Throughput Analysis for Layout Optimisation of Modular Conveyor Systems

In this paper, objective functions for the optimisation of modular conveyor systems will be introduced. Modular conveyor systems consist of conventional as well as modular conveyor hardware, which are arranged in form of matrix-like layouts. The aim of an ongoing research project is to provide small and medium-sized enterprises with a user-friendly decision support for the selection and planning of modular conveyor systems. For this purpose, the conveyor systems should be evaluated according to the objectives throughput and space requirement. Therefore, mathematical equations have been developed, which enable a fast and precise evaluation of layouts. The paper focuses mainly on the efficient calculation of the throughput. The result quality of the evaluation equations regarding the throughput was proven by a simulation of example systems

A Hidden Semi-Markov Model for Predicting Production Cycle Time Using Bluetooth Low Energy Data

This study proposes a statistical model to characterize the temporal characteristics of an entire production process. The model utilizes received signal strength indicator (RSSI) data obtained from a Bluetooth low energy (BLE) network. A hidden semi-Markov model (HSMM) is formulated based on the characteristics of the production process, and the forward-backward algorithm is employed to re-estimate the probability distribution of state durations. The proposed method is validated through numerical, simulation, and real-world experiments, yielding promising results. The results show that the Kullback-Leibler divergence (KLD) score of 0.1843, while the simulation achieves an average vector distance score of 0.9740. The real-time experiment also shows a reasonable accuracy, with an average HSMM estimated throughput time of 30.48 epochs, compared to the average real throughput time of 33.99 epochs. Overall, the model serves as a valuable tool for predicting the cycle time and throughput time of a production line

Production flow modeling based on BLE-based RSSI data with non-detectable areas

This study presents a method for modelling manufacturing processes to predict key performance indicators (KPIs) such as cycle time using Bluetooth Low Energy (BLE) data. We consider BLE applications to be similar to Radio-Frequency IDentification (RFID) scenarios, with a single BLE scanner indicating a single working area. This work considers the case when Received Signal Strength Indicator (RSSI) data are unavailable in some areas, such as, when products are in temporary storage areas away from the production areas. We solve this problem with a Duration and Interval Hidden Markov Model (DI-HMM), in which time spent in production areas is represented as duration and those with absence data as intervals. To parameterize the DIHMM model, we propose a two-stage machine-learning problem based on a classification tree and a Hidden Semi Markov Model (HSMM). To investigate the proposed model, the RSSI observation sequences are generated using MATLAB Bluetooth Toolbox and real-world experimentation. The runtime scenario compares estimated and original states, and the average accuracy of 100 test sequences is around 95%. In the offline forecast scenario, an estimated DI-HMM parameter is used to forecast 200 sequences, then compared with sequences with a vector distance with a similarity score of 0.4717

Multi-objective optimization of simultaneous buffer and service rate allocation in manufacturing systems based on a data-driven hybrid approach

The challenge presented by simultaneous buffer and service rate allocation in manufacturing systems represents a difficult non-deterministic polynomial problem. Previous studies solved this problem by iteratively utilizing a generative method and an evaluative method. However, it typically takes a long computation time for the evaluative method to achieve high evaluation accuracy, while the satisfactory solution quality realized by the generative method requires a certain number of iterations. In this study, a data-driven hybrid approach is developed by integrating a tabu search–non-dominated sorting genetic algorithm II with a whale optimization algorithm–gradient boosting regression tree to maximize the throughput and minimize the average buffer level of a manufacturing system subject to a total buffer capacity and total service rate. The former algorithm effectively searches for candidate simultaneous allocation solutions by integrating global and local search strategies. The prediction models built by the latter algorithm efficiently evaluate the candidate solutions. Numerical examples demonstrate the efficacy of the proposed approach. The proposed approach improves the solution efficiency of simultaneous allocation, contributing to dynamic production resource reconfiguration of manufacturing systems