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This research addresses the problem of multi-objective resource allocation or resource deficits, offering robust answers to planning decisions that involve the elementary question: “How is it done?”. The solution to the problem is realized using the general scheme of multi-criteria decision-making in uncertain conditions. The bases of the proposed scheme are associated with the possibilistic approach, which involves the generalization of fuzzy sets from the classical approach to process the uncertainty of information to produce robust (non-dominated) solutions in multi-criteria analysis. Applying this general scheme makes it possible to reduce regions of decision uncertainty through the maximum use of available quantitative information. In the case where quantitative information analysis is insufficient to obtain a unique solution, the proposed approach presupposes the appropriation of qualitative data extracted from experts, who express their opinions considering their knowledge, experience, and intuition. The information on the qualitative character can be represented in diverse preference formats processed by transformation functions to provide homogeneous information for decision procedures used at the final decision stage. The presented results have been implemented within the system of multi-criteria decision-making under uncertain conditions described in the paper. Its functioning is illustrated by solving the typical problem in investment planning activities.

This study presents a new technique for resource allocation in multiuser orthogonal frequency division multiplexing systems. The goal is to maximise the minimum data rate available to any user while minimising the total transmitted power. The strength Pareto evolutionary algorithm (SPEA-2) is used to achieve this goal. The SPEA-2 algorithm solves the contradicting multiple objectives by evaluating individual's fitness value based on the number of external non-dominated individuals that dominate it and then searching the solution space to minimise this fitness value. Most of the existing multi-objective solutions, for the problem under consideration, have used binary coded chromosomes which restricted the number of users to be in power of two only. This limitation is overcome in the proposed scheme by using an integer coded chromosome. The population density information is also incorporated into the fitness function to refine the search. Simulation results indicate that the proposed algorithm achieves higher data rates as compared with previous algorithms. Furthermore, the proposed scheme allocates both subcarriers and bits jointly, without being computationally expensive. The faster convergence of the algorithm to near-optimal value, as compared with previous algorithms is indicative of its reduced complexity, which is attributed to the modification in the power objective.

The limitation of freshwater resources and the growing demand for water, make the issue of water resource development planning and water allocation among stakeholders even more important. Ideally, water allocation should be economically efficient and socially equitable. In this study, a water allocation model is presented in an integrated framework that considers the interaction of water supply and demand according to economic and social factors. To achieve this, a reliability-based multi-objective optimization - simulation approach has been employed. The objective functions of the problem are: 1) maximizing GDP from agricultural sectors and 2) maximizing social equality in different provinces of the basin (measured using the Williamson coefficient). The fair development and allocation among the shared provinces in the basin can reduce conflicts in the region. Karkheh basin has been considered as a case study and decision variables of the problem are area under cultivation of agricultural development sectors in different provinces. The results show that, without harming the income of the agricultural sector, the spatial distribution of development projects can be done in such a way that equality (according to income level and the number of people working in each province) is achieved. One of the solutions of Pareto front compared to previous studies shows that, in addition to an increase of about 12% of the objective function 1 (GDP), the value of the objective function 2 (Williamson coefficient) decreased from 1.19 to 0.98. This indicates a decrease in income inequality among the provinces of the basin.

Public safety and health cannot be secured without the comprehensive recognition of characteristics and reliable emergency response schemes under the disaster chain. Distinct from emergency resource allocation that focuses primarily on a single disaster, dynamic response, periodic supply, and assisted decision-making are necessary. Therefore, we propose a multiobjective emergency resource allocation model considering uncertainty under the natural disaster chain. Resource allocation was creatively combined with path planning through the proposed multiobjective cellular genetic algorithm (MOCGA) and the improved A* algorithm with avoidance of unexpected road elements. Furthermore, timeliness, efficiency, and fairness in actual rescue were optimized by MOCGA. The visualization of emergency trips and intelligent avoidance of risk areas were achieved by the improved A* algorithm. The effects of logistics performance, coupling of disaster factors, and government regulation on emergency resource allocation were discussed based on different disaster chain scenarios. The results show that disruption in infrastructure support, cascading effect of disasters, and time urgency are additional environmental challenges. The proposed model and algorithm work in obtaining the optimal solution for potential regional coordination and resilient supply, with a 22.2% increase in the total supply rate. Cooperative allocation complemented by political regulation can be a positive action for successfully responding to disaster chains.

The paradigm of dynamic shared access aims to provide flexible spectrum usage. Recently, Federal Communications Commission (FCC) has proposed a new dynamic spectrum management framework for the sharing of a 3.5 GHz (3550–3700 MHz) federal band, called a citizen broadband radio service (CBRS) band, which is governed by spectrum access system (SAS). It is the responsibility of SAS to manage the set of CBRS-SAS users. The set of users are classified in three tiers: incumbent access (IA) users, primary access license (PAL) users and the general authorized access (GAA) users. In this article, dynamic channel assignment algorithm for PAL and GAA users is designed with the goal of maximizing the transmission rate and minimizing the total cost of GAA users accessing PAL reserved channels. We proposed a new mathematical model based on multi-objective optimization for the selection of PAL operators and idle PAL reserved channels allocation to GAA users considering the diversity of PAL reserved channels’ attributes and the diversification of GAA users’ business needs. The proposed model is estimated and validated on various performance metrics through extensive simulations and compared with existing algorithms such as Hungarian algorithm, auction algorithm and Gale–Shapley algorithm. The proposed model results indicate that overall transmission rate, net cost and data-rate per unit cost remain the same in comparison to the classical Hungarian method and auction algorithm. However, the improved model solves the resource allocation problem approximately up to four times faster with better load management, which validates the efficiency of our model.

The need for achieving efficient, equitable and sustainable use of water resources to meet water demands of different sectors is necessary, particularly in areas where water resources are decreasing. In the basins where water is required for both energy production and irrigation, allocation of water resources must be planned in such a way that both objectives can be achieved. In this research, a simulation-optimization approach has been used to solve the problem of optimal planning at the watershed scale. The water evaluation and planning system (WEAP) simulation model link with the multi-objective particle swarm optimization (MOPSO) model for optimal long term planning at the basin scale. Therefore, the objective functions of the problem are 1) maximize the cultivation area of agricultural development sectors and 2) maximize the energy produced by the hydropower plant. The developed simulation-optimization model was employed in the problem of optimal water resources planning in the Kashkan river basin in the west of Iran. The Pareto front obtained represents the best trade-off between hydropower and agricultural development in the basin and can be used for water-energy-food nexus planning. For example one of the solutions of Pareto front, in addition to an increase of about 8% of the objective function 2 (generated energy), the value of the objective function 1 (cultivation area) is approximately 5 times higher than the results of previous studies. This demonstrates the proper performance of the simulation-optimization model in the optimal allocation and planning of water resources at the basin scale based on the water-energy-food nexus approach.

With the development of big data and artificial intelligence, cloud resource requests present more complex features, such as being sudden, arriving in batches and being diverse, which cause the resource allocation to lag far behind the resource requests and an unbalanced resource utilization that wastes resources. To solve this issue, this paper proposes a proactive resource allocation method based on the adaptive prediction of the resource requests in cloud computing. Specifically, this method first proposes an adaptive prediction method based on the runs test that improves the prediction accuracy of resource requests, and then, it builds a multiobjective resource allocation optimization model, which alleviates the latency of the resource allocation and balances the utilizations of the different types of resources of a physical machine. Furthermore, a multiobjective evolutionary algorithm, the Nondominated Sorting Genetic Algorithm with the Elite Strategy (NSGA-II), is improved to further reduce the resource allocation time by accelerating the solution speed of the multiobjective optimization model. The experimental results show that this method realizes the balanced utilization between the CPU and memory resources and reduces the resource allocation time by at least 43% (10 threads) compared with the Improved Strength Pareto Evolutionary algorithm (SPEA2) and NSGA-II methods.

We consider the multiobjective optimization problem for the resource allocation of the multiagent network, where each agent contains multiple conflicting local objective functions. The goal is to find compromise solutions minimizing all local objective functions subject to resource constraints as much as possible, i.e., the Pareto optimums. To this end, we first reformulate the multiobjective optimization problem into one single-objective distributed optimization problem by using the weighted L p preference index, where the weighting factors of all local objective functions are obtained from the optimization procedure so that the optimizer of the latter is the desired Pareto optimum of the former. Next, we propose novel predefined-time algorithms to solve the reformulated problem by time-based generators. We show that the reformulated problem is solved within a predefined time if the local objective functions are strongly convex and smooth. Moreover, the settling time can be arbitrarily preset since it does not depend on the initial values and designed parameters. Finally, numerical simulations are presented to illustrate the effectiveness of the proposed algorithms.

Teamwork has increasingly become more popular in educational environments. With the also increasing mobility trends in the educational sector, internationalization and other diversity features have gained importance in the structure of teams. In this paper, we discuss an assignment problem arising in the allocation of students to business projects in a master program in Norway. Among other problem features, the students state their preferences on the projects they most want to conduct. There are also requirements from the companies that propose the projects and from the program administration. We develop a bi-objective approach to consider efficiency and fairness criteria in this assignment problem. We test the model using real data of 2017 and 2018, in joint collaboration with the administrative staff of the program. In light of the good results, our proposed solutions have been implemented in practice in 2019 and 2020. The implementation of these solutions have been beneficial for the administration, the students, and the companies.

In recent years, content-centric networks (CCN) have introduced the significant feature of in-network caching, which saves transmission energy consumption in content distribution. However, because of the extra logic needed for the caching mechanism, one of these networks’ main challenges is optimizing the trade-off between transmission and caching energy consumption. Moreover, in an energy-aware CCN, less popular content is cached near the content provider despite more popular content caching near end users. Therefore, in real-time or delay-sensitive traffic with less popularity, this caching strategy degrades the quality of service, drops delayed chunks, and wastes energy consumption. Accordingly, designing an appropriate content caching policy to improve energy efficiency and service quality is a long-term goal of the green CCN. This paper considers minimizing energy consumption and the queuing delay in CCN as a multi-objective optimization problem. Thus, to drive the proposed approach, called ED-CCN-MOP, the CCN queuing delay for receiving the Interest and Data packets is analyzed and formulated. Furthermore, the ED-CCN-MOP model is solved using the proposed Non-dominated Sorting Markov Approximation (NSMA) method. According to the numerical results, the NSMA algorithm outperforms the NSGA-II, NSGA-III, and MODA algorithms by about 49%, 46%, and 38%, respectively, in terms of their average energy-delay-product metric with the possibility of distributed implementation. Furthermore, the quality of NSMA solutions is evaluated and compared using performance metrics. The results of this evaluation indicate that NSMA consistently achieves a high level of performance.