A stochastic production planning problem with nonlinear cost

Most production planning models are deterministic and often assume a linear relation between production volume and production cost. In this paper, we investigate a production planning problem in a steel production process considering the energy consumption cost which is a nonlinear function of the p...

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Vydané v:Computers & operations research Ročník 39; číslo 9; s. 1977 - 1987
Hlavní autori: Tang, Lixin, Che, Ping, Liu, Jiyin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Kidlington Elsevier Ltd 01.09.2012
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Abstract Most production planning models are deterministic and often assume a linear relation between production volume and production cost. In this paper, we investigate a production planning problem in a steel production process considering the energy consumption cost which is a nonlinear function of the production quantity. Due to the uncertain environment, the production demands are stochastic. Taking a scenario-based approach to express the stochastic demands according to the knowledge of planners on the demand distributions, we formulate the stochastic production planning problem as a mixed integer nonlinear programming (MINLP) model. Approximated with the piecewise linear functions, the MINLP model is transformed into a mixed integer linear programming model. The approximation error can be improved by adjusting the linearization ranges repeatedly. Based on the piecewise linearization, a stepwise Lagrangian relaxation (SLR) heuristic for the problem is proposed where variable splitting is introduced during Lagrangian relaxation (LR). After decomposition, one subproblem is solved by linear programming and the other is solved by an effective polynomial time algorithm. The SLR heuristic is tested on a large set of problem instances and the results show that the algorithm generates solutions very close to optimums in an acceptable time. The impact of demand uncertainty on the solution is studied by a computational discussion on scenario generation.
AbstractList Most production planning models are deterministic and often assume a linear relation between production volume and production cost. In this paper, we investigate a production planning problem in a steel production process considering the energy consumption cost which is a nonlinear function of the production quantity. Due to the uncertain environment, the production demands are stochastic. Taking a scenario-based approach to express the stochastic demands according to the knowledge of planners on the demand distributions, we formulate the stochastic production planning problem as a mixed integer nonlinear programming (MINLP) model. Approximated with the piecewise linear functions, the MINLP model is transformed into a mixed integer linear programming model. The approximation error can be improved by adjusting the linearization ranges repeatedly. Based on the piecewise linearization, a stepwise Lagrangian relaxation (SLR) heuristic for the problem is proposed where variable splitting is introduced during Lagrangian relaxation (LR). After decomposition, one subproblem is solved by linear programming and the other is solved by an effective polynomial time algorithm. The SLR heuristic is tested on a large set of problem instances and the results show that the algorithm generates solutions very close to optimums in an acceptable time. The impact of demand uncertainty on the solution is studied by a computational discussion on scenario generation.
Most production planning models are deterministic and often assume a linear relation between production volume and production cost. In this paper, we investigate a production planning problem in a steel production process considering the energy consumption cost which is a nonlinear function of the production quantity. Due to the uncertain environment, the production demands are stochastic. Taking a scenario-based approach to express the stochastic demands according to the knowledge of planners on the demand distributions, we formulate the stochastic production planning problem as a mixed integer nonlinear programming (MINLP) model. Approximated with the piecewise linear functions, the MINLP model is transformed into a mixed integer linear programming model. The approximation error can be improved by adjusting the linearization ranges repeatedly. Based on the piecewise linearization, a stepwise Lagrangian relaxation (SLR) heuristic for the problem is proposed where variable splitting is introduced during Lagrangian relaxation (LR). After decomposition, one subproblem is solved by linear programming and the other is solved by an effective polynomial time algorithm. The SLR heuristic is tested on a large set of problem instances and the results show that the algorithm generates solutions very close to optimums in an acceptable time. The impact of demand uncertainty on the solution is studied by a computational discussion on scenario generation. [PUBLICATION ABSTRACT]
Most production planning models are deterministic and often assume a linear relation between production volume and production cost. In this paper, we investigate a production planning problem in a steel production process considering the energy consumption cost which is a nonlinear function of the production quantity. Due to the uncertain environment, the production demands are stochastic. Taking a scenario-based approach to express the stochastic demands according to the knowledge of planners on the demand distributions, we formulate the stochastic production planning problem as a mixed integer nonlinear programming (MINLP) model. Approximated with the piecewise linear functions, the MINLP model is transformed into a mixed integer linear programming model. The approximation error can be improved by adjusting the linearization ranges repeatedly. Based on the piecewise linearization, a stepwise Lagrangian relaxation (SLR) heuristic for the problem is proposed where variable splitting is introduced during Lagrangian relaxation (LR). After decomposition, one subproblem is solved by linear programming and the other is solved by an effective polynomial time algorithm. The SLR heuristic is tested on a large set of problem instances and the results show that the algorithm generates solutions very close to optimums in an acceptable time. The impact of demand uncertainty on the solution is studied by a computational discussion on scenario generation.
Author Che, Ping
Liu, Jiyin
Tang, Lixin
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  givenname: Jiyin
  surname: Liu
  fullname: Liu, Jiyin
  email: j.y.liu@lboro.ac.uk
  organization: Liaoning Key Laboratory of Manufacturing System and Logistics, The Logistics Institute, Northeastern University, 110004 Shenyang, China
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Cites_doi 10.1016/S0377-2217(03)00014-6
10.1287/mnsc.42.5.738
10.1016/0377-2217(90)90300-Z
10.1016/j.ejor.2005.12.008
10.1016/S0305-0483(03)00059-8
10.1016/j.jmateco.2007.04.011
10.1016/S0305-0548(00)00076-9
10.1016/0377-2217(86)90328-0
10.1016/S0377-2217(00)00116-8
10.1287/opre.48.1.91.12450
10.1016/S0167-6377(97)00005-9
10.1016/j.ijpe.2008.08.034
10.1016/j.ijpe.2009.04.007
10.1016/S0925-5273(98)00060-7
10.1016/S0167-6377(98)00050-9
10.1016/S0098-1354(01)00676-7
10.1016/j.ijpe.2005.09.001
10.1016/j.ijpe.2005.02.015
10.1287/moor.16.1.119
10.1287/mnsc.5.1.89
10.1080/00207540500435116
10.1016/S0898-1221(02)00146-3
10.1016/S0377-2217(00)00240-X
10.1287/opre.35.3.329
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Issue 9
Keywords MINLP
Stepwise Lagrangian relaxation
Stochastic production planning
Variable splitting
Scenario-based approach
Inventory
Energy consumption
Non linear programming
Production function
Polynomial method
Linear model
Optimization
Lagrange multiplier
Uncertain system
Production planning
Relaxation method
Production process
Script
Quantity production
Linear programming
Mixed integer programming
Stochastic programming
Polynomial time
Production management
Supply demand balance
Deterministic model
Non linear model
Heuristic method
Production cost
Approximation error
Mixed problem
Piecewise linearization
Piecewise-linear techniques
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References Bakir, Byrne (bib3) 1998; 55
Takriti, Birge (bib20) 2000; 48
Jörnsten, Näsberg (bib12) 1986; 27
Karimi, Fatemi Ghomi, Wilson (bib13) 2003; 31
Tang, Liu, Rong, Yang (bib21) 2001; 133
Yu, Du, Nan, Han, Ai (bib25) 1997; 4
Carøe, Schultz (bib6) 1999; 24
Tempelmeier, Derstroff (bib22) 1996; 42
Rizk, Martel, Ramudhin (bib17) 2006; 102
Xie, Dong (bib24) 2002; 44
Gutiérrez, Puerto, Sicilia (bib9) 2004; 156
Mula, Poler, García-Sabater, Lario (bib15) 2006; 103
Sox (bib19) 1997; 20
Barcia, Jörnsten (bib4) 1990; 46
Minner (bib14) 2009; 118
Azaron, Tang, Tavakkoli-Moghaddam (bib1) 2009; 120
Chazal, Jouini, Tahraoui (bib7) 2008; 44
Brandimarte (bib5) 2006; 44
Wagner, Whitin (bib23) 1958; 5
Rockafellar, Wets (bib18) 1991; 16
Oh, Karimi (bib16) 2001; 25
Haugen, Løkketangen, Woodruff (bib10) 2001; 132
Chen, Li, Tirupati (bib8) 2002; 29
Bahl, Ritzman, Gupta (bib2) 1987; 35
Jans, Degraeve (bib11) 2007; 177
Minner (10.1016/j.cor.2011.09.007_bib14) 2009; 118
Tempelmeier (10.1016/j.cor.2011.09.007_bib22) 1996; 42
Azaron (10.1016/j.cor.2011.09.007_bib1) 2009; 120
Barcia (10.1016/j.cor.2011.09.007_bib4) 1990; 46
Mula (10.1016/j.cor.2011.09.007_bib15) 2006; 103
Haugen (10.1016/j.cor.2011.09.007_bib10) 2001; 132
Jörnsten (10.1016/j.cor.2011.09.007_bib12) 1986; 27
Rockafellar (10.1016/j.cor.2011.09.007_bib18) 1991; 16
Carøe (10.1016/j.cor.2011.09.007_bib6) 1999; 24
Xie (10.1016/j.cor.2011.09.007_bib24) 2002; 44
Bakir (10.1016/j.cor.2011.09.007_bib3) 1998; 55
Takriti (10.1016/j.cor.2011.09.007_bib20) 2000; 48
Yu (10.1016/j.cor.2011.09.007_bib25) 1997; 4
Sox (10.1016/j.cor.2011.09.007_bib19) 1997; 20
Jans (10.1016/j.cor.2011.09.007_bib11) 2007; 177
Bahl (10.1016/j.cor.2011.09.007_bib2) 1987; 35
Wagner (10.1016/j.cor.2011.09.007_bib23) 1958; 5
Chen (10.1016/j.cor.2011.09.007_bib8) 2002; 29
Rizk (10.1016/j.cor.2011.09.007_bib17) 2006; 102
Tang (10.1016/j.cor.2011.09.007_bib21) 2001; 133
Gutiérrez (10.1016/j.cor.2011.09.007_bib9) 2004; 156
Oh (10.1016/j.cor.2011.09.007_bib16) 2001; 25
Karimi (10.1016/j.cor.2011.09.007_bib13) 2003; 31
Brandimarte (10.1016/j.cor.2011.09.007_bib5) 2006; 44
Chazal (10.1016/j.cor.2011.09.007_bib7) 2008; 44
References_xml – volume: 27
  start-page: 313
  year: 1986
  end-page: 323
  ident: bib12
  article-title: A new Lagrangian relaxation approach to the generalized assignment problem
  publication-title: European Journal of Operational Research
– volume: 24
  start-page: 37
  year: 1999
  end-page: 45
  ident: bib6
  article-title: Dual decomposition in stochastic integer programming
  publication-title: Operations Research Letters
– volume: 42
  start-page: 738
  year: 1996
  end-page: 756
  ident: bib22
  article-title: A Lagrangean-based heuristic for dynamic multilevel multiitem constrained lotsizing with setup times
  publication-title: Management Science
– volume: 44
  start-page: 997
  year: 2008
  end-page: 1023
  ident: bib7
  article-title: Production planning and inventories optimization: a backward approach in the convex storage cost case
  publication-title: Journal of Mathematical Economics
– volume: 5
  start-page: 89
  year: 1958
  end-page: 96
  ident: bib23
  article-title: Dynamic version of the economic lot size model
  publication-title: Management Science
– volume: 44
  start-page: 2997
  year: 2006
  end-page: 3022
  ident: bib5
  article-title: Multi-item capacitated lot-sizing with demand uncertainty
  publication-title: International Journal of Production Research
– volume: 120
  start-page: 607
  year: 2009
  end-page: 612
  ident: bib1
  article-title: Dynamic lot sizing problem with continuous-time Markovian production cost
  publication-title: International Journal of Production Economics
– volume: 16
  start-page: 119
  year: 1991
  end-page: 147
  ident: bib18
  article-title: Scenarios and policy aggregation in optimization under uncertainty
  publication-title: Mathematics of Operations Research
– volume: 4
  start-page: 39
  year: 1997
  end-page: 43
  ident: bib25
  article-title: A discussion of energy saving direction for rolling heating furnace
  publication-title: Industrial Furnace
– volume: 35
  start-page: 329
  year: 1987
  end-page: 345
  ident: bib2
  article-title: Determining lot sizes and resource requirements: a review
  publication-title: Operations Research
– volume: 133
  start-page: 1
  year: 2001
  end-page: 20
  ident: bib21
  article-title: A review of planning and scheduling systems and methods for integrated steel production
  publication-title: European Journal of Operational Research
– volume: 55
  start-page: 87
  year: 1998
  end-page: 96
  ident: bib3
  article-title: Stochastic linear optimization of an MPMP production planning model
  publication-title: International Journal of Production Economics
– volume: 156
  start-page: 162
  year: 2004
  end-page: 182
  ident: bib9
  article-title: The multiscenario lot size problem with concave costs
  publication-title: European Journal of Operational Research
– volume: 177
  start-page: 1855
  year: 2007
  end-page: 1875
  ident: bib11
  article-title: Meta-heuristics for dynamic lot sizing: a review and comparison of solution approaches
  publication-title: European Journal of Operational Research
– volume: 20
  start-page: 155
  year: 1997
  end-page: 164
  ident: bib19
  article-title: Dynamic lot sizing with random demand and non-stationary costs
  publication-title: Operations Research Letters
– volume: 132
  start-page: 116
  year: 2001
  end-page: 122
  ident: bib10
  article-title: Progressive hedging as a meta-heuristic applied to stochastic lot-sizing
  publication-title: European Journal of Operational Research
– volume: 103
  start-page: 271
  year: 2006
  end-page: 285
  ident: bib15
  article-title: Models for production planning under uncertainty: a review
  publication-title: International Journal of Production Economics
– volume: 44
  start-page: 263
  year: 2002
  end-page: 276
  ident: bib24
  article-title: Heuristic genetic algorithms for general capacitated lot-sizing problems
  publication-title: Computers and Mathematics with Applications
– volume: 118
  start-page: 305
  year: 2009
  end-page: 310
  ident: bib14
  article-title: A comparison of simple heuristics for multi-product dynamic demand lot-sizing with limited warehouse capacity
  publication-title: International Journal of Production Economics
– volume: 46
  start-page: 84
  year: 1990
  end-page: 92
  ident: bib4
  article-title: Improved Lagrangean decomposition: an application to the generalized assignment problem
  publication-title: European Journal of Operational Research
– volume: 102
  start-page: 344
  year: 2006
  end-page: 357
  ident: bib17
  article-title: A Lagrangean relaxation algorithm for multi-item lot-sizing problems with joint piecewise linear resource costs
  publication-title: International Journal of Production Economics
– volume: 29
  start-page: 781
  year: 2002
  end-page: 806
  ident: bib8
  article-title: A scenario based stochastic programming approach for technology and capacity planning
  publication-title: Computers and Operations Research
– volume: 31
  start-page: 365
  year: 2003
  end-page: 378
  ident: bib13
  article-title: The capacitated lot sizing problem: a review of models and algorithms
  publication-title: Omega
– volume: 25
  start-page: 1021
  year: 2001
  end-page: 1030
  ident: bib16
  article-title: Planning production on a single processor with sequence-dependent setups part 1: determination of campaigns
  publication-title: Computers and Chemical Engineering
– volume: 48
  start-page: 91
  year: 2000
  end-page: 98
  ident: bib20
  article-title: Lagrangian solution techniques and bounds for loosely coupled mixed-integer stochastic programs
  publication-title: Operations Research
– volume: 156
  start-page: 162
  year: 2004
  ident: 10.1016/j.cor.2011.09.007_bib9
  article-title: The multiscenario lot size problem with concave costs
  publication-title: European Journal of Operational Research
  doi: 10.1016/S0377-2217(03)00014-6
– volume: 42
  start-page: 738
  issue: 5
  year: 1996
  ident: 10.1016/j.cor.2011.09.007_bib22
  article-title: A Lagrangean-based heuristic for dynamic multilevel multiitem constrained lotsizing with setup times
  publication-title: Management Science
  doi: 10.1287/mnsc.42.5.738
– volume: 46
  start-page: 84
  year: 1990
  ident: 10.1016/j.cor.2011.09.007_bib4
  article-title: Improved Lagrangean decomposition: an application to the generalized assignment problem
  publication-title: European Journal of Operational Research
  doi: 10.1016/0377-2217(90)90300-Z
– volume: 4
  start-page: 39
  year: 1997
  ident: 10.1016/j.cor.2011.09.007_bib25
  article-title: A discussion of energy saving direction for rolling heating furnace
  publication-title: Industrial Furnace
– volume: 177
  start-page: 1855
  year: 2007
  ident: 10.1016/j.cor.2011.09.007_bib11
  article-title: Meta-heuristics for dynamic lot sizing: a review and comparison of solution approaches
  publication-title: European Journal of Operational Research
  doi: 10.1016/j.ejor.2005.12.008
– volume: 31
  start-page: 365
  year: 2003
  ident: 10.1016/j.cor.2011.09.007_bib13
  article-title: The capacitated lot sizing problem: a review of models and algorithms
  publication-title: Omega
  doi: 10.1016/S0305-0483(03)00059-8
– volume: 44
  start-page: 997
  year: 2008
  ident: 10.1016/j.cor.2011.09.007_bib7
  article-title: Production planning and inventories optimization: a backward approach in the convex storage cost case
  publication-title: Journal of Mathematical Economics
  doi: 10.1016/j.jmateco.2007.04.011
– volume: 29
  start-page: 781
  year: 2002
  ident: 10.1016/j.cor.2011.09.007_bib8
  article-title: A scenario based stochastic programming approach for technology and capacity planning
  publication-title: Computers and Operations Research
  doi: 10.1016/S0305-0548(00)00076-9
– volume: 27
  start-page: 313
  year: 1986
  ident: 10.1016/j.cor.2011.09.007_bib12
  article-title: A new Lagrangian relaxation approach to the generalized assignment problem
  publication-title: European Journal of Operational Research
  doi: 10.1016/0377-2217(86)90328-0
– volume: 132
  start-page: 116
  year: 2001
  ident: 10.1016/j.cor.2011.09.007_bib10
  article-title: Progressive hedging as a meta-heuristic applied to stochastic lot-sizing
  publication-title: European Journal of Operational Research
  doi: 10.1016/S0377-2217(00)00116-8
– volume: 48
  start-page: 91
  year: 2000
  ident: 10.1016/j.cor.2011.09.007_bib20
  article-title: Lagrangian solution techniques and bounds for loosely coupled mixed-integer stochastic programs
  publication-title: Operations Research
  doi: 10.1287/opre.48.1.91.12450
– volume: 20
  start-page: 155
  year: 1997
  ident: 10.1016/j.cor.2011.09.007_bib19
  article-title: Dynamic lot sizing with random demand and non-stationary costs
  publication-title: Operations Research Letters
  doi: 10.1016/S0167-6377(97)00005-9
– volume: 118
  start-page: 305
  year: 2009
  ident: 10.1016/j.cor.2011.09.007_bib14
  article-title: A comparison of simple heuristics for multi-product dynamic demand lot-sizing with limited warehouse capacity
  publication-title: International Journal of Production Economics
  doi: 10.1016/j.ijpe.2008.08.034
– volume: 120
  start-page: 607
  year: 2009
  ident: 10.1016/j.cor.2011.09.007_bib1
  article-title: Dynamic lot sizing problem with continuous-time Markovian production cost
  publication-title: International Journal of Production Economics
  doi: 10.1016/j.ijpe.2009.04.007
– volume: 55
  start-page: 87
  year: 1998
  ident: 10.1016/j.cor.2011.09.007_bib3
  article-title: Stochastic linear optimization of an MPMP production planning model
  publication-title: International Journal of Production Economics
  doi: 10.1016/S0925-5273(98)00060-7
– volume: 24
  start-page: 37
  year: 1999
  ident: 10.1016/j.cor.2011.09.007_bib6
  article-title: Dual decomposition in stochastic integer programming
  publication-title: Operations Research Letters
  doi: 10.1016/S0167-6377(98)00050-9
– volume: 25
  start-page: 1021
  year: 2001
  ident: 10.1016/j.cor.2011.09.007_bib16
  article-title: Planning production on a single processor with sequence-dependent setups part 1: determination of campaigns
  publication-title: Computers and Chemical Engineering
  doi: 10.1016/S0098-1354(01)00676-7
– volume: 103
  start-page: 271
  year: 2006
  ident: 10.1016/j.cor.2011.09.007_bib15
  article-title: Models for production planning under uncertainty: a review
  publication-title: International Journal of Production Economics
  doi: 10.1016/j.ijpe.2005.09.001
– volume: 102
  start-page: 344
  year: 2006
  ident: 10.1016/j.cor.2011.09.007_bib17
  article-title: A Lagrangean relaxation algorithm for multi-item lot-sizing problems with joint piecewise linear resource costs
  publication-title: International Journal of Production Economics
  doi: 10.1016/j.ijpe.2005.02.015
– volume: 16
  start-page: 119
  year: 1991
  ident: 10.1016/j.cor.2011.09.007_bib18
  article-title: Scenarios and policy aggregation in optimization under uncertainty
  publication-title: Mathematics of Operations Research
  doi: 10.1287/moor.16.1.119
– volume: 5
  start-page: 89
  year: 1958
  ident: 10.1016/j.cor.2011.09.007_bib23
  article-title: Dynamic version of the economic lot size model
  publication-title: Management Science
  doi: 10.1287/mnsc.5.1.89
– volume: 44
  start-page: 2997
  year: 2006
  ident: 10.1016/j.cor.2011.09.007_bib5
  article-title: Multi-item capacitated lot-sizing with demand uncertainty
  publication-title: International Journal of Production Research
  doi: 10.1080/00207540500435116
– volume: 44
  start-page: 263
  year: 2002
  ident: 10.1016/j.cor.2011.09.007_bib24
  article-title: Heuristic genetic algorithms for general capacitated lot-sizing problems
  publication-title: Computers and Mathematics with Applications
  doi: 10.1016/S0898-1221(02)00146-3
– volume: 133
  start-page: 1
  year: 2001
  ident: 10.1016/j.cor.2011.09.007_bib21
  article-title: A review of planning and scheduling systems and methods for integrated steel production
  publication-title: European Journal of Operational Research
  doi: 10.1016/S0377-2217(00)00240-X
– volume: 35
  start-page: 329
  year: 1987
  ident: 10.1016/j.cor.2011.09.007_bib2
  article-title: Determining lot sizes and resource requirements: a review
  publication-title: Operations Research
  doi: 10.1287/opre.35.3.329
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Snippet Most production planning models are deterministic and often assume a linear relation between production volume and production cost. In this paper, we...
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StartPage 1977
SubjectTerms Algorithms
Applied sciences
Demand
Energy consumption
Exact sciences and technology
Heuristic
Integer programming
Inventory
Inventory control, production control. Distribution
Linear programming
Marketing
Mathematical analysis
Mathematical models
Mathematical programming
Mathematics
MINLP
Nonlinearity
Operational research and scientific management
Operational research. Management science
Probability and statistics
Production planning
Sampling theory, sample surveys
Scenario-based approach
Sciences and techniques of general use
Statistics
Steel production
Stepwise Lagrangian relaxation
Stochastic models
Stochastic production planning
Stochasticity
Studies
Variable splitting
Title A stochastic production planning problem with nonlinear cost
URI https://dx.doi.org/10.1016/j.cor.2011.09.007
http://www.econis.eu/PPNSET?PPN=688146228
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https://www.proquest.com/docview/1019623549
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