Modeling to Generate Alternatives: The HSJ Approach and an Illustration Using a Problem in Land Use Planning

Brill, E. Downey; Chang, Shoou‐Yuh; Hopkins, Lewis D.

doi:10.1287/mnsc.28.3.221

Cited by 184 publications

(135 citation statements)

References 9 publications

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“…However, such diversity techniques have been demonstrated for semi-quantitative scenarios only. In energy systems modelling, three decades-old Modeling to Generate Alternatives (MGA) technique has been recently rediscovered [20]. MGA can be used with an energy system model to directly generate a small set of different energy scenarios [21,22] or to generate a large set of energy scenarios and afterwards select a small set of maximally-different ones [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Ensuring diversity of national energy scenarios: Bottom-up energy system model with Modeling to Generate Alternatives

Berntsen

Trutnevyte

2017

Energy

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Long-range energy scenarios are commonly used to inform national energy policy decisions. Although the scenario approach aims to expand the spectrum of futures considered, in the past energy scenarios have not been diverse enough to include less expected real-world developments. We use a bottom-up energy system model EXPANSE with Modeling to Generate Alternatives (MGA) to assess the diversity of the existing ensemble of multi-organization, multi-model Swiss electricity supply scenarios. We show that both for 2035 and 2050 existing scenarios cover well the various possibilities in deployment of individual electricity generation technologies in terms of installed capacity or produced electricity. When analyzing scenarios as a whole, we find that in 2035 there is a lack of scenarios with high electricity demand and greater deployment of renewable technologies occurring simultaneously, as well as scenarios with a negligible or negative share of net electricity import. In 2050, there is also a lack of scenarios that depict high electricity demand and conservative deployment of new renewable technologies, especially solar PV. We propose six additional scenarios to be included to increase the diversity of the ensemble of Swiss electricity supply scenarios and achieve better understanding of the future possibilities and uncertainties.

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Section: Introductionmentioning

confidence: 99%

Ensuring diversity of national energy scenarios: Bottom-up energy system model with Modeling to Generate Alternatives

Berntsen

Trutnevyte

2017

Energy

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“…After optimizing an initial problem formulation, the deterministic Hop, Skip, and Jump (HSJ) MGA technique creates supplementary problem instances by systematically adding target constraints on both the objective function value and the decision variables to force the generation of solution alternatives (Brill, 1979;Brill et al, 1981). Huang et al (1996b) combined GP with HSJ modelling to construct a procedure referred to as the Grey, Hop, Skip and Jump (GHSJ) method.…”

Section: Case 2: Policy Generation For the Expansion Of Waste Managemmentioning

confidence: 99%

“…In response to this option-generation requirement, several approaches collectively referred to as modelling-to-generatealternatives (MGA) have been developed Brill, 1979;Brill et al, 1981;Chang et al, 1980Chang et al, , 1982Church and Huber, 1979;Falkenhausen, 1979;Gidley and Bari, 1986;Rubenstein-Montano and Zandi, 1999;Rubenstein-Montano et al, 2000). The goal for all MGA methods is to create an optimal solution together with a set of several near-optimal alternatives (Gidley and Bari, 1986).…”

Section: Introductionmentioning

confidence: 99%

Applications of Simulation-Optimization Methods in Environmental Policy Planning under Uncertainty

Yeomans

2008

J ENV INFORM

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ABSTRACT. Environmental policy formulation can prove especially complicated, since in general, system components contain considerable degrees of uncertainty. However, simulation-optimization (SO) techniques can be adapted to model a wide variety of problem types in which system components are stochastic. In this paper, it is shown how multiple environmental policy alternatives meeting required system criteria, or modelling-to-generate-alternatives (MGA), can be quickly and efficiently created using SO. The efficacy of this MGA approach is illustrated using two case studies. Furthermore, since SO techniques can be adapted to problems in which many system components are stochastic, the practicality of this approach can be extended into many other operational and strategic planning applications containing significant sources of uncertainty.

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“…In HSJ modeling, after optimizing an initial problem formulation, supplementary problem instances are systematically solved with additional target constraints being placed upon both the objective function value and the decision variables which force the production of alternate solutions (Brill, 1979;Brill et al, 1981). Practice normally dictates that good alternative solutions should never be more than 10% worse than the initial problem formulation's optimal solution when additional unmodelled planning issues are included (Chang et al, 1982;Huang et al, 1996b).…”

Section: Situationmentioning

confidence: 99%

“…In response to this solution option requirement, several methods for modeling to generate alternatives (MGA) have been proposed (Baetz et al, 1990;Brill, 1979;Brill et al, 1981;Chang et al, 1980;Chang et al1982;Church and Huber, 1979;Falkenhausen, 1979;Gidley and Bari, 1986;Rubenstein-Montano and Zandi, 1999;Rubenstein-Montano et al 2000). MGA approaches provide an optimal solution and several near-optimal alternatives for planning problems (Gidley and Bari, 1986).…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary Grey, Hop, Skip, and Jump Approach: Generating Alternative Policies for the Expansion of Waste Management

Yeomans

Huang

2003

J ENV INFORM

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ABSTRACT. Evolutionary simulation-optimization methods are combined with a Grey Hop, Skip, and Jump (GHSJ) approach in an application to municipal solid waste management planning. GHSJ techniques have been effectively applied to problems containing uncertain information. Simulation-optimization methods can be adapted to a wide variety of problem types in which some or all of the system components are stochastic. In this paper, the advantages from both of these techniques are combined and used for efficiently generating improved decision alternatives. An illustrative application of the method is provided to demonstrate the usefulness of this approach in the planning design phase for the expansion of a waste management system. By using this approach, multiple different planning alternatives can be created that meet established system criteria, while simultaneously remaining acceptable and implementable in practice. Solid waste decision makers faced with difficult and controversial choices would then interpret and analyze these alternatives to internalize the environmental-economic tradeoffs prior to selecting their final policy.

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Modeling to Generate Alternatives: The HSJ Approach and an Illustration Using a Problem in Land Use Planning

Cited by 184 publications

References 9 publications

Ensuring diversity of national energy scenarios: Bottom-up energy system model with Modeling to Generate Alternatives

Ensuring diversity of national energy scenarios: Bottom-up energy system model with Modeling to Generate Alternatives

Applications of Simulation-Optimization Methods in Environmental Policy Planning under Uncertainty

An Evolutionary Grey, Hop, Skip, and Jump Approach: Generating Alternative Policies for the Expansion of Waste Management

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