Please wait a minute...
Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

CN 10-1205/N

Postal Subscription Code 80-905

Front. Eng    2017, Vol. 4 Issue (3) : 256-270    https://doi.org/10.15302/J-FEM-2017049
REVIEW ARTICLE
Perspectives in multilevel decision-making in the process industry
Braulio BRUNAUD, Ignacio E. GROSSMANN()
Carnegie Mellon University, Pittsburgh, PA 15213, US
 Download: PDF(803 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Decisions in supply chains are hierarchically organized. Strategic decisions involve the long-term planning of the structure of the supply chain network. Tactical decisions are mid-term plans to allocate the production and distribution of materials, while operational decisions are related to the daily planning of the execution of manufacturing operations. These planning processes are conducted independently with minimal exchange of information between them. Achieving a better coordination between these processes allows companies to capture benefits that are currently out of their reach and improve the communication among their functional areas. We propose a network representation for the multilevel decision structure and analyze the components that are involved in finding integrated solutions that maximize the sum of the benefits of all nodes of the decision network. Although such task is very challenging, significant research progress has been made in each component of this structure. An overview of strategic models, mid-term planning models, and scheduling models is presented to address the solution of each node in the decision network. Coordination mechanisms for converging the integrated solutions are also analyzed, including solving large-scale models, multiobjective optimization, bi-level programming, and decomposition. We conclude by summarizing the challenges that hinder the full integration of multilevel decision making in supply chain management.

Keywords supply chain optimization      enterprise-wide optimization      multilevel optimization      planning      scheduling     
Corresponding Author(s): Ignacio E. GROSSMANN   
Just Accepted Date: 17 August 2017   Online First Date: 27 September 2017    Issue Date: 30 October 2017
 Cite this article:   
Braulio BRUNAUD,Ignacio E. GROSSMANN. Perspectives in multilevel decision-making in the process industry[J]. Front. Eng, 2017, 4(3): 256-270.
 URL:  
https://academic.hep.com.cn/fem/EN/10.15302/J-FEM-2017049
https://academic.hep.com.cn/fem/EN/Y2017/V4/I3/256
Fig.1  Supply chain example
Fig.2  Supply chain decision pyramid
Fig.3  Planning matrix
Fig.4  Supply chain decision network structure
Fig.5  Example of a supply chain decision network structure
Fig.6  Concept of integrated solution as defined in the literature
Fig.7  Product life cycle curve
Fig.8  Site expansion modeling
Fig.9  Supply chain planning network
Fig.10  State-task-network example
Fig.11  Resource-task-network representation of the separation task from Fig.10
Fig.12  UOPSS representation example
Fig.13  Pareto front output from solving a bi-objective problem
Fig.14  Block angular structure with linking variables
1 Aytug H, Lawley M A, McKay K, Mohan S, Uzsoy R (2005). Executing production schedules in the face of uncertainties: a review and some future directions. European Journal of Operational Research, 161(1): 86–110
https://doi.org/10.1016/j.ejor.2003.08.027
2 Balas E (1998). Disjunctive programming: properties of the convex hull of feasible points. Discrete Applied Mathematics, 89(1–3): 3–44
https://doi.org/10.1016/S0166-218X(98)00136-X
3 Balas E, Jeroslow R (1972). Canonical cuts on the unit hypercube. SIAM Journal on Applied Mathematics, 23(1): 61–69
https://doi.org/10.1137/0123007
4 Baldea M, Harjunkoski I (2014). Integrated production scheduling and process control: a systematic review. Computers & Chemical Engineering, 71: 377–390
https://doi.org/10.1016/j.compchemeng.2014.09.002
5 Barbosa-Póvoa A P (2012). Progresses and challenges in process industry supply chains optimization. Current Opinion in Chemical Engineering, 1(4): 446–452
https://doi.org/10.1016/j.coche.2012.09.006
6 Benders J F (1962). Partitioning procedures for solving mixed-variables programming problems. Numerische Mathematik, 4(1): 238–252
https://doi.org/10.1007/BF01386316
7 Bezanzon J, Karpinski S, Shah V B, Edelman A(2012). Julia: a fast dynamic language for technical computing. Computer Science, arXiv: 1209.5145 [cs.PL]
8 Birge J R, Louveaux F (2011). Introduction to Stochastic Programming. Dordrecht: Springer Science & Business Media
9 Bixby R E, Fenelon M, Gu Z, Rothberg E, Wunderling R (2004). Mixed integer programming: a progress report. The Sharpest Cut, 309–324
10 Blum C, Roli A (2003). Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Computing Surveys, 35(3): 268–308
https://doi.org/10.1145/937503.937505
11 Colson B, Marcotte P, Savard G (2005). Bilevel programming: a survey. 4OR: A Quarterly Journal of Operations Research, 3(2): 87–107
12 de Weck O L, Suh E S, Chang D (2003). Product family and platform portfolio optimization. In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 175–185
13 Dias L S, Ierapetritou M G (2016). Integration of scheduling and control under uncertainties: review and challenges. Chemical Engineering Research & Design, 116: 98–113
https://doi.org/10.1016/j.cherd.2016.10.047
14 Drouven M G, Grossmann I E (2016). Multi-period planning, design and strategic models for long-term, quality-sensitive shale gas development. AIChE Journal, 62(7): 2296–2323
https://doi.org/10.1002/aic.15174
15 Dunning I, Huchette J, Lubin M (2017). JuMP: a modeling language for mathematical optimization. SIAM Review, 59(2): 295–320
16 Florensa C, Garcia-Herreros P, Misra P, Arslan E, Mehta S, Grossmann I E (2017). Capacity planning with competitive decision-makers: trilevel MILP formulation, degeneracy, and solution approaches. European Journal of Operational Research, 262(2): 449–463
https://doi.org/10.1016/j.ejor.2017.04.013
17 Floudas C A, Lin X (2004). Continuous-time versus discrete-time approaches for scheduling of chemical processes: a review. Computers & Chemical Engineering, 28(11): 2109–2129
https://doi.org/10.1016/j.compchemeng.2004.05.002
18 Funaki K (2009). State of the art survey of commercial software for supply chain design. Georgia Institute of Technology Report.
19 Gade D, Kücükyavuz S, Sen S (2014). Decomposition algorithms with parametric Gomory cuts for two-stage stochastic integer programs. Mathematical Programming, 144(1–2): 39–64
https://doi.org/10.1007/s10107-012-0615-y
20 Garcia D J, You F (2015). Supply chain design and optimization: challenges and opportunities. Computers & Chemical Engineering, 81: 153–170
https://doi.org/10.1016/j.compchemeng.2015.03.015
21 Garcia-Herreros P, Zhang L, Misra P, Arslan E, Mehta S, Grossmann I E (2016). Mixed-integer bilevel optimization for capacity planning with rational markets. Computers & Chemical Engineering, 86: 33–47
https://doi.org/10.1016/j.compchemeng.2015.12.007
22 Grossmann I E (2005). Enterprise-wide optimization: a new frontier in process systems engineering. AIChE Journal, 51(7): 1846–1857
https://doi.org/10.1002/aic.10617
23 Grossmann I E, Trespalacios F (2013). Systematic modeling of discrete-continuous optimization models through generalized disjunctive programming. AIChE Journal, 59(9): 3276–3295
https://doi.org/10.1002/aic.14088
24 Guignard M, Kim S (1987). Lagrangean decomposition: a model yielding stronger Lagrangean bounds. Mathematical Programming, 39(2): 215–228
https://doi.org/10.1007/BF02592954
25 Guillén-Gosálbez G, Grossmann I E (2009). Optimal design and planning of sustainable chemical supply chains under uncertainty. AIChE Journal, 55(1): 99–121
https://doi.org/10.1002/aic.11662
26 Harjunkoski I, Maravelias C T, Bongers P, Castro P M, Engell S, Grossmann I E, Hooker J, Méndez C, Sand G, Wassick J (2014). Scope for industrial applications of production scheduling models and solution methods. Computers & Chemical Engineering, 62: 161–193
https://doi.org/10.1016/j.compchemeng.2013.12.001
27 Hooker J N (2002). Logic, optimization, and constraint programming. INFORMS Journal on Computing, 14(4): 295–321
https://doi.org/10.1287/ijoc.14.4.295.2828
28 Iyer R R, Grossmann I E (1998). A bilevel decomposition algorithm for long-range planning of process networks. Industrial & Engineering Chemistry Research, 37(2): 474–481
https://doi.org/10.1021/ie970383i
29 Jain V, Grossmann I E (1999). Resource-constrained scheduling of tests in new product development. Industrial & Engineering Chemistry Research, 38(8): 3013–3026
https://doi.org/10.1021/ie9807809
30 Jain V, Grossmann I E (2001). Algorithms for hybrid MILP/CP models for a class of optimization problems. INFORMS Journal on Computing, 13(4): 258–276
https://doi.org/10.1287/ijoc.13.4.258.9733
31 Jalving J, Abhyankar S, Kim K, Hereld M, Zavala V M (2017). A graph-based computational framework for simulation and optimization of coupled infrastructure networks. IET Generation, Transmission & Distribution, in press
32 Karimi B, Fatemi Ghomi S M T, Wilson J M (2003). The capacitated lot sizing problem: a review of models and algorithms. Omega, 31(5): 365–378
https://doi.org/10.1016/S0305-0483(03)00059-8
33 Kondili E, Pantelides C, Sargent R (1993). A general algorithm for short-term scheduling of batch operations—I. MILP formulation. Computers & Chemical Engineering, 17(2): 211–227
34 Laporte G, Louveaux F V (1993). The integer L-shaped method for stochastic integer programs with complete recourse. Operations Research Letters, 13(3): 133–142
https://doi.org/10.1016/0167-6377(93)90002-X
35 Lappas N H, Gounaris C E (2016). Multi-stage adjustable robust optimization for process scheduling under uncertainty. AIChE Journal, 62(5): 1646–1667
https://doi.org/10.1002/aic.15183
36 Levis A A, Papageorgiou L G (2004). A hierarchical solution approach for multi-site capacity planning under uncertainty in the pharmaceutical industry. Computers & Chemical Engineering, 28(5): 707–725
https://doi.org/10.1016/j.compchemeng.2004.02.012
37 Li Z, Ierapetritou M (2008). Process scheduling under uncertainty: review and challenges. Computers & Chemical Engineering, 32(4–5): 715–727
https://doi.org/10.1016/j.compchemeng.2007.03.001
38 Linderoth J T, Savelsbergh M W (1999). A computational study of search strategies for mixed integer programming. INFORMS Journal on Computing, 11(2): 173–187
https://doi.org/10.1287/ijoc.11.2.173
39 Lotero I, Trespalacios F, Grossmann I E, Papageorgiou D J, Cheon M S (2016). An MILP- MINLP decomposition method for the global optimization of a source based model of the multiperiod blending problem. Computers & Chemical Engineering, 87: 13–35
https://doi.org/10.1016/j.compchemeng.2015.12.017
41 Mansoornejad B, Pistikopoulos E N, Stuart P (2011). Incorporating flexibility design into supply chain design for forest biorefinery. J-FOR Journal of Science & Technology for Forest Products and Processes, 1(2): 54–66
42 Maravelias C T, Grossmann I E (2004). A hybrid MILP/CP decomposition approach for the continuous time scheduling of multipurpose batch plants. Computers & Chemical Engineering, 28(10): 1921–1949
https://doi.org/10.1016/j.compchemeng.2004.03.016
43 Maravelias C T, Sung C (2009). Integration of production planning and scheduling: overview, challenges and opportunities. Computers & Chemical Engineering, 33(12): 1919–1930
https://doi.org/10.1016/j.compchemeng.2009.06.007
44 Martínez-Costa C, Mas-Machuca M, Benedito E, Corominas A (2014). A review of mathematical programming models for strategic capacity planning in manufacturing. International Journal of Production Economics, 153: 66–85
https://doi.org/10.1016/j.ijpe.2014.03.011
45 Melo M T, Nickel S, Saldanha-Da-Gama F (2009). Facility location and supply chain management—a review. European Journal of Operational Research, 196(2): 401–412
https://doi.org/10.1016/j.ejor.2008.05.007
40 Méndez C A, Cerdá J, Grossmann I E, Harjunkoski I, Fahl M (2006). State-of-the-art review of optimization methods for short-term scheduling of batch processes. Computers & Chemical Engineering, 30(6–7): 913–946
https://doi.org/10.1016/j.compchemeng.2006.02.008
46 Mesarovic M D, Macko D, Takahara Y (1970). Theory of Multilevel Hierarchical Systems. New York: Academic Press
47 Meyr H, Wagner M, Rohde J (2015). Structure of advanced planning systems. In Supply Chain Management and Advanced Planning, 99–106. New York: Springer
48 Mitra S, Garcia-Herreros P, Grossmann I E (2014). A novel cross-decomposition multi-cut scheme for two-stage stochastic programming. Computer-Aided Chemical Engineering, 33: 241–246
https://doi.org/10.1016/B978-0-444-63456-6.50041-7
49 Pantelides C C (1994). Unified frameworks for optimal process planning and scheduling. Proceedings on the Second Conference on Foundations of Computer Aided Process Operations, 253–274. New York: CACHE Publications
50 Papageorgiou L G (2009). Supply chain optimization for the process industries: advances and opportunities. Computers & Chemical Engineering, 33(12): 1931–1938
https://doi.org/10.1016/j.compchemeng.2009.06.014
51 Park M, Park S, Mele F D (2006). Modeling of purchase and sales contracts in supply chain optimization. In SICE-ICASE, 2006. International Joint Conference, 5727–5732
52 Perea-López E, Grossmann I E, Ydstie B E, Tahmassebi T (2001). Dynamic modeling and decentralized control of supply chains. Industrial & Engineering Chemistry Research, 40(15): 3369–3383
https://doi.org/10.1021/ie000573k
53 Pinto-Varela T, Barbosa-Póvoa A P F, Novais A Q (2011). Bi-objective optimization approach to the design and planning of supply chains: economic versus environmental performances. Computers & Chemical Engineering, 35(8): 1454–1468
https://doi.org/10.1016/j.compchemeng.2011.03.009
54 Rastogi A P, Fowler J W, Matthew Carlyle W, Araz O M, Maltz A, Büke B (2011). Supply network capacity planning for semiconductor manufacturing with uncertain demand and correlation in demand considerations. International Journal of Production Economics, 134(2): 322–332
https://doi.org/10.1016/j.ijpe.2009.11.006
55 Sahay N, Ierapetritou M (2015). Flexibility assessment and risk management in supply chains. AIChE Journal, 61(12): 4166–4178
https://doi.org/10.1002/aic.14971
56 Sargent R (2005). Process systems engineering: a retrospective view with questions for the future. Computers & Chemical Engineering, 29(6): 1237–1241
https://doi.org/10.1016/j.compchemeng.2005.02.008
57 Shah N (2005). Process industry supply chains: advances and challenges. Computers & Chemical Engineering, 29(6): 1225–1235
https://doi.org/10.1016/j.compchemeng.2005.02.023
58 Sherali H D, Fraticelli B M (2002). A modification of Benders’ decomposition algorithm for discrete subproblems: an approach for stochastic programs with integer recourse. Journal of Global Optimization, 22(1–4): 319–342
https://doi.org/10.1023/A:1013827731218
59 Širovnik D, Zore Ž, Čuček L, Kravanja Z (2016). System synthesis by maximizing sustainability net present value. Chemical Engineering Transactions, 52: 1075–1080
60 Snyder L V (2006). Facility location under uncertainty: a review. IIE Transactions, 38(7): 547–564
https://doi.org/10.1080/07408170500216480
61 Swaney R E, Grossmann I E (1985). An index for operational flexibility in chemical process design. Part i: Formulation and theory. AIChE Journal, 31(4): 621–630
https://doi.org/10.1002/ai
62 Van Roy T J (1983). Cross decomposition for mixed integer programming. Mathematical Programming, 25(1): 46–63
https://doi.org/10.1007/BF02591718
63 Vicente L N, Calamai P H (1994). Bilevel and multilevel programming: a bibliography review. Journal of Global Optimization, 5(3): 291–306
https://doi.org/10.1007/BF01096458
64 Wang H, Mastragostino R, Swartz C L (2016). Flexibility analysis of process supply chain networks. Computers & Chemical Engineering, 84: 409–421
https://doi.org/10.1016/j.compchemeng.2015.07.016
65 Yano C A (1992). Optimizing transportation contracts to support just-in-time deliveries: the case of one contracted truck per shipment. IIE Transactions, 24(2): 177–183
https://doi.org/10.1080/07408179208964215
66 You F, Grossmann I E (2011). Stochastic inventory management for tactical process planning under uncertainties: MINLP models and algorithms. AIChE Journal, 57(5): 1250–1277
https://doi.org/10.1002/aic.12338
67 You F, Grossmann I E, Wassick J M (2011). Multisite capacity, production, and distribution planning with reactor modifications: MILP model, bilevel decomposition algorithm versus Lagrangean decomposition scheme. Industrial & Engineering Chemistry Research, 50(9): 4831–4849
https://doi.org/10.1021/ie100559y
68 Zamarripa M A, Aguirre A M, Méndez C A, Espuña A (2013). Mathematical programming and game theory optimization-based tool for supply chain planning in cooperative/competitive environments. Chemical Engineering Research & Design, 91(8): 1588–1600
https://doi.org/10.1016/j.cherd.2013.06.008
69 Zhang Q, Grossmann I E, Lima R M (2016). On the relation between flexibility analysis and robust optimization for linear systems. AIChE Journal, 62(9): 3109–3123
https://doi.org/10.1002/aic.15221
70 Zou J, Ahmed S, Sun X A (2017). Stochastic dual dynamic integer programming. Optimization Online.
70 Zyngier D, Kelly J D (2009). Multi-product inventory logistics modeling in the process industries. In Optimization and Logistics Challenges in the Enterprise, 61–95. New York: Springer
71 Zyngier D, Kelly J D (2012). UOPSS: a new paradigm for modeling production planning & scheduling systems. In Symposium on Computer Aided Process Engineering, 17: 20
[1] Fikri KUCUKSAYACIGIL, Gündüz ULUSOY. Hybrid genetic algorithm for bi-objective resource-constrained project scheduling[J]. Front. Eng, 2020, 7(3): 426-446.
[2] Fupei LI, Minglei YANG, Wenli DU, Xin DAI. Development and challenges of planning and scheduling for petroleum and petrochemical production[J]. Front. Eng, 2020, 7(3): 373-383.
[3] Moulay Larbi CHALAL, Benachir MEDJDOUB, Nacer BEZAI, Raid SHRAHILY. Big Data to support sustainable urban energy planning: The EvoEnergy project[J]. Front. Eng, 2020, 7(2): 287-300.
[4] Sameh Al-SHIHABI, Mohammad AlDURGAM. Multi-objective optimization for the multi-mode finance-based project scheduling problem[J]. Front. Eng, 2020, 7(2): 223-237.
[5] Lei ZHOU, Zhe LIANG, Chun-An CHOU, Wanpracha Art CHAOVALITWONGSE. Airline planning and scheduling: Models and solution methodologies[J]. Front. Eng, 2020, 7(1): 1-26.
[6] Braulio BRUNAUD, Maria Paz OCHOA, Ignacio E. GROSSMANN. Product decomposition strategy for optimization of supply chain planning[J]. Front. Eng, 2018, 5(4): 466-478.
[7] Juan LIU, Fei QIAO, Yumin MA, Weichang KONG. Novel slack-based robust scheduling rule for a semiconductor manufacturing system with uncertain processing time[J]. Front. Eng, 2018, 5(4): 507-514.
[8] Gainanov Damir N., Mladenovic NENAD, Rasskazova V. A.. Maximum independent set in planning freight railway transportation[J]. Front. Eng, 2018, 5(4): 499-506.
[9] Lingxuan LIU, Zhongshun SHI, Leyuan SHI. Minimization of total energy consumption in an m-machine flow shop with an exponential time-dependent learning effect[J]. Front. Eng, 2018, 5(4): 487-498.
[10] Marcel JOLY, Darci ODLOAK, Mario Y. MIYAKE, Brenno C. MENEZES, Jeffrey D. KELLY. Refinery production scheduling toward Industry 4.0[J]. Front. Eng, 2018, 5(2): 202-213.
[11] Gang FU, Pedro A. Castillo CASTILLO, Vladimir MAHALEC. Impact of crude distillation unit model accuracy on refinery production planning[J]. Front. Eng, 2018, 5(2): 195-201.
[12] Lynda M. BOURNE, Patrick WEAVER. The origins of schedule management: the concepts used in planning, allocating, visualizing and managing time in a project[J]. Front. Eng, 2018, 5(2): 150-166.
[13] Mario VANHOUCKE. Planning projects with scarce resources: Yesterday, today and tomorrow’s research challenges[J]. Front. Eng, 2018, 5(2): 133-149.
[14] Jiateng YIN, Yihui WANG, Tao TANG, Jing XUN, Shuai SU. Metro train rescheduling by adding backup trains under disrupted scenarios[J]. Front. Eng, 2017, 4(4): 418-427.
[15] Marcel JOLY, Mario Y. MIYAKE. Lessons learned from developing and implementing refinery production scheduling technologies[J]. Front. Eng, 2017, 4(3): 325-337.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed