488 research outputs found
Design and Analysis of an Estimation of Distribution Approximation Algorithm for Single Machine Scheduling in Uncertain Environments
In the current work we introduce a novel estimation of distribution algorithm
to tackle a hard combinatorial optimization problem, namely the single-machine
scheduling problem, with uncertain delivery times. The majority of the existing
research coping with optimization problems in uncertain environment aims at
finding a single sufficiently robust solution so that random noise and
unpredictable circumstances would have the least possible detrimental effect on
the quality of the solution. The measures of robustness are usually based on
various kinds of empirically designed averaging techniques. In contrast to the
previous work, our algorithm aims at finding a collection of robust schedules
that allow for a more informative decision making. The notion of robustness is
measured quantitatively in terms of the classical mathematical notion of a norm
on a vector space. We provide a theoretical insight into the relationship
between the properties of the probability distribution over the uncertain
delivery times and the robustness quality of the schedules produced by the
algorithm after a polynomial runtime in terms of approximation ratios
Rescheduling in job-shop problems for sustainable manufacturing systems
[EN] Manufacturing industries are faced with environmental challenges, so their industrial processes must be
optimized in terms of both profitability and sustainability. Since most of these processes are dynamic, the
previously obtained solutions cannot be valid after disruptions. This paper focuses on recovery in dynamic
job-shop scheduling problems where machines can work at different rates. Machine speed scaling
is an alternative framework to the on/off control framework for production scheduling. Thus, given a
disruption, the main goal is to recover the original solution by rescheduling the minimum number of
tasks. To this end, a new match-up technique is developed to determine the rescheduling zone and a
feasible reschedule. Then, a memetic algorithm is proposed for finding a schedule that minimizes the
energy consumption within the rescheduling zone but that also maintains the makespan constraint. An
extensive study is carried out to analyze the behavior of our algorithms to recover the original solution
and minimize the energy reduction in different benchmarks, which are taken from the OR-Library. The
energy consumption and processing time of the tasks involved in the rescheduling zone will play an
important role in determining the best match-up point and the optimized rescheduling. Upon a
disruption, different rescheduling solutions can be obtained, all of which comply with the requirements
but that have different values of energy consumption. The results proposed in this paper may be useful
for application in real industries for energy-efficient production rescheduling.This research has been supported by the Seventh Framework Programme under the research project TETRACOM-GA609491 and the Spanish Government under research projects TIN2013-46511-C2-1, TIN2015-65515-C4-1-R and TIN2016-80856-R. The authors wish to thank reviewers and editors for their positive comments to improve the quality of the paper.Salido Gregorio, MA.; Escamilla Fuster, J.; Barber Sanchís, F.; Giret Boggino, AS. (2017). Rescheduling in job-shop problems for sustainable manufacturing systems. Journal of Cleaner Production. 162(20):121-132. https://doi.org/10.1016/j.jclepro.2016.11.002S1211321622
Bütünleşik tedarik zinciri çizelgeleme modelleri: Bir literatür taraması
Research on integration of supply chain and scheduling is relatively recent, and
number of studies on this topic is increasing. This study provides a comprehensive
literature survey about Integrated Supply Chain Scheduling (ISCS) models to help
identify deficiencies in this area. For this purpose, it is thought that this study will
contribute in terms of guiding researchers working in this field. In this study,
existing literature on ISCS problems are reviewed and summarized by introducing
the new classification scheme. The studies were categorized by considering the
features such as the number of customers (single or multiple), product lifespan
(limited or unlimited), order sizes (equal or general), vehicle characteristics
(limited/sufficient and homogeneous/heterogeneous), machine configurations and
number of objective function (single or multi objective). In addition, properties of
mathematical models applied for problems and solution approaches are also
discussed.Bütünleşik Tedarik Zinciri Çizelgeleme (BTZÇ) üzerine yapılan araştırmalar
nispeten yenidir ve bu konu üzerine yapılan çalışma sayısı artmaktadır. Bu çalışma,
bu alandaki eksiklikleri tespit etmeye yardımcı olmak için BTZÇ modelleri hakkında
kapsamlı bir literatür araştırması sunmaktadır. Bu amaçla, bu çalışmanın bu alanda
çalışan araştırmacılara rehberlik etmesi açısından katkı sağlayacağı
düşünülmektedir. Bu çalışmada, BTZÇ problemleri üzerine mevcut literatür gözden
geçirilmiş ve yeni sınıflandırma şeması tanıtılarak çalışmalar özetlenmiştir.
Çalışmalar; tek veya çoklu müşteri sayısı, sipariş büyüklüğü tipi (eşit veya genel),
ürün ömrü (sınırlı veya sınırsız), araç karakteristikleri (sınırlı/yeterli ve
homojen/heterojen), makine konfigürasyonları ve amaç fonksiyonu sayısı (tek veya
çok amaçlı) gibi özellikler dikkate alınarak kategorize edildi. Ayrıca problemler için
uygulanan matematiksel modellerin özellikleri ve çözüm yaklaşımları da
tartışılmıştır
Using real-time information to reschedule jobs in a flowshop with variable processing times
Versión revisada. Embargo 36 mesesIn a time where detailed, instantaneous and accurate information on shop-floor status is becoming available in many manufacturing companies due to Information Technologies initiatives such as Smart Factory or Industry 4.0, a question arises regarding when and how this data can be used to improve scheduling decisions. While it is acknowledged that a continuous rescheduling based on the updated information may be beneficial as it serves to adapt the schedule to unplanned events, this rather general intuition has not been supported by a thorough experimentation, particularly for multi-stage manufacturing systems where such continuous rescheduling may introduce a high degree of nervousness in the system and deteriorates its performance. In order to study this research problem, in this paper we investigate how real-time information on the completion times of the jobs in a flowshop with variable processing times can be used to reschedule the jobs. In an exhaustive computational experience, we show that rescheduling policies pay off as long as the variability of the processing times is not very high, and only if the initially generated schedule is of good quality. Furthermore, we propose several rescheduling policies to improve the performance of continuous rescheduling while greatly reducing the frequency of rescheduling. One of these policies, based on the concept of critical path of a flowshop, outperforms the rest of policies for a wide range of scenarios.Ministerio de Ciencia e Innovación DPI2016-80750-
A memetic algorithm to minimize the total sum of earliness tardiness and sequence dependent setup costs for flow shop scheduling problems with job distinct due windows
The research considers the flow shop scheduling problem under the Just-In-Time (JIT) philosophy. There are n jobs
waiting to be processed through m operations of a flow shop production system. The objective is to determine the job schedule
such that the total cost consisting of setup, earliness, and tardiness costs, is minimized. To represent the problem, the Integer
Linear Programming (ILP) mathematical model is created. A Memetic Algorithm (MA) is developed to determine the proper
solution. The evolutionary procedure, worked as the global search, is applied to seek for the good job sequences. In order to
conduct the local search, an optimal timing algorithm is developed and inserted in the procedure to determine the best schedule of
each job sequence. From the numerical experiment of 360 problems, the proposed MA can provide optimal solutions for 355
problems. It is obvious that the MA can provide the good solution in a reasonable amount of time
A survey of scheduling problems with setup times or costs
Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling
The following interdisciplinary article presents a memetic algorithm with
applying deep reinforcement learning (DRL) for solving practically oriented
dual resource constrained flexible job shop scheduling problems (DRC-FJSSP).
From research projects in industry, we recognize the need to consider flexible
machines, flexible human workers, worker capabilities, setup and processing
operations, material arrival times, complex job paths with parallel tasks for
bill of material (BOM) manufacturing, sequence-dependent setup times and
(partially) automated tasks in human-machine-collaboration. In recent years,
there has been extensive research on metaheuristics and DRL techniques but
focused on simple scheduling environments. However, there are few approaches
combining metaheuristics and DRL to generate schedules more reliably and
efficiently. In this paper, we first formulate a DRC-FJSSP to map complex
industry requirements beyond traditional job shop models. Then we propose a
scheduling framework integrating a discrete event simulation (DES) for schedule
evaluation, considering parallel computing and multicriteria optimization.
Here, a memetic algorithm is enriched with DRL to improve sequencing and
assignment decisions. Through numerical experiments with real-world production
data, we confirm that the framework generates feasible schedules efficiently
and reliably for a balanced optimization of makespan (MS) and total tardiness
(TT). Utilizing DRL instead of random metaheuristic operations leads to better
results in fewer algorithm iterations and outperforms traditional approaches in
such complex environments.Comment: This article has been accepted by IEEE Access on June 30, 202
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