Extended Genetic Algorithm for solving open-shop scheduling problem

Hosseinabadi, Ali Asghar Rahmani; Vahidi, Javad; Saemi, Behzad; Sangaiah, Arun Kumar; Elhoseny, Mohamed

doi:10.1007/s00500-018-3177-y

Cited by 154 publications

(62 citation statements)

References 34 publications

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“…The other three algorithms are applied to the HLFS problem and compared with the IMBO algorithm. They are MBO algorithm [27], extended genetic algorithm (EGA) [29], discrete invasive weed optimization (DIWO) algorithm [31], and artificial bee colony (ABC) algorithm [32]. According to the characteristics of the HLFS problem, we need to make necessary adaptions to these three algorithms to make comparison possible.…”

Section: E Evaluation Of the Proposed Imbo Algorithmmentioning

confidence: 99%

Improved Migrating Birds Optimization Algorithm to Solve Hybrid Flowshop Scheduling Problem With Lot-Streaming

et al. 2020

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Hybrid flowshop scheduling problem with lot-streaming (HLFS) has played an important role in modern industrial systems. In this paper, we preset an improved migrating birds optimization (IMBO) algorithm for HLFS to minimize makespan. To ensure the diversity of initial population, a Nawaz-Enscore-Ham (NEH) heuristic algorithm is used to generate the leader, and the remaining solutions are randomly generated. According to the characteristics of the HLFS problem, we propose a combined neighborhood search structure that consists of four different neighborhood operators. We design effective local search procedure to explore potential promising domains. In addition, a reset mechanism is added to avoid falling into local optimum. Extensive experiments and comparison demonstrate the feasibility and effectiveness of the proposed algorithm. INDEX TERMS Hybrid flowshop, lot-streaming, migrating birds optimization algorithm, scheduling problem.

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Section: E Evaluation Of the Proposed Imbo Algorithmmentioning

confidence: 99%

Improved Migrating Birds Optimization Algorithm to Solve Hybrid Flowshop Scheduling Problem With Lot-Streaming

et al. 2020

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“…An efficient GA was used to solve the resource-constrained project scheduling problem by [35]. An extended GA was proposed for solving open-shop scheduling problem by [36]. A hybrid GA approach was presented for preventive remanufacturing planning of production equipment under operational and imperfect maintenance constraints by [36].…”

Section: The Literature Reviewmentioning

confidence: 99%

“…An extended GA was proposed for solving open-shop scheduling problem by [36]. A hybrid GA approach was presented for preventive remanufacturing planning of production equipment under operational and imperfect maintenance constraints by [36]. In addition, GA is still of great interest to researchers not only for scheduling but also for nonlinear constrained optimization problems [37].…”

Section: The Literature Reviewmentioning

confidence: 99%

Scheduling of periodic services to customers in dispersed locations from heterogeneous multi-agent companies considering uncertainty: A real case study

2019

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The scheduling problem of periodic services from service providers to customers located in different places and need different services. The service centers are also located in different positions, each of which has limited number of teams with the capability of performing one or some services. The goal is to simultaneously minimize 'total service costs' and 'total earliness/tardiness' in providing services to customers. Providing an optimal maintenance schedule is a big challenge in those companies with dispersed supply centers. In this paper, a novel bi-objective mixed integer linear programming model along with augmented epsilon constraint method is presented to exactly solve this problem. Then, a biobjective meta-heuristic technique based on genetic algorithm is proposed and its performance in solving large-scale problems is assessed. The uncertain parameters are faced through robust possibilistic programming approach to diminish the risk of decision making. Finally, the performance of the proposed model and solution approaches are evaluated through a real case study in maintenance scheduling of compressed natural gas (CNG) stations equipment in Iran.

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“…In other words, most of the traffic could be controlled by a single gateway. If we have a huge amount of traffic between gateways, there will be a bottleneck in gateway level communication [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

IoT Resource Allocation and Optimization Based on Heuristic Algorithm

Sangaiah

Hosseinabadi

Shareh

et al. 2020

Sensors

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141

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The Internet of Things (IoT) is a distributed system that connects everything via internet. IoT infrastructure contains multiple resources and gateways. In such a system, the problem of optimizing IoT resource allocation and scheduling (IRAS) is vital, because resource allocation (RA) and scheduling deals with the mapping between recourses and gateways and is also responsible for optimally allocating resources to available gateways. In the IoT environment, a gateway may face hundreds of resources to connect. Therefore, manual resource allocation and scheduling is not possible. In this paper, the whale optimization algorithm (WOA) is used to solve the RA problem in IoT with the aim of optimal RA and reducing the total communication cost between resources and gateways. The proposed algorithm has been compared to the other existing algorithms. Results indicate the proper performance of the proposed algorithm. Based on various benchmarks, the proposed method, in terms of “total communication cost”, is better than other ones.

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Extended Genetic Algorithm for solving open-shop scheduling problem

Cited by 154 publications

References 34 publications

Improved Migrating Birds Optimization Algorithm to Solve Hybrid Flowshop Scheduling Problem With Lot-Streaming

Improved Migrating Birds Optimization Algorithm to Solve Hybrid Flowshop Scheduling Problem With Lot-Streaming

Scheduling of periodic services to customers in dispersed locations from heterogeneous multi-agent companies considering uncertainty: A real case study

IoT Resource Allocation and Optimization Based on Heuristic Algorithm

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