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2020 | Buch

Rescheduling Under Disruptions in Manufacturing Systems

Models and Algorithms

verfasst von: Assoc. Prof. Dujuan Wang, Prof. Yunqiang Yin, Prof. Yaochu Jin

Verlag: Springer Singapore

Buchreihe : Uncertainty and Operations Research

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Über dieses Buch

This book provides an introduction to the models, methods, and results of some rescheduling problems in the presence of unexpected disruption events, including job unavailability, arrival of new jobs, and machine breakdown. The occurrence of these unexpected disruptions may cause a change in the planned schedule, which may render the originally feasible schedule infeasible. Rescheduling, which involves adjusting the original schedule to account for a disruption, is necessary in order to minimize the effects of the disruption on the performance of the system. This involves a trade-off between finding a cost-effective new schedule and avoiding excessive changes to the original schedule.
This book views scheduling theory as practical theory, and it has made sure to emphasize the practical aspects of its topic coverage. Thus, this book considers some scenarios existing in most real-world environments, such as preventive machine maintenance, and deteriorating effect where the actual processing time of a job gets longer along with machine’s usage and age. To alleviate the effect of disruption events, some flexible strategies are adopted, including allocation extra resources to reduce job processing times or rejection the production of some jobs. For each considered scenario, depending on the model settings and on the disruption events, this book addresses the complexity, and the design of efficient exact or approximated algorithms. Especially when optimization methods and analytic tools fall short, this book stresses metaheuristics including improved elitist non-dominated sorting genetic algorithm and differential evolution algorithm. This book also provides extensive numerical studies to evaluate the performance of the proposed algorithms. The problem of rescheduling in the presence of unexpected disruption events is of great importance for the successful implementation of real-world scheduling systems. There is now an astounding body of knowledge in this field. This book is the first monograph on rescheduling. It aims at introducing the author's research achievements in rescheduling. It is written for researchers and Ph.D. students working in scheduling theory and other members of scientific community who are interested in recent scheduling models. Our goal is to enable the reader to know about some new achievements on this topic.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
This chapter introduces the basic concepts and notation in rescheduling under disruptions in manufacturing, and the basic notions related to the complexity of problems and algorithms, which provides a mathematical framework in which computational problems are studied so that they can be classified as “easy” or “hard”.
Dujuan Wang, Yunqiang Yin, Yaochu Jin
Chapter 2. Rescheduling on Identical Parallel Machines in the Presence of Machine Breakdowns
Abstract
This chapter considers a scheduling problem where a set of jobs has already been assigned to identical parallel machines that are subject to machine breakdowns with the objective of minimizing the total completion time. When machine breakdowns occur, the affected jobs need to be rescheduled with a view to not causing excessive schedule disruption with respect to the planned schedule. Schedule disruption is measured by the maximum time deviation or the total virtual tardiness, given that the completion time of any job in the planned schedule can be regarded as an implied due date for the job concerned. We focus on the trade-off between the total completion time of the adjusted schedule and schedule disruption by finding the set of Pareto-optimal solutions. We show that both variants of the problem are \(\mathcal {NP}\)-hard in the strong sense when the number of machines is considered to be part of the input, and \(\mathcal {NP}\)-hard when the number of machines is fixed. In addition, we develop pseudo-polynomial time algorithms for the two variants of the problem with a fixed number of machines, establishing that they are \(\mathcal {NP}\)-hard in the ordinary sense.
Dujuan Wang, Yunqiang Yin, Yaochu Jin
Chapter 3. Parallel-Machine Rescheduling with Job Rejection in the Presence of Job Unavailability
Abstract
This chapter focuses on a scheduling problem on identical parallel machines to minimize the total completion time under the assumption that all the jobs are available at time zero. However, before processing begins, some jobs are delayed and become unavailable at time zero, so all the jobs need to be rescheduled with a view to not causing excessive schedule disruption with respect to the planned schedule. To reduce the negative impact of job unavailability and achieve an acceptable service level, one option in rescheduling the jobs is to reject a subset of the jobs at a cost (the rejection cost). Three criteria are thus involved: the total completion time of the accepted jobs in the adjusted schedule, the degree of disruption measured by the maximum completion time disruption to any accepted job between the planned and adjusted schedules, and the total rejection cost. The overall objective is to minimize the former criterion, while keeping the objective values of the latter two criteria to no greater than the given limits.
Dujuan Wang, Yunqiang Yin, Yaochu Jin
Chapter 4. Rescheduling with Controllable Processing Times and Job Rejection in the Presence of New Arrival Jobs and Deterioration Effect
Abstract
This chapter considers a dynamic multi-objective machine scheduling problem in response to continuous arrival of new jobs with deterioration effect, under the assumption that jobs can be rejected and job processing time is controllable by allocating extra resources. By deterioration effect, we mean that each job’s processing time may be subject to change due to the capability deterioration with machine’s usage, i.e., the actual processing time of a job becomes longer if the job starts processing later. The operational cost and the disruption cost need to be optimized simultaneously. To solve these dynamic scheduling problems, a directed search strategy (DSS) is introduced into the elitist non-dominated sorting genetic algorithm (NSGA-II) to enhance its capability of tracking changing optimums while maintaining fast convergence. The DSS consists of a population re-initialization mechanism (PRM) to be adopted upon the arrival of new jobs and an offspring generation mechanism (OGM) during evolutionary optimization. PRM re-initializes the population by repairing the non-dominated solutions obtained before the disturbances occur, modifying randomly generated solutions according to the structural properties, as well as randomly generating solutions. OGM generates offspring individuals by fine-tuning a few randomly selected individuals in the parent population, employing intermediate crossover in combination with Gaussian mutations to generate offspring, and using intermediate crossover together with differential evolution based mutation operator. Both PRM and OGM aim to strike a good balance between exploration and exploitation in solving the dynamic multi-objective scheduling problem. Comparative studies are performed on a variety of problem instances of different sizes and with different changing dynamics. Experimental results demonstrate that the proposed DSS is effective in handling the dynamic scheduling problems under investigation.
Dujuan Wang, Yunqiang Yin, Yaochu Jin
Chapter 5. Rescheduling with Controllable Processing Times and Preventive Maintenance in the Presence of New Arrival Jobs and Deterioration Effect
Abstract
This chapter considers the rescheduling problem analogous to that investigated in Chap. 4. However, to alleviate the inherent deteriorating effect in manufacturing system and reduce the negative impact of new arrival jobs, the strategies of preventive maintenance together with controllable processing times are adopted here, where the machine could be totally recovered after being maintained, i.e., deteriorating effect for jobs arranged after the maintenance activity should be restarted from zero.
Dujuan Wang, Yunqiang Yin, Yaochu Jin
Chapter 6. A Knowledge-Based Evolutionary Proactive Scheduling Approach in the Presence of Machine Breakdown and Deterioration Effect
Abstract
This chapter considers proactive scheduling in response to stochastic machine breakdown under deteriorating production environments, where the actual processing time of a job gets longer along with machine’s usage and age. It is assumed that job processing times are controllable by allocating extra resources and the machine breakdown can be described using a given probability distribution. If a machine breaks down, it needs to be repaired and is no longer available during the repair. To absorb the repair duration, the subsequent unfinished jobs are compressed as much as possible to match up the baseline schedule. This work aims to find the optimal baseline sequence and the resource allocation strategy to minimize the operational cost consisting of the total completion time cost and resource consumption cost of the baseline schedule, and the rescheduling cost consisting of the match-up time cost and additional resource consumption cost. To this end, an efficient multi-objective evolutionary algorithm based on elitist non-dominated sorting is proposed, in which a support vector regression (SVR) surrogate model is built to replace the time-consuming simulations in evaluating the rescheduling cost, which represents the solution robustness of the baseline schedule. In addition, a priori domain knowledge is embedded in population initialization and offspring generation to further enhance the performance of the algorithm. Comparative results and statistical analysis show that the proposed algorithm is effective in finding non-dominated tradeoff solutions between operational cost and robustness in the presence of machine breakdown and deterioration effect.
Dujuan Wang, Yunqiang Yin, Yaochu Jin
Backmatter
Metadaten
Titel
Rescheduling Under Disruptions in Manufacturing Systems
verfasst von
Assoc. Prof. Dujuan Wang
Prof. Yunqiang Yin
Prof. Yaochu Jin
Copyright-Jahr
2020
Verlag
Springer Singapore
Electronic ISBN
978-981-15-3528-4
Print ISBN
978-981-15-3527-7
DOI
https://doi.org/10.1007/978-981-15-3528-4

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