Abstract. The lazy bureaucrat scheduling is a new class of scheduling problems that was introduced in [1]. In these problems, there is one employee (or more) who should perform the assigned jobs. The objective of the employee is to minimize the amount of work he performs and to be as inefficient as possible. He is subject to a constraint, however, that he should be busy when there is some work to do. In this paper, we focus on the cases of this problem where all jobs have the same common deadline. We show that with this constraint, the problem is still NP-hard, and prove some hardness results. We then present a tight 2-approximation algorithm for this problem under one of the defined objective functions. Moreover, we prove that this problem is weakly NP-hard under all objective functions, and present a pseudo-polynomial time algorithm for its general case.
Abstract-Many real world optimization problems are dynamic in which the landscape is time dependent and the optimums may change over time such as dynamic economic modeling, dynamic resource scheduling and dynamic vehicle routing. These problems challenge traditional optimization methods as well as conventional evolutionary optimization algorithms. In these environments, optimization algorithms must not just find the optima but also closely track the optima's trajectory. In this paper we propose a two phased and collaborative version of Cellular PSO, named Two Phased Cellular PSO (TP-CPSO), which introduces two search phases in order to create a more efficient balance between the exploration and exploitation of the optimums. We address the weaknesses of Cellular PSO and propose some modifications and ideas to tackle them including a modified PSO update rule and an efficient local search. Moreover, the cell capacity threshold which is a key parameter of Cellular PSO is eliminated due to these modifications. To demonstrate the performance and robustness of the proposed algorithm, Moving Peaks Benchmark (MPB) has been adopted. For all the experimented dynamic environments, TP-CPSO outperformed all compared evolutionary algorithms including Cellular PSO.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.