Domino effect risk management: Decision making methods

Abstract Process-based plants, including chemical complex units, due to having considerable complexity and interdependencies between the units carrying different types of hazardous materials, have a high potential to face disastrous domino effects. Thus, preventing dominoes and mitigating the domino effects are a vital and challenging task for onsite decision-makers. In this regard, decision-makers need to have a reliable tool to make feasible and optimum decisions to prevent and mitigate the occurrence and consequences of domino effects, respectively. This chapter reviews the advanced decision-making tools that complex chemical plants can effectively use to deal with domino effects.

[1]  Ehsan Raei,et al.  Multi-objective decision-making for green infrastructure planning (LID-BMPs) in urban storm water management under uncertainty , 2019 .

[2]  G. Sakthivel,et al.  Failure mode and effect analysis using fuzzy analytic hierarchy process and GRA TOPSIS in manufacturing industry , 2017 .

[3]  Gokhan Izbirak,et al.  Bi-objective covering tour location routing problem with replenishment at intermediate depots: Formulation and meta-heuristics , 2017, Comput. Ind. Eng..

[4]  Francisco Ruiz,et al.  ISTMO: An interval reference point-based method for stochastic multiobjective programming problems , 2009, Eur. J. Oper. Res..

[5]  Mohammad Yazdi,et al.  Application of fuzzy fault tree analysis based on modified fuzzy AHP and fuzzy TOPSIS for fire and explosion in the process industry , 2020, International journal of occupational safety and ergonomics : JOSE.

[6]  Jörg Fliege,et al.  Generalized Goal Programming: polynomial methods and applications , 2002, Math. Program..

[7]  Valerie Belton,et al.  Structuring problems for Multi-Criteria Decision Analysis in practice: A literature review of method combinations , 2017, Eur. J. Oper. Res..

[8]  Kaveh Khalili-Damghani,et al.  Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain , 2017 .

[9]  Luyao Zhang,et al.  Risk assessment of hydrogen generation unit considering dependencies using integrated DEMATEL and TOPSIS approach , 2020 .

[10]  Seungjae Lee,et al.  Stochastic multi-objective models for network design problem , 2010, Expert Syst. Appl..

[11]  Ronald R. Yager,et al.  Pythagorean Membership Grades, Complex Numbers, and Decision Making , 2013, Int. J. Intell. Syst..

[12]  Muhammet Gul,et al.  Application of Pythagorean fuzzy AHP and VIKOR methods in occupational health and safety risk assessment: the case of a gun and rifle barrel external surface oxidation and colouring unit , 2018, International journal of occupational safety and ergonomics : JOSE.

[13]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[14]  Sohag Kabir,et al.  A fuzzy Bayesian network approach for risk analysis in process industries , 2017 .

[15]  Walter J. Gutjahr,et al.  Stochastic multi-objective optimization: a survey on non-scalarizing methods , 2016, Ann. Oper. Res..

[16]  Thaddeus C. Nwaoha,et al.  Ship structural safety optimization: an integrated artificial intelligence and multi-criteria decision-making method , 2019 .

[17]  Carlos A. Coello Coello,et al.  Applications of multi-objective evolutionary algorithms in economics and finance: A survey , 2007, 2007 IEEE Congress on Evolutionary Computation.

[18]  ChangKyoo Yoo,et al.  Multi-objective decision-making and optimal sizing of a hybrid renewable energy system to meet the dynamic energy demands of a wastewater treatment plant , 2020 .

[19]  Abraham Charnes,et al.  Optimal Estimation of Executive Compensation by Linear Programming , 1955 .

[20]  Stanley Zionts,et al.  MCDM---If Not a Roman Numeral, Then What? , 1979 .

[21]  Noorbakhsh Amiri Golilarz,et al.  Optimized Wavelet-Based Satellite Image De-Noising With Multi-Population Differential Evolution-Assisted Harris Hawks Optimization Algorithm , 2020, IEEE Access.

[22]  Mohammad Yazdi,et al.  Risk assessment based on novel intuitionistic fuzzy-hybrid-modified TOPSIS approach , 2018, Safety Science.

[23]  Jun Yuan,et al.  Development of a multi-objective decision-making method to evaluate correlated decarbonization measures under uncertainty – The example of international shipping , 2019, Transport Policy.

[24]  Fouad Ben Abdelaziz,et al.  Multi-objective stochastic programming for portfolio selection , 2007, Eur. J. Oper. Res..

[25]  Faisal Khan,et al.  A dynamic risk model to analyze hydrogen infrastructure , 2020 .

[26]  Haozhong Cheng,et al.  Multi-Objective Stochastic Optimal Operation of a Grid-Connected Microgrid Considering an Energy Storage System , 2018 .

[27]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[28]  Alireza Alinezhad,et al.  New Methods and Applications in Multiple Attribute Decision Making (MADM) , 2019, International Series in Operations Research & Management Science.

[29]  Anirban Mukhopadhyay,et al.  Multi-objective uncapacitated facility location problem with customers’ preferences: Pareto-based and weighted sum GA-based approaches , 2019, Soft Comput..

[30]  Jacques Teghem,et al.  Two-phases Method and Branch and Bound Procedures to Solve the Bi–objective Knapsack Problem , 1998, J. Glob. Optim..

[31]  Devon Sigler,et al.  Pareto solutions in multicriteria optimization under uncertainty , 2020, Eur. J. Oper. Res..

[32]  Zita Vale,et al.  Weighted sum approach using parallel Particle Swarm Optimization to solve multi-objective energy scheduling , 2016, 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D).

[33]  Rouzbeh Abbassi,et al.  Vulnerability analysis of process plants subject to domino effects , 2016, Reliab. Eng. Syst. Saf..

[34]  C. Hwang,et al.  Fuzzy Multiple Objective Decision Making: Methods And Applications , 1996 .

[35]  Roni Habibi,et al.  Comparation of SAW Method and Topsis in Assesing The Best Area Using HSE Standards , 2020 .

[36]  Xia Li,et al.  An artificial bee colony algorithm for multi-objective optimisation , 2017, Appl. Soft Comput..

[37]  Fouad Ben Abdelaziz,et al.  A compromise solution for the multiobjective stochastic linear programming under partial uncertainty , 2010, Eur. J. Oper. Res..

[38]  J. Rezaei.  Best-worst multi-criteria decision-making method , 2015 .

[39]  Rafael Caballero,et al.  Stochastic approach versus multiobjective approach for obtaining efficient solutions in stochastic multiobjective programming problems , 2002, Eur. J. Oper. Res..

[40]  Omidvar Mohsen,et al.  An extended VIKOR method based on entropy measure for the failure modes risk assessment – A case study of the geothermal power plant (GPP) , 2017 .

[41]  Abolfazl Khalkhali,et al.  Multi-objective crashworthiness optimization of perforated square tubes using modified NSGAII and MOPSO , 2016, Structural and Multidisciplinary Optimization.

[42]  Amir Karimi,et al.  Analysis of interactions among sustainability supplier selection criteria using ISM and fuzzy DEMATEL , 2014, Int. J. Appl. Decis. Sci..

[43]  Arman Nedjati,et al.  An improved multi-criteria emergency decision-making method in environmental disasters , 2021, Soft Computing.

[44]  Sayyad Nojavan,et al.  A cost-emission model for fuel cell/PV/battery hybrid energy system in the presence of demand response program: ε-constraint method and fuzzy satisfying approach , 2017 .

[45]  Matthias Ehrgott,et al.  Solving biobjective combinatorial max-ordering problems by ranking methods and a two-phases approach , 2003, European Journal of Operational Research.

[46]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[47]  Edmundas Kazimieras Zavadskas,et al.  A Hybrid MCDM Technique for Risk Management in Construction Projects , 2018, Symmetry.

[48]  Ronald R. Yager,et al.  Pythagorean Membership Grades in Multicriteria Decision Making , 2014, IEEE Transactions on Fuzzy Systems.

[49]  Enrico Zio,et al.  Overview of risk-informed decision-making processes , 2012 .

[50]  Murat Köksalan,et al.  A multi-objective multi-period stochastic programming model for public debt management , 2010, Eur. J. Oper. Res..

[51]  Jairo R. Montoya-Torres,et al.  Robust solutions in multi-objective stochastic permutation flow shop problem , 2019, Comput. Ind. Eng..

[52]  Ji Young Lee,et al.  Multi-objective Environmental/Economic Dispatch Using the Bees Algorithm with Weighted Sum , 2008 .

[53]  Rouzbeh Abbassi,et al.  Improved DEMATEL methodology for effective safety management decision-making , 2020 .

[54]  George Mavrotas,et al.  Solving Multi-objective MILP Problems in Process Synthesis using the Multi-Criteria Branch and Bound Algorithm , 2005 .

[55]  Gisella Facchinetti,et al.  A New Proposal of Defuzzification of Intuitionistic Fuzzy Quantities , 2015, IWIFSGN@FQAS.

[56]  Antans Sauļus Sauhats,et al.  A multi-objective stochastic approach to hydroelectric power generation scheduling , 2016, 2016 Power Systems Computation Conference (PSCC).

[57]  Faisal Khan,et al.  Risk-based domino effect analysis for fire and explosion accidents considering uncertainty in processing facilities , 2018 .

[58]  Alexander Gutfraind,et al.  Optimizing Network Topology for Cascade Resilience , 2012 .

[59]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[60]  Abdolrasoul Parhizgarsharif,et al.  A hybrid approach based on the BWM-VIKOR and GRA for ranking facility location in construction site layout for Mehr project in Tehran , 2019, Decision Science Letters.

[61]  Long Ding,et al.  FSEM: An approach to model contribution of synergistic effect of fires for domino effects , 2019, Reliab. Eng. Syst. Saf..

[62]  Esmaeil Zarei,et al.  A reliable risk analysis approach using an extension of best-worst method based on democratic-autocratic decision-making style , 2020 .

[63]  Adel Hatami-Marbini,et al.  A fuzzy group Electre method for safety and health assessment in hazardous waste recycling facilities , 2013 .

[64]  Faisal Khan,et al.  A hybrid model for human factor analysis in process accidents: FBN-HFACS , 2019, Journal of Loss Prevention in the Process Industries.

[65]  Davood Eskandari,et al.  Development of a scale for assessing the organization's safety performance based fuzzy ANP , 2020 .

[66]  Yaroslav D. Sergeyev,et al.  Solving the Lexicographic Multi-Objective Mixed-Integer Linear Programming Problem using branch-and-bound and grossone methodology , 2020, Commun. Nonlinear Sci. Numer. Simul..

[67]  M. P. Biswal,et al.  Multi-objective solid transportation problem under stochastic environment , 2019, Sādhanā.

[68]  Bruno Urli,et al.  PROMISE/scenarios: An interactive method for multiobjective stochastic linear programming under partial uncertainty , 2004, Eur. J. Oper. Res..

[69]  J. Teghem,et al.  STRANGE: an interactive method for multi-objective linear programming under uncertainty , 1986 .

[70]  Mohammad Reza Hairi Yazdi,et al.  Hybrid Probabilistic Risk Assessment Using Fuzzy FTA and Fuzzy AHP in a Process Industry , 2017, Journal of Failure Analysis and Prevention.

[71]  M. Ehrgott.  Vilfredo Pareto and multi-objective optimization , 2012 .

[72]  Mohammad Reza Hairi Yazdi,et al.  Failure probability analysis by employing fuzzy fault tree analysis , 2017, Int. J. Syst. Assur. Eng. Manag..

[73]  Yun-Chia Liang,et al.  Multi-objective redundancy allocation optimization using a variable neighborhood search algorithm , 2010, J. Heuristics.

[74]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[75]  Gwo-Hshiung Tzeng,et al.  A VIKOR technique based on DEMATEL and ANP for information security risk control assessment , 2013, Inf. Sci..

[76]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[77]  Funda Samanlioglu,et al.  A Multi-Objective Stochastic Model for an Earthquake Relief Network , 2020 .

[78]  M. E. Niri,et al.  Estimation of subsurface geomodels by multi-objective stochastic optimization , 2016 .

[79]  Anthony Przybylski,et al.  Multi-objective branch and bound , 2017, Eur. J. Oper. Res..

[80]  Mehrdad Tamiz,et al.  Practical Goal Programming , 2010 .

[81]  Ambrose Goicoechea,et al.  Multiple objectives under uncertainty: An illustrative application of protrade , 1979 .

[82]  Panos M. Pardalos,et al.  Multi-Objective Branch and Bound , 2017 .

[83]  J. B. C..  Joseph Priestley , 1906, Nature.

[84]  James S. Dyer,et al.  A Time-Sharing Computer Program for the Solution of the Multiple Criteria Problem , 1973 .

[85]  Davide La Torre,et al.  The Stochastic Goal Programming Model: Theory and Applications , 2012 .

[86]  El-Ghazali Talbi,et al.  Dealing with Epistemic Uncertainty in Multi-objective Optimization: A Survey , 2018, IPMU.

[87]  John Ahmet Erkoyuncu,et al.  A Review of Multi-criteria Decision Making Methods for Enhanced Maintenance Delivery , 2015 .

[88]  Marc Roubens,et al.  Multiple criteria decision making , 1994 .

[89]  Zainab Al Ani,et al.  Towards process, energy and safety based criteria for multi-objective optimization of industrial acid gas removal process , 2020 .

[90]  Rabihah Md.Sum,et al.  Risk Management Decision Making , 2013 .

[91]  Antonio J. Nebro,et al.  A survey of multi-objective metaheuristics applied to structural optimization , 2014 .

[92]  Bernabé Dorronsoro,et al.  Solving the multi-objective flexible job shop scheduling problem with a novel parallel branch and bound algorithm , 2020, Swarm Evol. Comput..

[93]  Masoud Rabbani,et al.  Developing a hazardous waste management system with consideration of health, safety, and environment , 2020, Comput. Electr. Eng..

[94]  Jian Liu,et al.  Decision process in MCDM with large number of criteria and heterogeneous risk preferences , 2018 .

[95]  Fouad Ben Abdelaziz,et al.  Solution approaches for the multiobjective stochastic programming , 2012, Eur. J. Oper. Res..

[96]  Rubén Ruiz-Femenia,et al.  OFISI, a novel optimizable inherent safety index based on fuzzy logic , 2019, Comput. Chem. Eng..

[97]  Sohag Kabir,et al.  Uncertainty-Aware Dynamic Reliability Analysis Framework for Complex Systems , 2018, IEEE Access.

[98]  Mehdi Jahangiri,et al.  A Novel Fuzzy Bayesian Network-HFACS (FBN-HFACS) model for analyzing Human and Organization Factors (HOFs) in process accidents , 2019 .

[99]  Jyrki Wallenius,et al.  Multiple Criteria Decision Making: From Early History to the 21st Century , 2011 .

[100]  Thomas L. Saaty,et al.  The Modern Science of Multicriteria Decision Making and Its Practical Applications: The AHP/ANP Approach , 2013, Oper. Res..

[101]  Dinesh Kumar,et al.  Risk analysis of compressor house unit in thermal power plant using integrated fuzzy FMEA and GRA approach , 2017 .

[102]  Sule Yildirim Yayilgan,et al.  A Novel Hybrid IDS Based on Modified NSGAII-ANN and Random Forest , 2020, Electronics.

[103]  Joo-Young Lee,et al.  Optimization for allocating the explosive facilities in order to minimize the domino effect using nonlinear programming , 2005 .

[104]  Murat Köksalan,et al.  A visual interactive approach for scenario-based stochastic multi-objective problems and an application , 2012, J. Oper. Res. Soc..

[105]  Marley M. B. R. Vellasco,et al.  PROMETHEE and Fuzzy PROMETHEE Multicriteria Methods for Ranking Equipment Failure Modes , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[106]  Sayyad Nojavan,et al.  Corrigendum to “A cost-emission model for fuel cell/PV/battery hybrid energy system in the presence of demand response program: ε-constraint method and fuzzy satisfying approach” [Energy Convers. Manage 138 (2017) 383–392] , 2020 .

[107]  P. Pardalos,et al.  Generating properly efficient points in multi-objective programs by the nonlinear weighted sum scalarization method , 2014 .

[108]  Theodor J. Stewart,et al.  Multiple criteria decision analysis - an integrated approach , 2001 .

[109]  I. Little.  A critique of welfare economics , 1951 .

[110]  Mehdi Jahangiri,et al.  A novel Fuzzy Bayesian Network approach for safety analysis of process systems; An application of HFACS and SHIPP methodology , 2020 .

[111]  Mahmoud M. El-Halwagi,et al.  Optimizing safety-constrained solvent selection for process systems with economic uncertainties , 2013 .

[112]  Jan Van Impe,et al.  Robust multi-objective dynamic optimization of chemical processes using the Sigma Point method , 2016 .

[113]  Peng Jiang,et al.  A Multi-Objective Emergency Rescue Facilities Location Model for Catastrophic Interlocking Chemical Accidents in Chemical Parks , 2020, IEEE Transactions on Intelligent Transportation Systems.

[114]  L. Lasdon,et al.  On a bicriterion formation of the problems of integrated system identification and system optimization , 1971 .

[115]  M. Sam Mannan,et al.  A quantitative approach for optimal alarm identification , 2018, Journal of Loss Prevention in the Process Industries.

[116]  Carlos A. Coello Coello,et al.  An Introduction to Multi-Objective Particle Swarm Optimizers , 2011 .

[117]  Mohammad Yazdi,et al.  Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system , 2020, Qual. Reliab. Eng. Int..

[118]  Valerio Cozzani,et al.  Approaches to domino effect prevention and mitigation , 2013 .

[119]  Valerio Cozzani,et al.  Risk Management of Domino Effects Considering Dynamic Consequence Analysis , 2014, Risk analysis : an official publication of the Society for Risk Analysis.

[120]  Curtis Smith,et al.  Bayesian inference in probabilistic risk assessment - The current state of the art , 2009, Reliab. Eng. Syst. Saf..

[121]  Rouzbeh Abbassi,et al.  A novel extension of DEMATEL approach for probabilistic safety analysis in process systems , 2020 .

[122]  Noorbakhsh Amiri Golilarz,et al.  Satellite Multispectral and Hyperspectral Image De-Noising with Enhanced Adaptive Generalized Gaussian Distribution Threshold in the Wavelet Domain , 2020, Remote. Sens..

[123]  R. Benayoun,et al.  Linear programming with multiple objective functions: Step method (stem) , 1971, Math. Program..

[124]  Mahdi Sharifzadeh,et al.  Multi-objective design and operation of Solid Oxide Fuel Cell (SOFC) Triple Combined-cycle Power Generation systems: Integrating energy efficiency and operational safety , 2017 .

[125]  David Z. Zhang,et al.  Multi-objective ant colony optimisation: A meta-heuristic approach to supply chain design , 2011 .

[126]  R. Marler,et al.  The weighted sum method for multi-objective optimization: new insights , 2010 .

[127]  Ronald R. Yager,et al.  Pythagorean fuzzy subsets , 2013, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).

[128]  Marko D. Petković,et al.  On the linear weighted sum method for multi-objective optimization , 2011 .

[129]  Evangelos Triantaphyllou,et al.  Multi-Criteria Decision Making Methods , 2000 .

[130]  S. Kar,et al.  Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding , 2019 .

[131]  Diyar Akay,et al.  A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method , 2009, Expert Syst. Appl..

[132]  Teresa Peña,et al.  Multiobjective stochastic programming for feed formulation , 2009, J. Oper. Res. Soc..

[133]  Mohammad Reza Hairi Yazdi,et al.  An Extension to Fuzzy Developed Failure Mode and Effects Analysis (FDFMEA) Application for Aircraft Landing System , 2017 .

[134]  Vasiliki Kazantzi,et al.  Application of i-SDT for safer flare management operation , 2019 .

[135]  Amin Mahmoudi,et al.  A novel model for risk management of outsourced construction projects using decision-making methods: a case study , 2020, Grey Syst. Theory Appl..

[136]  Toly Chen,et al.  A fuzzy set approach for event tree analysis , 2001, Fuzzy Sets Syst..

[137]  F. B. Vernadat,et al.  Decisions with Multiple Objectives: Preferences and Value Tradeoffs , 1994 .