The resource-constrained elementary shortest path problem arises as a pricing subproblem in branch-and-price algorithms for vehicle routing problems with additional constraints. We address the optimization of the resource-constrained elementary shortest path problem and we present and compare three methods. The first method is a well-known exact dynamic programming algorithm improved by new ideas, such as bi-directional search with resource-based bounding. The second method consists of a branch-and-bound algorithm, where lower bounds are computed by dynamic programming with state space relaxation; we show how bounded bi-directional search can be combined with state space relaxation and we present different branching strategies and their hybridization. The third method, called decremental state space relaxation, is a new one; exact dynamic programming and state space relaxation are two special cases of this new method. The experimental comparison of the three methods is definitely favourable to decremental state space relaxation. Computational results are given for different kinds of resources, arising from the capacitated vehicle routing problem, the vehicle routing problem with distribution and collection and the vehicle routing problem with capacities and time windows.
The vehicle routing problem with simultaneous distribution and collection (VRPSDC) is the variation of the capacitated vehicle routing problem that arises when the distribution of goods from a depot to a set of customers and the collection of waste from the customers to the depot must be performed by the same vehicles of limited capacity and the customers can be visited in any order. We study how the branch-and-price technique can be applied to the solution of this problem and in particular we compare two different ways of solving the pricing subproblem: exact dynamic programming and state space relaxation. By applying a bi-directional search we experimentally prove its effectiveness in solving the subproblem. We also devise suitable branching strategies for both the exact and the relaxed approach and we report on an extensive set of computational experiments on benchmark instances with both simple and composite demands.
In this paper we integrate at the tactical level two decision problems arising in container terminals: the berth allocation problem, which consists of assigning and scheduling incoming ships to berthing positions, and the quay crane assignment problem, which assigns to incoming ships a certain QC profile (i.e. number of quay cranes per working shift). We present two formulations: a mixed integer quadratic program and a linearization which reduces to a mixed integer linear program. The objective function aims, on the one hand, to maximize the total value of chosen QC profiles and, on the other hand, to minimize the housekeeping costs generated by transshipment flows between ships. To solve the problem we developed a heuristic algorithm which combines tabu search methods and mathematical programming techniques. Computational results on instances based on real data are presented and compared to those obtained through a commercial solver.2
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