The use of Automated Guided Vehicles for container loading and unloading has emerged as a key solution in response to the increasing demand for port efficiency, driven by the growing volume of port operations. We formulate a routing problem, extended from existing literature, that determines the trajectories of these vehicles in an automated port environment while ensuring collision-free paths. We show that when the physical paths of the AGVs is fixed the problem reduces to a job shop scheduling problem with blocking constraints and no swap allowed, very little studied in the literature. We also develop an Iterative Path Reinsertion algorithm to improve the trajectories of the AGV for a given solution. Results show the efficiency of our approach both in terms of solution quality and of computational time for instances of various sizes. The conclusion outlines future research directions.

International audience

The Hydro Unit Commitment problem (HUC) specific to hydroelectric plants is part of the electricity production planning problem, called Unit Commitment Problem (UCP).More specifically, the studied case is that of the HUC with a single plant, denoted 1-HUC.The plant is located between two reservoirs.The horizon is discretized in time periods.The plant operates at a finite number of points defined as pairs of the generated power and the corresponding water flow.Several constraints are considered.Each reservoir has an initial volume, as well as window resource constraints, defined by a minimum and maximum volume per time period.At each time period, there is an additional positive, negative or zero intake of water in the reservoirs.The case of a price-taker revenue maximization problem is considered.An efficient exact A* variant, so called HA*, is proposed to solve the 1-HUC accounting for window constraints, with a reduced search space and a dedicated optimistic heuristic.This variant is compared to a classical Resource Constrained Shortest Path Problem (RCSPP) algorithm and a Mixed Integer Linear Programming formulation solved with CPLEX.Results show that the proposed algorithm outperforms both concurrent alternatives in terms of computational time in average on a set of realistic instances, meaning that HA* exhibits a more stable behavior with a larger number of instances solved.

International audience