A proactive approach to solve integrated production scheduling and maintenance planning problem in flow shops

Cui, Weiwei; Liu, Zhiqiang; Li, Chen; Han, Xiaohui

doi:10.1016/j.cie.2017.11.020

Cited by 79 publications

(53 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In all of these studies, the basis for constructing robustness and stability measures has been Cmax (e. g. see [1,9,15,16]). According to the literature [17], failure is considered for only one machine even in recent papers. However, in some related studies [1], the failure is considered for more than one machine.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraint (16) indicates that under scenario , the planned start time of the job in position on the first machine equals to the sum of the planned start time of the [ − 1] ℎ job on the first machine, plus its processing time and its additional time. Scenario-based Constraints (17) to (20) are required in an m-machine FSSP to calculate the total tardiness. Constraint (17) gives the completion time of the job in the ℎ position on the first machine.…”

Section: Surrogate Measure Of Robustnessmentioning

confidence: 99%

“…Scenario-based Constraints (17) to (20) are required in an m-machine FSSP to calculate the total tardiness. Constraint (17) gives the completion time of the job in the ℎ position on the first machine. Constraints (18) and (19) calculate the completion time of the job in the ℎ position on the other machines other than the first one.…”

Section: Surrogate Measure Of Robustnessmentioning

confidence: 99%

“…In this section, the MTTR is first set to the low level (for the instances 1-10 in Table 2) and then set to the high level (instances [11][12][13][14][15][16][17][18][19][20] to see the effect of its increment on the O.F. The results are demonstrated in Figure 4.…”

Section: 2 Testing the Effect Of Mttr Levelmentioning

confidence: 99%

See 3 more Smart Citations

A New Approach Generating Robust and Stable Schedules in m-Machine Flow Shop Scheduling Problems: A Case Study

Abtahi

Sahraeian

Rahmani

2020

IJE

View full text Add to dashboard Cite

This paper considers a scheduling problem with uncertain processing times and machine breakdowns in industriall/office workplaces and solves it via a novel robust optimization method. In the traditional robust optimization, the solution robustness is maintained only for a specific set of scenarios, which may worsen the situation for new scenarios. Thus, a two-stage predictive algorithm is proposed to efficiently handle the uncertainties and find robust and stable solutions. The first stage creates robust solutions and ensures their stability in the new scenarios. The second stage proposes a novel stability measure to proactively offset the effects of the machine breakdowns of the former stage. Moreover, a tri-component measure based on efficiency, robustness, and stability is proposed which aims to create a realistic schedule to satisfy the customers, manufacturers, and the staff. To meet the customer's requirements, the robustness measure is defined based on the tardiness and the delivery dates of jobs. Finally, the proposed algorithm is applied to a case study, and the findings are compared with the empirical data. The results emphasize the superiority of the proposed technique in satisfying the customers, staff, and increasing the profitability and accountability of the company.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Surrogate Measure Of Robustnessmentioning

confidence: 99%

Section: Surrogate Measure Of Robustnessmentioning

confidence: 99%

Section: 2 Testing the Effect Of Mttr Levelmentioning

confidence: 99%

See 2 more Smart Citations

A New Approach Generating Robust and Stable Schedules in m-Machine Flow Shop Scheduling Problems: A Case Study

Abtahi

Sahraeian

Rahmani

2020

IJE

View full text Add to dashboard Cite

show abstract

“…In manufacturing systems, there are usually existing two different categories of production activities. [1][2][3][4][5][6][7] One is the design activity such as allocating the resources into the production units to determine the production capacity. The other is the operation activity such as scheduling the jobs in the production units to satisfy the production demand.…”

Section: Introductionmentioning

confidence: 99%

A meta-heuristic algorithm for integrated optimization of dynamic resource allocation planning and production scheduling in parallel machine system

Wang

2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

With the wide application of advanced information technology and intelligent equipment in the manufacturing system, the decisions of design and operation have become more interdependent and their integration optimization has gained great concerns from the community of operational research recently. This article investigates an optimization problem of integrating dynamic resource allocation and production schedule in a parallel machine environment. A meta-heuristic algorithm, in which heuristic-based partition, genetic-based sampling, promising index calculation, and backtracking strategies are employed, is proposed for solving the investigated integration problem in order to minimize the makespan of the manufacturing system. The experimental results on a set of random-generated test instances indicate that the presented model is effective and the proposed algorithm exhibits the satisfactory performance that outperforms two stateof-the-art algorithms from literature.

show abstract

Predictive Models for Maintenance Optimization: An Analytical Literature Survey of Industrial Maintenance Strategies

Merkt

2020

Information Technology for Management: Current Research and Future Directions

View full text Add to dashboard Cite

A proactive approach to solve integrated production scheduling and maintenance planning problem in flow shops

Cited by 79 publications

References 44 publications

A New Approach Generating Robust and Stable Schedules in m-Machine Flow Shop Scheduling Problems: A Case Study

A New Approach Generating Robust and Stable Schedules in m-Machine Flow Shop Scheduling Problems: A Case Study

A meta-heuristic algorithm for integrated optimization of dynamic resource allocation planning and production scheduling in parallel machine system

Predictive Models for Maintenance Optimization: An Analytical Literature Survey of Industrial Maintenance Strategies

Contact Info

Product

Resources

About