Challenge
Job-Shop Scheduling
Optimize manufacturing schedules with complex job routing and machine assignments to minimize completion time.
Dr. Karim Tamssaouet
Challenge Owner & Designer
Associate Professor of Operations Research, BI Norwegian Business School (Oslo) and Co-Founder of Planimize.
We’re grateful to Dr. Tamssaouet for lending his expertise to the design of this challenge. After completing his Ph.D. at École des Mines de Saint-Étienne, he joined BI Norwegian Business School—first as an Assistant Professor and, since 2024, as an Associate Professor—following a post-doctoral stint at Mines Saint-Étienne and earlier industrial R&D with STMicroelectronics. Dr. Tamssaouet’s research bridges operations research and industrial engineering, focusing on metaheuristic and mathematical-programming approaches for complex scheduling and integrated inventory-transportation problems. A dedicated educator, he leads BI’s courses in Business Optimization and Supply Chain Analytics, equipping future analysts and managers with robust, data-driven decision-making skills. Beyond academia, he co-founded Planimize in 2021—a tech start-up that turns cutting-edge combinatorial-optimisation research into industrial-grade scheduling software for semiconductor and other capital-intensive manufacturing.
Problem Overview
The Job-Shop Scheduling Problem (JSP) is one of the most classical problems in combinatorial optimisation: given several jobs—each a fixed sequence of operations—find a schedule that respects machine capacity and operation order while optimising a chosen performance measure.
The Flexible Job-Shop Scheduling Problem (FJSP) generalises this by allowing every operation to run on any machine from a predefined eligible set. A solution must therefore (1) assign each operation to a specific machine and (2) sequence the resulting operations on all machines so that all technological constraints are met and the overall schedule is as efficient as possible. This added routing freedom dramatically increases complexity but better mirrors real-world systems—ranging from semiconductor fabs and flexible assembly lines to hospital laboratories and cloud data-centres—where tasks can be routed among interchangeable resources.
Applications
Job shop scheduling is about deciding which tasks are processed, and at what times, so that everything finishes as efficiently as possible. These problems arise in any system with shared resources. Flexible job-shop scheduling (FJSP) extends the classical job-shop model by allowing each operation to run on any of several alternative machines. This added routing freedom unlocks higher utilisation and shorter lead times in today’s multi-tool, multi-product environments [1][2]. Industry case studies confirm it; the examples below illustrate situations where smarter scheduling algorithms dramatically reduce costs.
- Semiconductor factories: Treating a wafer fab as a flexible job shop lets planners send each lot to whichever lithography, etch, or metrology tool is free, cutting queue times and boosting throughput[3].
- Custom metal-working shops: Smarter schedules spread one-off parts across interchangeable milling and grinding centres, trimming change-overs and delivering orders faster.
- 3-D-printing farms: Matching every print job to the most suitable machine (right volume, material, or speed) keeps printers busy and helps meet tight customer deadlines[4].
- Hospitals and laboratories: Viewing kitchens, test benches, and sample-prep stations as a flexible job shop smooths patient flow, shortens waits, and can even lower a facility’s carbon footprint[5].
- Reconfigurable assembly lines: Automotive and electronics plants with stations that swap tools or robots on the fly use flexible job-shop planning to keep lines running smoothly as product mixes change[6].
References
- Li, Y. et al.“A Comprehensive Survey of Flexible Job-Shop Scheduling.” European Journal of Operational Research (2023).
- Li, X. & Wang, H.“Flexible Job-Shop Scheduling: Wide-Application Problem and Recent Progress.” Computers & Operations Research (2022).
- Wang, J. et al.“Integrated Scheduling for Semiconductor Wafer Fabs Using FJSP Models.” European Journal of Operational Research (2021).
- Kriaučiūnas, K. et al.“Job-Shop Scheduling of Additive Manufacturing Farms.” Journal of Marine Science and Engineering (2024).
- Lemos, A. et al.“Applying Flexible Job-Shop Scheduling to Hospital Food-Service Operations.” Socio-Economic Planning Sciences (2022).
- Chen, Y. et al.“Dynamic Scheduling of Reconfigurable Assembly Lines via FJSP Techniques” Applied Soft Computing (2024).