Production planning: operations scheduling

with applications in manufacturing and services

Erwin Hans (T&M-OMST)BB-235, tel. 3523,

e.w.hans@sms.utwente.nl

Johann Hurink (TW-STOR)J.L.Hurink@math.utwente.nl

Faculty of Technology and Management

University of TwenteEnschede, The Netherlands

Literature

Book:Operations Scheduling with applicationsin manufacturing and services

Authors: M. Pinedo, X. Chao

Handouts, also downloadable from website

Exam

These methods must be learned entirely (one or two questions about these will be in the exam):• adaptive search• branch-and-bound, beam-search• shifting bottleneck

The idea (approach) and application of all other discussedmethods must be learned (i.e., no formulas)

One question will be asked about the software demonstration

Aside from the discussed chapters from the book, the handouts must be learned

Scheduling: definition

Allocation of jobs to scarce resources

the types of jobs and resources depend on the specific situation

Combinatorial optimization problemmaximize/minimize objectivesubject to constraints

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

Application areas

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

Application areas (cont.)

Scheduling in manufacturingDue to increasing market competition,

companies strive to:shorten delivery timesincrease variety in end-productsshorten production lead timesincrease resource utilizationimprove quality, reduce WIPprevent production disturbances

(machine breakdowns)--> more products in less time!

Different types of manufacturing control

Make and assemble to stockMake to stock, assemble to orderMake to orderEngineer to order

Scheduling in a manufacturing planning and control framework

Long range forecasting and sales planningFacility and resources planningDemand management, aggregate and

workforce planningOrder acceptance and resource group

loading

Shop floor scheduling, workforce scheduling

Relations with other management areas

Product and process designProcess planningInventory management and materials

planningPurchasing and procurement

managementWarehousing and physical distribution

Scheduling in servicesWorkforce Scheduling in

Call Centers Hospitals Employment agencies Schools, universities

Reservation Systems in Airlines Hotels Car Rentals Travel Agencies

Postal services

Our approach

Scheduling problem

Model

Conclusions

Problem formulation

Solve with algorithms

Scheduling models

Job shop schedulingProject schedulingFlexible Assembly SystemsLot sizing and schedulingWorkforce scheduling, staffingInterval scheduling, reservation

systems, timetabling

Scheduling algorithmsGeneral solution Techniques:Mathematical programming

linear, non-linear, (mixed) integer programmingExact methods (enumeration)

branch-and-bound dynamic programming cutting plane / column generation methods

Local search methods, heuristics simulated annealing tabu search adaptive search

k-opt methods genetic algorithms neural networks

Scheduling algorithms (cont.)Heuristics

dispatching rules composite dispatching rules beam-search

Decomposition Techniques Temporal decomposition (rolling horizon

approach) Machine decomposition (Shifting Bottleneck)

Hybrid Methods combined usage of scheduling methods

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?)

Local Search

ValueObjectiv

eFunctio

n

DispatchingRules

Beam Search Branch and Bound

CPU - Time

Consideration of software companies w.r.t. optimization techniques

Implementation costs(Are expensive LP-solvers required? Easy to implement?)

vs.

What solution quality does the customer require?(Is an immediate answer required, or are long calculations allowed? Does customer accept complex solutions?)

online scheduling offline scheduling

ERP-SYSTEMS SAP, Baan, JD Edwards, People Soft, Navision, MFG

Pro GENERAL OPTIMIZATION

Ilog, Dash, MINTO, OSL (IBM), XPRESS-MP, OML, XA GENERAL SCHEDULING

I2, Cybertec, AutoSimulation, IDS Professor Scheer, ORTEC

SCHEDULING OIL AND PROCESS INDUSTRIES Haverly Systems, Chesapeake, Finity, ORTEC

SCHEDULING CONSUMER PRODUCTS Manugistics, Numetrix

SCHEDULING WORKFORCE IN CALL CENTERS AIX, TCS, Siebel

Commercial Packages

Decision Support Systems

Important issues in design of DSS:Database design and managementData collection (e.g. barcoding system)Module Design and InterfacingGUI Design (Gantt-charts, etc.)Design of link between GUI and algorithm

library (data organization before transfer)Internal Re-optimizationExternal Re-optimization

GUI’S should allow:Interactive Optimization

Freezing Jobs and Re-optimizing Creating New Schedules by Combining

Different Parts from Different SchedulesCascading and Propagation Effects

After a Change or Mutation by the User, the system:

does Feasibility Analysis takes care of Cascading and Propagation

Effects, does Internal Re-optimization

Graphics user interfaces for scheduling production processes

Gantt Chart InterfaceDispatch List InterfaceTime Buckets (resource capacity loading)Throughput DiagramsTime tables

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