Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set

Human Factors-Based Many-Objective Personnel Recruitment for Safety-Critical Work Environments

In spite of many improvements in industrial safety of the last decades, nowadays four people per minute die in the world for occupational illnesses and accidents at work. Besides equipping machines with the most advanced technologies, industrial safety has become more and more interested in human factors in recent years, since many accidents at work are proven to be blamed on dangerous behaviours of workers. Recruiting workers with proper risk perception and caution can increase how safely they will deal with the task assigned, thus reducing devastating events. This paper presents a many-objective optimization framework for personnel recruitment in safety-critical work environments. Four objectives are considered: cost and learning time (which are minimized), and risk perception and caution (which are maximized). A neural network-based module computes each candidate’s risk perception and caution for every single task he/she applies for. Pareto optimal solutions are generated using the Multi-Objective Particle Swarm Optimizer based on hypervolume (MOPSOhv). The best personnel recruitment is selected by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The effectiveness of the proposed framework was validated on two real-world recruitment processes involving 100 and 300 candidates, respectively

Home healthcare staff scheduling: a clustering particle swarm optimization approach

The home healthcare staff scheduling problem is concerned with the allocation of care tasks to healthcare staff at a minimal cost, subject to healthcare service requirements, labor law, organizational requirements, staff preferences, and other restrictions. Healthcare service providers strive to meet the time window restrictions specified by the patients to improve their service quality. This paper proposes a clustering particle swam optimization methodology (CPSO) for addressing the scheduling problem. The approach utilizes the strengths of unique grouping techniques to efficiently exploit the group structure of the scheduling problem, enabling the algorithm to provide good solutions within reasonable computation times. Computational results obtained in this study demonstrate the efficiency and effectiveness of CPSO approach

Research Trends and Outlooks in Assembly Line Balancing Problems

This paper presents the findings from the survey of articles published on the assembly line balancing problems (ALBPs) during 2014-2018. Before proceeding a comprehensive literature review, the ineffectiveness of the previous ALBP classification structures is discussed and a new classification scheme based on the layout configurations of assembly lines is subsequently proposed. The research trend in each layout of assembly lines is highlighted through the graphical presentations. The challenges in the ALBPs are also pinpointed as a technical guideline for future research works

A fuzzy-based particle swarm optimization algorithm for nurse scheduling

The nurse scheduling problem (NSP) has a great impact on the quality and efficiency of health care operations. Healthcare Operations Analysts have to assign daily shifts to nurses over the planning horizon, so that operations costs are minimized, health care quality is improved, and the nursing staff is satisfied. Due to conflicting objectives and a myriad of restrictions imposed by labor laws, company requirements, and other legislative laws, the NSP is a hard problem. In this paper we present a particle swarm optimization-based algorithm that relies on a heuristic mechanism that incorporates hard constraints to improve the computational efficiency of the algorithm. Further, we incorporate soft constraints into objective function evaluation to guide the algorithm. Results from illustrative examples show that the algorithm is effective and efficient, even over large scale problems

SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization

Computer vision is experiencing an AI renaissance, in which machine learning models are expediting important breakthroughs in academic research and commercial applications. Effectively training these models, however, is not trivial due in part to hyperparameters: user-configured values that control a model's ability to learn from data. Existing hyperparameter optimization methods are highly parallel but make no effort to balance the search across heterogeneous hardware or to prioritize searching high-impact spaces. In this paper, we introduce a framework for massively Scalable Hardware-Aware Distributed Hyperparameter Optimization (SHADHO). Our framework calculates the relative complexity of each search space and monitors performance on the learning task over all trials. These metrics are then used as heuristics to assign hyperparameters to distributed workers based on their hardware. We first demonstrate that our framework achieves double the throughput of a standard distributed hyperparameter optimization framework by optimizing SVM for MNIST using 150 distributed workers. We then conduct model search with SHADHO over the course of one week using 74 GPUs across two compute clusters to optimize U-Net for a cell segmentation task, discovering 515 models that achieve a lower validation loss than standard U-Net.Comment: 10 pages, 6 figure

Mercedes-Benz USA Labor Planning Dashboard

Mercedes-Benz USA specializes in producing high-quality vehicles that exceed customer expectations at a cost-effective rate. The company utilizes a labor planning dashboard that predicts the daily use of their lines at their part distribution centers by allocating their employees to different zones in inbound, outbound, or both. The supervisors manually input all the data to designate employees to various sections within those zones. Our team was tasked with improving and proposing an updated version of the labor planning dashboard by meeting their requirements while making it effective, responsive, and user-friendly. Through trial and error, the new labor planning dashboard combats these issues by eliminating an excessive amount of manual input and creates an automated dashboard by implementing a linear program solver known as an Assignment Problem