Previous chromosomal representations for solving the FJSP include a parallel machine and a parallel job representation by Mesghouni et al.
Figure 7 presents the flowchart for decoding an OOMS to obtain an active FJSP schedule.
As we all know, the GA is a well-known, widely used algorithm, and many researchers have used it to solve FJSP [17-19].
We use the proposed seven algorithms to solve the five FJSP instances proposed in Reference , and the performance of these algorithms is illustrated to answer the two questions mentioned above.
A two-stage hybrid genetic algorithm is proposed to solve FJSP with random machine breakdowns by Al-Hinai and Elmekkawy .
Therefore, in this paper, the dynamic FJSP with several uncertain dynamic events is studied.
The existing literatures [2-5] about solving single-objective FJSP (SOFJSP) over the past decades mainly concentrated on minimizing one specific objective such as makespan.
Disjunctive graph model G = (V, U, E) has been adapted for representing feasible schedules of FJSP. V denotes nodes set, and each of them represents an operation.
The FJSP is firstly addressed by Brucker and Schlie .
As is shown above, the single-objective optimization of FJSP (SO-FJSP) has been extensively studied, which generally minimizes the makespan that is the time required to complete all jobs.
As a FJSP model with fuzzy processing time, FJSPF is also highly difficult to solve and several intelligent algorithms based on fuzzy number have been developed to optimize it.
Therefore, two types of FJSP, partial flexibility and total flexibility, are defined as follows: