Flow-time estimation in dynamic job shops with priority scheduling using a hybrid modelling approach

A new approach for due date assignment in dynamic job shops with priority scheduling is presented. The future temporal development of the production system, eventually determining the flow-time of a job, is governed by both the processing of the jobs already present in the system as well as the processing of future arriving jobs. We combine a simulation-like approach for the already known jobs with a stochastic model describing the influence of future arriving jobs. The resulting model is a hybrid system dynamics model that can be solved numerically, leading to estimates for the flow-time of all available jobs. In a simulation study, we compare the new approach with other popular methods known in literature. Our results indicate that the new method significantly outperforms all other studied methods in terms of accuracy of the estimates, in most cases by at least a factor of two. Furthermore, the effect of priority scheduling can be modelled correctly, yielding good estimates for jobs of different priorities

Concurrent solution of WATC scheduling with WPPW due date assignment for environmentally weighted customers, jobs and services using SA and its hybrid

After industrial revolution environmental problems increased drastically. Air, water and soil pollution became a serious threat for the mankind. In order to overcome this threat everyone should take responsibility and try to preserve environment as much as possible. Environmentally conscious actions, people, law and foundations should be supported. When it came to determining due dates and scheduling, one of the important criteria should be the supporting the environment. In this study environmentally conscious customers, jobs, and services are rewarded, on the other hand unconscious customers, jobs, and services are penalized, while determining due dates and schedules. Simulated annealing and its hybrid with random search are applied to get environmentally better due dates and schedules

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

New approaches to due date assignment in job shops

In this study, two new approaches for due date assignment in job shops are evaluated. Proposed approaches use statistical prediction techniques for dynamic prediction of job flowtimes in a job shop environment as the job arrives to the shop floor. Primary objective of this research is to compare the performance of the proposed due date assignment model (PDDAM) with several conventional due date assignment models (CDDAM). For this purpose, simulation models are developed and comparisons of the PDDAM and CDDAM are made in terms of the mean absolute percent error (MAPE), mean percent error (MPE) and mean tardiness (MT). Simulation experiments showed that for many test conditions, PDDAM dominates CDDAM. Therefore, case by case findings are summarized in the paper. (c) 2007 Elsevier B.V. All rights reserved

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

Voraussicht zur Verbesserung der Zielerreichung bei prioritätsregelgesteuerter Produktion

In der vorliegenden Arbeit wird ein umfassender Überblick zum Thema Voraussicht im Rahmen der Ablaufplanung gewährt. Zunächst werden die unterschiedlichen Verfahren aus allen Bereichen der Ablaufplanung dargestellt und klassifiziert. Darauf folgend werden neu entwickelte Verfahren vorgestellt, die als Repräsentanten ihrer Klassen zur Erweiterung von Prioritätsregeln eingesetzt werden können. Dabei handelt es sich um ein modulares Konzept, das es gestattet, die Einzelmechanismen in beliebiger Kombination einzusetzen. Zur Überprüfung der Wirksamkeit findet eine Untersuchung aller entwickelten Verfahren im Rahmen eines stochastisch dynamischen Job Shops mithilfe umfangreicher Experimente statt. Bestandteil dieser experimentellen Studie ist auch die Untersuchung der Wechselwirkungen der verschiedenen Voraussichtsmechanismen. Die Experimentergebnisse zeigen auf, dass Voraussicht in vielen Situationen zur Verbesserung der Zielerreichung eingesetzt werden kann und liefern zudem zahlreiche Erkenntnisse über den Einfluss der Systemparameter auf die Vorteilhaftigkeit der einzelnen Voraussichtsklassen