ACO-IH: An improved ant colony optimization algorithm for Airport Ground Service Scheduling

Abstract

Many Airport Ground Service Scheduling(AGSS) problems can be formulated as Vehicle Routing Problem with Tight time windows, Short travel time and Re-used Vehicles(VRPTSR). A model with multiple objectives to minimize the number of vehicles used, the total start time of serving flights and the total flow time of vehicles for VRPTSR is presented. Based on the facts that Ant Colony Optimization (ACO) has good performance in solving combinatorial optimization problems with multi objectives, the ACO algorithm with MAX-MIN and Rankbased Ant System is proposed; an efficient heuristic called Earliest Due Date First (EDD) is incorporated into ACO as a comparative ant in order to improve the performance of ACO. Numerical computations show that the proposed ACO can construct good solutions in reasonable time. At last, policies for dealing with Non-Homogeneous vehicles are presented, which makes it more applicable.

Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling

“…Ho and Leung evaluated the performance of both tabu search and simulated annealing for solving the scheduling problem with respect to catering operation [5]. Du et al modeled the scheduling problem for fueling operation as a multi-objective VRPTW and solved it by an ant colony optimization (ACO) heuristic [6]. Zhou et al tackled the trailer scheduling problem by developing a multi-target scheduling model with rolling windows [7].…”

“…(5) ensures the equivalence between the number of vehicles leaving and returning to the depot. (6) ensures that all routes are from the initial depot node to final depot node. ( 7) defines capacity constraints in each fleet.…”

“…To this end, we generate 20 instances with 200 flights, and solve them by solvers (i.e., CPLEX and OR-Tools), the decomposition method [14] following previous setting, and LNS methods with CPLEX and OR-Tools as the repair operators, respectively. 6 For all these methods, we set the time limit and total runtime to 30 m and 2 h, respectively. In specific, we set mipgap to 0.2 for CPLEX in each iteration, and set the same destroy degree D d = 0.2 for all LNS methods to keep sub-problems tractable.…”

“…In those methods, certain hand-crafted rules are always designed to cope with the hard constraints so that the computation could be expedited when searching a solution of satisfactory quality. Following this principle, a number of heuristic methods have been attempted to handle the simple variants of VRP in AGH, which only consider a single operation, i.e., de-icing [3], towing [4], catering [5], fueling [6], trailering [7], and baggage loading and unloading [8], respectively. Although some success has been achieved, they fail to consider those operations simultaneously and their precedence relation, which may render them less effective for solving the real world routing problems in AGH.…”

Balanced Job Scheduling Based on Ant Algorithm for Grid Network

“…The ACO has become a very popular algorithm in solving grid scheduling problems. (Du et al, 2008;Lorpunmanee et al, 2007;Thangavel, Karnan, Jeganathan, Petha, Sivakumar, & Geetharamani, 2006). The ACO method is used to refer to the class of algorithms that are inspired in the process of foraging for food by natural ants for the optimization of hard-tosolve problems.…”