One-machine rescheduling heuristics with efficiency and stability as criteria

Wu, S. David; Storer, Robert H.; Chang, Pei‐Chann

doi:10.1016/0305-0548(93)90091-v

Cited by 218 publications

(112 citation statements)

References 8 publications

Supporting

Mentioning

104

Contrasting

Unclassified

Order By: Relevance

“…Un buen ejemplo para la programación On-line es la aplicación de las reglas de secuenciación en un entorno dinámico. Entre estos dos extremos, otra alternativa podría ser el scheduling Quasi-line, en el que un subconjunto de las operaciones del conjunto de trabajos ya se ha programado y el resto se deja para períodos futuros [31,38]. El enfoque On-line tiene capacidad para una considerable flexibilidad en el schedule, a fin de compensar las perturbaciones imprevistas del sistema, pero carece de la perspectiva global proporcionada por un enfoque Off-line.…”

Section: Materials Y Métodosunclassified

Scheduling With Uncertainty

Lujan¹,

Segura²,

Moreno³

et al. 2015

Sel.mat

View full text Add to dashboard Cite

1. Introducción. La incertidumbre es un tema de estudio en los diversos tipos de sistemas, debido a que siempre se producen cambios que alteran su comportamiento, en particular en los sistemas relacionados con problemas de scheduling, la incertidumbre en este tipo de problemas se puede presentar por ejemplo en: interrupciones o caídas de máquinas, cancelación de pedidos, etc., los cuales hacen que la programación de tareas sea más compleja. Con el fin de solucionar o al menos traten de mitigar los efectos producidos por estos cambios, se proponen diferentes métodos.Los dos elementos clave de la programación son: la generación de schedules y la revisión del schedule [8,30]. La generación de schedules actúa como un mecanismo de predicción que determina la programación de los tiempos de inicio y de finalización de las tareas de producción en base a los requisitos y restricciones dadas antes del proceso de producción. La revisión de scheduling es una parte reactiva, que supervisa la ejecución de lo previsto y se ocupa de acontecimientos inesperados. Los enfoques de programación se pueden dividir en: scheduling On-line y scheduling Off-line, [16]. En el scheduling off-line, todos los trabajos disponibles se programan a la vez para todo el horizonte de planificación, mientras que para el scheduling On-line se toman decisiones en el momento en que se necesitan.Para tratar la incertidumbre en los problemas de scheduling, se tiene dos enfoques, el scheduling reactivo y el scheduling proactivo. El scheduling reactivo es un proceso para modificar la programación

show abstract

Section: Materials Y Métodosunclassified

Scheduling With Uncertainty

Lujan¹,

Segura²,

Moreno³

et al. 2015

Sel.mat

View full text Add to dashboard Cite

show abstract

“…The earlier works on this problem were due to [18][19][20]. Belouadah et al [21] dealt with a similar problem with the objective of minimizing the total weighted completion time.…”

Section: Surveys Of the Earliness/tardiness Single Machine Schedulingmentioning

confidence: 99%

“…This assumption reflects that the cost of machine idleness is higher than the early cost stemming from completing any job before its due date in a production setting or the capacity of the machine is limited as compared with its demand so that the machine must remain in operation all the time. In [20] and [28], they provided some specific examples of production arrangements with these characteristics. A characteristic of the deterministic problem is an assumption that the set of jobs is ready to process jobs in the beginning.…”

Section: Problem Statementsmentioning

confidence: 99%

EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs

Chen

Chang

et al. 2011

Entropy

View full text Add to dashboard Cite

An Estimation of Distribution Algorithm (EDA), which depends on explicitly sampling mechanisms based on probabilistic models with information extracted from the parental solutions to generate new solutions, has constituted one of the major research areas in the field of evolutionary computation. The fact that no genetic operators are used in EDAs is a major characteristic differentiating EDAs from other genetic algorithms (GAs). This advantage, however, could lead to premature convergence of EDAs as the probabilistic models are no longer generating diversified solutions. In our previous research [1], we have presented the evidences that EDAs suffer from the drawback of premature convergency, thus several important guidelines are provided for the design of effective EDAs. In this paper, we validated one guideline for incorporating other meta-heuristics into the EDAs. An algorithm named "EA/G-GA" is proposed by selecting a well-known EDA, EA/G, to work with GAs. The proposed algorithm was tested on the NP-Hard single machine scheduling problems with the total weighted earliness/tardiness cost in a just-in-time environment. The experimental results indicated that the EA/G-GA outperforms the compared algorithms statistically significantly across different stopping criteria and demonstrated the robustness of the proposed algorithm. Consequently, this paper is of interest and importance in the field of EDAs.

show abstract

“…Another match-up procedure for a modified flow shop subject to machine breakdowns is developed in [3]. In another line of research, explicit stability measures are developed and minimized alongside traditional performance measures [e.g., 1,7,26,35]. A detailed review of such reactive studies can be found in [23,25,34].…”

Section: Introductionmentioning

confidence: 99%

Optimization of schedule stability and efficiency under processing time variability and random machine breakdowns in a job shop environment

Goren

Sabuncuoğlu

Koc

2011

Naval Research Logistics

View full text Add to dashboard Cite

Abstract:The ability to cope with uncertainty in dynamic scheduling environments is becoming an increasingly important issue. In such environments, any disruption in the production schedule will translate into a disturbance of the plans for several external activities as well. Hence, from a practical point of view, deviations between the planned and realized schedules are to be avoided as much as possible. The term stability refers to this concern. We propose a proactive approach to generate efficient and stable schedules for a job shop subject to processing time variability and random machine breakdowns. In our approach, efficiency is measured by the makespan, and the stability measure is the sum of the variances of the realized completion times. Because the calculation of the original measure is mathematically intractable, we develop a surrogate stability measure. The version of the problem with the surrogate stability measure is proven to be NP-hard, even without machine breakdowns; a branch-and-bound algorithm is developed for this problem variant. A tabu search algorithm is proposed to handle larger instances of the problem with machine breakdowns. The results of extensive computational experiments indicate that the proposed algorithms are quite promising in performance.

show abstract

One-machine rescheduling heuristics with efficiency and stability as criteria

Cited by 218 publications

References 8 publications

Scheduling With Uncertainty

Scheduling With Uncertainty

EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs

Optimization of schedule stability and efficiency under processing time variability and random machine breakdowns in a job shop environment

Contact Info

Product

Resources

About