Abstract
Scheduling problems play a pivotal role in many of the fields today, such as manufacturing systems, industrial processes, production, etc. The job-shop scheduling problems are categorized as a non-deterministic NP hard problem. It is very complicated to find a better algorithm which can give an optimal solution under a given time constraint. A scheduling can be generally categorized as a set of jobs where each job can have greater than one operation to perform. The main aim of the scheduling would be to resolve the scheduling of the jobs that would minimize a measure of performance. A common measure of performance for dealing with the problems of the scheduling is the makespan of a schedule. Technique for calculating the shop scheduling problem ranges from simple FIFO and SPT to more sophisticated procedures, for instance branch and bound, tabu search, ant colony, genetic algorithm, and many others. The modified version of ABC is parallel ABC (PABC) for solving the JSSP. Projected method basically uses the concept of parallelism for the concept of scheduling.
TopBackground
The scheduling not so new concept it was introduced in the past 3000- year ago. The science behind scheduling is to identify the tasks and resources that need to perform. In the 1980s, scheduling was used for very costly affairs which required extensive manual scheduling calculations (Jaiswal et al., 2023). In the current scenario, scheduling helps to develop a computer and information technology. The application of scheduling becomes very essential in every field such as computer science, business sector, cooperation, etc. to organize the data correctly, creating plains and structure complex processes efficiently and reliably (Gupta et al., 2023). It improves the effectiveness of the system by dividing the given problem into the process and assign them to the processors. The scheduling of the process is very tedious task in field of computer science. So, these tasks need a specific algorithm to reduce the time frame of the process in the waiting queue of the process onto the processor. Scheduling technique is a hot research topic these days, Today's many of the scheduling techniques in use have been adapted from management science and operations research studies. Multi-programed operation systems required scheduling to make the computer more productive. There is various stochastic scheduling techniques study as available to schedule the processor and process vice versa. It helps to find out that which process will get the computer resource to execute the process while another process is keeps on hold condition.
Key Terms in this Chapter
Scheduling: The process of arranging and allocating tasks or jobs to resources (such as machines, workers, or time slots) in an efficient and optimal manner to meet specified objectives.
Ant Colony: An optimization algorithm inspired by the foraging behavior of ants, employed to solve complex problems by simulating the way ants find the shortest path to food sources.
Genetic Algorithm (GA): An optimization technique based on the principles of natural selection and genetics, used to find solutions to complex problems by evolving a population of potential solutions over multiple generations.
Computing: The use of computers and algorithms to perform various tasks, including data processing, problem solving, and information retrieval, encompassing a wide range of applications from scientific research to everyday tasks.
Parallel Artificial Bee Colony: A parallel optimization algorithm inspired by the foraging behavior of honeybees, used for solving complex optimization problems by employing multiple simultaneous search agents.
FIFO (First-In-First-Out): A scheduling and data management strategy where the items or tasks are processed in the order they arrive, with the first item received being the first one to be processed.
Shortest Processing Time (SPT): A scheduling rule used to prioritize tasks or jobs based on their processing time, with the shortest tasks being scheduled first to optimize throughput or minimize completion time.
Job Shop Scheduling Problem (JSSP): A classic combinatorial optimization challenge where a set of jobs must be scheduled on a set of machines while minimizing makespan or other criteria.