Over the past few years, a continually increasing number of research efforts have investigated the application of evolutionary computation techniques for the solution of scheduling problems. Scheduling problems can pose extremely complex combinatorial optimization problems. The necessity to satisfy various kinds of constraint makes the scheduling task even more difficult. The major portion of t...

This paper addresses the multistage hybrid flow shop (HFS) scheduling problems. The HFS is the special case of flowshop problem. Multiple parallel machines are considered in each stage in the HFS. The HFS scheduling problem is known to be strongly NPhard. Hence, many researchers proposed metaheuristic algorithms for solving the HFS scheduling problems. This paper develops a bat algorithm (BA) t...

Planning and scheduling attracts an unceasing attention of computer science community. However, despite of similar character of both tasks, in most current systems planning and scheduling problems are solved independently using different methods. Recent development of Constraint Programming brings a new breeze to these areas. It allows using the same techniques for modelling planning and schedu...

Process scheduling is a classical problem in the field of production planning and control; in particular, effective job shop scheduling remains an essential component in today’s highly dynamic and agile production environment. This paper presents unified framework for solving generic job shop scheduling problems based on the formulation of a job shop into three main classes of problem, namely, ...

This paper presents a new approach to genetic algorithm based scheduling. We use genetic algorithms augmented with a case-based memory, containing population members from past problem solving attempts, to obtain better performance over time on sets of similar job shop scheduling problems. Rather than starting with a randomly initialized population on each scheduling problem, we periodically inj...