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Mathematical Programming Approach to Supply Chain Optimization and Humanitarian Logistics

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Mathematical Programming Approach to Supply Chain Optimization and Humanitarian Logistics Mikio Kubo Tokyo University of Marine Science and Technology – PowerPoint PPT presentation

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Title: Mathematical Programming Approach to Supply Chain Optimization and Humanitarian Logistics


1
Mathematical Programming Approach to Supply Chain
Optimization and Humanitarian Logistics  
  • Mikio Kubo
  • Tokyo University of  Marine Science and Technology

2
Supply Chain Risk Management (SCRM)
  • Proactive and response approaches to cope with
    supply chain disruptions.

Disruption
Performance
Recovery
Proactive
Response
Time
3
Humanitarian Logistics (HL)
  • is a branch of logistics which specializes in
    organizing the delivery and warehousing of
    supplies during natural disasters to the affected
    area and people.
  • Decentralized
  • No SCM unit nor trained staffs
  • Everything is ad hoc
  • No performance measure (fairness, speed, )
  • No information communication technology
  • Many players (government, NGOs)

4
Mathematical Optimization Approach to SCRM and HL
  • Stochastic Optimization a classical mathematical
    programming approach to cope with uncertainty
  • Disruption (Recovery) Management an approach to
    recover from disruption quickly (mainly used in
    airline and rail industries)
  • Risk Optimization a new framework Stochastic
    Optimization Disruption Management

5
Stochastic Optimization (1)
Here Now Variables
Recourse Variables
gtflexibility
scenarios
Disruption
Performance
Proactive
Response
Time
6
Stochastic Optimization (2)
  • Scenario approach ( of typical scenarios is
    not so large)
  • S set of scenarios x here now variable
    vector Xs recourse variable vector for scenario
    s

7
Stochastic Optimization (3)
  • CVaR approach (disruption is a rare event
    decision maker is risk averse)
  • (1- ? ) Expectation ? ß-CVaR

8
Disruption Management (1)
Response Action X
Base Solution x
Disruption
Performance
Deviation from x
Proactive
Response
Time
9
Disruption Management (2)
  • Recovery Optimization After a disruption
    (scenario), find a recovery solution that is
    close to the base solution x

10
Risk Optimization
  • A new framework to copy with disruptions
  • Stochastic Recovery Optimization

11
Supply Chain Risk Optimization Models
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12
Probabilistic Inventory Model (1)
Multi period, Single stage, Static policy, Nominal
Variables I inventory B backorder x ordering
amount
Parameters h inventory cost b backorder
cost p probability d 0 disruption occurs
1 otherwise
13
Probabilistic Inventory Model (2)
Multi period, Single stage, Static policy, CVaR
14
Probabilistic Inventory Model (3)
Multi period, Multi stage, Adaptive policy,
Nominal
15
Resource Constrained Scheduling Problem (1)
16
Resource Constrained Scheduling Problem (2)
Resource constraints
Precedence constraints
Processing time (p), resource upper bound (RUB),
and resource usage (a) depend on scenarios
17
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