Scheduling a dynamic job shop production system with sequence-dependent setups: An experimental study

Vinod, V.V.; Sridharan, R.

doi:10.1016/j.rcim.2007.05.001

Cited by 68 publications

(44 citation statements)

References 20 publications

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“…A directed disjunctive arc (i, j) is "satisfied" if the subset containing operation i is not preceded by the subset containing operation j. With respect to the previous example (see Figure 2 here), if the disjunctive arcs (1,8) and (6,8) have been oriented by CP, then the illustrated decomposition policy satisfies (1,8) According to this definition, B has to satisfy:…”

Section: Finding a Decomposition Policymentioning

confidence: 99%

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A Simulated Annealing-Based Heuristic Algorithm for Job Shop Scheduling to Minimize Lateness

Zhang

2013

International Journal of Advanced Robotic Systems

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A decomposition-based optimization algorithm is proposed for solving large job shop scheduling problems with the objective of minimizing the maximum lateness. First, we use the constraint propagation theory to derive the orientation of a portion of disjunctive arcs. Then we use a simulated annealing algorithm to find a decomposition policy which satisfies the maximum number of oriented disjunctive arcs. Subsequently, each subproblem (corresponding to a subset of operations as determined by the decomposition policy) is successively solved with a simulated annealing algorithm, which leads to a feasible solution to the original job shop scheduling problem. Computational experiments are carried out for adapted benchmark problems, and the results show the proposed algorithm is effective and efficient in terms of solution quality and time performance.

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Section: Finding a Decomposition Policymentioning

confidence: 99%

“…Scheduling has been an important research field in robotics and computer-integrated manufacturing [1,2]. The job shop scheduling problem (JSSP) is one of the most frequently adopted models in the area of scheduling research [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Simulated Annealing-Based Heuristic Algorithm for Job Shop Scheduling to Minimize Lateness

Zhang

2013

International Journal of Advanced Robotic Systems

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“…Proper scheduling leads to increased efficiency and capacity utilization, reduced time required to complete tasks, and consequently increased profitability of an organization (Vinod and Sridharan, 2008). The most common high-mix-low-volume environment is in a job shop.…”

Section: Introductionmentioning

confidence: 99%

“…Every machine requires a setup period before any operation. Setup time is defined as the time interval between the end of processing of the current job and the beginning of processing of the next job (Vinod and Sridharan, 2008). Setup is the activities to prepare a machine or workstation to perform the next operation.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Sequencing Rules Using Witness in a Milling Job Shop

Ang¹,

Wong²,

Wong³

2011

CIBIMA

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Simulation is essential when studying manufacturing processes or designing production systems. This project was a real case study which involved a job shop with five similar CNC milling machines. A total of six jobs were performed and each of them consisted of a different set of operations. The sequence of the six jobs to enter the system was determined by the sequencing rules including shortest setup time (SST), shortest processing time (SPT), shortest processing and setup time (SPST), earliest due date (EDD), least process (LP), and lowest volume (LV). The setup time was taken into consideration to make the results more realistic. Due to the complexity of the model, WITNESS was used to simulate all the sequencing rules. The best approach was then determined by comparing the results of each rule. By doing this, the case company would be able to make a better decision on which job should be processed first instead of selecting it randomly among the jobs.

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“…Gupta and Sivakumar [9] present a simulation study of a single machine with due dates and sequence-dependent setup times, where the multiple objectives are to minimize average cycle time and average tardiness and to maximize machine utilization. Vinod and Sridharan [10] present a simulation-based experimental study of scheduling rules for scheduling a dynamic job shop in which the setup times are sequence dependent. Their results indicate that setup-oriented rules provide better performance than ordinary rules.…”

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 a dynamic job shop production system with sequence-dependent setups: An experimental study

Cited by 68 publications

References 20 publications

A Simulated Annealing-Based Heuristic Algorithm for Job Shop Scheduling to Minimize Lateness

A Simulated Annealing-Based Heuristic Algorithm for Job Shop Scheduling to Minimize Lateness

Simulation of Sequencing Rules Using Witness in a Milling Job Shop

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

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