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Multi-Objective Combinatorial Optimization(MOCO), which consists of several conflicting objectives to be optimized, finds an ever-increasing number of uses in many real-world applications. In past years, the research of MOCO mainly focuses on evolutionary algorithms. Recently, constraint-based methods come into the view and have been proved to be effective on MOCO problems. This paper builds on the previous works of constraint-based algorithm MCSEnumPD(AAAI-18) using path diversification method. Due to the inadequacy that the original method fails to prune the redundant search space effectively, this paper proposes the definition of infeasible path and develops a novel algorithm that exploits the properties of unsatisfiable cores, referred as CgPDMCS. The approach extends MCSEnumPD algorithm with a core-guided path diversification method, which avoids solving infeasible paths representing the supersets of the unsatisfiable cores. Experimental results show that the novel approach provides further performance gains over the previous constraint-based algorithms, especially for the instances tightly constrained. [ABSTRACT FROM AUTHOR]

Personnel rostering is a challenging combinatorial optimization problem in which shifts are assigned to employees over a scheduling period while subject to organizational, legislative, and personal constraints. Academic models for personnel rostering typically abstractly conceptualize complex real-world problem characteristics. Often only one isolated scheduling period is considered, contradicting common practice where personnel rostering inherently spans multiple dependent periods. The state of the art offers no systematic approach to address this modeling challenge, and consequently, few models capture the requirements imposed by practice. The present paper introduces the concepts of local and global consistency in constraint evaluation processes and proposes a general methodology to address these challenges in integer programming approaches. The impact of inconsistent constraint evaluation is analyzed in a case study concerning rostering nurses in a hospital ward, of which the data have been made publicly available. The results demonstrate that the proposed methodology approximates the optimal solution. [ABSTRACT FROM AUTHOR]

Submodular maximization is one of the central topics in combinatorial optimization. It has found numerous applications in the real world. In the past decades, a series of algorithms have been proposed for this problem. However, most of the state-of-the-art algorithms are randomized. There remain non-negligible gaps with respect to approximation ratios between deterministic and randomized algorithms in submodular maximization. In this paper, we propose deterministic algorithms with improved approximation ratios for non-monotone submodular maximization. Specifically, for the matroid constraint, we provide a deterministic 0.283 − o (1) approximation algorithm, while the previous best deterministic algorithm only achieves a 1/4 approximation ratio. For the knapsack constraint, we provide a deterministic 1/4 approximation algorithm, while the previous best deterministic algorithm only achieves a 1/6 approximation ratio. For the linear packing constraints with large widths, we provide a deterministic 1 / 6 − ϵ approximation algorithm. To the best of our knowledge, there is currently no deterministic approximation algorithm for the constraints. [ABSTRACT FROM AUTHOR]

• A broker based resource allocation model for Federated Cloud environment has been proposed. • A genetic algorithm for constraint based multi-objective optimization has been proposed. • Simulated results show the effectiveness of proposed model. Cloud brokering has emerged a new service paradigm because of the proliferation of a large number of cloud service providers. Cloud brokering attracts users because it aggregates and extends computing services from multiple cloud service providers. Service providers offer services with varying capabilities, technologies and pricing models which creates a heterogeneous service environment. Cloud users are also increasing day by day with varied requirements. Users prefer services from multiple service providers in order to fulfill their money and application requirements. Therefore, service allocation becomes one of most challenging task to fulfill all the requirements of users within resource constraints. Service allocation problem in multiple cloud environment has been studied as combinatorial optimization problem. Broker guided service allocation increases application resilience, provides unified billing and simplifies service management. In this paper we have proposed a broker guided service allocation (BGSA) model for Federated Cloud. BGSA employs a genetic algorithm based technique for service allocation. BGSA optimizes service allocation and derives well distributed set of non-dominated solutions. Simulation based evaluation of BGSA has been done and obtained results are compared with other two multi-objective algorithms. Observed results improve both execution time and total utilization. [ABSTRACT FROM AUTHOR]