The impact of special days in call arrivals forecasting: A neural network approach to modelling special days

Barrow, Devon K.; Kourentzes, Nikolaos

doi:10.1016/j.ejor.2016.07.015

Cited by 43 publications

(23 citation statements)

References 63 publications

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“…Table 5 provides detailed information of the parameters optimization process for the RF using Scikit Learn's GridSearchCV function [49]. Minimum number of samples required to be at a leaf node 1x10 -2 , 1x10 -3 , 1x10 -4 , 1x10 -5 , 1x10 -6 Minimum impurity required to split a node 1x10 -2 , 1x10 -3 , 1x10 -4 , 1x10 -5 , 1x10 -6 Lastly, NNs are widely recognized for their capability to model complex statistical interactions between variables [56,57]. An NN is a system of interconnected neurons, organized by independent layers, inspired by biological nervous functioning [58].…”

Section: Design and Development: The Tested Modelling Approachesmentioning

confidence: 99%

Improving healthcare access management by predicting patient no-show behaviour

Barrera

Brailsford

Bravo

et al. 2020

Decision Support Systems

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Low attendance levels in medical appointments have been associated with poor health outcomes and efficiency problems for service providers. To address this problem, healthcare managers could aim at improving attendance levels or minimizing the operational impact of no-shows by adapting resource allocation policies. However, given the uncertainty of patient behaviour, generating relevant information regarding no-show probabilities could support the decision-making process for both approaches. In this context many researchers have used multiple regression models to identify patient and appointment characteristics than can be used as good predictors for no-show probabilities. This work develops a Decision Support System (DSS) to support the implementation of strategies to encourage attendance, for a preventive care program targeted at underserved communities in Bogotá, Colombia. Our contribution to literature is threefold. Firstly, we assess the effectiveness of different machine learning approaches to improve the accuracy of regression models. In particular, Random Forest and Neural Networks are used to model the problem accounting for non-linearity and variable interactions. Secondly, we propose a novel use of Layer-wise Relevance Propagation in order to improve the explainability of neural network predictions and obtain insights from the modelling step. Thirdly, we identify variables explaining no-show probabilities in a developing context and study its policy implications and potential for improving healthcare access. In addition to quantifying relationships reported in previous studies, we find that income and neighbourhood crime statistics affect no-show probabilities. Our results will support patient prioritization in a pilot behavioural intervention and will inform appointment planning decisions.

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Section: Design and Development: The Tested Modelling Approachesmentioning

confidence: 99%

Improving healthcare access management by predicting patient no-show behaviour

Barrera

Brailsford

Bravo

et al. 2020

Decision Support Systems

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“…The literature on the operations management in call centers is rich (Akşin and Harker, 2003;Avramidis et al, 2010;Jouini et al, 2010;Aktekin, 2014;Ibrahim et al, 2016;Barrow and Kourentzes, 2018). For some background on this literature, we refer the reader to the two surveys by and Akşin et al (2007).…”

Section: Literature Reviewmentioning

confidence: 99%

On the scheduling of operations in a chat contact center

Legros¹,

Jouini

2019

European Journal of Operational Research

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Unlike calls, the chat channel allows a contact center agent to simultaneously work with many customers. The benefit of this flexibility can be however challenged by a new abandonment feature during service. In this context, new flow routing questions arise. How many chats should an agent serve? Which agent should be selected? Or may a chat be served by more than one agent? We aim to answer these questions so as to find the best trade-off between time spent in service, queueing delay and abandonment.For this purpose, we determine conditions under which the traditional Least Busy First or Most Busy First policies are optimal for agent selection. Using a dynamic programming analysis, we prove that a state-dependent threshold reservation policy is optimal when a chat can be handed to other agents without loss of efficiency. This analysis reveals that reservation is useful as it allows to maintain of sufficient agent productivity and abandonment from the queue which in turn reduce the system's congestion. In addition, we show the closeness between the optimal policy and the easier-to-implement fixed capacity policy if this fixed capacity is optimized. When a chat cannot be handed to other agents, we develop a dispatching policy improvement algorithm based on the explicit computation of the relative value function for an initial policy with fixed probabilities. The dispatching policy significantly outperforms non state-dependent ones, as it partly compensates the agents' inability to share a chat service.

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“…Artificial neural networks to forecast the number of call arrivals were used in [10]. Time series statistical and machine learning methods to forecast call volume in a call centre were used in [11]. Call center performance with machine learning was predicted in [12] and call center arrivals at a call center was forecasted using dynamic linear model in [13].…”

Section: Introductionmentioning

confidence: 99%

Makine Öğrenmesi Kullanarak Çağrı Merkezine Gelen Çağrıların Tahmin Edilmesi

Ballouch

Akay

Erdem

et al. 2021

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Çağrı merkezi, bir kuruluş için çok sayıda telefon görüşmesini idare edebilecek şekilde donatılmış bir ofistir ve aramaları tahmin etme yeteneği kilit bir faktördür. Bir şirket, arama sayısını doğru bir şekilde tahmin ederek personel ihtiyaçlarını planlayabilir, hizmet seviyesi gereksinimlerini karşılayabilir, müşteri memnuniyetini artırabilir ve diğer birçok optimizasyondan yararlanabilir. Bu çalışmada, bir çağrı merkezindeki gelen çağrı sayısını tahmin etmek için zaman gecikmeleri ile entegreli Çok Katmanlı Algılayıcı (Multilayer Perceptron -MLP) ve Uzun Kısa Vadeli Bellek (Long-Short Term Memory -LSTM) tabanlı modeller geliştirilmiştir. 12, 24, 36 ve 48'lik tahminler üretilip, tahmin modellerinin performansı Ortalama Mutlak Hata (Mean Absolute Error -MAE) kullanılarak değerlendirilmiştir. Sonuçlar, MLP tabanlı modellerin MAE değerlerinin 1,50 ile 13,58 arasında, LSTM tabanlı modellerin ise 19,99 ile 66,74 arasında değiştiğini göstermektedir.

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The impact of special days in call arrivals forecasting: A neural network approach to modelling special days

Cited by 43 publications

References 63 publications

Improving healthcare access management by predicting patient no-show behaviour

Improving healthcare access management by predicting patient no-show behaviour

On the scheduling of operations in a chat contact center

Makine Öğrenmesi Kullanarak Çağrı Merkezine Gelen Çağrıların Tahmin Edilmesi

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