An effective teaching–learning-based optimisation algorithm for RCPSP with ordinal interval numbers

Abstract

To solve the resource-constrained project-scheduling problem (RCPSP) with ordinal interval numbers, this paper presents an effective teaching–learning-based optimisation (TLBO) algorithm. Ordinal interval number is introduced as a novel tool for handling vague information to describe the RCPSP under uncertain environment. An ordinal interval-based parallel schedule generation scheme is used to generate feasible schedules. Two new phases including the self-study phase and the exam phase are incorporated into the TLBO to enhance the teaching–learning process. In the self-study phase, the population is updated by a mutation operator to prevent premature convergence and to enhance exploration search. In the exam phase, elite students are selected to enhance exploitation search. Moreover, a novel ordinal interval resource-based crossover operator (OIRBCO) is well designed for both the teacher phase and the student phase of the TLBO. Computational comparisons between the OIRBCO and the existing two-point crossover show that OIRBCO is more effective due to the utilisation of the resource information. In addition, statistical comparisons with particle swarm optimisation and simulated annealing show that the proposed TLBO is more effective in solving the RCPSP with ordinal interval numbers.

Keywords:

• Resource-constrained project scheduling
• ordinal interval number
• teaching–learning-based optimisation
• self-study phase
• exam phase
• ordinal interval resource-based crossover

Additional information

Funding

Funding. This research is partially supported by the National Key Basic Research and Development Program of China [2013CB329503], the National Science Foundation of China [61174189], and the Doctoral Program Foundation of Institutions of Higher Education of China [20130002110057].