In this paper, we study a smooth-abrupt changepoint model through two testing procedures based on likelihood ratiotest and Schwarz information criterion under a normal distribution.Simulations are implemented to show the performance of these two procedures. The proposed testingprocedures are applied to detect changes in gene expression patterns and in predator versus prey population patterns of the Isle Royale National Park

Alizadeh, M

Matilal, S

Mehdi, A-A

Mustafee, N

European Journal of Operational Research

In this paper, a Casualty Collection Points (CCPs) location problem is formulated as a two-stage robust stochastic optimization model in an uncertain environment. In this modelling approach, the network design decisions are integrated with the multi-period response operational decisions where the number of casualties with different levels of injuries coming from the affected areas is uncertain. Furthermore, the transportation capacity for the evacuation of casualties to CCPs and hospitals is also uncertain. To solve this complex problem, a robust sample average approximation method with the feasibility restoration technique is proposed, and its efficiency is examined through a statistical validation procedure. We then evaluate the proposed methodology in the backdrop of a hypothetical case of Bhopal gas tragedy (with the same hazard propagation profile) at the present day. We also report the solution robustness and model robustness of 144 instances of the case-study to show the proficiency of our proposed solution approach. Results analysis reveals that our modelling approach enables the decision makers to design a humanitarian logistic network in which not only the proximity and accessibility to CCPs is improved, but also the number of lives lost is decreased. Moreover, it is shown that the proposed robust stochastic optimization approach convergences rapidly and more efficiently. We hope that our methodology will encourage urban city planners to pre-identify CCP locations, and, in the event of a disaster, help them decide on the subset of these CCPs that could be rapidly mobilised for disaster response

Alizadeh, Morteza

Amiri-Aref, Mehdi

Mustafee, Navonil

Matilal, Sumohon

White Rose Research Online

A robust stochastic Casualty Collection Points location problem

Ning, Wei

Chen, Ying-Ju

ESE - Salento University Publishing

Tests for smooth-sbrupt changes with applications

Open Research Exeter

English

A robust stochastic casualty collection points location problem

Università del Salento: ESE - Salento University Publishing

Mustafee, Nav

Hal-Diderot

A Robust Stochastic Casualty Collection Points Location Problem: .

In this paper, a Casualty Collection Points (CCPs) location problem is formulated as a two-stage robust stochastic optimization model in an uncertain environment. In this modelling approach, the network design decisions are integrated with the multi-period response operational decisions where the number of casualties with different levels of injuries coming from the affected areas is uncertain. Furthermore, the transportation capacity for the evacuation of casualties to CCPs and hospitals is also uncertain. To solve this complex problem, a robust sample average approximation method with the feasibility restoration technique is proposed, and its efficiency is examined through a statistical validation procedure. We then evaluate the proposed methodology in the backdrop of a hypothetical case of Bhopal gas tragedy (with the same hazard propagation profile) at the present day. We also report the solution robustness and model robustness of 144 instances of the case-study to show the proficiency of our proposed solution approach. Results analysis reveals that our modelling approach enables the decision makers to design a humanitarian logistic network in which not only the proximity and accessibility to CCPs are improved, but also the number of lives lost is decreased. Moreover, it is shown that the proposed robust stochastic optimization approach converges rapidly and more efficiently. We hope that our methodology will encourage urban city planners to pre-identify CCP locations, and, in the event of a disaster, help them decide on the subset of these CCPs that could be rapidly mobilised for disaster response

Morteza Alizadeh (13192466)

Mehdi Amiri-Aref (16282595)

Navonil Mustafee (12647678)

Sumohon Matilal (8498202)

Sussex Research Online

https://ore.exeter.ac.uk/repository/bitstream/10871/37644/1/Accepted%20Manuscript_A%20Robust%20Stochastic%20Casualty%20Collection%20Points%20Location%20Problem.pdf

Tests for smooth-sbrupt changes with applications

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