Methods for off-line/on-line optimization under uncertainty
Authors: Allegra De Filippo, Michele Lombardi, Michela Milano
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed an experimentation to compare the solution quality and run times of our methods. As references for comparison we use the baseline approaches, plus an optimal solver operating under perfect information. Additionally, on the VRP, BOON obtains the same solution quality as EXPECTATION, which gives a third term of comparison. Experimental Setup: Our methods are evaluated over different uncertainty realizations, obtained by sampling the random variables for the loads and RES generation in the VPP model, and for the travel times in the VRP model. We consider a sample of 100 realizations for each of the six different instances of each problem. We then run each approach on each relization and measure the cost and run time. The scenarios in our models, conversely, are not sampled, but programmatically chosen: for the VPP we consider four extreme scenarios where (resp.) the load and the RES generation are at low/high values. For the VRP, each scenario corresponds to the mean travel times in one mode of the distribution. The VPP has 24 on-line stages, while in the VRP the number depends on how many customers are assigned to each vehicle. We solve our LPs and MILPs using Gurobi, while for the non-linear problems we use BARON via the GAMS modeling system on the Neos server for optimization. |
| Researcher Affiliation | Academia | Allegra De Filippo, Michele Lombardi and Michela Milano Department of Computer Science and Engineering, University of Bologna allegra.defilippo@unibo.it, michele.lombardi2@unibo.it, michela.milano@unibo.it |
| Pseudocode | No | The paper presents mathematical formulations and describes methods, but it does not contain structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper; there is no explicit code release statement or repository link. |
| Open Datasets | Yes | For the VPP, we use data from two public datasets to define problem instances for a residential [Espinosa and Ochoa, 2015] and industrial plant1. For the VRP we use modified versions of classical instances2. 1https://data.lab.fiware.org/dataset/ 2http://myweb.uiowa.edu/bthoa/TSPTWBenchmark Data Sets.htm |
| Dataset Splits | No | The paper states 'Our methods are evaluated over different uncertainty realizations, obtained by sampling the random variables for the loads and RES generation in the VPP model, and for the travel times in the VRP model. We consider a sample of 100 realizations for each of the six different instances of each problem.' However, it does not provide specific dataset split information (e.g., percentages, sample counts, or predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper mentions that 'We solve our LPs and MILPs using Gurobi, while for the non-linear problems we use BARON via the GAMS modeling system on the Neos server for optimization.' However, it does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper states, 'We solve our LPs and MILPs using Gurobi, while for the non-linear problems we use BARON via the GAMS modeling system on the Neos server for optimization.' However, it does not provide specific version numbers for Gurobi, BARON, or GAMS, which are necessary for reproducible software dependencies. |
| Experiment Setup | Yes | Experimental Setup: Our methods are evaluated over different uncertainty realizations, obtained by sampling the random variables for the loads and RES generation in the VPP model, and for the travel times in the VRP model. We consider a sample of 100 realizations for each of the six different instances of each problem. We then run each approach on each relization and measure the cost and run time. The scenarios in our models, conversely, are not sampled, but programmatically chosen: for the VPP we consider four extreme scenarios where (resp.) the load and the RES generation are at low/high values. For the VRP, each scenario corresponds to the mean travel times in one mode of the distribution. The VPP has 24 on-line stages, while in the VRP the number depends on how many customers are assigned to each vehicle. We solve our LPs and MILPs using Gurobi, while for the non-linear problems we use BARON via the GAMS modeling system on the Neos server for optimization. The time limit is 100 seconds for the VPP, and 500 seconds for the VRP. |