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Top2. Literature Review
It is often a complicated mission to devise solution methods that are general and able to sort out large integrated problems, when various sub-problems are recognized as NP-hard. A novel technique based on Simulated Annealing was applied in a framework suggested for a general SSP by (Kletzander and Musliu 2020). It was implemented to different benchmark instances for comparison and instances from a novel instance generator that allows the utilization of several Heuristic Algorithms (HA) and their application to a large scope of problems. The algorithm offers better results for all problems and might integrate novel constraints like the equity constraint achieving good results or it may even allow changing the constraints. The proposed framework also enables an easy integration of new moves as well as their reusability across different algorithms.
(Porto et al., 2019) evaluate the possible profits of integrating flexible labor into SSP. The solution technique elaborates, in a novel way, a mixed approach of labor flexibility unifies, first, flexible contracts, that enable the shifts length relaxation and the amount of working weekly hours of staff; and second polyvalent staff, employees specialized to work with diverse types of task, on the other. The Mixed Integer Linear Programming Model (MILPM) is drawn up to sort out the employees’ number needed in every contract, and the number of employees who will be multi skilled and in which type of tasks. From The results it is demonstrated that the Hybrid Flexibility Strategy (HFS) proposed produce the best savings in the overall cost. Uniting single-skilled employees and polyvalent employees produce the best staff arrangements. In addition, it is illustrating that the most interesting contracts are those that have the shortest workdays.
To deal with Staff Scheduling (SS) in marine company, (Ammar, Benaissa and Chabchoub, 2013) proposed an MF of SSSP based on a Goal Programming (GP). Knowing that they are incapable to work out real cases using their advocated formulation, they shift to heuristic method advocating a Greedy Randomized Adaptative Search Procedure (GRASP) meta-heuristic which is constructed in two phases; First, the solution construction and then the improvement which is guaranteed by the authors through a Local Simple Research (LSR), then a Taboo Algorithm (TA) utilizing a real case data from a Tunisian ship-owner to validate their tests. For future works, the authors propose to use other heuristics or to include the ship scheduling to the problem.
In this context (Ammar, Benaissa and Chabchoub, 2014) adapted the Analytical Hierarchy Process (AHP) method to the MF of SSSP proposed in (Ammar, Benaissa and Chabchoub, 2013) to define weights of diverse objectives then they offered the adjustment of the GRASP meta-heuristic method to find the solution to the problem utilizing a real cases data adopted from a Tunisian ship-owner.