Joint optimization of capacity, production and maintenance planning of leased machines

Hajej, Zied; Rezg, Nidhal; Askri, Tarek

doi:10.1007/s10845-018-1450-7

Cited by 10 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Equations ( 21) and ( 22), among the seven orders, orders 1 and 2 are high-level orders, orders 3 and 4 are intermediate-level orders, and orders 5, 6 and 7 are basic-level orders. Among the 36 machines, machines 1, 2, 3,4,10,11,12,13,19,20,21,22,28,29,30 and 31 belong to the high-health group machine set, machines 5,6,7,14,15,16,23,24,25,32,33 and 34 belong to the medium-health group machine set, and machines 8, 9, 17, 18, 26, 27, 35 and 36 belong to the sub-health group machine set.…”

Section: Production Line Configuration and Random Cases Generation 1)...mentioning

confidence: 99%

“…Xu [9] considered stochastic periodic maintenance in parallel machine scheduling and set minimising the maximum completion time as an optimization goal. Zied [10] conducted joint optimization of capacity, production, and maintenance plans under the constraint of leased machines. They utilised the branch and bound algorithm to reduce machine maintenance costs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on joint scheduling method of order grading and machine maintenance

Zeng,

Li,

Jiang

et al. 2024

IET Collab Intel Manufact

View full text Add to dashboard Cite

In the multi‐variety and large‐scale order production mode, enterprises must balance delivery deadlines and maintain customer satisfaction while also considering the health status of machines. Therefore, the authors propose a method for jointly optimising production scheduling and machine maintenance. Before machine processing, an order value grading and sorting model and a machine health‐status group partitioning model are constructed to classify orders into different production value levels and machines into different health‐status groups, respectively. During machine processing, based on the Weibull distribution theory, a ‘health evaluation function value’ constraint machine preventive maintenance (PM) model and PM strategy are proposed to account for the changing health status of machines; these are integrated with the order allocation machine strategy as decision‐making elements in the production schedule. Finally, two case studies are used to verify the effectiveness of this proposed model and method. The results show that compared to general scheduling schemes, the proposed method can reduce total delay and improve customer satisfaction. Additionally, the PM plan proposed in this method can improve production efficiency and line stability compared to periodic maintenance.

show abstract

Section: Production Line Configuration and Random Cases Generation 1)...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on joint scheduling method of order grading and machine maintenance

Zeng,

Li,

Jiang

et al. 2024

IET Collab Intel Manufact

View full text Add to dashboard Cite

show abstract

“…Besides, [7] used an artificial neural network approach coupled with a simulation tool to estimate the minimum number and the best scheduling of machines that enhance the mean tardiness and the mean flow time. In fact, [8] used two analytical methods to firstly, determine the optimal number of machines and the quantities produced in each period and secondly, determine the production plan and the periodic preventive maintenance scheduling to reduce the failures and minimize the total costs. Moreover, [9] used Discrete-Event Simulation (DES) and OptQuest to find out the optimal machine number.…”

Section: Machine Selection Problemmentioning

confidence: 99%

Improvement opportunities of a Simulation/Expert System Approach for Manufacturing System Sizing: A review and proposal

Imen¹,

Masmoudi²,

Elleuch³

et al. 2019

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

Manufacturing System (MS) sizing is a crucial task to complete in order to obtain the desired MS performance and efficiency. It involves selecting the required number of resources from each used type in a given planning horizon. In fact, different approaches coupling simulation/optimization tools have been developed to solve this issue and evaluate the MS performance. One of these approaches is the Simulation Expert System Approach (SESA). Unfortunately, the application domain of this approach is limited in sizing only the production resources (machines and labor) but neglects the material handling system (MHS) components. Moreover, omitting the transferring problem is not viable in the real world due to its importance in each shop floor. Thus, the aim of this paper is to describe the evolution of SESA, then, to check if the simulation optimization tools used in SESA are still relevant. This paper also investigates the importance of incorporating MHS in this approach and finally proposes some improvement opportunities for SESA including the tackling of the MHS fleet sizing problem. In fact, the wide literature review performed in this research indicates that SESA is still a pertinent approach but it must be improved. Therefore, it is expected that SESA improvement opportunities proposed in this work will greatly assist industrialists in enhancing the overall MS performance, providing a significant productivity increase and a minimization of the total production costs.

show abstract