Prioritization of stockpile maintenance with layered Pareto fronts

Burke, Sarah E.; Anderson‐Cook, Christine M.; Lu, Lu; Montgomery, Douglas C.

doi:10.1080/08982112.2017.1390585

Cited by 4 publications

(11 citation statements)

References 10 publications

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“…We use N to denote the number of top designs to evaluate using the proposed method to choose the final design. The Top N strategy for multiple objective decision‐making was first introduced by Burke et al to select multiple leading solutions by considering their robustness to different weight combinations. The method identifies the Top N layers of PFs, which form multiple groups of ranked solutions with the top layer containing solutions strictly better than the second layer on all objectives and so on.…”

Section: Introductionmentioning

confidence: 99%

“…The method identifies the Top N layers of PFs, which form multiple groups of ranked solutions with the top layer containing solutions strictly better than the second layer on all objectives and so on. Burke et al demonstrated the Top N layers of PFs must include the Top N solutions for any possible weight combinations and hence offer an objective set of solutions before evaluating the weight impact and robustness. Burke et al illustrate how the Top N strategy (described in Section ) can be effectively used to identify multiple top contenders when the goal is to choose several results, such as the most critical stockpiles to receive additional funding.…”

Section: Introductionmentioning

confidence: 99%

“…Burke et al demonstrated the Top N layers of PFs must include the Top N solutions for any possible weight combinations and hence offer an objective set of solutions before evaluating the weight impact and robustness. Burke et al illustrate how the Top N strategy (described in Section ) can be effectively used to identify multiple top contenders when the goal is to choose several results, such as the most critical stockpiles to receive additional funding. This approach can also work well in the context of designed experiments even though only a single experiment will ultimately be run.…”

Section: Introductionmentioning

confidence: 99%

“…To accommodate the desire to consider near‐optimal solutions in the design selection, we have adapted the PF approach to include the Top N solutions, not just those which are strictly best. Layered PFs allow all designs that could rank in the Top N choices to be identified as they consider potential solutions that lie just behind the PF as potential candidates. Considering optimal and near‐optimal designs provides a larger pool of potential solutions to evaluate.…”

Section: Introductionmentioning

confidence: 99%

“…How best i and worst i are defined depends on the judgment of the decision‐maker or on the properties of the particular data set under consideration. In the JMP add‐in that was developed in Burke et al, four options are available: The best and worst values are calculated from the data only in the layered PFs. The best and worst values are calculated from the entire dataset. The best value is calculated from the data in the layered PFs and the user provides the worst values for each criterion. The user provides the best and worst values for each criterion. In the case of scaling options 1 or 2, for example, if criterion i is maximized, the worst value would be the minimum in the applicable dataset, while the best value would be the maximum. If criterion i is minimized in these scaling scenarios, the reverse is true.…”

Section: Introductionmentioning

confidence: 99%

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Selecting a near‐optimal design for multiple criteria with improved robustness to different user priorities

Burke¹,

Anderson‐Cook

et al. 2018

Quality & Reliability Eng

Self Cite

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In a decision‐making process, relying on only one objective can often lead to oversimplified decisions that ignore important considerations. Incorporating multiple, and likely competing, objectives is critical for balancing trade‐offs on different aspects of performance. When multiple objectives are considered, it is often hard to make a precise decision on how to weight the different objectives when combining their performance for ranking and selecting designs. We show that there are situations when selecting a design with near‐optimality for a broad range of weight combinations of the criteria is a better test selection strategy compared with choosing a design that is strictly optimal under very restricted conditions. We propose a new design selection strategy that identifies several top‐ranked solutions across broad weight combinations using layered Pareto fronts and then selects the final design that offers the best robustness to different user priorities. This method involves identifying multiple leading solutions based on the primary objectives and comparing the alternatives using secondary objectives to make the final decision. We focus on the selection of screening designs because they are widely used both in industrial research, development, and operational testing. The method is illustrated with an example of selecting a single design from a catalog of designs of a fixed size. However, the method can be adapted to more general designed experiment selection problems that involve searching through a large design space.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Selecting a near‐optimal design for multiple criteria with improved robustness to different user priorities

Burke¹,

Anderson‐Cook

et al. 2018

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

show abstract

Graphics to facilitate informative discussion and team decision making

Anderson‐Cook

2018

Appl Stoch Models Bus & Ind

Self Cite

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Everyone knows the expression "A picture is worth a thousand words," and this effectively summarizes the ability of graphical summaries to convey information and persuade. However, in many cases, the goal for the right visualization is to encourage and guide discussion while helping focus a team to make carefully considered, defensible, and data-driven decisions. The aims of graphics differ if we are trying to communicate the merits of a single choice versus outlining several contending alternatives for further comparison and discussion.These choices each have their own strengths and weaknesses depending on how we value different criteria. They also serve different purposes at various stages of decision making. Often the role of statisticians is not to provide a single answer but to provide rich information and summaries in a manageable and compact form to enable productive discussion among team members. Through a series of diverse examples, we present principles and strategies for encouraging discussion and informed decision making and discuss how they can be integrated with versatile use of graphical tools for examining multiple objectives, framing trade-offs between alternatives, and examining the impact of subjective priorities and uncertainty on the final decision.

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Robust estimation of dependent competing risk model under interval monitoring and determining optimal inspection intervals

Baghel,

Mondal

2024

Appl Stoch Models Bus & Ind

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Recently, a growing interest is evident in modelling dependent competing risks in lifetime prognosis problems. In this work, we propose to model the dependent competing risks by Marshal‐Olkin bivariate exponential distribution. The observable data consists of a number of failures due to different causes across different time intervals. The failure count data is common in instances like one‐shot devices where the state of the subjects is inspected at different inspection times rather than the exact failure times. The point estimation of the lifetime distribution in the presence of competing risk has been studied through a divergence‐based robust estimation method called minimum density power divergence estimation (MDPDE) with and without constraint. The optimal value of the tuning parameter has been obtained. The testing of the hypothesis is performed based on a Wald‐type test statistic. The influence function is derived for the point estimator and the test statistic, reflecting the degree of robustness. Another key contribution of this work is determining the optimal inspection times based on predefined objectives. This article presents the determination of multi‐criteria‐based optimal design. Population‐based heuristic algorithm nondominated sorting‐based multiobjective Genetic algorithm is exploited to solve this optimization problem.

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Prioritization of stockpile maintenance with layered Pareto fronts

Cited by 4 publications

References 10 publications

Selecting a near‐optimal design for multiple criteria with improved robustness to different user priorities

Selecting a near‐optimal design for multiple criteria with improved robustness to different user priorities

Graphics to facilitate informative discussion and team decision making

Robust estimation of dependent competing risk model under interval monitoring and determining optimal inspection intervals

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