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Optimizing gas entry-exit capacity utilization under uncertainty. [PDF]
Comput Manag SciMarkhorst B +3 more
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Optimal bus bridging service for urban rail transit disruptions with stochastic passenger demand. [PDF]
PLoS OneLiu Y, Yang T, Su J.
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Quantum annealing applications, challenges and limitations for optimisation problems compared to classical solvers. [PDF]
Sci RepQuinton FA +4 more
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Presolve Reductions in Mixed Integer Programming
INFORMS Journal on Computing, 2020Mixed integer programming has become a very powerful tool for modeling and solving real-world planning and scheduling problems, with the breadth of applications appearing to be almost unlimited. A critical component in the solution of these mixed integer
Tobias Achterberg +4 more
semanticscholar +3 more sources
Chemical Production Scheduling, 2020
Introduction Linear programming maximizes (or minimizes) a linear objective function subject to one or more constraints. Mixed integer programming adds one additional condition that at least one of the variables can only take on integer values.
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Introduction Linear programming maximizes (or minimizes) a linear objective function subject to one or more constraints. Mixed integer programming adds one additional condition that at least one of the variables can only take on integer values.
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Trajectory Optimization for High-Speed Trains via a Mixed Integer Linear Programming Approach
IEEE transactions on intelligent transportation systems (Print), 2022This paper proposes a trajectory optimization approach for high-speed trains to reduce traction energy consumption and increase riding comfort. Besides, the proposed approach can also achieve energy-saving effects by optimizing the operation time between
Yuan Cao +3 more
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Mathematical programming, 2020
We study multistage distributionally robust mixed-integer programs under endogenous uncertainty, where the probability distribution of stage-wise uncertainty depends on the decisions made in previous stages.
Xian Yu, Siqian Shen
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We study multistage distributionally robust mixed-integer programs under endogenous uncertainty, where the probability distribution of stage-wise uncertainty depends on the decisions made in previous stages.
Xian Yu, Siqian Shen
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Strong mixed-integer programming formulations for trained neural networks
Mathematical programming, 2018We present strong mixed-integer programming (MIP) formulations for high-dimensional piecewise linear functions that correspond to trained neural networks.
Ross Anderson +4 more
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Computers & industrial engineering, 2020
This paper intends to address the distributed flexible job shop scheduling problem (DFJSP) with minimizing maximum completion time (makespan). In order to solve this problem, we propose four mixed integer linear programming (MILP) models as well as a ...
Leilei Meng +4 more
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This paper intends to address the distributed flexible job shop scheduling problem (DFJSP) with minimizing maximum completion time (makespan). In order to solve this problem, we propose four mixed integer linear programming (MILP) models as well as a ...
Leilei Meng +4 more
semanticscholar +1 more source