Capability of cumulative delay based reactive scheduling for job shops with machine breakdowns

Suwa, Haruhiko; Sandoh, Hiroaki

doi:10.1016/j.cie.2007.04.002

Cited by 38 publications

(3 citation statements)

References 18 publications

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“…As the reconfiguration period is equal to 15 units of time, and by following the Delay Time function, the value of cumulative delay time is very small: 0,005238 units of time (considering the data values from [8] for the estimated parameters a and b for the instance I (8,120,1.3,0.1,1). For this reason, the cumulative value was not considered in the results presented, as they have a very low impact on the final results.…”

Section: Simulations and Resultsmentioning

confidence: 99%

Cyber-Physical Production System (CPPS) decision making duration time impact on manufacturing system performance

Alveš¹,

Putnik²

2019

FME Transactions

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Cyber-Physical Production Systems (CPPS) are inherent to i4.0 to enhance actual manufacturing system control and management processes, and, consequently, the decision making process. This paper presents research on the influence of decision making duration time in CPPS on manufacturing system performance, for different scheduling paradigms. Different scenarios were investigated considering different decision making duration times and different variations of the environement dynamics. Results show that with the increase of the decision making duration time, the completion time of the jobs given at the input, as one of the principal manufacturing system performance measures, increases. Also, considering stable environments, the completion time variation growth in function of CPPS decision making duration time is approximately linear, while when considering dynamic environments, completion time variation growth in the function of CPPS decision making duration time is nonlinear.

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Section: Simulations and Resultsmentioning

confidence: 99%

Cyber-Physical Production System (CPPS) decision making duration time impact on manufacturing system performance

Alveš¹,

Putnik²

2019

FME Transactions

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“…They examined the single machine case in detail in order to find the optimal sequence of jobs. Suwa and Sandoh [16] suggested a scheduling policy for job-shop reactive scheduling under the circumstances of machines' unavailability. Hao and Lin [17] studied one of the classic scheduling in a dynamic environment that emphasizes responsiveness to environmental changes.…”

Section: Literature Reviewmentioning

confidence: 99%

A reactive approach for flexible job shop scheduling problem with tardiness penalty under uncertainty

et al. 2022

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Flexible job shop scheduling under uncertainty plays an important role in real-world manufacturing systems. This paper deals with the flexible job shop scheduling problem to minimize the sum of jobs' tardiness considering machines breakdown and order due date modification as two important disruptions in this production system. To this end, the problem is formulated as a mixed-integer linear programming model. In addition, two strategies are proposed including allocating multiple machines to each job and selecting the best alternative process from other jobs to handle these disruptions. Since the problem is well-known strongly NP-hard, a hybrid metaheuristic algorithm based on the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) is proposed to solve the real-sized instances of these problems. Numerical experiments are used to evaluate the performance and effectiveness of the proposed hybrid algorithm. Obtained results for the small-sized instances show that the proposed algorithm provides proper solutions in terms of optimality and CPU Time. In addition, results for the medium-and large-sized scales validate the efficiency of the proposed algorithm and indicate that the proposed hybrid solution approach outperformed the classic GA in terms of the objective function value and the CPU time.

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“…Examples of the first kind of approach are the work of Jain and Elmaraghy (1997), Fang and Xi (1997), Rangsaritratsamee et al (2004), Li et al (1993) and Suwa and Sandoh (2007). The first three make use of genetic algorithms, the fourth develops an heuristic algorithm that adapts ideas from MRP systems and the last one uses tabu search to generate schedules.…”

Section: Introductionmentioning

confidence: 99%

A discrete time reactive scheduling model for new order insertion in job shop, make-to-order industries

Gomes

Barbosa‐Póvoa²,

Novais

2010

International Journal of Production Research

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Capability of cumulative delay based reactive scheduling for job shops with machine breakdowns

Cited by 38 publications

References 18 publications

Cyber-Physical Production System (CPPS) decision making duration time impact on manufacturing system performance

Cyber-Physical Production System (CPPS) decision making duration time impact on manufacturing system performance

A reactive approach for flexible job shop scheduling problem with tardiness penalty under uncertainty

A discrete time reactive scheduling model for new order insertion in job shop, make-to-order industries

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