ADAPTIVE, MULTI-OBJECTIVE JOB SHOP SCHEDULING USING GENETIC ALGORITHMS

This research proposes a method to solve the adaptive, multi-objective job shop scheduling problem. Adaptive scheduling is necessary to deal with internal and external disruptions faced in real life manufacturing environments. Minimizing the mean tardiness for jobs to effectively meet customer due date requirements and minimizing mean flow time to reduce the lead time jobs spend in the system are optimized simultaneously. An asexual reproduction genetic algorithm with multiple mutation strategies is developed to solve the multi-objective optimization problem. The model is tested for single day and multi-day adaptive scheduling. Results are compared with those available in the literature for standard problems and using priority dispatching rules. The findings indicate that the genetic algorithm model can find good solutions within short computational time

Decentralized Scheduling Using The Multi-Agent System Approach For Smart Manufacturing Systems: Investigation And Design

The advent of industry 4.0 has resulted in increased availability, velocity, and volume of data as well as increased data processing capabilities. There is a need to determine how best to incorporate these advancements to improve the performance of manufacturing systems. The purpose of this research is to present a solution for incorporating industry 4.0 into manufacturing systems. It will focus on how such a system would operate, how to select resources for the system, and how to configure the system. Our proposed solution is a smart manufacturing system that operates as a self-coordinating system. It utilizes a multi-agent system (MAS) approach, where individual entities within the system have autonomy to make dynamic scheduling decisions in real-time. This solution was shown to outperform alternative scheduling strategies (right shifting and dispatching priority rule) in manufacturing environments subject to uncertainty in our simulation experiments. The second phase of our research focused on system design. This phase involved developing models for two problems: (1) resource selection, and (2) layout configuration. Both models developed use simulation-based optimization. We first present a model for determining machine resources using a genetic algorithm (GA). This model yielded results comparable to an exhaustive search whilst significantly reducing the number of required experiments to find the solution. To address layout configuration, we developed a model that combines hierarchical clustering and GA. Our numerical experiments demonstrated that the hybrid layouts derived from the model result in shorter and less variable order completion times compared to alternative layout configurations. Overall, our research showed that MAS-based scheduling can outperform alternative dynamic scheduling approaches in manufacturing environments subject to uncertainty. We also show that this performance can further be improved through optimal resource selection and layout design

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development