The Experts below are selected from a list of 18090 Experts worldwide ranked by ideXlab platform
Gabriel Luque - One of the best experts on this subject based on the ideXlab platform.
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New Local Search Procedure for Workforce Planning Problem
Cybernetics and Information Technologies, 2020Co-Authors: Stefka Fidanova, Gabriel LuqueAbstract:AbstractOptimization of Workforce Planning is important for any production area. This leads to an improvement in production process. The aim is minimization of the assignment costs of the workers, who will do the jobs. The problem is to select and assign employers to the jobs to be performed. The constraints are very strong, coming both from the specifics of the production process and from the legislation. Sometimes it is difficult to find feasible solutions. The complexity of the problem is very high and the needed number of calculations is exponential, therefore only specially developed algorithms can be applied. The objective is to minimize the assignment cost, while respecting all requirements. We propose a new hybrid metaheuristic algorithm to solve the Workforce-Planning problem, which is a combination between Ant Colony Optimization (ACO) and suitable local search procedure.
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Ant Colony Optimization Algorithm for Workforce Planning: Influence of the Algorithm Parameters
Advanced Computing in Industrial Mathematics, 2018Co-Authors: Stefka Fidanova, Olympia Roeva, Gabriel LuqueAbstract:The Workforce Planning is a difficult optimization problem. It is important real life problem which helps organizations to determine Workforce which they need. A Workforce Planning problem is very complex and needs special algorithms to be solved using reasonable computational resources. The problem consists to select set of employers from a set of available workers and to assign this staff to the tasks to be performed. The objective is to minimize the costs associated to the human resources needed to fulfil the work requirements. A good Workforce planing is important for an organization to accomplish its objectives. The complexity of this problem does not allow the application of exact methods for instances of realistic size. Therefore we will apply Ant Colony Optimization (ACO) method which is a stochastic method for solving combinatorial optimization problems. On this paper we focus on influence of the parameters on ACO algorithm performance.
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WCO@FedCSIS - Intercriteria Analysis of ACO Performance for Workforce Planning Problem
Recent Advances in Computational Optimization, 2018Co-Authors: Olympia Roeva, Gabriel Luque, Stefka Fidanova, Marcin PaprzyckiAbstract:The Workforce Planning helps organizations to optimize the production process with the aim to minimize the assigning costs. The problem is to select a set of employees from a set of available workers and to assign this staff to the jobs to be performed. A Workforce Planning problem is very complex and requires special algorithms to be solved. The complexity of this problem does not allow the application of exact methods for instances of realistic size. Therefore, we will apply Ant Colony Optimization (ACO) algorithm, which is a stochastic method for solving combinatorial optimization problems. The ACO algorithm is tested on a set of 20 Workforce Planning problem instances. The obtained solutions are compared with other methods, as scatter search and genetic algorithm. The results show that ACO algorithm performs better than other the two algorithms. Further, we focus on the influence of the number of ants and the number of iterations on ACO algorithm performance. The tests are done on 16 different problem instances – ten structured and six unstructured problems. The results from ACO optimization procedures are discussed. In order to evaluate the influence of considered ACO parameters additional investigation is done. InterCriteria Analysis is performed on the ACO results for the regarded 16 problems. The results show that for the considered here Workforce Planning problem the best performance is achieved by the ACO algorithm with five ants in population.
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Parallel Genetic Algorithm for the Workforce Planning Problem
Studies in Computational Intelligence, 2011Co-Authors: Gabriel Luque, Enrique AlbaAbstract:Decision making associated with Workforce Planning results in difficult optimization problems, this is so it involves multiple levels of complexity. In fact, the Workforce Planning problem that we tackle in this chapter consists of two sets of decisions: selection and assignment. The first step selects a small set of employees from a large number of available workers and the second (decision) assigns this staff to the tasks to be performed. The objective is to minimize the costs associated to the human resources needed to fulfill the work requirements. An effective Workforce plan is an essential tool to identify appropriate workload staffing levels and justify budget allocations so that organizations can meet their objectives.
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Parallel Metaheuristics for Workforce Planning
Journal of Mathematical Modelling and Algorithms, 2007Co-Authors: Enrique Alba, Gabriel Luque, Francisco LunaAbstract:Workforce Planning is an important activity that enables organizations to determine the Workforce needed for continued success. A Workforce Planning problem is a very complex task requiring modern techniques to be solved adequately. In this work, we describe the development of three parallel metaheuristic methods, a parallel genetic algorithm, a parallel scatter search, and a parallel hybrid genetic algorithm, which can find high-quality solutions to 20 different problem instances. Our experiments show that parallel versions do not only allow to reduce the execution time but they also improve the solution quality.
Enrique Alba - One of the best experts on this subject based on the ideXlab platform.
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Parallel Genetic Algorithm for the Workforce Planning Problem
Studies in Computational Intelligence, 2011Co-Authors: Gabriel Luque, Enrique AlbaAbstract:Decision making associated with Workforce Planning results in difficult optimization problems, this is so it involves multiple levels of complexity. In fact, the Workforce Planning problem that we tackle in this chapter consists of two sets of decisions: selection and assignment. The first step selects a small set of employees from a large number of available workers and the second (decision) assigns this staff to the tasks to be performed. The objective is to minimize the costs associated to the human resources needed to fulfill the work requirements. An effective Workforce plan is an essential tool to identify appropriate workload staffing levels and justify budget allocations so that organizations can meet their objectives.
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Parallel Metaheuristics for Workforce Planning
Journal of Mathematical Modelling and Algorithms, 2007Co-Authors: Enrique Alba, Gabriel Luque, Francisco LunaAbstract:Workforce Planning is an important activity that enables organizations to determine the Workforce needed for continued success. A Workforce Planning problem is a very complex task requiring modern techniques to be solved adequately. In this work, we describe the development of three parallel metaheuristic methods, a parallel genetic algorithm, a parallel scatter search, and a parallel hybrid genetic algorithm, which can find high-quality solutions to 20 different problem instances. Our experiments show that parallel versions do not only allow to reduce the execution time but they also improve the solution quality.
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ISDA - Designing a Parallel GA for Large Instances of the Workforce Planning Problem
Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), 2007Co-Authors: Enrique Alba, Gabriel LuqueAbstract:Workforce Planning is an important activity that enables organizations to determine the Workforce needed for a given task. Solving a Workforce Planning problem is a hard combinatorial process requiring modern techniques such advanced metaheuristics. In this work, we analyze several options to design a parallel genetic algorithm, which can find high-quality solutions to realistic size problem instances.
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IPDPS - Workforce Planning with parallel algorithms
Proceedings 20th IEEE International Parallel & Distributed Processing Symposium, 2006Co-Authors: Enrique Alba, Gabriel Luque, Francisco LunaAbstract:Workforce Planning is an important activity that enables organizations to determine the Workforce needed for continued success. A Workforce Planning problem is a very complex task that requires modern techniques to be solved adequately. In this work, we describe the development of two parallel metaheuristic methods, a parallel genetic algorithm and a parallel scatter search, which can find high-quality solutions to 20 different problem instances. Our experiments show that parallel versions do not only allow to reduce the execution time but they also improve the solution quality.
Michael Brundage - One of the best experts on this subject based on the ideXlab platform.
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managing a national radiation oncologist Workforce a Workforce Planning model
Radiotherapy and Oncology, 2012Co-Authors: Teri Stuckless, Catherine De Metz, Brent Tompkins, Matthew Parliament, Michael Milosevic, Michael BrundageAbstract:Abstract Purpose The specialty of radiation oncology has experienced significant Workforce Planning challenges in many countries. Our purpose was to develop and validate a Workforce-Planning model that would forecast the balance between supply of, and demand for, radiation oncologists in Canada over a minimum 10-year time frame, to identify the model parameters that most influenced this balance, and to suggest how this model may be applicable to other countries. Methods A forward calculation model was created and populated with data obtained from national sources. Validation was confirmed using a historical prospective approach. Results Under baseline assumptions, the model predicts a short-term surplus of RO trainees followed by a projected deficit in 2020. Sensitivity analyses showed that access to radiotherapy (proportion of incident cases referred), individual RO workload, average age of retirement and resident training intake most influenced balance of supply and demand. Within plausible ranges of these parameters, substantial shortages or excess of graduates is possible, underscoring the need for ongoing monitoring. Conclusions Workforce Planning in radiation oncology is possible using a projection calculation model based on current system characteristics and modifiable parameters that influence projections. The workload projections should inform policy decision making regarding growth of the specialty and training program resident intake required to meet oncology health services needs. The methods used are applicable to Workforce Planning for radiation oncology in other countries and for other comparable medical specialties.
Raik Stolletz - One of the best experts on this subject based on the ideXlab platform.
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operational Workforce Planning for check in counters at airports
Transportation Research Part E-logistics and Transportation Review, 2010Co-Authors: Raik StolletzAbstract:Abstract This paper addresses operation models for Workforce Planning for check-in systems at airports. We characterize different tasks of the hierarchical Workforce Planning problem with time-dependent demand. A binary linear programming formulation is developed for the fortnightly tour scheduling problem with flexible employee contracts. This binary programming model is solved for optimality by CPLEX for real-world demand scenarios with different Workforce sizes. The numerical study analyzes the impact of the degree of flexibility and economies of scale. The model formulation is extended to generate convenient tours with regard to employee preferences.
Martin Kunc - One of the best experts on this subject based on the ideXlab platform.
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Strategic Workforce Planning in healthcare: A multi-methodology approach
European Journal of Operational Research, 2018Co-Authors: Graham Willis, Siôn Cave, Martin KuncAbstract:Abstract This paper presents a description of the development and use of a framework for strategic Workforce Planning for healthcare at the national level. The framework is called the Robust Workforce Planning Framework, and was developed by the Centre for Workforce Intelligence. The Centre for Workforce Intelligence was a national organisation that delivered Workforce Planning advice, and was active from July 2010 until March 2016. The Centre was a key contributor to the Planning of future Workforce requirements for healthcare in England and was primarily commissioned by the English Department of Health, Health Education England and Public Health England, supporting them in national and local strategic Workforce Planning. The framework involved the use of multiple methodologies, including the development of strategic Workforce models based on System Dynamics, and the framework evolved through practise. This paper describes contributions to three areas in the field: healthcare Workforce Planning models using System Dynamics, the use of System Dynamics to support strategic Planning with the integration of multiple methodologies, and facilitated modelling through building and using System Dynamics models in workshops.
