2016 Best Paper Prize
The winner of the IMA Journal of Management Mathematics Best Paper Prize 2016 is:
Strategic design of a competing supply chain network for markets with deterministic demands
by Shabnam Rezapour, Reza Zanjirani Farahani, Ding Zhang, and Faeghe Mohammaddust
This work looks at the development of a supply chain network design in the existence of market competitors who offer the same or substitutable products or services. Demands are assumed to be price-dependent leading to variable delivered price competition over time. The very network design is assumed to be a strategic (long term) decision but setting prices is treated as a short term commitment. The findings suggest that collaboration may be preferable to competition as a means to increasing profit. Very importantly, supply chains may also decide to compete at the retailer level but collaborate (horizontally) upstream the supply chain levels. Many interesting insights are offered to supply chain managers or directors.
Measuring the risk of a non-linear portfolio with fat-tailed risk factors through a probability conserving transformation
by Paresh Date and Roberto Bustreo
Measuring the risk of a non-linear portfolio with fat-tailed risk factors through a probability conserving transformation 2016/2 The authors provide a fast and accurate approximation of VaR (Value-at-Risk) and CVaR (conditional Value-at-Risk) for financial portfolios dependent on non-Gaussian risk factors. The algorithms and heuristics in the paper have direct impact and are beneficial to financial risk managers and market regulators in line with the practical considerations and implementation of the Basel II Accord on Banking Supervision.
Parallel variable neighbourhood search strategies for the cutwidth minimization problem
by Abraham Duarte, Juan J. Pantrigo, Eduardo G. Pardo, and Jesús Sánchez-Oro
The authors present a methodology paper in optimization that proposes and tests various schemes for parallelising variable neighbourhood search (VNS), with particular application to the cutwidth minimisation problem. Parallelisation of VNS is a new and important direction for developments of this metaheuristic, and the parallel implementation of VNS is shown to outperform existing state-of the-art methods for the cutwidth minimisation problem.
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