Many manufacturing facilities generate and update production schedules, which are plans that state when certain controllable activities (e.g., processing of jobs by resources) should take place. Production schedules help managers and supervisors coordinate activities to increase productivity and reduce operating costs. Because a manufacturing system is dynamic and unexpected events occur, rescheduling is necessary to update a production schedule when the state of the manufacturing system makes it infeasible. Rescheduling updates an existing production schedule in response to disruptions or other changes. Though many studies discuss rescheduling, there are no standard definitions or classification of the strategies, policies, and methods presented in the rescheduling literature. This paper presents definitions appropriate for most applications of rescheduling manufacturing systems and describes a framework for understanding rescheduling strategies, policies, and methods. This framework is based on a wide variety of experimental and practical approaches that have been described in the rescheduling literature. The paper also discusses studies that show how rescheduling affects the performance of a manufacturing system, and it concludes with a discussion of how understanding rescheduling can bring closer some aspects of scheduling theory and practice.

/pdf/rescheduling-manufacturing-systems-a-framework-of-strategies-2ihvpab9tj.pdf

Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods

In most real-world environments, scheduling is an ongoing reactive process where the presence of a variety of unexpected disruptions is usually inevitable, and continually forces reconsideration and revision of pre-established schedules. Many of the approaches developed to solve the problem of static scheduling are often impractical in real-world environments, and the near-optimal schedules with respect to the estimated data may become obsolete when they are released to the shop floor. This paper outlines the limitations of the static approaches to scheduling in the presence of real-time information and presents a number of issues that have come up in recent years on dynamic scheduling.

The paper defines the problem of dynamic scheduling and provides a review of the state-of-the-art of currently developing research on dynamic scheduling. The principles of several dynamic scheduling techniques, namely, heuristics, meta-heuristics, multi-agent systems, and other artificial intelligence techniques are described in detail, followed by a discussion and comparison of their potential.

/pdf/a-survey-of-dynamic-scheduling-in-manufacturing-systems-1u4bc63f51.pdf

A survey of dynamic scheduling in manufacturing systems

http://akturk.bilkent.edu.tr/ie573/aytug.pdf

Executing production schedules in the face of uncertainties: a review and some future directions

Process scheduling under uncertainty: Review and challenges

http://yoksis.bilkent.edu.tr/pdf/files/10.1016-S0377-2217(99)00311-2.pdf

Analysis of reactive scheduling problems in a job shop environment

We address the problem of rescheduling production systems in the face of dynamic job arrivals. Using simple single-and parallel-machine models to gain insight, we provide worst-case and computational analyses of periodic and event-driven rescheduling policies. Our results indicate that event-driven policies can obtain high-quality schedules with less rescheduling than continuous rescheduling policies. We also show that if structure in the job arrival process is exploited very effective periodic rescheduling policies can be designed.

Analysis of periodic and event-driven rescheduling policies in dynamic shops

We evaluate the performance of several dispatching rules in a semiconductor testing environment and examine the effects of uncertainties in problem data and job arrival patterns. A series of simulation experiments shows that a single dispatching rule seldom performs well for several different performance measures simultaneously, non-homogenous job arrival patterns significantly affect several aspects of system performance while uncertainty in problem data does not, and the choice of dispatching rules for a given performance measure is robust to both arrival patterns and uncertainties.

Performance evaluation of dispatching rules for semiconductor testing operations

In this paper we compare the performance of different dispatching rules in a semiconductor testing environment for a variety of due-date and cycle time related performance measures. We also examine the effect of different models of job arrivals and uncertainties in the problem data on the performance of the dispatching rules. Our results indicate that no one rule performs well for all performance measures, and that performanceis robust to uncertainties and non-homogeneity in the arrival process.

http://ieeexplore.ieee.org/iel3/5088/13880/00639903.pdf

Laura K. Church

Papers

Analysis of periodic and event-driven rescheduling policies in dynamic shops

Performance evaluation of dispatching rules for semiconductor testing operations

Dispatching Rules For Semiconductor Testing Operations: A Computational Study