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The paper aims to build the connections between game theory and the resource allocation problem with general uncertainty. It proposes modeling the distributed resource allocation problem by Bayesian game. During this paper, three basic kinds of uncertainties are discussed. Therefore, the purpose of this paper is to build the connections between game theory and the resource allocation problem with general uncertainty.

In this paper, the Bayesian game is proposed for modeling the resource allocation problem with uncertainty. The basic game theoretical model contains three parts: agents, utility function, and decision-making process. Therefore, the probabilistic weighted Shapley value (WSV) is applied to design the utility function of the agents. For achieving the Bayesian Nash equilibrium point, the rational learning method is introduced for optimizing the decision-making process of the agents.

The paper provides empirical insights about how the game theoretical model deals with the resource allocation problem uncertainty. A probabilistic WSV function was proposed to design the utility function of agents. Moreover, the rational learning was used to optimize the decision-making process of agents for achieving Bayesian Nash equilibrium point. By comparing with the models with full information, the simulation results illustrated the effectiveness of the Bayesian game theoretical methods for the resource allocation problem under uncertainty.

This paper designs a Bayesian theoretical model for the resource allocation problem under uncertainty. The relationships between the Bayesian game and the resource allocation problem are discussed.

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.

This paper presents a general model of resource allocation problems based on the concept of the system of the complex of operations type. Using the terminology of this model two wide classes of models of allocation problems has been described and compared. In the first class, resource requirements of activities concern amounts of resources belonging to given finite sets, while in the second they refer to given intervals. Attention has been paid to the interpretation of assumptions made in these models. Some desirable directions of further development in the theory and application of the considered problems have been indicated.

Modelling construction resources and their dynamic interactions and constraints are a challenging problem. The allocation of these resources to competing activities is usually a function required in any scheduling process. Performing such allocation under a dynamic and diverse set of constraints adds more complexity to the problem. This study seeks a structured approach for representing resources and their allocation to different activities through the use of an agent‐oriented modelling framework.

A model is developed for a real case of assembly operations of industrial construction modules. The model follows a multi‐agent resource allocation structure and is implemented within an agent‐based simulation environment. The model is used to evaluate the effects of different optimization algorithms and modelling parameters on the generation of a construction schedule. Different experiments run through the model and their results are analyzed and discussed.

The model showed sensitivity only under large and continuous workloads. Overall the structured approach followed in developing the model provided a flexible medium for experimenting with different elements of the resource allocation problem.

The work is limited to the studied case and the results cannot be generalized beyond similar cases. The modelling approach used in the study provides a platform that can facilitate future research in construction resource allocation strategies.

The presented work demonstrates a new approach for modelling construction resource allocation problems that enables structured experimentation with alternative allocation algorithms. It also presents a novel way for modelling modular industrial construction operations.

The purpose of this paper is to determine and evaluate resource allocation algorithms for mixed‐structure operation systems with unknown parameters characterized by experts using the formalism of C‐uncertain variables.

Aggregation and decomposition of mathematical models of operations, performed using analytical‐numerical optimization methods, lead to serial and parallel structures for which allocation algorithms are known. Evaluation of allocations carried out by means of a computer simulation.

Resource allocation problems for mixed‐structure operation systems may be solved by applying aggregation, decomposition and allocation algorithms determined for simple structures. Allocation algorithms based on C‐uncertain variables outperform these based on basic uncertain variables.

Application of the presented algorithms is limited to some mixed structures, however, the methodology used appears general enough to allow developing algorithms for other mixed structures.

The algorithms developed may be embedded into a knowledge‐based system supporting management‐level decisions concerning optimal distribution of limited financial resources.

Originally determined rules for aggregation and decomposition, as well as resulting allocation algorithms. The presented methodology seems promising for developing a general resource allocation algorithm – valid for any mixed structure of an operation system.

In many decision‐making problems under parameter uncertainty, the most popular stochastic approach is not used because of its serious drawbacks. The purpose of this paper is to present another approach, which copes with the uncertainty of parameters. It uses a precise criterion evaluating a decision with respect to uncertain parameters. This precision by the maximum operator is performed on a term based on the criterion and called the relative regret. The approach is applied to the allocation problems in a complex of operations.

The resource allocation problems in a complex of operations of independent and dependent structures to minimize a total execution time of all operations are investigated. Then, the results are extended for the problem of a task allocation in the complex of independent operations. The case is considered when the parameters in the functional models of the operations are uncertain, and their values belong to the intervals of known bounds. The solution algorithms for the uncertain problems are based on known solution algorithms for the corresponding deterministic problems. The solution algorithms for the latter problems are outlined in the paper.

The main contribution of the paper consists in presenting the property that it is possible for the uncertain problems considered to replace the solution of the uncertain allocation problems by solving a number of corresponding deterministic problems.

The useful and interesting property of the solution algorithm for the allocation problems, in general, cannot be applied to the other decision‐making problems under uncertainty. As an example of such a problem, a simple routing‐scheduling problem is presented for which, however, a number of possible parameter scenarios can be substantially limited.

The allocation problems addressed in the paper have a variety of applications in computer systems and in manufacturing systems. Moreover, a lack of crisp values for the parameters in models of individual operations is rather common.

The paper extends previous results for the allocation problems in a complex of operations.

Universities are social and economic instruments for investment in man and thereby for the development of human resources at the highest level. This is truer in the case of developing countries where science and technology have not yet extended their beneficial aspects to whole spheres of social life. While preserving culture and heritage, universities are the most powerful institutions for social change and innovation. At the same time, universities and colleges themselves are subject to changes and need to adapt to these.

Optimum allocation of resources is today's means of designing a logistic network to deal with tomorrow's demands. Accepting this and in order to ensure that a logistic network is being managed efficiently, it is argued that it is essential to look at what is now achievable, given the advances in computer technology and Artificial Intelligence. The parameters necessary for each market segment in the resource allocation process are defined and the characteristics and problems of data describing the parameters encountered in practice are presented.

Optimum allocation of resources is today′s means of designing a logistic network to deal with tomorrow′s demands. Accepting this and in order to ensure that a logistic network is being managed efficiently, it is argued that it is essential to look at what is now achievable, given the advances in computer technology and Artificial Intelligence. The parameters necessary for each market segment in the resource allocation process are defined and the characteristics and problems of data describing the parameters encountered in practice are presented.

Cycle time reduction is important for order fulling process but often subject to resource constraints. This study considers an unrelated parallel machine environment where orders with random demands arrive dynamically. Processing speeds are controlled by resource allocation and subject to diminishing marginal returns. The objective is to minimize long-run expected order cycle time via order schedule and resource allocation decisions.

A stochastic optimization algorithm named CAP is proposed based on particle swarm optimization framework. It takes advantage of derived bound information to improve local search efficiency. Parameter impacts including demand variance, product type number, machine speed and resource coefficient are also analyzed through theoretic studies. The algorithm is evaluated and benchmarked with four well-known algorithms via extensive numerical experiments.

First, cycle time can be significantly improved when demand randomness is reduced via better forecasting. Second, achieving processing balance should be of top priority when considering resource allocation. Third, given marginal returns on resource consumption, it is advisable to allocate more resources to resource-sensitive machines.

A novel PSO-based optimization algorithm is proposed to jointly optimize order schedule and resource allocation decisions in a dynamic environment with random demands and stochastic arrivals. A general quadratic resource consumption function is adopted to better capture diminishing marginal returns.