Name:Industry@Grid
Description:Soves the Product Mix Stochastic Problem
Abstract:The Industry@Grid ShopScheduling application uses LiSA (http://lisa.math.uni-magdeburg.de/) routines to solve scheduling problems representing decisions that manufacturing industries face on daily or weekly basis. Business restrictions such as due dates, machinery availability, operations precedence’s among other, can be modeled to represent factory floors. The application solves the scheduling problem to help the decision makers to plan the production in order to respond to market demands on time, trying to keep costs down so the company can generate bigger profits or charge cheaper prices. These decisions are hard to take due to the computational complexity to solve this kind of problems to optimality. Although many different heuristics available generate good solutions, they may not be close to the absolute maximum in the space of parameters. Neighborhood searches may find local maxima and stop execution while significantly better solutions may exist. The use of grid computing allows researchers and practitioners to experiment and run the application millions of times that otherwise could take months running on a single desktop machine. One of the latest experiments was run with a sample of one million problems. It was designed to run on 150 CPU cores, with an average solution time of 11 CPU hours. The total computational time was 1650 hours (more than 2 months). Although the results were not optimal, they are close to the benchmark solution, yielding considerably good results in an affordable amount of time for the industry. The application is designed to work in parallel solving millions of instances of the same problem but in a different fashion. From an initial problem, the application is capable of generating random initial solutions and to apply neighborhood search algorithms to improve each different solution. After execution, the results are filtered and only the better solution is brought back to the user (and a log reporting all the objectives found, so a graphic can be drawn and different analysis made). The dissemination of this approach may allow not only better solutions for companies’ shop scheduling operations but also substantial economies in hardware and software investments.
In order to have better control of execution and try to better exploit the benefits of grid computing, we designed a set of scripts (Industry@Grid) to allow automatic execution of a considerable amount of experiments on GISELA infrastructure, reducing the hassle and effort involved in frequent simulations. Industry@Grid profits from the grid infrastructure with three modules: (1) ModelBuilder: responsible for initializing the experiments inside the grid; (2) Runner: runs the experiments to produce results; and (3) Merger: puts together and fetches the results to the user. After execution, the product-mix solution to be implemented is chosen with k-means clustering technique, which separates the solutions in clusters using similarity measures between them. The following sections present a summarized description of each module and the k-means method.
https://link.springer.com/article/10.1007%2Fs10723-015-9325-z
Created:2017-07-22
Last updated:2017-07-22
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