MILP-based model for approximating non-stationary (R, S) policies with correlated demands

Mengyuan Xiang; Roberto Rossi; Belen Martin-Barragan; S. Armagan Tarim

MILP-based model for approximating non-stationary (R, S) policies with correlated demands

Mengyuan Xiang, Roberto Rossi, Belen Martin-Barragan, S. Armagan Tarim

Research output: Contribution to conference › Paper › peer-review

Abstract / Description of output

This paper addresses the single-item single-stock location stochastic lotsizing
problem under (R, S) policy. We assume demands in different periods
are dependent. We present a mixed integer linear programming (MILP) model
for computing optimal (R, S) policy parameters, which is built upon the conditional
distribution. Our model can be extended to cover time-series-based
demand processes as well. Our computational experiments demonstrate the
effectiveness and versatility of this model.

Original language	English
Pages	17-22
Number of pages	6
Publication status	Published - Aug 2017
Event	the 8th International Workshop on Lot Sizing - Glasgow, United Kingdom Duration: 23 Aug 2017 → 25 Aug 2017

Workshop

Workshop	the 8th International Workshop on Lot Sizing
Abbreviated title	IWLS 2017
Country/Territory	United Kingdom
City	Glasgow
Period	23/08/17 → 25/08/17

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title = "MILP-based model for approximating non-stationary (R, S) policies with correlated demands",

abstract = "This paper addresses the single-item single-stock location stochastic lotsizingproblem under (R, S) policy. We assume demands in different periodsare dependent. We present a mixed integer linear programming (MILP) modelfor computing optimal (R, S) policy parameters, which is built upon the conditionaldistribution. Our model can be extended to cover time-series-baseddemand processes as well. Our computational experiments demonstrate theeffectiveness and versatility of this model.",

author = "Mengyuan Xiang and Roberto Rossi and Belen Martin-Barragan and Tarim, {S. Armagan}",

year = "2017",

month = aug,

language = "English",

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T1 - MILP-based model for approximating non-stationary (R, S) policies with correlated demands

AU - Xiang, Mengyuan

AU - Rossi, Roberto

AU - Martin-Barragan, Belen

AU - Tarim, S. Armagan

PY - 2017/8

Y1 - 2017/8

N2 - This paper addresses the single-item single-stock location stochastic lotsizingproblem under (R, S) policy. We assume demands in different periodsare dependent. We present a mixed integer linear programming (MILP) modelfor computing optimal (R, S) policy parameters, which is built upon the conditionaldistribution. Our model can be extended to cover time-series-baseddemand processes as well. Our computational experiments demonstrate theeffectiveness and versatility of this model.

AB - This paper addresses the single-item single-stock location stochastic lotsizingproblem under (R, S) policy. We assume demands in different periodsare dependent. We present a mixed integer linear programming (MILP) modelfor computing optimal (R, S) policy parameters, which is built upon the conditionaldistribution. Our model can be extended to cover time-series-baseddemand processes as well. Our computational experiments demonstrate theeffectiveness and versatility of this model.

UR - http://iwls2017.emse.fr/program.html

M3 - Paper

SP - 17

EP - 22

T2 - the 8th International Workshop on Lot Sizing

Y2 - 23 August 2017 through 25 August 2017

ER -

MILP-based model for approximating non-stationary (R, S) policies with correlated demands

Abstract / Description of output

Workshop

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