Optimizing Shift Scheduling for Tank Trucks Using an Effective Stochastic Variable Neighbourhood Approach
In this contribution we introduce the application of a stochastic variable neighbourhood algorithm in order to solve a problem taken from a real world situation faced by a small oil company. The problem at hand is an optimization problem and relates to shift scheduling of tank trucks. Input data consists of a set of tank trucks with different properties along with a set of drivers having different driving skills. The objective is to establish a one-to-one assignment between the set of available drivers and the set of tank trucks shifts so as all related hard and soft constraints are satisfied and the tank truck shifts are optimized. This specific problem has been already addressed at the literature and some encouraging results have been published (Knust and Schumacher, 2011). Nevertheless, the optimization algorithm proposed in current contribution manages to achieve better solutions for all but one instance among thirty of them in the same reasonable amount of computational time.
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