Discrete Particle Swarm Optimization Algorithm for Flowshop Scheduling

In the context of manufacturing systems, scheduling refers to allocation of resources over time to perform a set of operations. Manufacturing systems scheduling has many applications ranging from manufacturing, computer processing, transportation, communication, health care, space exploration, education, distribution networks, etc. Scheduling is a process by which limited resources are allocated over time among parallel or sequential activities. Solving such a problem amounts to making discrete choices such that an optimal solution is found among a finite or a countably infinite number of alternatives. Such problems are called combinatorial optimization problems. Typically, the task is complex, limiting the practical utility of combinatorial, mathematical programming and other analytical methods in solving scheduling problems effectively. Manufacturing system entails the acquisition and allocation of limited resources to production activities so as to reduce the manufacturing cycle time and in-process inventory and to satisfy customer demand in specified time. Successful achievement of these objectives lies in efficient scheduling of the system. Scheduling plays an important role in shop floor planning. A schedule shows the planned time when processing of a specific job will start on a machine. It also indicates when a job will get completed on a machine. Scheduling is a decisionmaking process of sequencing a set of operations on different machines in a manufacturing unit. The objective of scheduling is generally to improve the utilization of resources and profitability of production lines. Scheduling problem is characterized by three components namely: 1. Number of machines, number of jobs and the processing time for each job using appropriate machine 2. A set of constraints such as operation precedence constraint for a given job and operation non-overlapping constraint for a given machine 3. A target function called objective function consisting of single or multiple criteria that must be optimized. Traditionally, scheduling researchers has shown interest in optimizing a single-objective or performance measure while scheduling, which is not a reality. Practical scheduling problems acquire consideration of several objectives as desired by the scheduler. When multiple criteria are considered, scheduler may wish to generate a schedule which performs