ABSTRACT
JSSPs • Development of MATLAB® programs for solving JSSP using intelligent algorithms • Computational parameters of JSSP instances with different job and machine sizes.
Scheduling has been the subject of a vast amount of literature in the operations research (OR) field since the early 1950s. The main objective of scheduling is to determine an efficient allocation of shared resources over time to competing activities. Emphasis has been laid on investigating machine scheduling problems, where jobs represent activities and machines represent resources. Such problems are referred to as job shop scheduling problems (JSSPs). JSSP is not only NP-hard but also has a well-earned reputation of being one of the most computationally difficult combinatorial optimization problems considered to date (Sonmez and Baykasoglu 1998). The research on JSSP promotes not only the development of relative algorithms in the field of artificial intelligence but also the means of solutions and applications for complex JSSPs. JSSPs can be thought of as the allocation of resources over a specified time to perform a predetermined collection of tasks. JSSP can be considered as comprising two problems: first, assigning a proper machine from a set of machines to each operation, and second, sequencing each operation on every given machine. The former problem can be seen as a parallel machine problem, which is also an NP-hard problem, and the latter is similar to a classical job shop problem (Li et al. 2009).
