Dr. Christopher Sürie
Expert Consultant
SCM Optimization
Optimization in Supply Chain Planning
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 2
Agenda
Introduction
Hierarchical Planning Approach and Modeling Capability
Optimizer Architecture and Optimization Strategies
Customer Cases
System Demo
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 3
Introduction: Supply Chain Management
DCPlantSupplier Customer
Supply Chain Management: Set of approaches utilized
� to integrate suppliers, manufactures, warehouses and stores
� so that merchandise is produced and distributed
� with the correct quantity
� to/from the correct locations
� at the correct time
� in order to minimize cost while satisfying service level requirements
Prerequisite: Integrated Supply Chain Model
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 4
Introduction: Supply Chain Management
DCPlantSupplier CustomerProductResource
Master Data Model
Operational Data
� Location (Plant, DC, Supplier, ...)
� Lane
� Product
� Production Process Model (PPM)
� Resource
� Demands
� Orders
� Capacity Profiles
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 5
mySAP SCM Solution Overview
Network
Supply
Chain
Exchange
Network
Supplier
Partner Partner
Customer
Direct
Procurement
Source Deliver
Order
Fulfillment
Make
Manufacturing
Supply Chain Design
Strategize
Demand and
Supply Planning
Plan
Supply Chain Performance Management
Measure
Supply Chain Event Management
Track
Supply Chain Collaboratio
n
Collaborate
Supply Chain Collaboration
Collaborate
Supply
Chain
Exchange
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 6
Supply Chain Planning Matrix
long-
term
short-
term
Procurement SalesProduction Distribution
Strategic network planning
Master planning
Material
requirements
planning
Demand
planning
Available
to
promise
Production
planning
Detailed
scheduling
Distribution
planning
Transportation
planning
(Stadtler/Kilger, 2005)
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 7
Agenda
Introduction
Hierarchical Planning Approach and Modeling Capability
Optimizer Architecture and Optimization Strategies
Customer Cases
System Demo
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 8
How to deal with planning complexity?
Basic idea: Hierarchy of relaxations
Relaxations are derived by Aggregation
� Time →→→→ Periods
� Product →→→→ Product groups
(e.g. ignore country specific documentation in packaging a product)
� Resource →→→→ Resource Families
(e.g. summarize similar resources into one resource with cumulative
capacity)
� Locations →→→→ Regions
(e.g. aggregate different locations into a transportation zone (postal
code areas)
Integration between different relaxations: Disaggregation
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 9
Hierarchical Planning
Direct
Procurement
Source Deliver
Order
Fulfillment
Make
Manufacturing
Supply Chain Design
Strategize
Supply Chain Design
Strategize
Demand and
Supply Planning
Plan
Supply Chain Design
Demand and
Supply Planning
Plan
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 10
Supply Network Planning (SNP)
Supply Chain Design
Strategize
Direct
Procurement
Source Deliver
Order
Fulfillment
Make
Manufacturing
Demand and
Supply Planning
Plan
Supply Network Planning (SNP)
� Combined production and distribution
planning
� Mid-term to long-term planning horizon
� Quantity-based
� Aggregation
�Time →→→→ Buckets, max. daily precision
�Products, Resources →→→→ Families
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 11
Supply Network Planning Procedures
SNP Heuristics
� Material availability constraints
� Rule-based
� First feasible plan
CTM (Capable To Match)
� Material availability and production capacity constraints
� Constraint-based propagation with backtracking search
� First feasible plan
SNP Optimizer
� Material availability and all capacity constraints
� (MI)LP and others
� Cost-based optimization
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 12
SNP Optimizer Application
Sourcing
� Product-Mix
Which products and how much of them should be produced, transported, procured and stored?
� Technology-Mix
� Which recipes (PPMs) should be applied?
� Which transportation type should be used?
� Which resource should be used?
� Temporal: When should we produce, transport, procure and store?
� Spatial: Where should we produce, procure and store? Wherefrom and
whereto should we transport?
Finite Planning
Lot-Sizing
� Multi-Level-Capacitated Lot Sizing (MLCLSP)
� Campaign Planning
Inventory Control
� Target Days of Supply
� Shelf Life
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 13
Supply Network Optimization: Model Building
TransportDiscrete Lots
Minimal Lots
Piecewise linear Costs
Handling-In
Capacity
Satisfy Demandwith Demand Classes
Delay Costs
Non-Delivery Costs
ProcurePiecewise linear Costs
PPM
Products
ProduceDiscrete Lots
Minimal Lots
Fixed Resource Consumption
Piecewise linear Costs
Storewith Shelf life
Storage
Capacity
Safety Stock
Transport
Capacity
Handling-Out
Capacity
Production
Capacity
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 14
SNP Optimization Run
Demand Forecast
SNP Optimization
Resource Selection
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 15
Supply Network Optimization: Lot-Sizing
Multi-Level Capacitated Lot Sizing Problem (MLCLSP)
� Setup cost and/or consumption in each bucket
� Good results
�Setup cost small compared to storage cost ( →→→→ Small lots)
�Setup consumption << bucket capacity
� Bad results
�Setup cost large compared to storage cost (large lots)
�Setup consumption big compared to bucket capacity
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 16
Supply Network Optimization: Lot-Sizing
Proportional Lot Sizing Problem (PLSP)
� Setup cost and/or setup consumption only if different PPM starts
� At most one startup per bucket
�Constraints on cross-period lots (= campaign quantity)
� Minimal campaign quantity
� Campaign quantity integer multiple of batch size
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 17
Manufacturing (PP/DS)
Supply Chain Design
Strategize
Direct
Procurement
Source Deliver
Order
Fulfillment
Make
Manufacturing
Demand and
Supply Planning
Plan
�Manufacturing (PP/DS)
� Combined material and capacity
planning
� Short-term to mid-term planning
horizon
� Order based
� Time continuous
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 18
PP/DS Planning Procedures
PP/DS Heuristics
� Material availability, single-level finite
� Priority-based planning
� First partial plan
CTM (Capable To Match)
� Material availability and production capacity constraints
� Constraint-based propagation with backtracking search
� First feasible plan
PP/DS Optimizer
� All constraints
� Genetic Algorithm and Constraint Programming
� Cost-based Optimization
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 19
PP/DS Optimizer Application
Feasible compact schedule
Delay Reduction
Makespan Minimization
Setup Minimization
� Time
� Cost
Resource Selection
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 20
Time Windows
earliest starting time
due date, deadline
delay costs
Distances
(with calendars)
minimal
maximal
Setupsequence dependent
setup costs
Resource Selection
Alternative resources
Resource costs
Unary
Resources
Product Flow
discrete
continuous
Storage
resources
Multi-Cap
Resources
PP/DS Optimizer: Model Overview
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 21
PP/DS Optimization Run
SNP
PP run
DS
optimization
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 22
Integrated hierarchical planning
PP/DS
SNP
SNP Horizon
�SNP
� Planning only in SNP horizon
� Release SNP Orders only PP/DS
horizon
� Respect PP/DS orders as fixed
� capacity reduction
�material flow
� Respect PP/DS setup state
�PP/DS
� Respect pegged SNP Orders as due
dates
�No capacity reduction
�But material flow
� No restrictions for scheduling
PP/DS orders
PP/DS Horizon
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 23
demand bj
time window [lj,rj]
time window [l,r]
supply a
VSR: Model Building
Classical vehicle routing problems: CVRP, CVRPTW
capacity c
m vehicles
7
2
8
4
1
3
9
6
5
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 24
Extensions towards APO VSR (1):
� Order-based model
� Source location and destination location per order (Pickup and delivery problem)
� Quantity regarding loading dimensions (tons, m3, ...)
� Material type (chemicals, food, ...)
� Service times for loading and unloading (depends on vehicle)
VSR: Model Building (ct‘d)
O4
O3
O2O1
6
24
1
3
5
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 25
Extensions towards APO VSR (2):
� Cost for not delivering an order
� Soft/hard time constraints per order:
� Earliest date for pickup
� Due date for pickup
� Earliest date for delivery
� Due date for delivery
VSR: Model Building (ct‘d)
time
cost
Late
Pickup
Early
Pickup
[ ] ][
Late
Delivery
Early
Delivery
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 26
VSR: Model Building (ct‘d)
Extensions towards APO VSR (3):
� Per vehicle:
� Travel characteristics (time, distance per lane)
� Start location and end location
� Constraints
� Capacity per loading dimension (tons, m3, ...)
� Limit for time, distance, number of stops
� Break calendar
� Costs:
� Fixed cost
� Traveled time
� Traveled distance
� Number of stops
� Distance x Load (e.g. miles x tons)
� Vehicle type = vehicles with identical travel & cost characteristics
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 27
VSR: Model Building (ct‘d)
Extensions towards APO VSR (4):
� Per location:
� Deliveries require inbound resource
� Opening times
� Capacities
� Pickups require outbound resource
� Opening times
� Capacities
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 28
VSR: Model Building (ct‘d)
Extensions towards APO VSR (5):
� Incompatibility constraints:
� Between material types
� Between vehicle types and material types
� Between vehicle types and locations
� Schedule vehicles (e.g. trains, ships)
� Route and schedule is fixed a priori
� Hubs
� Indirect shipment through hub(s) versus direct shipment
� Maximum waiting time at hub
H1 2
1 H2
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 29
Agenda
Introduction
Hierarchical Planning Approach and Modeling Capability
Optimizer Architecture and Optimization Strategies
Customer Cases
System Demo
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 30
Challenge: Generic Optimizer
Generic and Best of Breed
� planning level
� vertical industries
� run time requirement
� model complexity (size, constraints, objectives)
Generic Model (-> planning level)
� aggregated planning (LP / MILP)
� detailed planning (scheduling)
Customization (-> vertical industries)
� specialization the generic model to customer problem
� scripting the strategies (decomposition, goal programming)
Scalability (-> run time)
� greedy versus complex optimizations strategies
� parallelization
Open Architecture
� internal: adding new special optimizer (software evolution)
� external: integration of optimizer packages
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 31
SNP Optimizer Architecture
Core-Model
LiveCache/DB
Model Generator
SNPLP/MILP
DeploymentLP/MILP
Basic-Optimizers
SNPRule based
Time-
Decomposition
Product-
Decomposition
Meta-Heuristics
Priority-
Decomposition
Reporting
GUI
Control
Checking
Resource-
Decomposition
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 32
Scheduling Optimizer Architecture
Core Model
LiveCache
Model Generator
Campaign
Optimizer
Constraint
Programming
Genetic
Algorithm
Basic Optimizer
Sequence
Optimizer
Time
Decomposition
Bottleneck
Meta-Heuristics
Multi Agent
Reporting
GUI
Control
Checking
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 33
Mastering the algorithmic complexity: Decomposition
Global versus local optimality -> SNP + DS
� Local optimality depends on neighborhood
� High solution quality by local optimization
� Local Optimization = Decomposition
Decomposition strategies
� SNP: time, resource, product, procurement
� DS: time, resource
� (Parallelization by “Agents”)
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 34
SNP Product Decomposition
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 35
solved solved
SNP Time Decomposition
Time-
Decomposition
1 2 3 4 5 6
1 2 3-6
extract
merge
SNP LP/MILP
solve
store
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 36
DS Time Decomposition - Local Improvement
Resources
TimeCurrent window
Gliding window script
1. Optimize only in current window
2. Move window by a time delta
3. Go to first step
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 37
DS Metaheuristics - Bottleneck
Bottleneck Script
1. Determine bottleneck
2. Schedule bottleneck resources only
3. Fix sequence on bottleneck resource
4. Schedule all resources
Time
Resources
Bottleneck
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 38
VSR: The Optimizer
� „Generic“ Optimizer
� Preprocessing
� Which orders cannot be delivered at all?
� Which order can be processed by which vehicle?
� Postprocessing
� Shift travel activities forward or backward
S-1 P1 1-2 D1 (forward)
S-1 P1 1-2 D1 (backward)
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 39
VSR: The Optimizer (ct‘d)
� Evolutionary local search (ELS) with small population (3)
� Uses GENEAL (GENeral Evolutionary Algorithm Library)
� Direct solution representation
� Assignment of orders to vehicles
� Routing of activities on vehicles
� Scheduling of activities on vehicles
� Each „atomic“ move has three phases:
1. Change assignment
2. Change routing
3. Change scheduling
� 19 „atomic“ moves, classified into
� Assignment moves
� Routing moves
� Scheduling moves
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 40
Agenda
Introduction
Hierarchical Planning Approach and Modeling Capability
Optimizer Architecture and Optimization Strategies
Customer Cases
System Demo
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 41
Optimal
model detail
Challenges in modeling real-world problems
Generic nature of the SNP optimization model restricts exploitation of specific problem structure
Business
acceptability
of computed
solutions
Model detail
Solution
quality gap
(fixed run-time)
Model too
simplified
Model too
detailed
SAP AG 2005, Optimization in SC Planning, Dr. Christopher Sürie, 42
Agenda
Introduction
Hierarchical Planning Approach and Modeling Capability
Optimizer Architecture and Optimization Strategies
Customer Cases
System Demo