Workforce planning in a lotsizing mail processing problem

Júdice, Joaquim J.; Martins, Pedro; Nunes, Jacinto

doi:10.1016/j.cor.2004.04.011

Cited by 15 publications

(11 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A wide range of research areas are available on workforce optimisation problem, ranging from lot-sizing consideration (Judice et al 2004;Ighravwe and Oke 2014) to workforce modelling using artificial intelligence tools such as simulated annealing, genetic algorithms and some defined metrics (Safaei et al 2008;Ighravwe and Oke 2014;Ighravwe et al 2015Ighravwe et al , 2016a. Nonetheless, studies are rare on the maintenance workforce optimisation problem.…”

Section: Literature Review Survey Of Literaturementioning

confidence: 99%

An enhanced reliability-oriented workforce planning model for process industry using combined fuzzy goal programming and differential evolution approach

2017

View full text Add to dashboard Cite

This paper draws on the ''human reliability'' concept as a structure for gaining insight into the maintenance workforce assessment in a process industry. Human reliability hinges on developing the reliability of humans to a threshold that guides the maintenance workforce to execute accurate decisions within the limits of resources and time allocations. This concept offers a worthwhile point of deviation to encompass three elegant adjustments to literature model in terms of maintenance time, workforce performance and return-on-workforce investments. These fully explain the results of our influence. The presented structure breaks new grounds in maintenance workforce theory and practice from a number of perspectives. First, we have successfully implemented fuzzy goal programming (FGP) and differential evolution (DE) techniques for the solution of optimisation problem in maintenance of a process plant for the first time. The results obtained in this work showed better quality of solution from the DE algorithm compared with those of genetic algorithm and particle swarm optimisation algorithm, thus expressing superiority of the proposed procedure over them. Second, the analytical discourse, which was framed on stochastic theory, focusing on specific application to a process plant in Nigeria is a novelty. The work provides more insights into maintenance workforce planning during overhaul rework and overtime maintenance activities in manufacturing systems and demonstrated capacity in generating substantially helpful information for practice.

show abstract

Section: Literature Review Survey Of Literaturementioning

confidence: 99%

An enhanced reliability-oriented workforce planning model for process industry using combined fuzzy goal programming and differential evolution approach

2017

View full text Add to dashboard Cite

show abstract

“…Srour, Haas, and Morton (2006) generated an LP scheme for efficient fit of demand and supply of construction personnel considering training, hiring, and allocation. MILP has been used in a demand-based model to optimize the size and arrangement of long-term employees in a postal service office (Qi & Bard, 2006), and to minimize the costs associated with the workforce size in a mail-processing center (Judice, Martins, & Nunes, 2005). Weigel and Wilcox (1993) combined GP, LP, and Markov inventory techniques to model the supply side of the US Army's enlisted workforce decision support system.…”

Section: Optimization Modelsmentioning

confidence: 99%

Workforce forecasting models: A systematic review

Safarishahrbijari

2018

Journal of Forecasting

View full text Add to dashboard Cite

Workforce analytics involves using models that integrate internal and external data to predict future workforce and help organizations in any industry examine factors that have a prognostic effect. This paper assesses workforce modeling and prediction methods by examining their rationale, strengths, and constraints. It aims to identify enhancements for further development of workforce forecasting models and compares the capacity and reliability of different forecasting methods. Past and present modeling trends are described and critiqued based on their relevance to current requirements. Several approaches are reviewed, such as time series modeling and system dynamics simulation. Sensitivity analysis in models is assessed. The models are decomposed into three modes: supply‐based, demand‐based, and need‐based, which in some cases provide substantially different estimates of future workforce need. The chronological progression of models' development is analyzed. The articles are also classified based on the countries and the sectors that have paid great attention to workforce prediction research. Consideration of the use of workforce models and the inputs into such models is not within the scope of this paper.

show abstract

“…This limitation makes it harder to construct and analyze alternative strategies. Judice et al (2005) analyze the assignment of staff to shifts within a daily working time horizon, considering differences in shift durations and start times. In this case the model is not solved over known workload requirements, but it must cover the workloads generated by a batch decision problem running over the same time horizon.…”

Section: Introductionmentioning

confidence: 99%