Abstract
The assembly job shop scheduling problem (AJSSP) widely exists in the production process of many complex products. Robust scheduling methods aim to optimize the given criteria for improving the robustness of the schedule by organizing the assembly processes under uncertainty. In this work, the uncertainty of process setup time and processing time is considered, and a framework for the robust scheduling of AJSSP using data-driven methodologies is proposed. The framework consists of obtaining the distribution information of uncertain parameters based on historical data and using a particle swarm optimization (PSO) algorithm to optimize the production schedule. Firstly, the kernel density estimation method is used to estimate the probability density function of uncertain parameters. To control the robustness of the schedule, the concept of confidence level is introduced when determining the range of uncertain parameters. Secondly, an interval scheduling method constructed using interval theory and a customized discrete PSO algorithm are used to optimize the AJSSP with assembly constraints. Several computational experiments are introduced to illustrate the proposed method, and these were proven effective in improving the performance and robustness of the schedule.
Highlights
Scheduling is one of the core issues in production management, which is very important for improving the production efficiency and resource utilization of the manufacturing system [1]
This paper proposes a data-driven robust scheduling method for the assembly job shop scheduling problem (AJSSP) with uncertain production parameters, which can generate adjustable robust schedule
The improvements of the proposed method consist of three points: (1) using the kernel density estimation method to obtain the probability density function of uncertain parameters, and introducing the confidence level to adjust the bounds of uncertain parameters; (2) establishing a decoding scheme based on interval scheduling to realize the combination of interval numbers of uncertain parameters and schedule; (3) designing a discrete particle swarm optimization (PSO) algorithm to deal with the assembly constraints and optimize solutions
Summary
Scheduling is one of the core issues in production management, which is very important for improving the production efficiency and resource utilization of the manufacturing system [1]. With the recently emerging industrial informatics technologies such as the Internet of Things (IoT), sensors, and artificial intelligence, the historical data of the production process can be continuously collected and can be a new source for scheduling optimization [26,27] These data have motivated a shift from a priori reasoning and assumptions to a new datadriven paradigm in dealing with uncertainties in the production process, which is the focus of this research. This paper considers two types of uncertain parameters that have an important impact on the production scheduling of the assembly manufacturing system, namely process processing time and setup time, and from there we aim to develop a data-driven robust scheduling framework. The proposed framework consists of statistical analysis and interval scheduling methods used to describe the distribution of uncertainty parameters On this basis, a PSO algorithm considering assembly constraints is designed to hedge against the risk of system performance degradation in uncertain environments. A summary covering the conclusions is presented in the last section
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