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The additional introduction of heuristic improvements further optimizes the performance in real network scenarios.
All these methods are relatively simple to implement, heuristic improvements can easily be added, but they can be time consuming.
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We use a local search (LS) procedure employing a heuristic improvement to standard simulated annealing (SA) to solve the APG problem.
This aims at a heuristic improvement of runtime, while still computing exact probability values.
The matheuristic is composed of four main algorithmic components: a construction heuristic, an improvement heuristic, a reinsertion heuristic, and a perturbation heuristic.
The improvement heuristic uses an integer program to select a set of improving port insertions and removals on each service.
Our improvement heuristic is applicable as a real-time decision support tool for a liner shipping company.
The solution method is an improvement heuristic, where an integer program is solved iteratively to perform moves in a large neighborhood search.
We use simulated annealing and an iterative improvement heuristic algorithm to find the efficient solutions for different objective functions and budget sizes.
A comprehensive computational experiment is conducted to show the effectiveness of the heuristic and the improvement upon real-world terminal operations.
We also show that the running time of one iteration of the best-exchange-by one improvement heuristic is of the same order.
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