The human resources allocation (HRA) is a major issue for container terminals. The workload is organized in shifts covering 24 hours a day and, due to union and work rules, shifts are typically requested to be planned a number of months before their implementation. As a consequence, information on the workforce demand is imprecise when HRA must be planned. This problem must be addressed by a superior exibility, which can be achieved by the two planning levels: the long term stage and the short term one, which encompasses one or more days. In this talk we address the operational planning level which inherits some unchangea- ble decisions from the long term plan and considers the terminal resources involved in real workload. The related literature on HRA does not consider personnel shortfall, which is the most risky situation for terminals, because it may result in vessel delays and in high penalties charged by shipping companies to terminals. The ability to correct manpower shortfalls in a timely fashion is a key factor for the overall productivity of container terminals and their com- petitiveness in the shipping industry. In this talk we present an Integer Linear Programming Model, which addresses this gap by modelling shortages as appropriate decision variables. The objective of our model is to determine the optimal allocation of workers to tasks, shifts and terminal activities, as well as to minimize shortfalls. The model has been used in a rolling horizon fashion, to compare here-and-now decisions deriving from a one-day and a two-day planning horizon. It is exactly solved by a state-of-art solver within reasonable times for the needs of terminal containers. Our experimentation shows that personnel shortfalls can be signi cantly reduced if the model encompasses a longer-than-one-day planning horizon in a rolling horizon fashion. Moreover, whenever shortfalls are observed, they are clustered in low level tasks.
Human Resource Allocation for Maritime Container Terminals: Effects of Planning Horizon Length
DI FRANCESCO, MASSIMO;FADDA, PAOLO;FANCELLO, GIANFRANCO;SERRA, PATRIZIA;ZUDDAS, PAOLA
2012-01-01
Abstract
The human resources allocation (HRA) is a major issue for container terminals. The workload is organized in shifts covering 24 hours a day and, due to union and work rules, shifts are typically requested to be planned a number of months before their implementation. As a consequence, information on the workforce demand is imprecise when HRA must be planned. This problem must be addressed by a superior exibility, which can be achieved by the two planning levels: the long term stage and the short term one, which encompasses one or more days. In this talk we address the operational planning level which inherits some unchangea- ble decisions from the long term plan and considers the terminal resources involved in real workload. The related literature on HRA does not consider personnel shortfall, which is the most risky situation for terminals, because it may result in vessel delays and in high penalties charged by shipping companies to terminals. The ability to correct manpower shortfalls in a timely fashion is a key factor for the overall productivity of container terminals and their com- petitiveness in the shipping industry. In this talk we present an Integer Linear Programming Model, which addresses this gap by modelling shortages as appropriate decision variables. The objective of our model is to determine the optimal allocation of workers to tasks, shifts and terminal activities, as well as to minimize shortfalls. The model has been used in a rolling horizon fashion, to compare here-and-now decisions deriving from a one-day and a two-day planning horizon. It is exactly solved by a state-of-art solver within reasonable times for the needs of terminal containers. Our experimentation shows that personnel shortfalls can be signi cantly reduced if the model encompasses a longer-than-one-day planning horizon in a rolling horizon fashion. Moreover, whenever shortfalls are observed, they are clustered in low level tasks.
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