Large Neighborhood Search with Decision Diagrams

Authors: Xavier Gillard, Pierre Schaus

IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental In order to evaluate the effectiveness of DD-LNS, we considered two sequencing problems: a discrete lot sizing and scheduling problem (DSLP) and the traveling salesman problem with time windows (TSPTW). All our experiments were performed on the same physical machine... Table 1 shows the number of DLSP instances for which the best solution found by each solver matches the best known solution.
Researcher Affiliation Academia Xavier Gillard , Pierre Schaus Universit e Catholique de Louvain, BELGIUM {xavier.gillard, pierre.schaus}@uclouvain.be
Pseudocode Yes Algorithm 1 Top Down Compilation of an Exact DD, Algorithm 2 Top Down Compilation of a Restricted DD, Algorithm 3 LNS with Decision Diagrams, Algorithm 4 Restrict Procedure
Open Source Code Yes All source code and data are freely available online at: https://github.com/xgillard/ijcai 22 DDLNS. Contact Author
Open Datasets Yes All source code and data are freely available online at: https://github.com/xgillard/ijcai 22 DDLNS. Contact Author. Our experiments cover the 467 instances of the benchmark suites which are usually used to assess the efficiency of new TSPTW solvers.
Dataset Splits No The paper mentions using 500 generated instances for DSLP and 467 instances from benchmark suites for TSPTW, but does not provide specific details on train/validation/test splits, percentages, or methodology for creating such splits.
Hardware Specification Yes All our experiments were performed on the same physical machine equipped with two Intel(R) Xeon(R) CPU E52687W v3 and 128Gb of RAM.
Software Dependencies Yes All MIP models originate from [Pochet and Wolsey, 2006] and are written using FICO Xpress Mosel v8.11. We compared the performance of DD-LNS against an implementation of the CP model from section 6.2 in Choco 4.10.6.
Experiment Setup Yes On that machine, each considered solver was given a 10 minutes timespan using a single thread and a maximum memory quota of 2Gb in order to solve each instance. using a LNS that re-optimizes a small sequence4 of 5 decision variables xi . . . xi+4 randomly selected at each restart.