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It is widely known that the assembly line balancing problem (ALBP) is an NP-hard optimization problem. Although different meta-heuristics have been proposed for solving this problem so far, there is no convincing support that what type of algorithms can perform more efficiently than the others. Thus, using some statistical measures, the landscape of the simple ALBP is studied for the first time in the literature. The results indicate a flat landscape for the problem where the local optima are uniformly scattered over the search space. Accordingly, the efficiency of population-based algorithms in addressing the considered problem is statistically validated. ; CC BY-NC-ND 3.0 Edited by Peter Butala, Edvard Govekar, Rok Vrabič This study is supported by the European Union's Horizon 2020 research and innovation program under grant agreement no. 723711 through the MANUWORK project. ; This study is supported by the European Union's Horizon 2020 research and innovation program under grant agreement no. 723711 through the MANUWORK project

Assembly line designs in manufacturing commonly face the key problem of dividing the assembly tasks among the working stations so that the efficiency of the line is optimized. This problem is known as the assembly line balancing problem which is known to be NP-hard. This study, proposes a bi-objective genetic algorithm to cope with the assembly line balancing problem where the considered objectives are the utilization of the assembly line and the workload smoothness measured as the line efficiency and the variation of workload, respectively. The performance of the proposed genetic algorithm is tested through solving a set of standard problems existing in the literature. The computational results show that the genetic algorithm is promising in providing good solutions to the assembly line balancing problem. ; This study is supported by the European Union's Horizon 2020 research and innovation program under grant agreement no. 723711 through the MANUWORK project.

Purpose – This study aims to propose an efficient optimization algorithm to solve the assembly line balancing problem (ALBP). The ALBP arises in high-volume, lean production systems when decision makers aim to design an efficient assembly line while satisfying a set of constraints. Design/methodology/approach – An improved genetic algorithm (IGA) is proposed in this study to deal with ALBP in order to optimize the number of stations and the workload smoothness. Findings – To evaluate the performance of the IGA, it is used to solve a set of well-known benchmark problems and a real-life problem faced by an automobile manufacturer. The solutions obtained are compared against two existing algorithms in the literature and the basic genetic algorithm. The comparisons show the high efficiency and effectiveness of the IGA in dealing with ALBPs. Originality/value – The proposed IGA benefits from a novel generation transfer mechanism that improves the diversification capability of the algorithm by allowing population transfer between different generations. In addition, an effective variable neighborhood search is employed in the IGA to enhance its local search capability. ; CC BY-NC 4.0 ; This study is supported by the European Union's Horizon 2020 research and innovation program under grant agreement no. 723711 through the MANUWORK project.

The improvement of emergency department processes involves the need to take into considerationmultiple variables and objectives in a highly dynamic and unpredictable environment, which makes thedecision-making task extremely challenging. The use of different methodologies and tools to support thedecision-making process is therefore a key issue. This article presents a novel approach in healthcarein which Discrete Event Simulation, Simulation-Based Multi-Objective Optimization and Data Miningtechniques are used in combination. This methodology has been applied for a system improvementanalysis in a Swedish emergency department. As a result of the project, the decision makers were providedwith a range of nearly optimal solutions and design rules which reduce considerably the length of stayand waiting times for emergency department patients. These solutions include the optimal number ofresources and the required level of improvement in key processes. The article presents and discussesthe benefits achieved by applying this methodology, which has proven to be remarkably valuable fordecision-making support, with regard to complex healthcare system design and improvement.