Particle swarm optimization and opposite-based particle swarm optimization for two-agent multi-facility customer order scheduling with ready times

Lin, Win-Chin; Yin, Yunqiang; Cheng, Shuenn‐Ren; Cheng, T.C.E.; Wu, Chin‐Chia

doi:10.1016/j.asoc.2016.09.038

Cited by 35 publications

(8 citation statements)

References 44 publications

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“…In this study m was chosen as equal to 15, which is lower than the number of particles suggested by [53], i.e., 20-50, since these require considerable computing resources which were not available for the present study. However, it has been reported that there are no drawbacks from using a lower number of particles provided that this is higher than the number of parameters [53,54], which was nine in our case. Each particle can modify its position and velocity based on both the best point in current generation (p id ) and the best point of all particles in the swarm (p gd ).…”

Section: Pso Algorithm For Aquacrop Model Assimilationmentioning

confidence: 77%

Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models

et al. 2017

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Accurate yield estimation at the field scale is essential for the development of precision agriculture management, whereas at the district level it can provide valuable information for supply chain management. In this paper, Huan Jing (HJ) satellite HJ1A/B and Landsat 8 Operational Land Imager (OLI) images were employed to retrieve leaf area index (LAI) and canopy cover (CC) in the Yangling area (Central China). These variables were then assimilated into two crop models, Aquacrop and simple algorithm for yield (SAFY), in order to compare their performances and practicalities. Due to the models' specificities and computational constraints, different assimilation methods were used. For SAFY, the ensemble Kalman filter (EnKF) was applied using LAI as the observed variable, while for Aquacrop, particle swarm optimization (PSO) was used, using canopy cover (CC). These techniques were applied and validated both at the field and at the district scale. In the field application, the lowest relative root-mean-square error (RRMSE) value of 18% was obtained using EnKF with SAFY. On a district scale, both methods were able to provide production estimates in agreement with data provided by the official statistical offices. From an operational point of view, SAFY with the EnKF method was more suitable than Aquacrop with PSO, in a data assimilation context. Keywords: leaf area index (LAI); canopy cover (CC); Landsat 8; HJ1A/B; artificial neural network (ANN); ensemble Kalman filter (EnKF); particle swarm optimization (PSO)

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Section: Pso Algorithm For Aquacrop Model Assimilationmentioning

confidence: 77%

Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models

et al. 2017

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“…For the specific two machines/components case and weighted jobs, Sung and Yoon (1998) propose some constructive heuristics. Finally, Lin et al (2017) address the version with interfering jobs,…”

Section: Referencementioning

confidence: 99%

Deterministic assembly scheduling problems: A review and classification of concurrent-type scheduling models and solution procedures

Framiñán

2019

European Journal of Operational Research

104

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Many activities in industry and services require the scheduling of tasks that can be concurrently executed, the most clear example being perhaps the assembly of products carried out in manufacturing. Although numerous scientific contributions have been produced on this area over the last decades, the wide extension of the problems covered and the lack of a unified approach have lead to a situation where the state of the art in the field is unclear, which in turn hinders new research and makes translating the scientific knowledge into practice difficult. In this paper we propose a unified notation for assembly scheduling models that encompass all concurrent-type scheduling problems. Using this notation, the existing contributions are reviewed and classified into a single framework, so a comprehensive, unified picture of the field is obtained. In addition, a number of conclusions regarding the state of the art in the topic are presented, as well as some opportunities for future research.

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“…(2015) and Lin et al. (2017) present a honeybee optimization algorithm and a particle swarm optimization algorithm for the problem of two‐agent single‐machine scheduling problem, respectively. In this study, a three‐agent problem is considered and a novel evolutionary‐based reference point determination of NSGA‐III (ERNSGA‐III) is used to solve it.…”

Section: Literature Reviewmentioning

confidence: 99%

An ERNSGA‐III algorithm for the production and distribution planning problem in the multiagent supply chain

Gharaei

Jolai

2019

Int Trans Operational Res

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Nowadays, new issues have been emerged in industry by increasing the supply chain boundaries, especially when some supply chain members seek their own interests. In the literature, this case is referred to as a multiagent problem in which each agent has his/her own set of jobs and objectives. Here, an integrated production scheduling and distribution problem in a multisite supply chain is investigated from the three‐agent perspective of the manufacturer, distributor, and customer. Due to the complexity of the problem, a novel evolutionary‐based reference point determination of the third version of the non‐dominated sorting genetic algorithm (ERNSGA‐III) is proposed. In this algorithm, some modifications to the classic version of NSGA‐III have been made and a novel reference point determination method based on Bees algorithm is used in the selection operation. Some statistical tests are performed to compare the proposed algorithm with three other algorithms in the literature. The results show that the proposed algorithm is more effective than other algorithms.

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Particle swarm optimization and opposite-based particle swarm optimization for two-agent multi-facility customer order scheduling with ready times

Cited by 35 publications

References 44 publications

Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models

Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models

Deterministic assembly scheduling problems: A review and classification of concurrent-type scheduling models and solution procedures

An ERNSGA‐III algorithm for the production and distribution planning problem in the multiagent supply chain

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