Robust absolute single machine makespan scheduling-location problem on trees

Krumke, Sven O.; Le, Huy Minh

doi:10.1016/j.orl.2019.11.001

Cited by 12 publications

(3 citation statements)

References 6 publications

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“…The service level is measured by the probability of ensuring an on‐time schedule. Krumke and Le (2020) studied a robust single‐machine ScheLoc problem with uncertain edge lengths of a given tree. They considered the concept of gamma robustness and proposed a polynomial time algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Scheduling‐location problem with drones

Kim,

Moon

2023

Int Trans Operational Res

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Drone operation, a new driving force for logistics innovation, is struggling to overcome practical challenges. One of the concerns for drone utilization is limited flight ranges, and different concepts of facilities are continually developed to support drone delivery. These new facilities prompt the need to integrate decision‐making across different phases. In particular, the deployment of facilities that complement the physical limitations of drones and the scheduling of drones to perform delivery tasks are closely related. Therefore, we developed a scheduling‐location problem with drones, a new methodology for integrating operational and strategic planning decisions. The integrated decision‐making determines the location of the drone facilities by not only considering the critical distance of facilities but also by taking into account whether the delivery schedule is implemented. In our model, additional drone facilities are sometimes opened considering available drones due to the feasibility of the delivery schedule. An extended formulation and a restricted master heuristic are proposed to solve problems time‐efficiently. Computational results show that the restricted master heuristic outperforms the mathematical model in finding solutions for large‐scale instances. The developed model and heuristic algorithm provide drone delivery services even in areas that are not easily reachable by drones due to being located far from the warehouse and can be effectively applied to humanitarian logistics.

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Section: Literature Reviewmentioning

confidence: 99%

Scheduling‐location problem with drones

Kim,

Moon

2023

Int Trans Operational Res

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“…In the first stage, the decisions related to machine locations are taken, while in the second stage, when the full information of processing times is obtained, the scheduling problem is solved. Krumke and Le (2020) also investigate the ScheLoc with uncertain job-processing times, but involving only a single machine and aiming at minimizing the makespan value in the worst-case.…”

Section: Literature Reviewmentioning

confidence: 99%

Exact and heuristic methods for the discrete parallel machine scheduling location problem

Kramer,

Kramer

2020

Preprint

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The discrete parallel machine makespan scheduling location (ScheLoc) problem is an integrated combinatorial optimization problem that combines facility location and job scheduling. The problem consists in choosing the locations of p machines among a finite set of candidates and scheduling a set of jobs on these machines, aiming to minimize the makespan. Depending on the machine location, the jobs may have different release dates, and thus the location decisions have a direct impact on the scheduling decisions. To solve the problem, it is proposed a new arc-flow formulation, a column generation and three heuristic procedures that are evaluated through extensive computational experiments. By embedding the proposed procedures into a framework algorithm, we are able to find proven optimal solutions for all benchmark instances from the related literature and to obtain small percentage gaps for a new set of challenging instances.

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“…A class of scheduling problems with uncertain parameters is discussed by Kasperski and Zielinski [3], where the complexity of various (regret) scheduling problems and some algorithms for solving them are described for both the absolute and regret criteria. The concept of gammarobustness for combined scheduling-location problems is studied in [5] in the sense that the total deviation of the uncertainty parameters cannot exceed some threshold. For other approaches to robust optimization, we refer to [9,1].…”

Section: Introductionmentioning

confidence: 99%

Robust Single Machine Makespan Scheduling with Release Date Uncertainty

Bachtler,

Krumke,

2020

Preprint

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This paper addresses the robust single machine makespan scheduling with uncertain release dates of the jobs. The release dates take values within know intervals. We use the concept of gamma-robustness in two different settings and address both the robust absolute and robust regret criteria. Our main results are polynomial time algorithms which have the same running time (O(n log n)) as the best algorithms for the non-robust case.

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Robust absolute single machine makespan scheduling-location problem on trees

Cited by 12 publications

References 6 publications

Scheduling‐location problem with drones

Scheduling‐location problem with drones

Exact and heuristic methods for the discrete parallel machine scheduling location problem

Robust Single Machine Makespan Scheduling with Release Date Uncertainty

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