iMOPSE library

We provided some iMOPSE tools to support other scienticts and make more usefull iMOPSE MS-RCPSP model.
We developed and published some iMOPSE tools, such as:
Please cite works as:
Myszkowski P.B., Laszczyk M., Nikulin I. and Skowroński M.E. "iMOPSE: a library for bicriteria optimization in Multi–Skill Resource–Constrained Project Scheduling Problem", in review process, Soft Computing Journal.

MS-RCPSP instance Generator

Orginally we developed two datasets (d36 and EDU). Hoever in some situations the d36 is too small. In this case and application has been created to generate new MS–RCPSP instances.
To build new instance several task’s attributes are needed: number of tasks, duration (minimal and maximal) and number of (precedence) relations. Next, resource configuration should be given: number of resources, standard rate (minimal and maximal), overtime rate (minimal and maximal) and number of skills (minimal and maximal).

Fig. An example of iMOPSE generator usage.

The generator file can be downloaded (~3,4MB, the ZIP package includes documentation and readme files).

Details of instances difficulties (measures) are presented in:
Myszkowski P.B., Skowroński M., Sikora K., "A new benchmark dataset for Multi-Skill Resource-Constrained Project Scheduling Problem", Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 5, pages 129–138 (2015)

MS-RCPSP instance Validator

In MS–RCPSP there are several constraints that should be satisfied: every task must have an assigned resource, assigned resource must have skills required by the task and each resource cannot realize more than one task at a given time. Moreover, realiza tion of a task must be valid according to precedence relations. The duration and cost of the schedule can be calculated only if the solution is valid and represents a feasible schedule. In Fig.4 an example of an invalid schedule is presented where some constraints are broken. The Validator application reports where/which constraints are not satisfied. It potentially helps the developer/researcher to fix the solving method.

Example of iMOPSE Validator application usag -- inspection results of infeasible schedule are presented.

The validator application (jar) can be downloaded (~800kB, the ZIP package includes documentation and readme files).

MS-RCPSP instance Visualiser

In the iMOPSE library, the visualization of MS–RCPSP solutions has been implemented to improve the analysis of optimization approaches. The visualization focuses on the time-based optimization and does not take cost factor into consideration. The visualization is represented graphically as table, where rows correspond to resources and columns correspond to timeslots.

Example of iMOPSE schedule visualiser usage.

The visualiser application (jar) can be downloaded (~540kB, the ZIP package includes documentation and readme files).

MS-RCPSP library

The iMOPSE library consists of several applications: instances generator, two datasets of predefined instances, solution validator and solution visualizer. These tools are developed for all researches to make possible not only to generate new instances, but also to check if provided solution is valid and optimal. For educational purpose we have developed Java source codes of Greedy Algorithm and Genetic Algorithm to show how easy it is to extend it.

General class diagram schema of iMOPSE library

We developed Java code that bases on above class diagram and implements two MS-RCPSP solving approaches:
  1. (ZIP, 525kB) that implements Greedy algorithm. The pack includes documentation and quicksart how to use the whole iMOPSE library.
  2. (ZIP, 528kB) that implements example of metaheuristic usage. It implements in Java using iMOPSE library the Greedy algorithm guided by Genetic Algorrithm.

Last update: 18.04.2017.