An improved multi-objective imperialist competitive algorithm for surgical case scheduling problem with switching and preparation times.

Surgical case scheduling is a key issue in the field of medician, which is a challenging work because of the difficulty in assigning resources to patients. This study regards the surgical case scheduling problem as a flexible job shop scheduling problem (FJSP). Considering the switching and preparation time of patients in different stage, an improved multi-objective imperialist competitive algorithm (IMOICA), which adopts the non-dominant sorting method, is proposed to optimize the whole scheduling. First, the social hierarchy strategy is developed to initialize the empire. Then, to enhance the global search ability of the algorithm, the concept of attraction and repulsion (AR) is introduced into the assimilation strategy. Moreover, to increase the diversity of the population, the revolution strategy is utilized. Finally, the variable neighborhood search (VNS) strategy is embedded to improve its exploitation capacity further. Experiments show that scheduling in advance saves time and cost, and IMOICA can solve the surgical case scheduling problem studied efficiently.

Hybrid Artificial Bee Colony Algorithm for a Parallel Batching Distributed Flow-Shop Problem With Deteriorating Jobs.

In this article, we propose a hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs. In the considered problem, there are two stages as follows: 1) in the first stage, a DFSP is studied and 2) after the first stage has been completed, each job is transferred and assembled in the second stage, where the parallel batching constraint is investigated. In the two stages, the deteriorating job constraint is considered. In the proposed algorithm, first, two types of problem-specific heuristics are proposed, namely, the batch assignment and the right-shifting heuristics, which can substantially improve the makespan. Next, the encoding and decoding approaches are developed according to the problem constraints and objectives. Five types of local search operators are designed for the distributed flow shop and parallel batching stages. In addition, a novel scout bee heuristic that considers the useful information that is collected by the global and local best solutions is investigated, which can enhance searching performance. Finally, based on several well-known benchmarks and realistic industrial instances and via comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several algorithms in terms of both solution quality and population diversity.

A hybrid invasive weed optimization algorithm for the economic load dispatch problem in power systems.

In this study, a hybrid invasive weed optimization (HIWO) algorithm that hybridizes the invasive weed optimization (IWO) algorithm and genetic algorithm (GA) has been proposed to solve economic dispatch (ED) problems in power systems. In the proposed algorithm, the IWO algorithm is used as the main optimizer to explore the solution space, whereas the crossover and mutation operations of the GA are developed to significantly improve the optimization ability of IWO. In addition, an effective repair method is embedded in the proposed algorithm to repair infeasible solutions by handing various practical constraints of ED problems. To verify the optimization performance of the proposed algorithm and the effectiveness of the repair method, six ED problems in the different-scale power systems were tested and compared with other algorithms proposed in the literature. The experimental results indicated that the proposed HIWO algorithm can obtain the more economical dispatch solutions, and the proposed repair method can effectively repair each infeasible dispatch solution to a feasible solution. The convergence capability, applicability and effectiveness of HIWO were also demonstrated through the comprehensive comparison results.