Planning and Scheduling in

Manufacturing and Services

Michael Pinedo

Stern School of Business

New York University

Planning and Scheduling

Issues in Manufacturing Planning and Scheduling Issues in Services System Implementation Issues Research Issues

Planning and Scheduling

Issues in Manufacturing Planning and Scheduling Issues in Services System Implementation Issues Research Issues

Plants DCs Customers Products Resources Supplier

Nowadays - Global Supply Chains

Supply Chains in Different Industries

PC-ASSEMBLY AUTOMOTIVE ASSEMBLY PAPER PRODUCTS

(Process Industries)

Glass

Monitors

Micro Processors

Memories

PC-Assembly

Iron ore Bauxite

Steel Aluminum

Electronic

Components

Engine

Car Assembly

Wood Chips

Chemicals

Reels

Rolls Ink

Paper Products

(Cutsize; Bags)

Supply Chain Characteristics

ASSEMBLY CONSUMER

PRODUCTS

(PC; Xerox machines)

AUTOMOTIVE

ASSEMBLY

BULK/PROCESS INDUSTRIES

(Paper; Pharmaceuticals; Plastics)

No Setup Times

In Assembly

Distribution (Fast)

(UPS)

MTO/MTS High

Impact of

E-Commerce High

B2C

DTC

Call-Center

Webcentric

Planning Horizon

Short

Scheduling:

More Dynamic (reactive)

Setup Costs in

Paint Shop

Distribution

(Trailer, (fast) ships)

MTO/MTS Medium

Impact of

E-Commerce Lower

B2C-B2B

EDI

Planning Horizon

Medium

Major Setup Costs

(Long Runs (Affect Quality))

Distribution (Slow)

(Trains, Barges, (slow) ships)

MTO/MTS Low

Impact of

E-Commerce Lowest

B2B

Planning Horizon

Long

Scheduling:

More Static

Applications of Planning and Scheduling

Theory in Various Supply Chain Models

• Applications in Process Industries

• Upstream: multi-echelon inventory theory

• Downstream: scheduling theory

• Applications in Automobile Manufacturing

• Finite horizon grouping and spacing heuristics

• Reactive (adaptive) scheduling

• Applications in PC Assembly

• Reactive (adaptive) scheduling

• Scheduling subject to disruptions

Classification of planning and

scheduling problems

Detailed

Scheduling

Production

Planning

Deployment

Supply Network

Planning

Detailed Planning

Aggregated Planning

Tactical (medium

term)

Operational (short term)

Aggregation

Medium vs. Short Term Planning

Medium Term Planning

Global Optimization

Maximum Profit

Product Hierarchies

Time Buckets (days, weeks, …)

Decide

Where to produce

How much to produce

How much to deliver

How much capacities

Medium vs. Short Term Planning

Short Term Scheduling

Local Optimization

Disaggregate global plan

Time continuous (seconds)

Decide

When to produce

On which resources to produce

Optimize production sequence

Buffer inventory may decouple medium Term Planning

Medium

Term

Planning

Short

Term

Scheduling

Upstream downstream

Stage 1 Stage 2 Stage 3 Stage 4 Stage 5

Planning and Scheduling in Supply Chains

facility

Planning system

Scheduling system

Integration between Medium and

Short Term Planning

Scheduling in Medium and Short

Term Planning Medium Term Planning

• Respects Short Term planning

orders as fixed

Capacity reduction from

Short Term Planning Orders

Material flow

Short Term Planning

• Respects medium term planned demands as due dates

No capacity reduction from medium term planning orders

Material flow

Short Term Horizon

Medium Term Horizon

Example: Short Term Optimization

Genetic Algorithm Optimized Period: 7 days

Only one operation per product

Sequence dependent set-up times

Due dates are the planned transports

from the medium term optimization

Up to three alternatives resources

Number of operations: 20-30

Runtime: 1 min

Scheduling Optimizer Architecture

Core Model

LiveCache

Model Generator

Campaign

Optimizer

Constraint

Programming

Genetic

Algorithm

Basic Optimizer

Time

Decomposition

Bottleneck

Meta-Heuristics

Reporting

GUI

Control

Checking

Short Term Scheduling User Interface

Example: Medium Term Optimization

MIP Program (3 MIPs)

309 number of products – 3184 number of location products (1.run)

Planning period 12 weeks – the first 5 weeks in daily buckets

432631 number of variables (1.run)

132649 number of constraints (1.run)

66083 number of discrete variables (1.run)

Using product decomposition (5% partition)

Pre-Phase to calculate starting solution

Pre-Phase to calculate product priority for the product decomposition

Result quality

Runtime 10 hours

LP solves problem in less than 15 minutes optimal

Solution quality: 1-3% > compared to relaxation solution

Medium Term Planning User Interface

• Two Schools of Thought

Mathematical Programming

Constraint Programming

• Hybrid Algorithms

Research Issues in Planning and

Scheduling in Supply Chains

Research Issues in Planning and

Scheduling in Supply Chains

• Robustness; how to measure the robustness

of a schedule

• Interrelationship between scheduling

and quality control

• The longer the run lengths in the process industries,

the higher the quality

• The better the grouping and spacing in automobile

manufacturing, the higher the quality

Impact of Internet on Planning and

Scheduling Algorithms and Systems

• Adaptive (Reactive) Scheduling after

Disruptions (Repair Algorithms)

• Bidding and Pricing Mechanisms; Auctions

• Remote use and Development of

Scheduling Algorithms and Engines

• Relevant information (with regard to disruptions)

moves forward and backward through the supply chain.

• Adaptive scheduling after disruptions

• Repair algorithms

MRP Production

Production Upstream Downstream Pricing

Disruption

JOB AGENT

CALL FOR BIDS

(DESIRED TIME

FRAMES)

BIDS (PRICES, SPECIFIC

TIME FRAMES)

AWARD

COMPARISON

ALGORITHM

PRICING AND

TIMING

ALGORITHM

$

MACHINE AGENT

Bidding and Pricing Mechanism

Bidding and Pricing Mechanisms

• Budgeting Constraints

• Information Infrastructure

• Bidding and Pricing Rules and Algorithms

Bidding and Pricing

Rules and Algorithms

• Goodness of Fit of Operations in Current

Machine Schedule

• Anticipated Supply of Machine Capacity

and Competition Level

• Anticipated Demand for Machine Capacity

• Learning Mechanisms

Some System Implementation Companies

SAP — APO (Advanced Planning and Optimization)

ASPROVA — No: 1 in Japan

Preactor — Subsidiary of Siemens

ORTEMS — Based in Lyon

Cybertec — Based in Trieste

Taylor --- Based in Canada

Then and now

• A lot of work had been done then on

theoretical scheduling (classification

schemes, complexity results, etc.)

• Now a lot of work is going on in applied

scheduling (unfortunately, no classification

scheme - a little bit of chaos)

Planning and Scheduling Issues in

Manufacturing Planning and Scheduling Issues in Services Systems Implementations Research Issues

General Scheduling Areas

in Service Industries:

1. Project planning and scheduling.

2. Tournament scheduling.

3. Scheduling in Health Care.

4. Transportation scheduling.

5. Workforce scheduling.

Application Areas:

• Consulting projects.

• Scheduling meetings, exams.

• Broadcast television network scheduling.

• Operating Room Scheduling

• Tanker scheduling, aircraft scheduling.

• Train timetabling.

• Workforce scheduling in call centers.

Thank You !