Hybrid Ant Colony Optimization Algorithm for Workforce Planning

Every organization and factory optimize their production process with a help of workforce planing. The aim is minimization of the assignment costs of the workers, who will do the jobs. The problem is very complex and needs exponential number of calculations, therefore special algorithms are developed to be solved. The problem is to select employers and to assign them to the jobs to be performed. This problem has very strong constraints and it is difficult to find feasible solutions. The objective is to fulfil the requirements and to minimize the assignment cost. We propose a hybrid Ant Colony Optimization (ACO) algorithm to solve the workforce problem, which is a combination between ACO and an appropriate local search procedure.

[1]  Hongxun Jiang,et al.  A genetic algorithm-based decomposition approach to solve an integrated equipment-workforce-service planning problem☆ , 2015 .

[2]  Maria Ganzha,et al.  Introducing the Environment in Ant Colony Optimization , 2016 .

[3]  İlker Özdemir,et al.  A State of Art Review on Metaheuristic Methods in Time-Cost Trade-off Problems , 2017 .

[4]  Wansheng Tang,et al.  An uncertain workforce planning problem with job satisfaction , 2017, Int. J. Mach. Learn. Cybern..

[5]  G. M. Campbell,et al.  A two-stage stochastic program for scheduling and allocating cross-trained workers , 2011, J. Oper. Res. Soc..

[6]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[7]  Scott E. Grasman,et al.  Integer programming techniques for solving non-linear workforce planning models with learning , 2015, Eur. J. Oper. Res..

[8]  Wansheng Tang,et al.  An uncertain search model for recruitment problem with enterprise performance , 2017, J. Intell. Manuf..

[9]  Frederick Easton,et al.  Service Completion Estimates for Cross‐trained Workforce Schedules under Uncertain Attendance and Demand , 2014 .

[10]  Enrique Alba,et al.  Parallel Metaheuristics for Workforce Planning , 2007, J. Math. Model. Algorithms.

[11]  Mitsuo Gen,et al.  A new model for single machine scheduling with uncertain processing time , 2017, J. Intell. Manuf..

[12]  Surafel Luleseged Tilahun,et al.  Firefly algorithm for discrete optimization problems: A survey , 2017, KSCE Journal of Civil Engineering.

[13]  Olympia Roeva,et al.  Cuckoo Search Algorithm for Model Parameter Identification , 2017 .

[14]  Colin N. Jones,et al.  A two-stage stochastic programming approach to employee scheduling in retail outlets with uncertain demand , 2015 .

[15]  Alberto Gómez,et al.  Review of metaheuristics applied to heat exchanger network design , 2017, Int. Trans. Oper. Res..

[16]  Thomas Stützle,et al.  Ant Colony Optimization Theory , 2004 .

[17]  Katarzyna Grzybowska,et al.  Sustainable supply chain - Supporting tools , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[18]  Aziz Moukrim,et al.  A Memetic Algorithm for staff scheduling problem in airport security service , 2013, Expert Syst. Appl..

[19]  Limei Yan,et al.  Uncertain aggregate production planning , 2012, Soft Computing.

[20]  Mohammed Othman,et al.  Integrating workers' differences into workforce planning , 2012, Comput. Ind. Eng..

[21]  Gang Liu,et al.  An uncertain goal programming model for machine scheduling problem , 2017, J. Intell. Manuf..

[22]  Olympia Roeva,et al.  Ant colony optimization algorithm for workforce planning , 2017, 2017 Federated Conference on Computer Science and Information Systems (FedCSIS).

[23]  Olympia Roeva,et al.  InterCriteria Analysis of ACO start startegies , 2016, 2016 Federated Conference on Computer Science and Information Systems (FedCSIS).