Comparison of new metaheuristics, for the solution of an integrated jobs-maintenance scheduling problem

Bertolini, Massimo; Mezzogori, Davide; Zammori, Francesco

doi:10.1016/j.eswa.2018.12.034

Cited by 19 publications

(9 citation statements)

References 34 publications

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“…One of the main scheduling problems in the airline industry is the flight scheduling problem. Scheduling involves how limited resources should be allocated to related jobs within a specified time interval to meet customer demand (Bertolini et al, 2019). Flight scheduling is a problem that defines the destinations and frequency of services.…”

Section: Literaturementioning

confidence: 99%

Flight scheduling incorporating bad weather conditions through big data analytics: A comparison of metaheuristics

2021

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The most important mode of long-distance transportation for people to use time efficiently is air transportation. One of the most significant factors determining air traffic performance is the scheduling created by companies. When creating schedules, various parameters are used, mainly the demand for the determined route, and the availability of airplanes and flight crews. Known climatic conditions on the route are generally neglected. Within the scope of this study, the creation of flight schedules, which are normally the preparation of flight schedules by considering the monthly, daily, and hourly constraints that could cause flight delays, were determined by also considering weather conditions. In this determination process, 32-year flight data between 1987 and 2018 were used as a basis. Apache Spark, one of the big data technologies, was used to determine these constraints. The scheduling was optimized by using the constraints used to prevent flight delays. It was observed in the results obtained that simulated annealing (SA) achieved the most optimal results compared to the genetic algorithm (GA) and the artificial bee colony (ABC) algorithm. As the dimensions of the searched space increased, solving the problem with metaheuristic approaches was more advantageous in terms of time compared to the classical method.

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Section: Literaturementioning

confidence: 99%

Flight scheduling incorporating bad weather conditions through big data analytics: A comparison of metaheuristics

2021

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“…They addressed a problem on single machine scheduling subject to planned maintenance and sequence-dependent setup times with the objective of minimizing total earliness and tardiness penalties. Later on, Bertolini et al went on to present and compare new metaheuristics to solve an integrated jobs-maintenance scheduling problem on a single machine subjected to aging and failures [30]. Nie et al addressed the problem of a fuzzy random time window for maintenance planning on single machine scheduling problems with multi-objective functions of minimizing the total weighted completion time, and maximizing the average timeliness level under a fuzzy environment [31].…”

Section: Literature Reviementioning

confidence: 99%

An Exact Method and Ant Colony Optimization for Single Machine Scheduling Problem With Time Window Periodic Maintenance

et al. 2020

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This paper considers a time window periodic maintenance strategy with different duration windows and job scheduling activities in a single machine environment. The aim is to minimize the number of tardy jobs through the integration of production scheduling and periodic maintenance intervals. A mixedinteger linear programming model (MILP) is proposed to optimize small-sized test instances. Furthermore, an ant colony optimization (ACO) algorithm is developed to solve larger sized test instances. Subsequently, to measure the efficiency of the solutions obtained by ACO, Moore's algorithm is also developed to benchmark with ACO. To test the efficiency and the effectiveness of the ACO algorithm, a set of data for small and large sized problems was generated in which several parameters were adopted and then ten replicates were solved for each combination. The small sized instances were solved by the MILP. Then, the results obtained showed that the proposed ACO was able to obtain the exact solutions within reasonable CPU times, thus, it outperformed the CPLEX solver with respect to CPU. The large sized instances were solved by the Moore's algorithm and compared to ACO. Then, the results obtained showed that the ACO outperforms Moore's algorithm for all the instances tested. It can be concluded that the developed ACOis very efficient and effective in solving the problem considered in this paper. INDEX TERMS Scheduling, MILP, single machine, periodic Maintenance, ant colony. AHMED BADWELAN received the B.Sc. degree in mechanical engineering from the Faculty of Engineering, University of Aden, in 2012, and the M.S. degree in industrial engineering from King Saud University, Saudi Arabia. He is currently pursuing the Ph.D. degree with the

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“…Rasiulis et al (2016) proposed the decision model for selection of optimal combinations of modernization measures. Bertolini et al (2019) conducted comparison of new metaheuristics, for the solution of an integrated jobs-maintenance scheduling problem. Chansombat et al (2019) proposed a mixed-integer linear programming model for integrated production and preventive maintenance scheduling in the capital goods industry.…”

Section: Literature Reviewmentioning

confidence: 99%

Proposed Procedure for Optimal Maintenance Scheduling Under Emergent Failures

Al–Refaie

Al-Shalaldeh

Lepkova

2020

Journal of Civil Engineering and Management

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Production lines are usually subjected to emergent machine failures. Such emergent failures disrupt pre-established maintenance schedules, which challenge maintenance engineers to react to those failures in real time. This research proposes an optimization procedure for optimizing scheduling repairs of emergent failures. Three optimization models are developed. Model I schedules failures in newly idle repair shops with the objective of maximizing the number of scheduled repairs. Model II maximizes the number of assigned repairs to untapped ranges. Model III maximizes both the number of assigned failure repairs and satisfaction on regular and emergency repairs by resequencing regular and emergent failures in the shop that contains the largest free margin. A real case study is provided to illustrate the proposed optimization procedure. Results reveal that the proposed models efficiently scheduled and sequenced emergent failures in the idle maintenance shops, the untapped ranges between repairs of regular failures, and in the maintenance shop with the largest free margin. In conclusions, the proposed models can greatly support maintenance engineers in planning repairs under unexpected failures.

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Comparison of new metaheuristics, for the solution of an integrated jobs-maintenance scheduling problem

Cited by 19 publications

References 34 publications

Flight scheduling incorporating bad weather conditions through big data analytics: A comparison of metaheuristics

Flight scheduling incorporating bad weather conditions through big data analytics: A comparison of metaheuristics

An Exact Method and Ant Colony Optimization for Single Machine Scheduling Problem With Time Window Periodic Maintenance

Proposed Procedure for Optimal Maintenance Scheduling Under Emergent Failures

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