A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

Aqel, Ghiath Al; Li, Xinyu; Gao, Liang

doi:10.1186/s10033-019-0337-7

Cited by 34 publications

(12 citation statements)

References 51 publications

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“…Processed data tables for two scenarios: Based on the triage code and specialization treatments of casualties, and available resources of nearest hospitals, processed data tables for these scenarios are created as shown in Tables 1 and 2. The processing time for rescue O 1 and first-aid treatment O2 can vary in the range of [5,30] minutes. The transport time to hospitals depends on the distance between the scene and the target hospital.…”

Section: Data Scenariosmentioning

confidence: 99%

“…Recently, mathematically modeling the CPSP has been received significant attention from the research community. Many studies have attempted to develop optimization algorithms to search for an optimal schedule for processing casualties, such as GA, NSGA-II, deep reinforcement learning (DRL) [1][2][3][4][5][6]. First, CPSP is modeled as a flexible Job Shop schedule problem (FJSSP) [1,2], and then optimization-based algorithms are employed to search for an optimal schedule.…”

Section: Introductionmentioning

confidence: 99%

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Genetic Algorithm Approach for Casualty Processing Schedule

Nistor¹,

Pham²,

Pickl³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Searching for an optimal casualty processing schedule can be considered a key element in the MCI response phase. Genetic algorithm (GA) has been widely applied for solving this problem. In this paper, it is proposed a GA-based optimization model for addressing the casualty processing scheduling problem (CPSP). It aims to develop a GA-based optimization model in which only a part of the chromosome (solution) involves in the evolutionary process. This can result in a less complex training process than previous GA-based approaches. Moreover, the study attempts to investigate two common objectives in CPSP: maximizing the number of survivals and minimizing the makespan. The proposed GA-based model is evaluated on two real-world scenarios in the Republic of Moldova, FIRE, and FLOOD. The paper suggests that GA models with a population size of 500 or smaller can be applied for MCI scenarios. The first objective can help many casualties receiving specialization treatments at hospitals.

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Section: Data Scenariosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Approach for Casualty Processing Schedule

Nistor¹,

Pham²,

Pickl³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…In order to verify the advantage of the proposed algorithm in solving the physical examination scheduling problem, we compared the ICA GS with other current popular algorithms, including enhanced GA (EGA) [37], AIA [38], and modified IG (MIG) [39]. We coded these four algorithms and ran each in the same environment.…”

Section: Multi-algorithm Comparisonmentioning

confidence: 99%

An imperialist competition algorithm using a global search strategy for physical examination scheduling

Zhang²,

Duan

2020

Appl Intell

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The outbreak of the novel coronavirus clearly highlights the importance of the need of effective physical examination scheduling. As treatment times for patients are uncertain, this remains a strongly NP-hard problem. Therefore, we introduce a complex flexible job shop scheduling model. In the process of physical examination for suspected patients, the physical examiner is considered a job, and the physical examination item and equipment correspond to an operation and a machine, respectively. We incorporate the processing time of the patient during the physical examination, the transportation time between equipment, and the setup time of the patient. A unique scheduling algorithm, called imperialist competition algorithm with global search strategy (ICA GS) is developed for solving the physical examination scheduling problem. A local search strategy is embedded into ICA GS for enhancing the searching behaviors, and a global search strategy is investigated to prevent falling into local optimality. Finally, the proposed algorithm is tested by simulating the execution of the physical examination scheduling processes, which verify that the proposed algorithm can better solve the physical examination scheduling problem.

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“…Kim et al [25] used iterated greedy algorithm to solve job shop scheduling problem with job family and sequence related setup time. Aqel et al [26] applied the iterated greedy algorithm to solve the flexible job shop scheduling problem. Thus, the iterated greedy algorithm has been widely used in the flow shop scheduling problem, and in the process of deconstruction and reconstruction, the reconstruction mechanism based on the NEH (Nawaz-Enscore-Ham) algorithm [27] is usually adopted.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Iterated Greedy Algorithm for Job Shop Scheduling Problem

Zhao

Huang

Han

2022

Preprint

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Aiming at the job shop scheduling problem (JSP), an iterated greedy hybrid algorithm is proposed to optimize the maximum completion time. In the sections of deconstruction and reconstruction of iterated greedy algorithm, any number of operations can be moved to realize the deconstruction and reconstruction of operation-level. When deconstructing, after removing multiple operations, the calculation method of the head and tail length of the partial scheduling based on adjacency matrix and topological sorting is studied. The corresponding directed graph model and adjacency matrix are updated, to solve the topological sorting based on the adjacency matrix, and then the head and tail length of the operations in the partial scheduling are calculated. In the reconstruction, multiple operations should be inserted into the existing partial scheduling. The reconstruction method is based on the insertion algorithm of constructing the initial scheduling, and each previously removed operation should be inserted in turn, which not only ensures the feasibility after inserting multiple operations, but also scientifically inserts operations with better strategies. In view of the strong local search ability of tabu search algorithm, the tabu search is used in the local search section of iterated greedy algorithm. The effectiveness of the proposed algorithm is verified by testing international JSP benchmarks.

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A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

Cited by 34 publications

References 51 publications

Genetic Algorithm Approach for Casualty Processing Schedule

Genetic Algorithm Approach for Casualty Processing Schedule

An imperialist competition algorithm using a global search strategy for physical examination scheduling

A Hybrid Iterated Greedy Algorithm for Job Shop Scheduling Problem

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