María Ángeles Rodríguez¹, Pedro Gomez-Gasquet¹, Llanos Cuenca¹ and M. M. E. Alemany¹

¹ Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València, Camino de Vera S/N, 46022 València, Spain.

marodsa4@cigip.upv.es, pgomez@cigip.upv.es, llcuenca@cigip.upv.es, mareva@cigip.upv.es

1. Introduction

For almost 80 years researchers have been analyzing problems related to production scheduling. It is well known that a large part of the problems addressed can be classified as NP-Complete, which often leads researchers to propose resolution methods that do not guarantee optimal solutions, such as heuristics or metaheuristics. The validation of a new algorithm should be accompanied by a design of experiments (DoE) that will be supported with statistical data related to the measure or measures that are used for its evaluation as is highlighted in [1][2]. This experimental study can have a double objective. On the one hand, carry out a parametric adjustment of the pro-posed method, since in most cases there are one or more parameters whose values are difficult to specify. On the other hand, they would allow algorithms to be compared with each other, with the idea of establishing a classification against a well contextualized problem. This paper propose a service that allows you to create experiments for a wide group of production scheduling problems, apply them to algorithms already available or to your own, and obtain the results when they are available without waiting and without consuming your own resources. Users of this service can use a class written in Python language. This allows initialize the problem, as first step in each execution of the experimenter as:

• Type: Machine, flowshop o job shop problem. Allows adapted some calculation methods to make them more efficient.
• And the following True/False parameter to define the problem context. Multi (True if a set of machines μij ⊆ {M1, . . . , Mm}where m>1), Setup (True if setup is considered), Ready (True if any machine is not available at the beginning), Release (True if any job is not available at the beginning), Duedate (True if due date is considered for jobs) and Weight (True if weights are considered for jobs).

2. Production Scheduling Web Application for Experiments

The web application (http://niotome.cigip.upv.es/) has been designed and implemented according to Web information system development methodology (WISDM) [3]. the web application is mainly designed to create experiments for a wide group of production scheduling problems, apply them to algorithms already available or to your own, and obtain the results.

This translates into a basic functionalities that are obtained due to the user requirements, which are described as follows:

• Initialize the problem (between a wide range of workshops).
• Instantiation of a concrete problem with data (randomly generated by machine or uploaded by user).
• Calculation of a schedule of the instantiated problem.
• Calculation any of the objective functions associated with schedule.
• Show results of calculation in Gantt diagram format and the summary of experimentation.

These functionalities are implemented as methods in Python class explained in previous section. And the web application called the methods through API Rest designed. This application web is in developing progress and there are still some details to be completed. For this purpose, in future work we will evaluate the user satisfaction to web application using and we will develop the data model associated to algorithms production scheduling. As well as, uploading the web application to the cloud via Docker containers.

Acknowledgements. This research is being funded by project entitled NIOTOME (Ref. RTI2018-102020-B-I00) (MCI/AEI/FEDER, UE).

References

1. Blackstone, John H., Don T. Phillips, and L. GARY. “A state-of-the-art survey of dispatching rules for manufacturing job shop operations.” The International Journal of Production Research. Taylor & Francis https://doi.org/10.1080/00207548208947745
2. Ruiz, R. & Stützle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European journal of operational research. Elsevier. https://doi.org/10.1016/j.ejor.2005.12.009
3. Vidgen R. (2002). Constructing a Web Information System Development Methodology. Information Systems Journal (Oxford, England), vol. 12, no. 3, Blackwell Science Ltd, 2002, pp. 247–61, doi:10.1046/j.1365-2575.2002.00129