Multiple-Attribute-Based Expert Support System for Customer/Order Evaluation

Vasko, Francis J.; Cregger, Michael L.; Lutsko, Larry G.; Newhart, Dennis D.; Renn, Douglas J.

doi:10.1057/jors.1990.168

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…Wen et al [9] presented a mixed integer programming formulation and used numerical analysis to study simultaneous pricing, order acceptance, scheduling, and lead-time decisions. Vasko et al [10] appraised buyer order using multiple attribute expert support systems with analytic hierarchy process (AHP). Xiao [11] introduced a novel evidential fuzzy multi-criteria decision making method (EFMCDM) by integrating Dempster-Shafer theory with belief entropy to determine the optimal alternative.…”

Section: Introductionmentioning

confidence: 99%

Order Priority Evaluation Based on Kriging Model Under Supply Chain Environment

Zhu

Ouyang

2021

IEEE Access

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How to tackle the order production priority in production scheduling is a key issue for make-to-order enterprises. Some approaches of determining the order production priority have been proposed from different angles such as linear programming, entropy weight and analytic hierarchy process. Nevertheless, under the supply chain environment, the determination of order production priority becomes a complex problem, and traditional approaches have their limitations. Hence, in this paper, a new evaluation index system of order production priority is established under supply chain environment, and then an evaluation model of order priority based on Kriging model is proposed to determine the order production priority. The performance of proposed model is demonstrated by simulation experiments. The results show that the proposed model is suitable for the problems of small samples and it is a feasible, effective evaluation model for order priority. Compared to other models, the proposed model has improved the evaluation precision of order priority. Meanwhile, the proposed model performs more reliable and stable and it augments the methods for order production priority.

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Section: Introductionmentioning

confidence: 99%