Optimization Days 2019
HEC Montréal, May 13-15, 2019
JOPT2019
HEC Montréal, 13 — 15 May 2019
WB10 Operations Research and Computer Science in Transportation, Mobility and Logistics
May 15, 2019 10:45 AM – 12:25 PM
Location: TAL Gestion globale d'actifs inc.
Chaired by Bernard Gendron
4 Presentations
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10:45 AM - 11:10 AM
Parallel machine scheduling with periodic maintenance, job rejection and weighted sum of completion times
We consider a bi-objective scheduling problem on two parallel, non identical machines with a periodic preventive maintenance policy. The two objectives involve minimization of job rejection costs and weighted sum of completion times. Two metaheuristics based on tabu search are proposed to solve this problem. Computational results on test instances of different sizes are reported.
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11:10 AM - 11:35 AM
Decision-aiding approach to embed persons with disability to public transport
We focus on the decision-aiding approach to imbed of disabled persons to public transport, with the goal to provide full independence in their displacement. We propose to frame the transport chain depending on the information of the network in order to the meet needs and remove barriers for the disabled persons.
Key-words:
Decision aiding, transport, persons with disabilities -
11:35 AM - 12:00 PM
Variable neighborhood search for the set union knapsack problem
The set-union knapsack problem (SUKP) is a generalization of knapsack problem where an item corresponds to a set of elements. SUKP has various applications including information security systems. We propose a variable neighborhood search for the SUKP and the computational results on a set of benchmark instances show its efficiency.
Mots-Clés: set-union knapsack problem, knapsack problem, variable neighborhood search, metaheuristic
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12:00 PM - 12:25 PM
Efficient matheuristics for multicommodity capacitated network design
We present matheuristics for the multicommodity capacitated fixed-charge network design problem (MCND). The matheuristics are based on combining iterative linear programming (ILP) methods and slope scaling (SS) heuristics. Each iteration alternates between solving a linear program obtained by adding pseudo-cuts and a restricted mixed-integer programming (MIP) model. The SS heuristic is used as a warm start to a state-of-the-art generic method that solves the restricted MIP model. The resulting ILP/SS matheuristics are compared against state-of-the-art heuristics for the MCND on a set of large-scale difficult instances.