SPL-LNS replaces the greedy proposal step in neural Large Neighborhood Search with sampling over locally-informed proposals, trained by hindsight relabeling on self-generated data, and reports large gains over prior neural LNS solvers on integer linear programs.
Alternative Mixed Integer Linear Programming Optimization for Joint Job Scheduling and Data Allocation in Grid Computing
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abstract
This paper presents a novel approach to the joint optimization of job scheduling and data allocation in grid computing environments. We formulate this joint optimization problem as a mixed integer quadratically constrained program. To tackle the nonlinearity in the constraint, we alternatively fix a subset of decision variables and optimize the remaining ones via Mixed Integer Linear Programming (MILP). We solve the MILP problem at each iteration via an off-the-shelf MILP solver. Our experimental results show that our method significantly outperforms existing heuristic methods, employing either independent optimization or joint optimization strategies. We have also verified the generalization ability of our method over grid environments with various sizes and its high robustness to the algorithm hyper-parameters.
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cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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SPL-LNS: Sampling-Enhanced Large Neighborhood Search for Solving Integer Linear Programs
SPL-LNS replaces the greedy proposal step in neural Large Neighborhood Search with sampling over locally-informed proposals, trained by hindsight relabeling on self-generated data, and reports large gains over prior neural LNS solvers on integer linear programs.