Artificial Bee Colony Algorithm with Nelder–Mead Method to Solve Nurse Scheduling Problem

Rajeswari Muniyan, Rajakumar Ramalingam, Sultan S. Alshamrani, Durgaprasad Gangodkar, Ankur Dumka, Rajesh Singh, Anita Gehlot and Mamoon Rashid
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Rajeswari Muniyan: Department of Information Technology, Sri Manakula Vinayagar Engineering College, Pondicherry 605107, India
Rajakumar Ramalingam: Department of Computer Science and Technology, Madanapalle Institute of Technology & Science, Madanapalle 517325, India
Sultan S. Alshamrani: Department of Information Technology, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Durgaprasad Gangodkar: Department of Computer Science & Engineering, Graphic Era Deemed to Be University, Dehradun 248002, India
Ankur Dumka: Department of Computer Science & Engineering, Graphic Era Deemed to Be University, Dehradun 248002, India
Rajesh Singh: Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India
Anita Gehlot: Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India
Mamoon Rashid: Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411048, India

Mathematics, 2022, vol. 10, issue 15, 1-24

Abstract: The nurse scheduling problem (NSP) is an NP-Hard combinatorial optimization scheduling problem that allocates a set of shifts to the group of nurses concerning the schedule period subject to the constraints. The objective of the NSP is to create a schedule that satisfies both hard and soft constraints suggested by the healthcare management. This work explores the meta-heuristic approach to an artificial bee colony algorithm with the Nelder–Mead method (NM-ABC) to perform efficient nurse scheduling. Nelder–Mead (NM) method is used as a local search in the onlooker bee phase of ABC to enhance the intensification process of ABC. Thus, the author proposed an improvised solution strategy at the onlooker bee phase with the benefits of the NM method. The proposed algorithm NM-ABC is evaluated using the standard dataset NSPLib, and the experiments are performed on various-sized NSP instances. The performance of the NM-ABC is measured using eight performance metrics: best time, standard deviation, least error rate, success percentage, cost reduction, gap, and feasibility analysis. The results of our experiment reveal that the proposed NM-ABC algorithm attains highly significant achievements compared to other existing algorithms. The cost of our algorithm is reduced by 0.66%, and the gap percentage to move towards the optimum value is 94.30%. Instances have been successfully solved to obtain the best deal with the known optimal value recorded in NSPLib.

Keywords: meta-heuristic approach; evolutionary algorithm; nurse scheduling problem; artificial bee colony algorithm; Nelder–Mead method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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