Resource allocation problems: algorithmic approaches
Resource allocation problems: algorithmic approaches
An efficient algorithm for a task allocation problem
Journal of the ACM (JACM)
A fuzzy dynamic approach to the multicriterion resource allocation problem
Fuzzy Sets and Systems
A pegging algorithm for the nonlinear resource allocation problem
Computers and Operations Research
Optimal testing-resource allocation with genetic algorithm for modular software systems
Journal of Systems and Software
Resource allocation during tests for optimally reliable software
Computers and Operations Research
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems
Computer Standards & Interfaces
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Operations Research Letters
A note on a general non-linear knapsack problem
Operations Research Letters
Fuzzy multi-objective project management decisions using two-phase fuzzy goal programming approach
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
A heuristic method for the inventory routing and pricing problem in a supply chain
Expert Systems with Applications: An International Journal
Variable neighborhood search for multi-objective resource allocation problems
Robotics and Computer-Integrated Manufacturing
Information Sciences: an International Journal
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The multiple-objective resource allocation problem (MORAP) seeks for an allocation of resource to a number of activities such that a set of objectives are optimized simultaneously and the resource constraints are satisfied. MORAP has many applications, such as resource distribution, project budgeting, software testing, health care resource allocation, etc. This paper addresses the nonlinear MORAP with integer decision variable constraint. To guarantee that all the resource constraints are satisfied, we devise an adaptive-resource-bound technique to construct feasible solutions. The proposed method employs the particle swarm optimization (PSO) paradigm and presents a hybrid execution plan which embeds a hill-climbing heuristic into the PSO for expediting the convergence. To cope with the optimization problem with multiple objectives, we evaluate the candidate solutions based on dominance relationship and a score function. Experimental results manifest that the hybrid PSO derives solution sets which are very close to the exact Pareto sets. The proposed method also outperforms several representatives of the state-of-the-art algorithms on a simulation data set of the MORAP.