Production Scheduling: operations scheduling with applications in manufacturing and services

1. Production Scheduling: operations scheduling with applications in manufacturing and services Pei-Chann Chang RM 2614, tel. 2305, iepchang@saturn.yzu.edu.tw Industrial Engineering and Management Yuan Ze University, Taiwan

2. Literature Book: Operations Scheduling with applications in manufacturing and services Authors: M. Pinedo, X. Chao Handouts, also downloadable from website

3. Exam • The following methods must be studied thoroughly • (one or two questions about these will be in the exam): • adaptive search • branch-and-bound, beam-search • shifting bottleneck • Aside from the discussed chapters from the book, • the handouts must be well studied.

4. Scheduling: definition Allocation of jobs to scarce resources the types of jobs and resources depend on the specific situation Combinatorial optimization problem maximize/minimize objective subject to constraints

5. Application of Scheduling Sales Dept. Production Dept. Inventory Dept. order shipping Production Management Dept. customer Problem：Complexity↑、Machine ↑ 、Order ↑ 、Variety ↑

6. Application of Scheduling MTO (Make to Order) MTS (Make to Stock) Produce way Tendency of Business: BTO (Build To Order)CTO (Configuration To Order)

7. M1 M2 M3 M4 M4 M3 M1 M2 Theory of Production Scheduling • Shop Type • Single Machine • Parallel Machine • (Flow Shop : Uni-direction) • (Job Shop : Multi-direction) • (Open Shop: No direction) Total identical Partial identical

8. Theory of Production Scheduling • Job Type • Dependent Job order product operation b. Independent Job part assembly

9. Theory of Production Scheduling • Objective Function 1. Completion time - Min Max Ci 2. Tardiness - Min Tmax Note：Reasonable Due Date 3. Flow time - Min F Objectives

10. Application areas • Manufacturing, e.g.: • job shop / flow shop scheduling • workforce scheduling • tool scheduling • Services, e.g.: • Hotel / airline reservation systems • Hospitals (operating rooms) • Transportation and distribution, e.g.: • vehicle scheduling, and routing • railways

11. Application areas (cont.) • Information processing and communications: • CPU’s, series and parallel computing • call centers • Time-tabling, e.g.: • lecture planning at a University • soccer competition • flight scheduling • Warehousing, e.g.: • AGV scheduling, and routing • Maintenance, e.g.: • scheduling maintenance of a fleet of ships

12. Scheduling in manufacturing Due to increasing market competition, companies strive to: • shorten delivery times • increase variety in end-products • shorten production lead times • increase resource utilization • improve quality, reduce WIP • prevent production disturbances (machine breakdowns) --> more products in less time!

13. Scheduling in services • Workforce Scheduling in • Call Centers • Hospitals • Employment agencies • Schools, universities • Reservation Systems in • Airlines • Hotels • Car Rentals • Travel Agencies • Postal services

14. Important objectives to be displayed • Due Date Related • Number of late jobs • Maximum lateness • Average lateness, tardiness • Productivity and Inventory Related • Total Setup Time • Total Machine Idle Time • Average Time Jobs Remain in System, WIP • Resource usage • resource shortage

15. Important characteristics of optimization techniques • Quality of Solutions Obtained(How Close to Optimal?) • Amount of CPU-Time Needed(Real-Time on a PC?) • Ease of Development and Implementation(How much time needed to code, test, adjust and modify) • Implementation costs (Are expensive LP-solvers required?)

16. Our approach Scheduling problem Problem formulation Model Solve with algorithms Conclusions

17. Time NP problem #jobs 10 20 30 40 Theory of Production Scheduling • Methodology • Mixed Integer Linear Programming • Dynamic Programming • Branch and Bound • Constraint Programming • Heuristics • Genetic Algorithm • Neural Network • Simulated Annealing • Tabu Search • Ant Colony • Evolutionary Algorithm • Fuzzy Logistics • . • . • .

18. Future Development • Alternate Routing • Multiple Objectives • Machine break down -Rescheduling

19. Topic 1 • Setting up the Scheduling Problem

20. Modeling • Three components to any model: • 1. Decision variables • This is what we can change to affect the system, that is, the variables we can decide upon • 2. Objective function • E.g, cost to be minimized, quality measure to be maximized • 3. Constraints • Which values the decision variables can be set to

21. Decision “Variables” • Three basic types of solutions: • A sequence: a permutation of the jobs • A schedule: allocation of the jobs in a more complicated setting of the environment • A scheduling policy: determines the next job given the current state of the system

22. Model Characteristics • Multiple factors: • Number of machine and resources, • configuration and layout, • level of automation, etc. • Our terminology: Resource = machine (m) Entity requiring the resource = job (n)

23. Example: Scheduling Problem: The data for the newspaper reading problem Ask: What is the earliest time they may leave?

24. Sol: Estimation based on jobs (persons): Lower Bound 1 (Jobs base bound)

25. Sol: Estimation based on machine (newspaper): Why? Lower Bound 2 (machine base bound) LB = Max(LB1, LB2) = Max(11:03, 11:30) = 11:30

26. HW. • How many different schedules, feasible and infeasible are there? • What is the earliest time that Algy and his friends can leave for the country? • Digby decides that the delights for a day in the country are not for him, He will spend the morning in bed. What is the earliest time that Algy, Bertie and Charles may leave ? • Do you need to list every feasible solution when solving prob.2 & 3? If not, please explain in detail the procedure to your answer without listing every feasible solution.