Multi-objective Resource Allocation Problem Research Articles

Distributed Event-Triggered Algorithms for Multi-Objective Resource Allocation Problem

This paper considers a multi-objective resource allocation problem for a multi-agent network where each agent has multiple local objective functions. The goal is to obtain the Pareto optimum by exchanging information between agents. To this end, first, we introduce the weighted [Formula: see text] preference index to reformulate this problem into a single-objective resource allocation problem, in which the weighting factor of each objective depends on its relative importance. Moreover, in order to reduce the communication burden, we propose distributed event-triggered algorithms to solve the reformulated problem. When local objective functions are strongly convex and have Lipschitz gradients, we prove that the proposed algorithms are free of Zeno behavior and achieve exponential convergence. Finally, we demonstrate the effectiveness of the proposed algorithms by a microgrid network example.

Deep Reinforcement Learning for Multi-Objective Resource Allocation in Multi-Platoon Cooperative Vehicular Networks

Grouping vehicles into platoons is a promising cooperative driving scenario to enhance the traffic safety and capacity of future vehicular networks. However, fast changing channel conditions in multi-platoon vehicular networks cause tremendous uncertainty for resource allocation. In addition, the unprecedented proliferation of various emerging vehicle-to-infrastructure (V2I) applications may result in some service demands with conflicting quality of experience. In this paper, we formulate a multi-objective resource allocation problem, which maximizes the transmission success rate of intra-platoon communications and the mean opinion score (MOS) of V2I communication links. To efficiently solve this multi-objective optimization problem, we resort to a deep reinforcement learning (DRL) framework. Specifically, we divide it into a set of scalar optimization subproblems based on the weighted sum approach and model each one as a partially observable stochastic game (P-OSG), where each platoon acts as an agent and the actions taken by all platoons correspond to the resource allocation solution. We further propose a Contribution-based Dual-Clip Proximal Policy Optimization (CD-PPO) algorithm to deal with each subproblem, which is a DRL algorithm based on the actor-critic framework. The network parameters of all subproblems are then optimized collaboratively by using the proposed training algorithm and the neighborhood parameter transfer strategy. The desired Pareto front is obtained when all the subproblems are solved. Simulation results reveal that proposed algorithm can outperform the other algorithms in terms of the MOS and transmission success rate.

Resource Management in UAV Enabled MEC Networks

Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things (IoT) devices to nearby mobile edge servers, thereby lowering energy consumption and response time for ground mobile users or IoT devices. Integration of Unmanned Aerial Vehicles (UAVs) and the mobile edge computing (MEC) server will significantly benefit small, battery-powered, and energy-constrained devices in 5G and future wireless networks. We address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator (OI), the computational capacity (CC), the power consumption, the time duration, and the optimal location planning simultaneously. It is possible to assign some heavy tasks to the UAV for faster processing and small ones to the mobile users (MUs) locally. This paper utilizes the k-means clustering algorithm, the interior point method, and the conjugate gradient method to iteratively solve the non-convex multi-objective resource allocation problem. According to simulation results, both local and offloading schemes give optimal solution.

Resource Allocation for Secure SWIPT-Enabled D2D Communications With $\alpha$ Fairness

Device-to-device (D2D) communication is an emerging paradigm that can improve system capacity and spectral efficiency by using cooperative communication coexisting with cellular networks. In spite of these advantages, D2D communication suffers from unfair resource usage, security risks posed by eavesdroppers, and limited energy storage. To deal with these issues, in this paper, we propose a resource allocation algorithm to maximize the security-aware energy efficiency (EE) for D2D users (DUs) in a simultaneous wireless information and power transfer (SWIPT)-enabled D2D communication system with <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> fairness, where multiple random eavesdroppers are present. In particular, we formulate a multi-objective resource allocation problem by jointly optimizing the transmit power, power-splitting (PS) factors of DUs, and the sub-channel allocation factor under multiple constraints, including the maximum interference power for each cellular user, the maximum transmit power of each DU, the PS factor, and the integer sub-channel assignment. To solve the non-convex problem, an iterative algorithm is developed to obtain the sub-optimal solution. Simulation results verify that the proposed algorithm outperforms benchmark algorithms in terms of balancing secrecy EE and fairness.

Solving Multi-Objective Resource Allocation Problem Using Multi-Objective Binary Artificial Bee Colony Algorithm

Resource allocation is the optimal distribution in a limited number of resources available for certain activities. The allocation of the resources for a large number of activities requires exponentially multiplying a computation cost. Therefore, the resource allocation problem is known as NP-Hard problem in the literature. In this study, a multi-objective binary artificial bee colony algorithm has been proposed for solving the multi-objective resource allocation problems. The proposed algorithm has benefited from the robust structure and easy implementation properties of the artificial bee colony algorithm. The contribution is to introduce the multi-objective version of the artificial bee colony algorithm with advanced local search and binary format using transfer functions. The multi-objective binary artificial bee colony algorithm has been improved as two versions using sigmoid and hyperbolic tangent transfer functions to be able to search in the binary search space. With the proposed algorithms, the multi-objective resource allocation problems in the literature are solved, and the algorithms are compared with other algorithms that develop for the same problems. The results obtained show that the proposed algorithms give effective results on the problem. Especially, in large-scale problems, higher accuracy values are reached with a smaller number of evaluations.

Autonomous resource allocation of smart workshop for cloud machining orders

AbstractIn order to realize the online allocation of collaborative processing resource of smart workshop in the context of cloud manufacturing, a multi-objective optimization model of workshop collaborative resources (MOM-WCR) was proposed. Considering the optimization objectives of processing time, processing cost, product qualification rate, and resource utilization, MOM-WCR was constructed. Based on the time sequence of workshop processing tasks, the workshop collaborative manufacturing resource was integrated in MOM-WCR. Fuzzy analytic hierarchy process (FAHP) was adopted to simplified the multi-objective problem into the single-objective problem. Then, the improved firefly algorithm which integrated the particle swarm algorithm (IFA-PSA) was used to solve MOM-WCR. Finally, a group of connecting rod processing experiments were used to verify the model proposed in this paper. The results show that the model is feasible in the application of workshop-level resource allocation in the context of cloud manufacturing, and the improved firefly algorithm shows good performance in solving the multi-objective resource allocation problem.

Assessment of Resource Allocation and Tradeoff Analysis Approaches in Transportation Asset Management

Transportation asset management (TAM) practices continue to grow and to develop as transportation agencies seek to make more objective and defensible decisions, as well as responding to recent legislation. One primary goal of TAM is to provide a structure in which decisions on how to distribute resources across many disparate assets can be made using a systematic process. Resource allocation is analogous to multi-objective optimization, and thus presents the complication that many potential optimal solutions (i.e., a Pareto set) can be found. To solve the multi-objective resource allocation problem, many approaches have been recommended, such as the use of utility theory and weighting functions to express preferences that result in a single solution being selected. This paper discusses those recommendations, as well as describing the current state of the resource allocation process in an effort to identify gaps in practice and, in turn, to provide recommendations for addressing those gaps. First, the process of cross-asset resource allocation is deconstructed to highlight the key steps. Then, current practices identified from the literature, as well as from interviews with five State agencies are discussed. Finally, a set of recommendations for improving resource allocation and the supporting processes within resource allocation are presented.

K-means-Clustering Based Evolutionary Algorithm for Multi-objective Resource Allocation Problems

K-means-Clustering Based Evolutionary Algorithm for Multi-objective Resource Allocation Problems

Applying Ant Colony System algorithm in multi-objective resource allocation for virtual services

ABSTRACTOver the past few years, using cloud computing technology has become popular. With the cloud computing service providers, reducing the number of physical machines providing resources for virtual services in cloud computing is one of the efficient ways to reduce the amount of energy consumption which in turn enhance the performance of data centres. However, using a minimum of physical machines to allocate resources for virtual services can result in system overload and break the SLA of service. Consequently, providing resources for virtual services which do not only satisfy the constraint of reducing the energy consumption but also ensure the load balancing of the whole system is necessary. In this study, we present the multi-objective resource allocation problem for virtual services. This problem aims at both reducing the energy consumption and balancing the load of physical machines. The MORA-ACS algorithm is proposed to resolve the problem by the Ant Colony System method. The experimental results show that in the CloudSim environment, the MORA-ACS algorithm could balance the load as well as reduce the energy consumption better than the Round Robin algorithm.

English

English

Genetic Algorithm Based on K-means-Clustering Technique for Multi-objective Resource Allocation Problems

This paper presents genetic algorithm based on K - means clustering technique for solving multi objective resource allocation problem (MORAP). By using k - means clustering technique, population can be divided int o a specific number of subpopulations with dynamic size. In this way, different GA operators (crossover and mutation) can be applied to each subpopulation instead of one GA operators applied to the whole population. The purpose of implementing K - means clus tering technique is preserving and introducing diversity. Also it enable the algorithm to avoid local minima by preventing the population of chromosomes from becoming too similar to each other. Two test problems taken from the literature are used to compar e the performance of the proposed approach with the competing algorithms. The results have been demonstrated the superiority of the proposed algorithm and its capability to solve MORAP.

An effective modified binary particle swarm optimization (mBPSO) algorithm for multi-objective resource allocation problem (MORAP)

A modified binary particle swarm optimization (mBPSO) algorithm is proposed for solving the multi-objective resource allocation problem (MORAP). First, the generation mechanism for initial particles is established to guarantee that the algorithm can begin to search optimal particle in the feasible solution space. Second, we develop the update mechanism for iterative particles which includes setting up the memory array, modifying Sig function and verifying the constraint condition to assure that the regenerated particles meet the constraint and algorithm can quickly converge. Third, the selection mechanism for pbesti and gbest is proposed which uses the dynamic neighborhood strategy to ensure that the algorithm to find Pareto optimal solutions. Through comparing the example simulation results of our mBPSO with hGA and ACO published in references, we find that proposed mBPSO outperforms hGA and ACO. Finally, the effectiveness of the different improved methods is analyzed, and the synergism effect and the convergence behavior of the mBPSO algorithm show its good performances.

An Efficient Resource Allocation Scheme Using Particle Swarm Optimization

Developing techniques for optimal allocation of limited resources to a set of activities has received increasing attention in recent years. In this paper, an efficient resource allocation scheme based on particle swarm optimization (PSO) is developed. Different from many existing evolutionary algorithms for solving resource allocation problems (RAPs), this PSO algorithm incorporates a novel representation of each particle in the population and a comprehensive learning strategy for the PSO search process. The novelty of this representation lies in that the position of each particle is represented by a pair of points, one on each side of the constraint hyper-plane in the problem space. The line joining these two points intersects the constraint hyper-plane and their intersection point indicates a feasible solution. With the evaluation value of the feasible solution used as the fitness value of the particle, such a representation provides an effective way to ensure the equality resource constraints in RAPs are met. Without the distraction of infeasible solutions, the particle thus searches the space smoothly. In addition, particles search for optimal solutions by learning from themselves and their neighborhood using the comprehensive learning strategy, helping prevent premature convergence and improve the solution quality for multimodal problems. This new algorithm is shown to be applicable to both single-objective and multiobjective RAPs, with performance validated by a number of benchmarks and by a real-world bed capacity planning problem. Experimental results verify the effectiveness and efficiency of the proposed algorithm.

Variable neighborhood search for multi-objective resource allocation problems

The Resource Allocation Problem (RAP) is a classical problem in the field of operations management that has been broadly applied to real problems such as product allocation, project budgeting, resource distribution, and weapon-target assignment. In addition to focusing on a single objective, the RAP may seek to simultaneously optimize several expected but conflicting goals under conditions of resources scarcity. Thus, the single-objective RAP can be intuitively extended to become a Multi-Objective Resource Allocation Problem (MORAP) that also falls in the category of NP-Hard. Due to the complexity of the problem, metaheuristics have been proposed as a practical alternative in the selection of techniques for finding a solution. This study uses Variable Neighborhood Search (VNS) algorithms, one of the extensively used metaheuristic approaches, to solve the MORAP with two important but conflicting objectives-minimization of cost and maximization of efficiency. VNS searches the solution space by systematically changing the neighborhoods. Therefore, proper design of neighborhood structures, base solution selection strategy, and perturbation operators are used to help build a well-balanced set of non-dominated solutions. Two test instances from the literature are used to compare the performance of the competing algorithms including a hybrid genetic algorithm and an ant colony optimization algorithm. Moreover, two large instances are generated to further verify the performance of the proposed VNS algorithms. The approximated Pareto front obtained from the competing algorithms is compared with a reference Pareto front by the exhaustive search method. Three measures are considered to evaluate algorithm performance: D1R, the Accuracy Ratio, and the number of non-dominated solutions. The results demonstrate the practicability and promise of VNS for solving multi-objective resource allocation problems.

A memetic algorithm for multi-objective resource allocation problems

This paper presents one application of the multi-objective resource allocation problem, in which decision makers need to allocate between limited resources for the performance of tasks so that the best overall work efficiency is accomplished at the lowest labor cost. With this problem, we apply the memetic algorithm to obtain a set of Pareto optimal solutions. The search procedure consists of three main steps selection, crossover, and mutation. In addition, once the new solutions are generated, a local search scheme will be employed to help exploit the search space. The proposed memetic algorithm is tested on two benchmark instances and compared with two other methods –hGA and ACO in the literature. Three performance metrics are considered – (1) the hit ratio; (2) the accuracy ratio; (3) the D1 R value. Computational results show that, with test instance 1, our MA outperforms hGA and ACO in terms of all three performance measures. While, in test instance 2, our MA is compared with the only available method –hGA from literature. The results of MA show its superiority in all categories, even with a larger instance. Decision makers can be benefited from a set of abundant alternatives provided by the effective and efficient MA.

Applying Memetic Algorithm in Multi-Objective Resource Allocation among Competing Projects

This paper presents a memetic algorithm for solving project resource allocation problems, where the resource requirements of each project concern numbers of monetary units and never exceeds the amount of capital available. Our objectives are to obtain the best overall result for which returns are maximized and costs are minimized. Such problem, considered as a multi-objective optimization, is too complex to be solved by exact methods. In the proposed MA, the population generated by the crossover or the mutation operator is further improved by a local search method. The approximated Pareto front is updated using all new solutions generated. The performance of MA is demonstrated via an instance, consisting of six projects, and 120 units of capitals, and compared with the reference Pareto front found by executing the exhaustive method . Based on both solution quality and CPU time, the results of such comparison have proved our MA to be an effective optimal method in the multi-objective resource allocation problems (MORAP) .

Hybrid particle swarm optimization for multiobjective resource allocation *

Resource allocation (RA) is the problem of allocating resources among various artifacts or business units to meet one or more expected goals, such as maximizing the profits, minimizing the costs, or achieving the best qualities. A complex multiobjective RA is addressed, and a multiobjective mathematical model is used to find solutions efficiently. Then, an improved particle swarm algorithm (mO PSO) is proposed combined with a new particle diversity controller policies and dissipation operation. Meanwhile, a modified Pareto methods used in PSO to deal with multiobjectives optimization is presented. The effectiveness of the provided algorithm is validated by its application to some illustrative example dealing with multiobjective RA problems and with the comparative experiment with other algorithm.

An effective ant colony optimization algorithm (ACO) for multi-objective resource allocation problem (MORAP)

The multi-objective resource allocation problem (MORAP) addresses the important issue which seeks to find the expected objectives by allocating the limited amount of resource to various activates. Resources may be manpower, assets, raw material or anything else in limited supply which can be used to accomplish the goals. The goals may be objectives (i.e., minimizing costs, or maximizing efficiency) usually driven by specific future needs. In this paper, in order to obtain a set of Pareto solution efficiently, we proposed a modified version of ant colony optimization (ACO), in this algorithm we try to increase the efficiency of algorithm by increasing the learning of ants. Effectiveness and efficiency of proposed algorithm was validated by comparing the result of ACO with hybrid genetic algorithm (hGA) which was applied to MORAP later.

Multi-criteria human resource allocation for solving multistage combinatorial optimization problems using multiobjective hybrid genetic algorithm

Multi-criteria human resource allocation involves deciding how to divide human resource of limited availability among multiple demands in a way that optimizes current objectives. In this paper, we focus on multi-criteria human resource allocation for solving multistage combinatorial optimization problem. Hence we tackle this problem via a multistage decision-making model. A multistage decision-making model is similar to a complex problem solving, in which a suitable sequence of decisions is to be found. The task can be interpreted as a series of interactions between a decision maker and an outside world, at each stage of which some decisions are available and their immediate effect can be easily computed. Eventually, goals would be reached due to the found of optimized variables. In order to obtain a set of Pareto solutions efficiently, we propose a multiobjective hybrid genetic algorithm (mohGA) approach based on the multistage decision-making model for solving combinatorial optimization problems. According to the proposed method, we apply the mohGA to seek feasible solutions for all stages. The effectiveness of the proposed algorithm was validated by its application to an illustrative example dealing with multiobjective resource allocation problem.

Multiobjective resource allocation problem by multistage decision-based hybrid genetic algorithm

Multiobjective resource allocation problem (MORAP) is the process of allocating resources among the various projects or business units to meet the expected objectives. Resources may be manpower, assets, raw materials, capital or anything else in limited supply which can be used to accomplish the goals. The goal may be objectives or targets (i.e., maximizing profits, minimizing costs, or achieving the best possible quality), usually driven by specific future needs. For this reason, the MORAP will be formulated as a complex multiobjective optimization model. Hence, we tackle this problem via a multistage decision making model. A multistage decision making model is similar to a complex problem solving, in which a suitable sequence of decisions is to be found. The task can be interpreted as a series of interactions between a decision maker and an outside world, at each stage of which some decisions are available and their immediate effect can be easily computed. Eventually, goals would be reached due to the found of optimized variables. In this paper, in order to obtain a set of Pareto solutions efficiently, we propose a multiobjective hybrid genetic algorithm (mo-hGA) approach based on the multistage decision making model. According to the proposed method, we apply the mo-hGA to seek feasible solutions for all stages. The effectiveness of the proposed algorithm was validated by its application to an illustrative example dealing with multiobjective resource allocation problem.