Scheduling in dynamic assembly job-shops to minimize the sum of weighted earliness, weighted tardiness and weighted flowtime of jobs

Thiagarajan, S.; Rajendran, Chandrasekharan

doi:10.1016/j.cie.2005.06.005

Cited by 71 publications

(32 citation statements)

References 39 publications

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“…Encouraging results were reported for both approaches; however, no comparison with alternative multiobjective optimization techniques was attempted. Thiagarajan 96 addresses the problem of scheduling in dynamic assembly job-shops with the consideration of jobs having different earliness, tardiness and holding costs. In the first phase of the study, relative costs, earliness and tardiness of jobs are considered, and the dispatching rules are presented in order to minimize the sum of weighted earliness and weighted tardiness of jobs.…”

Section: Background On Multi-criteria Scheduling (Job-shop)mentioning

confidence: 99%

Review on Job-Shop and Flow-Shop Scheduling Using

Parveen

Ullah

2011

J. mech. eng.

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Section: Background On Multi-criteria Scheduling (Job-shop)mentioning

confidence: 99%

Review on Job-Shop and Flow-Shop Scheduling Using

Parveen

Ullah

2011

J. mech. eng.

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“…Most studies on due-date based scheduling assume uniform distribution of due dates. However, in real production system, this assumption does not always hold true (Thiagarajan and Rajendran, 2005). Sourd (2005) presented a different method for generating due dates that defines the interval bounds in which due dates belong.…”

Section: The Data Set and The Objective Functionmentioning

confidence: 99%

Benchmarking flexible job-shop scheduling and control systems

Trentesaux

Pach

Bekrar

et al. 2013

Control Engineering Practice

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a b s t r a c tBenchmarking is comparing the output of different systems for a given set of input data in order to improve the system's performance. Faced with the lack of realistic and operational benchmarks that can be used for testing optimization methods and control systems in flexible systems, this paper proposes a benchmark system based on a real production cell. A three-step method is presented: data preparation, experimentation, and reporting. This benchmark allows the evaluation of static optimization performances using traditional operation research tools and the evaluation of control system's robustness faced with unexpected events.

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“…They present scheduling rules for minimizing the maximum standard deviation of flow time and staging delay and the maximum conditional tardiness and standard deviation of conditional tardiness. Thiagarajan and Rajendran [4] present another simulation study of a dynamic assembly shop. They present dispatching rules for minimizing the sum of weighted tardiness and earliness, and the sum of weighted tardiness, earliness, and flow time.…”

Section: Introductionmentioning

confidence: 99%

Scheduling rules for a small dynamic job-shop: a simulation approach

Dileepan¹,

Ahmadi²

2010

Int. j. simul. model.

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The objective of this paper is to study a selected set of scheduling criteria commonly used in scheduling research and develop a set of simple rules that can be easily implemented in relatively small dynamic job-shops. Simulation models were developed for two dynamic production shop environments, namely, a job-shop with jumbled flow and a job-shop with limited jumbled flow. Five scheduling criteria were used with each of the three models. They are, (i) Slack per Remaining Operations (SPRO), (ii) Priority Ratio (PR), (iii) Earliest Due Date (EDD), (iv) Total Work Content Remaining (TWKR), and (v) Smallest Processing Time (SPT). In each of these cases, performance statistics relating to work-in-process (WIP) inventory and job lateness were gathered. The results show that in most cases, simple rules such as SPT and EDD outperform the other rules. SPT rule minimizes the performance metrics that measure WIP inventory levels and the EDD rule minimizes the performance metrics that measure deviation of job completion time from the promised due date.

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Scheduling in dynamic assembly job-shops to minimize the sum of weighted earliness, weighted tardiness and weighted flowtime of jobs

Cited by 71 publications

References 39 publications

Review on Job-Shop and Flow-Shop Scheduling Using

Review on Job-Shop and Flow-Shop Scheduling Using

Benchmarking flexible job-shop scheduling and control systems

Scheduling rules for a small dynamic job-shop: a simulation approach

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