288 research outputs found
Polynomial algorithms for p-dispersion problems in a 2d Pareto Front
Having many best compromise solutions for bi-objective optimization problems,
this paper studies p-dispersion problems to select
representative points in the Pareto Front(PF). Four standard variants of
p-dispersion are considered. A novel variant, denoted Max-Sum-Neighbor
p-dispersion, is introduced for the specific case of a 2d PF. Firstly, it is
proven that -dispersion and -dispersion problems are solvable in
time in a 2d PF. Secondly, dynamic programming algorithms are designed for
three p-dispersion variants, proving polynomial complexities in a 2d PF. The
Max-Min p-dispersion problem is proven solvable in time and
memory space. The Max-Sum-Min p-dispersion problem is proven solvable in
time and space. The Max-Sum-Neighbor p-dispersion problem
is proven solvable in time and space. Complexity results and
parallelization issues are discussed in regards to practical implementation
Matheuristics to stabilize column generation: application to a technician routing problem
International audienceThis paper considers a simplified Technician Routing and Scheduling Problem with skill constraints, where a Column Generation (CG) heuristic was shown effective. This work proposes new CG schemes to stabilize the CG process and thus to accelerate the CG heuristics. Solving CG subproblems with matheuristics allows to diversify the column generation. Furthermore, a tabu search matheuristic allows to generate aggressively columns at each iteration. Both techniques imply a better stability of the CG scheme: the diversification is interesting for the first iterations whereas tabu intensification is especially useful for the last iterations. It implies a significant acceleration of the CG convergence. A perspective of these CG strategies proposed is an extension to other problems where CG induces independent and heterogeneous subprob-lems
Maintenance planning on French military aircraft operations
The Military Flight and Maintenance Planning Problem considered here aims to assign missions and maintenance tasks to military aircraft. It is a variant of the Flight and Maintenance Planning problem where flights are not modeled geographically since a round-trip to the base is assumed for each flight. It also includes different objectives and constraints
A new simplified daylight evaluation tool, description and validation against the standard method of EN 17037
A new daylight evaluation tool using a simplified assessment method to determine the daylight quantity provided to a typical room was developed. Its calculation method is based on a set of formulas integrating the main factors characterizing the indoor space and the outdoor context. The results are expressed as a corrected Glass-to-Floor ratio (GFR*) which is used as a proxy for the daylight provision. This value can then be used to attribute a rating or “Daylight score” to each space. The main finding is that the simplified method is an easy and relatively reliable estimation for daylight provision. A comparison of the tool with detailed daylight simulations according to the daylight factor method of EN 17037:2018 shows that a high correlation is obtained. The tool is applicable for any case which has conditions matching closely to the models and situations defined. Due to its easy implementation and the limited number of input parameters this evaluation method could be well suited for building passport schemes.publishedVersio
A Branch-and-Price Algorithm for the Electric Autonomous Dial-A-Ride Problem
The Electric Autonomous Dial-A-Ride Problem (E-ADARP) consists in scheduling
a fleet of electric autonomous vehicles to provide ride-sharing services for
customers that specify their origins and destinations. The E-ADARP differs from
the classical DARP in two aspects: (i) a weighted-sum objective that minimizes
both total travel time and total excess user ride time; (ii) the employment of
electric autonomous vehicles and a partial recharging policy. This paper
presents a highly-efficient labeling algorithm, which is integrated into
Branch-and-Price (B&P) algorithms to solve the E-ADARP. To handle (i), we
introduce a fragment-based representation of paths. A novel approach is invoked
to abstract fragments to arcs while ensuring excess-user-ride-time optimality.
We then construct a new graph that preserves all feasible routes of the
original graph by enumerating all feasible fragments, abstracting them to arcs,
and connecting them with each other, depots, and recharging stations in a
feasible way. On the new graph, partial recharging (ii) is tackled exactly by
tailored Resource Extension Functions (REFs). We apply strong dominance rules
and constant-time feasibility checks to compute the shortest paths efficiently.
These methods construct the first labeling algorithm that can deal with
minimizing (excess) user ride time. In the computational experiments, the B&P
algorithm achieves optimality in 71 out of 84 instances. Remarkably, among
these instances, 50 were solved optimally at the root node without branching.
We identify 26 new best solutions, improve 30 previously reported lower bounds,
and provide 17 new lower bounds for large-scale instances with up to 8 vehicles
and 96 requests. In total 42 new best solutions are generated on previously
solved and unsolved instances
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