An improved multiverse optimization (IMVO) algorithm is proposed herein for the fuzzy flexible job-shop scheduling problem pertaining to non-deterministic polynomial-time hard (NP-hard) problem. First, we designed a hybrid initialization method improve quality of initial solution, and thereafter, introduced self-crossing technique along with an insert-based heuristic simulate process exchanging...

Scheduling is an important process widely used in manufacturing, production, management, computer science, and so on. Appropriate scheduling can reduce material handling costs and time. Finding good schedules for given sets of jobs can thus help factory supervisors effectively control job flows and provide solutions for job sequencing. In simple flow shop problems, each machine operation center...

Flexible job-shop scheduling problem (FJSP) has been well known as one of the most difficult NP-hard combinatorial optimization problems. This problem FJSP is an extension of job-shop scheduling problem (JSP) which is to create a sequence of operations satisfying the precedence relationship together with assignment of time and resources for each operation. Thus, the FJSP model is closer to the ...

This paper proposes a novel genetic algorithm (GA) approach that utilizes multichromosome to solve the flexible job-shop scheduling problem (FJSP), which involves two kinds of decisions: machine selection and operation sequencing. Typically, former is represented by string categorical values, whereas latter forms sequence operations. Consequently, chromosome conventional GAs for solving FJSP co...

Scheduling for job shop is very important in both fields of production management and combinatorial op-timization. However, it is quite difficult to achieve an optimal solution to this problem with traditional opti-mization approaches owing to the high computational complexity. The combination of several optimization criteria induces additional complexity and new problems. In this paper, we pro...