The request-trip-vehicle assignment problem is at the heart of popular decomposition strategies for online vehicle routing. We study an integer linear programming formulation and its linear programming relaxation. Our main result is a simple, linear programming based randomized algorithm that, whenever the instance is feasible, leverages assumptions typically met in practice to return an assignment whose: i) expected cost is at most that of an optimal solution, and ii) expected fraction of unassigned requests is at most 1/e. If trip-vehicle assignment costs can only be \alpha-approximated , we pay an additional factor of \alpha in the expected cost. Unassigned requests are assigned in future rounds with high probability. We can relax the feasibility requirement by including a penalty term for unassigned requests, in which case our performance guarantee is with respect to a modified objective function. Our techniques generalize to a class of set-partitioning problems .
The request-trip-vehicle assignment problem is at the heart of a popular decomposition strategy for online vehicle routing. In this framework, assignments are done in batches in order to exploit any shareability among vehicles and incoming travel requests. We study a natural ILP formulation and its LP relaxation. Our main result is an LP-based randomized rounding algorithm that, whenever the instance is feasible, leverages mild assumptions to return an assignment whose: i) expected cost is at most that of an optimal solution, and ii) expected fraction of unassigned requests is at most 1/e. If trip-vehicle assignment costs are \alpha-approximate , we pay an additional factor of \alpha in the expected cost. We can relax the feasibility requirement by considering the penalty version of the problem, in which a penalty is paid for each unassigned request. We find that, whenever a request is repeatedly unassigned after a number of rounds, with high probability it is so in accordance with the sequence of LP solutions and not because of a rounding error. We additionally introduce a deterministic rounding heuristic inspired by our randomized technique. Our computational experiments show that our rounding algorithms achieve a performance similar to that of the ILP at a reduced computation time, far improving on our theoretical guarantee. The reason for this is that, although the assignment problem is hard in theory, the natural LP relaxation tends to be very tight in practice. We conclude with observations and open questions related to this phenomenon .
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popular decomposition strategies
a popular decomposition strategy
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We study an integer linear programming
In this framework, assignments are done in batches in order to exploit any shareability among vehicles and incoming travel requests. We study a natural ILP
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linear programming
LP
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a simple, linear programming based randomized
an LP-based randomized rounding
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assumptions typically met in practice
mild assumptions
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can only be \alpha-approximated
are \alpha-approximate
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Unassigned requests are assigned in future rounds with high probability.
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including a penalty term for unassigned requests, in which case our performance guarantee is with respect to a modified objective function. Our techniques generalize to a class of set-partitioning problems
considering the penalty version of the problem, in which a penalty is paid for each unassigned request. We find that, whenever a request is repeatedly unassigned after a number of rounds, with high probability it is so in accordance with the sequence of LP solutions and not because of a rounding error. We additionally introduce a deterministic rounding heuristic inspired by our randomized technique. Our computational experiments show that our rounding algorithms achieve a performance similar to that of the ILP at a reduced computation time, far improving on our theoretical guarantee. The reason for this is that, although the assignment problem is hard in theory, the natural LP relaxation tends to be very tight in practice. We conclude with observations and open questions related to this phenomenon