An ant colony optimization approach for solving an operating room surgery scheduling problem

Xiang, Wei; Yin, Jiao; Lim, Gino J.

doi:10.1016/j.cie.2015.04.010

Cited by 83 publications

(68 citation statements)

References 24 publications

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“…Evolutionary heuristic methods make it possible to model dynamic integrated forward/reverse logistics network for 3rd party logistics (3PL) providers, where GA optimizes the network plan to help in the determination of resource plans for capacities of technical and human resources [9]. Ant colony based heuristics support open shop scheduling problems with a multi-skills resource constraint [10] and the solution of operating room surgery scheduling [11]. Scheduling of temporary and skilled-workers in dynamic cellular manufacturing systems can be solved by hybrid algorithms [12].…”

Section: State Of the Art In Human Resource Researchmentioning

confidence: 99%

Markov-Chain Simulation-Based Analysis of Human Resource Structure: How Staff Deployment and Staffing Affect Sustainable Human Resource Strategy

2018

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Manufacturing and service processes are composed of several elements: Technical, financial, logistics, information and human resources. Staff deployment and staffing is an essential problem in the human resource management domain because the structure of employees would be continuously in an optimal relationship to the jobs to be performed. This paper proposes a conceptual model for the analysis of human resource deployment processes. After a systematic literature review, it was found that algorithms are important tools for the design and control of human resource problems since a wide range of models determines an optimization problem. According to that, the main focus of this research is the modelling and analysis of human resource deployment processes of manufacturing companies using Markov-chain mathematics, also taking into account the absorbing phenomena of employees’ promotion. The main contribution of this article includes the model framework of Markov-chain simulation of a human resource deployment problem; the mathematical description of different human resource deployment strategies with subdiagonal and superdiagonal promotion matrices; the computational results of the described model with different datasets and scenarios. In the case of a given human resource strategy, the Markovian human resource deployment process of a company was analyzed. The analyzed model was the HR deployment of assembly line operators in a multinational company, including six levels of promotion. The results of the scenario analysis show that promotion and recruitment rates have a great impact on the future employees’ structure.

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Section: State Of the Art In Human Resource Researchmentioning

confidence: 99%

Markov-Chain Simulation-Based Analysis of Human Resource Structure: How Staff Deployment and Staffing Affect Sustainable Human Resource Strategy

2018

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“…NP-hard problems in large-scale are tackled by meta-heuristic and swarm intelligence algorithms (Dekhici andBelkadi, 2015, Arun andKumar, 2017). In many researches in the field of the SCS problem, some heuristic or meta-heuristic procedures such as genetic algorithm (Marques et al, 2014), simulated annealing Demeulemeester, 2007, Beliën et al, 2009), tabu search (Lamiri et al, 2009, Saremi et al, 2013, and ant colony optimization (Xiang et al, 2015 were developed to achieve near-optimal solutions, because this problem is NP-hard combinatorial optimization problem (Marques et al, 2014). The ACO is compatible with the MRSCS problem since this is classified into the constructive algorithms and these always generate a feasible solution in a short time,while improvement approaches may generate infeasible solutions for the MRSCS after applying their operators and hence more time may be needed to repair the infeasible solutions.…”

Section: Background and Related Workmentioning

confidence: 99%

Multiple Resource Surgical Case Scheduling Problem: Ant Colony System Approach

Behmanesh¹,

Zandieh²,

Molana³

2020

ECECSR

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In this paper, we address the multiple-resource surgical case scheduling(MRSCS) problem in multi-operating theatre to minimize makespan. The constraints of no-wait multiple resource flexible job shop problem (FJSP) are considered for formulating MRSCS problem because the environment of the operating room is similar to the FJSP. Minimization of makespan forMRSCS is NP-hard combinatorial optimization problem; hence we employ the ant colony optimization algorithm so as to tackle this problem. These proposed approaches are illustrated by three test cases include small, medium, and large dataset. Consequently, the results of the experiments indicate that ant colony system and elitism ant system outperform traditional ant system since, the mean, the standard deviation, the best, and the worst solutions of proposed algorithms are better than the results of the traditional algorithm.

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“…In solving strongly NP-hard problems, researchers use either hybrid Artificial Intelligence methods based on combining the positive properties of individual evolutionary computing methods, or solving multi-objective optimization problems [23,46]. Particularly deeply explored is the field of Planning and Scheduling, from service activities [42] to production systems [37]. The authors implement Artificial Intelligence algorithms to benchmark examples [28], as well as to realworld examples [33].…”

Section: Literature Reviewmentioning

confidence: 99%

Use of a Simulation Environment and Metaheuristic Algorithm for Human Resource Management in a Cyber-Physical System

Zhang

Buchmeister

Liu

et al. 2019

Springer Series in Advanced Manufacturing

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At the time of Industry 4.0 and the emergence of collaborative workplaces based on the cooperation of robots (machines) and humans, the number of human workplaces in the Industry 4.0 production system is crucial. In this chapter, we present the use of the evolutionary computation methods that use the input data of a real production system and transfer it through the five-stage Cyber-Physical System architecture into the simulation environment in order to determine the optimal number of workers. By using these methods, we confirm the hypothesis of the importance of correctly determining the number of workers in the manufacturing process in Industry 4.0. Number of workers' determination has a key influence on the product flow time, machine utilization and cost-effectiveness of a production system. Research results show the importance and effectiveness of combining evolutionary computation methods and simulation modelling for the purpose of implementing the advanced approaches of Industry 4.0. The demonstrated approach of combining evolutionary computing, simulation environments and methods of Industry 4.0 can be used from mass customization to mass production systems for the purpose of single-criteria or multi-criteria optimization.

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An ant colony optimization approach for solving an operating room surgery scheduling problem

Cited by 83 publications

References 24 publications

Markov-Chain Simulation-Based Analysis of Human Resource Structure: How Staff Deployment and Staffing Affect Sustainable Human Resource Strategy

Markov-Chain Simulation-Based Analysis of Human Resource Structure: How Staff Deployment and Staffing Affect Sustainable Human Resource Strategy

Multiple Resource Surgical Case Scheduling Problem: Ant Colony System Approach

Use of a Simulation Environment and Metaheuristic Algorithm for Human Resource Management in a Cyber-Physical System

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