Improving workforce scheduling using artificial neural networks model

This paper demonstrates a decision support tool for workforce planning and scheduling. The research conducted in this study is oriented on batch type production typical for smaller production systems, workshops and service systems. The derived model in the research is based on historical data from Public utility service billing company. Model uses Artificial Neural Networks (ANN) fitting techniques. A set of eight input indicators is designed and two variants were tested in the model with two different outputs. Several comprehensive parameter setting experiments were performed to improve prediction performances. Real case studies using historic data from public weather database and communal consolidated billing service show that it is difficult to predict the required number of servers-workers in front office. In a similar way, this model is adequate for complex production systems with unpredictable and volatile demand. Therefore, manufacturing systems which create short cycle products, typical for food processing industry, or production for inventory, may benefit of the research presented in this paper. ANN simulation model with its unique set of features and chosen set of training parameters illustrate that presented model may serve as a valuable decision support system in workforce scheduling for service and production systems. © 2017 PEI, University of Maribor. All rights reserved.

[1]  Fulya Altiparmak,et al.  Buffer allocation and performance modeling in asynchronous assembly system operations: An artificial neural network metamodeling approach , 2007, Appl. Soft Comput..

[2]  John A. Czepiel,et al.  A role theory perspective on dyadic interactions: The service encounter. , 1985 .

[3]  Roger G. Schroeder,et al.  Operations Management: Decision Making in the Operations Function , 1981 .

[4]  David A. Aaker,et al.  Converting Image Into Equity , 2013 .

[5]  X. Y. Ren,et al.  Vehicle scheduling based on plant growth simulation algorithm and distribution staff behavior , 2017 .

[6]  Yongheng Yang,et al.  On the Development of Public-Private Partnerships in Transitional Economies: An Explanatory Framework , 2013 .

[7]  Ali Fuat Guneri,et al.  Forecasting patient length of stay in an emergency department by artificial neural networks , 2015 .

[8]  Teresa Wu,et al.  Evolution of operations management: past, present and future , 2007 .

[9]  Marcus O'Connor,et al.  Artificial neural network models for forecasting and decision making , 1994 .

[10]  R. Kerin,et al.  Store shopping experience and consumer price-quality-value perceptions. , 1992 .

[11]  Sunil Chopra,et al.  Five Decades of Operations Management and the Prospects Ahead , 2004, Manag. Sci..

[12]  A. O'Cass,et al.  Examining service experiences and post-consumption evaluations , 2004 .

[13]  X. Chao,et al.  Operations scheduling with applications in manufacturing and services , 1999 .

[14]  Harun Resit Yazgan,et al.  Demand Forecasting in Pharmaceutical Industry Using Artificial Intelligence: Neuro-Fuzzy Approach , 2014 .

[15]  Armando Calabrese,et al.  Service productivity and service quality: A necessary trade-off? , 2012 .

[16]  David Taylor,et al.  Manufacturing Operations and Supply Chain Management: The LEAN Approach , 2000 .

[17]  Erik Demeulemeester,et al.  Personnel scheduling: A literature review , 2013, Eur. J. Oper. Res..

[18]  S. Asensio-Cuesta,et al.  A genetic algorithm for the design of job rotation schedules considering ergonomic and competence criteria , 2012 .

[19]  Krystsina Bakhrankova,et al.  Production Planning in Continuous Process Industries:Theoretical and Optimization Issues , 2009 .

[20]  A. Calabrese,et al.  The Impact of Workforce Management Systems on Productivity and Quality: A Case Study in the Information and Communication Technology Service Industry , 2013 .

[21]  Dušan Marković,et al.  Management and estimation of thermal comfort, carbon dioxide emission and economic growth by support vector machine , 2016 .

[22]  Nathalie Demoulin,et al.  Waiting time influence on the satisfaction‐loyalty relationship in services , 2007 .

[23]  Gail Tom,et al.  Waiting time delays and customer satisfaction in supermarkets , 1995 .

[24]  Gary M. Thompson,et al.  Variable employee productivity in workforce scheduling , 2006, Eur. J. Oper. Res..

[25]  Stephen L. Vargo,et al.  The Four Service Marketing Myths , 2004 .

[26]  M.Sivakami Sundaria,et al.  Simulation of M/M/1 Queuing System Using ANN , 2015 .

[27]  Giner Alor-Hernández,et al.  The impact of information and communication technologies (ICT) on agility, operating, and economical performance of supply chain , 2017 .

[28]  Felipe Baesler,et al.  Simulation Optimization for Operating Room Scheduling , 2015 .

[29]  Volker Nissen,et al.  Staff Scheduling with Particle Swarm Optimisation and Evolution Strategies , 2009, EvoCOP.

[30]  Maher Rebai,et al.  Scheduling jobs and maintenance activities on parallel machines , 2013, Oper. Res..

[31]  S. Keyvan Mirrazavi,et al.  A web-based workforce management system for Sainsburys Supermarkets Ltd , 2007, Ann. Oper. Res..

[32]  B. Milović,et al.  Prediction and Decision Making in Health Care using Data Mining , 2012 .

[33]  M. Sacramento Quintanilla,et al.  Skilled workforce scheduling in Service Centres , 2009, Eur. J. Oper. Res..

[34]  Werner Sandmann,et al.  Quantitative fairness for assessing perceived service quality in queues , 2011, Operational Research.

[35]  Uwe Aickelin,et al.  An Indirect Genetic Algorithm for a Nurse Scheduling Problem , 2004, Comput. Oper. Res..

[36]  Inneke Van Nieuwenhuyse,et al.  A decision support system for capacity planning in emergency departments , 2015 .

[37]  Pisal Yenradee,et al.  PSO-based algorithm for home care worker scheduling in the UK , 2007, Comput. Ind. Eng..