Case study background and problem formulations
PROBLEM: Supply Chain Planning
Minimize Avg (Recourse) (minimizing average of recourse function)
subject to
ConstVector1 ≤ Linearmulti ≤ ConstVector2 (linear constraints on the first stage variables)
Box constraints (bounds on the first stage variables)
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Avg = Average for Recourse
Linearmulti = Linear Multiple
Box constraints = constraints on individual decision variables
Minimize Avg (Recourse) (minimizing average of recourse function)
subject to
ConstVector1 ≤ Linearmulti ≤ ConstVector2 (linear constraints on the first stage variables)
Box constraints (bounds on the first stage variables)
——————————————————————–
Avg = Average for Recourse
Linearmulti = Linear Multiple
Box constraints = constraints on individual decision variables
Recourse = Minimal value of the following second stage subproblem depending on scenarios for the given first stage variables
Minimize Linear (minimizing linear objective of the second stage subproblem)
subject to
ConstVector3 ≤ Linearmulti ≤ ConstVector4 (linear constraints on the second stage variables depending on scenarios)
Box constraints (bounds on the second stage variables)
Minimize Linear (minimizing linear objective of the second stage subproblem)
subject to
ConstVector3 ≤ Linearmulti ≤ ConstVector4 (linear constraints on the second stage variables depending on scenarios)
Box constraints (bounds on the second stage variables)
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Problem ” problem_ Supply_Chain_Planning”
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Problem ” problem_ Supply_Chain_Planning”
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| Description | # of Variables | # of Scenarios | Objective Value | Solving Time, PC 3.14GHz (sec) | |||
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| Dataset1 | 5,602,526=22,676+74,398*75 | 75 | -93456355.9873 | 56.32 | |||
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| Matlab Subroutines | Matlab code | Data | ||||
CASE STUDY SUMMARY
A supply chain design problem is modeled as a sequence of splitting and combining processes. The problem is formulated as a two-stage stochastic program. The first-stage decisions are strategic location decisions, whereas the second stage consists of operational decisions. The objective is to maximize the expected profits over the planning horizon.
References
• Schütz, P., and A. Tomasgard (2011): The impact of flexibility on operational supply chain planning. International Journal of Production Economics 134(2), 300-311.
A supply chain design problem is modeled as a sequence of splitting and combining processes. The problem is formulated as a two-stage stochastic program. The first-stage decisions are strategic location decisions, whereas the second stage consists of operational decisions. The objective is to maximize the expected profits over the planning horizon.
References
• Schütz, P., and A. Tomasgard (2011): The impact of flexibility on operational supply chain planning. International Journal of Production Economics 134(2), 300-311.