Models and Methodologies for Supply Chain Decision Making

Because of the inherent complexity of decision making in supply chains, there is a growing need for modeling methodologies. A large number of manufacturing and service organizations are therefore seeking modeling systems that can help identify and implement strategies for designing and improving their supply chain networks. In the supply chain context, both deterministic and stochastic models are relevant. Also, we are concerned with both descriptive (or analysis) models and prescriptive (or optimization) models. Important descriptive models for supply chains include: Markov chains, queueing networks, stochastic Petri nets, and simulation. Important prescriptive models include: linear programming, mixed integer linear programming, heuristic optimization models, and simulation.

Some of the typical models and methodologies used in supply chain decision making are shown in Figure . We now provide a sampling of important problems from Section and the methodologies used in solving them.

  

Figure: Mathematical Models for Supply Chain

1.

Supplier Selection

• Game Theory [#!tayur99!#]
• Markov Decision Processes [#!tayur99!#]

2.

Supply Chain Facilities Layout Problem

• Integer Linear Programming [#!sankaran97!#]
• Mixed Integer Linear Programming [#!arntzen95!#]

3.

Constrained Supply

• Linear Programming [#!dietrich95!#]
• Simulation [#!feigin96!#]

4.

Manufacturing Capacity Planning

• Non-Linear Programming [#!Bretthauer1997!#]
• Assignment Problem [#!Shtub1998!#]
• Lagrangian Relaxation [#!Fumero1996!#]

5.

Inventory Control

• Stochastic and Dynamic Inventory Policies [#!factory96!#,#!nahmias93!#]
• Queueing Network Model [#!ettl96!#]

6.

Manufacturing Strategy

• Simulation [#!feigin96!#,#!connor!#]
• Queueing and Petri Net Analysis [#!ragthesis!#]

7.

Production Scheduling

• Heuristics [#!WILLIAMS81!#,#!karm92!#]
• Game Theory [#!Bai96!#]

8.

Transportation Strategy

• Integer Linear Programming [#!GEOFF74!#,#!mart!#]