DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of...
-
Upload
heidi-sheerer -
Category
Documents
-
view
216 -
download
1
Transcript of DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of...
![Page 1: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/1.jpg)
DECISION SUPPORT SYSTEMS FOR PLANNING AND
SCHEDULING IN PRACTICE
Michael PinedoStern School of Business
New York University
![Page 2: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/2.jpg)
DECISION SUPPORT SYSTEMS FOR PLANNING AND
SCHEDULING IN PRACTICE
I. Application Areas, Infrastructures,General Architectural Issues
II. System Requirements
III. Planning and Scheduling Techniques
IV. System Implementations Commercial Packages
![Page 3: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/3.jpg)
Part I.
Application Areas, Infrastructures, General
Architectural Issues
![Page 4: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/4.jpg)
• Application Areas– Planning and Scheduling in
Manufacturing and Services
• Infrastructures– In Manufacturing– In Supply Chain Management– In Services
• General Issues regarding– Systems Architecture– For Production Scheduling– For Workforce Scheduling
![Page 5: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/5.jpg)
APPLICATION AREAS OF PLANNING AND SCHEDULING
• Manufacturing– Process– Discrete– Automotive– Food and Snacks
• Services:– Crew Scheduling (Airlines)– Workforce Scheduling (Call Centers)– Reservation Systems and Yield
Management
![Page 6: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/6.jpg)
INFORMATION SYSTEM INFRASTRUCTURE IN
MANUFACTURING ENVIRONMENTS
• Interfaces with Forecasting, Medium Term, and Long Term Planning
• Interfaces with Product Design and Facility Layout
![Page 7: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/7.jpg)
•Workforce Scheduling in–Cell Centers–Hospitals
•Reservation Systems in –Airlines–Hotels–Car Rentals
INFORMATION SYSTEM INFRASTRUCTURE IN
SERVICE ENVIRONMENTS
![Page 8: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/8.jpg)
Part II.
Important Issues in Design of Decision Support Systems
![Page 9: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/9.jpg)
IMPORTANT ISSUES IN DESIGN OF DECISION SUPPORT
SYSTEMS•Module Design and Interfacing•GUI Design•Design of Link Between GUI and Algorithm Library•Internal Reoptimization•External Reoptimization
![Page 10: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/10.jpg)
MODULAR (OBJECT-ORIENTED) DESIGN
Standardization of Data Transfers Between Modules.Data Concerning:
– Jobs (Operations)– Work Centers (Machines)– Schedules
Have to be Properly Organized in order to make Transfer of Data Easy.
EXAMPLE: Plugging in New Algorithm in Existing System should be Easy.
![Page 11: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/11.jpg)
GUI’SSHOULD ALLOW:
• Interactive Optimization– Freezing Jobs and Reoptimize– Creating New Schedules by Combining
Different Parts from Different Schedules
• Cascading and Propagation EffectsAfter a Change or Mutation by the User, the
System– does Feasibility Analysis– takes care of Cascading and Propagation
Effects,– does Internal Reoptimization
![Page 12: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/12.jpg)
GRAPHICS USER INTERFACES FOR SCHEDULING
PRODUCTION PROCESSES
•Gantt Chart Interface•Dispatch List Interface•Time Buckets•Throughput Diagrams
![Page 13: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/13.jpg)
IMPORTANT OBJECTIVES TO BE DISPLAYED
• Due Date Related– Number of Late Jobs– Maximum Lateness– Average Lateness
• Productivity and Inventory Related– Total Setup Time– Total Machine Idle Time– Average Time Jobs Remain in
System
![Page 14: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/14.jpg)
SEQUENCE DEPENDENTSETUP TIMES
Sijk = The Time it Takes to Setup for Job k at the Completion of Job j on Machine i.
•One way to Retrieve These Data is Through a Table Look-up
•Another way is Through a FormulaJob j Carries a Number of Parameters in its Data String
aij, bij, cij (color, sizes, etc.)
Sijk = fi (aij, aik) + gi (bij, bik) + hi (cij, cik)
or
Sijk = MAX (fI (aij, aik), gi (bij, bik), hi (cij, cik))
![Page 15: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/15.jpg)
INTERNAL RE OPTIMIZATION AFTER A CHANGE BY THE USER
Internal Reoptimization Should Satisfy Certain Conditions:
C.U. = Change by the UserI. R. = Internal Reoptimization
Internal Reoptimizaton Should be ReversibleC. U. I. R. Reverse C. U. I. R.
Original ScheduleInternal Reoptimization Should be CommutativeC. U. 1 I. R. C. U. 2 I. R.
Same ScheduleC. U. 2 I. R. C. U. 1 I. R.
![Page 16: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/16.jpg)
Part III.
Planning and Scheduling Optimization Techniques
![Page 17: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/17.jpg)
PLANNING AND SCHEDULING OPTIMIZATION TECHNIQUES
• Dispatching Rules• Composite Dispatching
Rules• Dynamic Programming• Integer Programming• Column Generation• Branch and Bound• Beam Search
![Page 18: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/18.jpg)
• Local Search• Decomposition Techniques
– Temporal– Machine (Shifting Bottleneck)
• Drum-Buffer-Rope• Hybrid Methods
PLANNING AND SCHEDULING OPTIMIZATION TECHNIQUES
(continued)
![Page 19: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/19.jpg)
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)
![Page 20: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/20.jpg)
Local Search
ValueObjectiv
eFunctio
n
Dispatching
Rules
Beam Search Branch and
BoundCPU - Time
![Page 21: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/21.jpg)
COMPOSITE PRIORITY RULE THAT IS MIXTURE OF
THREE BASIC PRIORITY RULES:
• Weighted Shortest Processing Time First
• Earliest Due Date First• Shortest Setup Time First
![Page 22: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/22.jpg)
DYNAMIC PROGRAMMINGCharacterizing Equations:
(i) Initial Conditions(ii) Recursive Relation(iii) Optimal Value FunctionExample: Consider a Single Machine and
Objective Function
Let J Denote a Subset of the n Jobs. Assume J is Processed First.Let V(J) = hj (Cj)
![Page 23: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/23.jpg)
Initial Conditions:V({j}) = hj (pj) j = 1, …, n
Recursive Relation:
V(J) = min (V(J- {j}) +
hj( pk)) j J
Optimal Value Function:V({1,…., n})
![Page 24: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/24.jpg)
INTEGER PROGRAMMING FORMULATIONS
•Hard Problems can often be Formulated as I.P.s.
•These I.P.s are often Solved via Branch and Bound
•Many Applications of I.P. Formulations in– Workforce scheduling– Crew Scheduling
![Page 25: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/25.jpg)
I.P. FORMULATION OF WORKFORCE SCHEDULING
PROBLEM•Predetermined Cycle of m Periods
•During Period i presence of bi needed
•n Different Shift Patterns•Shift Pattern j a1j 0
a2j 1. 1. 1. 0amj 0
•cj is Cost of Assigning one Person to Shift j
•xj is Integer Decision Variable Representing Number Assigned to Shift j
![Page 26: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/26.jpg)
MINIMIZEc1 x1 + c2 x2 + …. + cn xn
SUBJECT TO:a11 x1 + a12 x2 + … + a1n xn > b1
a12 x1 + a22 x2
. .
. .
. .
am1 x1 + am2 x2 + … + amn xn > bm
xj Integer
![Page 27: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/27.jpg)
I.P. FORMULATION OF CREW SCHEDULING PROBLEM
• m Jobs (Flight Legs)• n Feasible Combinations of Jobs one Crew can
Handle (Round Trips)
• cj Cost of Round Trip j
INTEGER PROGRAM
• min c1 x1 + x2 x2 + …. + cn xn
• S.T. a11 x1 + a12 x2+ …. + ain xn > 1
am1 x1 + am2 x2+ ….+ amn xn > 1
xj {0, 1}
• Each Column is a Round Trip• Each Row is a Job that must be Covered
SET PARTITIONING PROBLEM
![Page 28: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/28.jpg)
DISJUNCTIVE PROGRAMMING FORMULATIONS
•Hard Problems can often be Formulated as Disjunctive Programs
•These Programs are often Solved via Branch and Bound
•Many Applications of Disjunctive Programs in Job Shop Scheduling
![Page 29: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/29.jpg)
MINIMIZING THE MAKESPANIN A JOB SHOP
pij = processing time of job j on machine i
yij = starting time of job j on machine i
DISJUNCTIVE PROGRAMMinimize Cmax
Subject toykj - yij > pij For All (i, j) (k, j)
Cmax - yij > pij For All (i, j)
yij - yiℓ > pi ℓ or yiℓ - yij > pij For All (i, ℓ) ( i, j)
yij > 0 For All (i,j)
There are Disjunctive Programs for Job Shopswith other Objectives
![Page 30: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/30.jpg)
LOCAL (NEIGHBORHOOD) SEARCH METHODS
•Simulated Annealing(Probabilistic
Method)•Tabu-Search
(Deterministic Method)
•Genetic Algorithms
![Page 31: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/31.jpg)
IMPORTANT CHARACTERISTICS OF LOCAL SEARCH PROCEDURES
•Schedule Representation Needed for Procedure
•The Neighborhood Design•The Search Process within
the Neighborhood•The Acceptance-Rejection
Criterion
![Page 32: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/32.jpg)
DECOMPOSITION TECHNIQUES•Machine Decomposition
(Shifting Bottleneck Techniques)•Temporal Decomposition
IMPORTANT CHARACTERISTICS OF DECOMPOSITION TECHNIQUES
•Select as the next Subproblem to Solve always the one that Appears the Hardest
(“Follow the Path of the Most Resistance”)•After the Completion of Each Step, Reoptimize
all the Steps that were Done Before
![Page 33: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/33.jpg)
HYBRID METHODS• Scheduling techniques can be
Combined in Series•E.G., FIRST USE A DISPATCHING RULE,
THEN FOLLOW UP WITH A LOCAL SEARCH
• Scheduling Techniques can be Combined in an Integrated Manner
•E.G., A DISPATCHING RULE CAN BE USED WITHIN A BRANCH AND BOUND TO OBTAIN UPPER BOUNDS.
•DYNAMIC PROGRAMMING ROUTINE CAN BE USED FOR A SINGLE MACHINE SUBPROBLEM WITHIN A MACHINE DECOMPOSITION TECHNIQUE
![Page 34: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/34.jpg)
Part IV.
System Implementation Issues
Commercial Packages
![Page 35: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/35.jpg)
ERP-SYSTEMSSAP, Baan, JD Edwards, People Soft
GENERAL OPTIMIZATIONIlog, Dash
GENERAL SCHEDULING(Often in Framework of Supply Chain
Management)I2, Cybertec, AutoSimulation, IDS Professor
ScheerSCHEDULING OIL AND PROCESS INDUSTRIES
Haverly Systems, Chesapeake, FinitySCHEDULING CONSUMER PRODUCTS
Manugistics, NumetrixSCHEDULING WORKFORCE IN CALL CENTERS
AIX, TCS, Siebel
![Page 36: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/36.jpg)
ROBUSTNESS
• Unexpected (Random) Events• Inaccuracy of Data
CAUSES OF PERTURBATIONS:
ValueObjective
Solution Space
![Page 37: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/37.jpg)
MEASURES OF ROBUSTNESS• δ - Perturbation; Amount of Time
Completion of a Task is Postponed
•Z - Value of the Objective UnderOriginal Schedule
•Z1 - Value of the Objective Under New Situation (without rescheduling)
Z1 - Zδ
= L (δ) L (δ)
δ
![Page 38: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/38.jpg)
OPTION: Reschedule After Perturbation
•Local Rescheduling•Global Rescheduling
PRACTICAL CONSIDERATION:•New Schedule Should be Similar to Old Schedule(Distance Measure)
![Page 39: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/39.jpg)
RULES TO FOLLOW IN ORDER TO GENERATE ROBUST SCHEDULES
• Insert Idle times(Especially Where Perturbationsare to be Expected)
• Less Flexible Job FirstMore Flexible Jobs Later
• Do NOT Postpone Processing when Possible(NOTE: This Would Go Against JIT Principles)
![Page 40: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/40.jpg)
LEARNING MECHANISMS
• Rote Learning(When Solution Space is Relatively
Small)• Classifier Systems
(Often Based on Genetic Algorithms)
• Case Based Reasoning(Parameter Adjustment Methods)
• Induction Methods and Neural Nets
![Page 41: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/41.jpg)
PARAMETER ADJUSTMENTATCS - RuleIj (t, ℓ ) =wj exp (- (dj-pj-t)+) exp (- sℓ j)pj k1p k2 s
k1 and k2 are hard to determine
k1 and k2 Functions of–Due Date tightness τ–Due Date Range R–Setup Time Severity η
![Page 42: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/42.jpg)
ON-LINE LEARNING
Every Time the Problem is Solved, the Problem is also Solved for k1 + δ, k1 - δ, k2 +
δ, k2 - δ
Dependent Upon the Outcome the Parameters are Adjusted for the Next Time.
![Page 43: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/43.jpg)
NEURAL NETApplication:
•m Resources in Parallel•Different Speeds•Setups
INPUT UNIT
OUTPUT UNIT
HIDDEN UNITS• Jobs Arrive at Different Times• Jobs Have Due Dates
Machines have Attributes• Increase in Total Weighted Completion Time• Increase in Number of Late Jobs•Current Number of Jobs on Machine
![Page 44: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/44.jpg)
OFF-LINE TRAINING BY AN EXPERT
Expert Plus LearningAlgorithm (Back Propagation)Determine the Connection Weights
![Page 45: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/45.jpg)
MULTIPLE OBJECTIVESEXAMPLE:
•m Resources in Parallel•n Jobs•Due Dates•Sequence Dependent Setups
OBJECTIVES:•Minimize Sum of Setup Times•Minimize Penalties Due to Late Delivery
Weights of the Two Objectives Vary over Time and Depend on Status Quo.
![Page 46: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/46.jpg)
GENERAL FRAMEWORK:
• Mixing of Priority Rules• Switching Over Between Rules
Scaling Parameters and Switch-Over Times Depend on the Data Set
Framework Above can be Combined with Local Search Heuristic.
![Page 47: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/47.jpg)
DESIGN ISSUES WITH REGARD TO DECISION SUPPORT SYSTEMS
FOR PLANNING AND SCHEDULING
•Robustness•Multiple Objectives•Learning mechanisms
Michael PinedoStern School of BusinessNew York University
![Page 48: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/48.jpg)
DECISION SUPPORT SYSTEMS
•Forecasting•Facility Location•Supply Chain
Management•Routing and Distribution
![Page 49: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/49.jpg)
PLANNING AND SCHEDULING
•Characteristics:–Engines Often Based on
Combinatorial Algorithms
–Systems Often have to Operate in Real Time
![Page 50: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/50.jpg)
IMPORTANCE OF PLANNING AND SCHEDULING SYSTEMS
• 150 Software Companies– I2– Manugistics– Bender-Synquest– IDS - Scheer– SAP– Bran
![Page 51: DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING IN PRACTICE Michael Pinedo Stern School of Business New York University.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551635ea550346c6758b4f7c/html5/thumbnails/51.jpg)
PLANNING AND SCHEDULING FRAMEWORK
• Resources (Machines)• Tasks (Jobs)• Due Dates• Objectives
GOAL:
• Determine a Schedule (solution)That Minimizes the Objective(s)