Ph.D. Project: Parallel Batch Processor Scheduling

Broad Category: Tabulated Analytics + Optimization

Program: Ph.D. in Decision Sciences, Indian Institute of Science

Project Focus: Scheduling parallel batch processors to minimize weighted tardiness and completion time using metaheuristics.

Overview

This research project examined high-complexity scheduling problems in manufacturing and computing systems, focusing on efficient heuristics for practical problem sizes.

Key Contributions

• Developed ant colony optimization algorithms for scheduling parallel processors with incompatible job families.
• Designed heuristics to minimize total weighted tardiness and total weighted completion time.
• Implemented solutions in C and LINGO, and compared results against benchmark scheduling methods.
• Introduced local lower bounds for performance evaluation of proposed heuristics.

Technology

C, LINGO, metaheuristics, ant colony optimization, scheduling theory.

Outcome

The work produced robust scheduling algorithms for NP-hard problems and contributed to peer-reviewed publications in manufacturing technology and operations research.