#Interval scheduling greedy code

However, itвЂ™s been proved by Pezzella et al. Demir and Д°Еџleyen evaluated some mathematical models of the FJSP. Exact algorithms include mathematical programming (MP), while the approximation algorithms include some dispatching rules (DRs) and artificial intelligence (AI) based approaches.Д«rucker and Schlie proposed a polynomial graphical algorithm when they first presented the FJSP with two jobs. These techniques are classified into two groups the exact methods and the approximation methods. Several methods have been used to deal with the FJSP. Section 5 provides the conclusions and future work. Experimental studies are discussed in Section 4. IG approach and the modified iterated greedy (MIG) is proposed in Section 3. Literature review and problem definition are presented in Section 2. The remainder of this paper is arranged as follows. The simplicity and the efficiency of IG and DRs shall result in an effective method which consumes less computation time and is easy to implement. The classical IG is separated into two phases to deal with the two sub-problems of FJSP and itвЂ™s combined with a set of dispatching rules (DRs) to solve the FJSP.

#Interval scheduling greedy code

In this research, we try to present a modified iterated greedy (IG) algorithm, a simpler metaheuristic that is easier to code and reproduce. This makes it difficult to apply these methods in real-life problems. Many of these algorithms are also complicated and in many cases they are too difficult to reproduce. While exact algorithms tend to be inefficient with large scale FJSP, other methods, such as EA and SI, are much more expensive regarding the consumption of computation time. The current methods for solving FJSP can be mainly categorized into exact algorithm, dispatching rules (DR), evolutionary algorithm (EA), swarm intelligence (SI) based approaches, local search (LS) algorithms, and so on. Many approaches have been proposed to solve FJSP since first presented in 1990.

Furthermore, the FJSP is considered to be more complicated in comparison to the traditional JSP, as it dictates an extra decision level for the same scale in addition to the sequencing one, such as the operations routes. ДЄs an extension of the JSP, Flexible Job-shop Scheduling Problem (FJSP), which is known to be NP-hard, considers the flexible machines for each operation. This gives the permission to break the definition of JSP where an operation can be processed by a single machine. These machines are capable of processing several types of the operations. Modern manufacturing systems contain many flexible machines to increase the production efficiency. JSP considers no flexibility of any resources (such as machines and tools) for each operation. Job-shop scheduling problem (JSP) is basically an NP-complete challenge. The optimization of production scheduling can bring in considerable improvements in the manufacturing efficiency. The simplicity of the proposed IG provides an effective method that is also easy to apply and consumes less CPU time in solving the FJSP problem. By compared with other algorithms, the experimental results show that the presented algorithm is competitive and able to find global optimum for most instances. To evaluate the performance of proposed algorithm, some experiments including some famous FJSP benchmarks have been conducted. A set of dispatching rules are employed in the proposed algorithm for the sequencing and machine selection in the construction phase of the solution. Each phase is used to solve a sub-problem of the FJSP: sequencing and routing sub-problems. This is done by separating the classical IG into two phases.

This paper proposes a modified iterated greedy (IG) algorithm to deal with FJSP problem in order to provide a simpler metaheuristic, which is easier to code and to reproduce than some other much more complex methods. Some of these methods normally consume more CPU time and some other methods are more complicated which make them difficult to code and not easy to reproduce.

Most of the algorithms used in solving FJSP problem are categorized as metaheuristic methods. The flexible job shop scheduling problem (FJSP) is considered as an important problem in the modern manufacturing system.