning processes.

This chapter deals with the development of banking in the Crown of Aragon from the end of the thirteenth century through the establishment of money changers, which followed similar patterns as in other Western European territories. It starts with a review of existing literature and follows with an explanation on the different banking services provided by money changers and the specific legal framework that supported such activities. It then examines the geographical distribution of private banks in cities and towns within the domains of the kings of Aragon, as well as their evolution throughout the fourteenth century. After that, it offers an analysis of the most common professional profiles among these bankers and financers. Finally, drawing on a heterogeneous pool of unpublished data, it seeks to shed light on the diversity of investors and clients of these establishments, a crucial proof of their role in integrated financial markets.