Open source solution approaches to a class of stochastic supply chain problems

This research proposes a variety of solution approaches to a class of stochastic supply chain problems, with normally distributed demand in a certain period of time in the future. These problems aim to provide the decisions regarding the production levels; supplier selection for raw materials; and optimal order quantity. The typical problem could be formulated as a mixed integer nonlinear program model, and the objective function for maximizing the expected profit is expressed in an integral format. In order to solve the problem, an open source solution package BONMIN is first employed to get the exact optimum result for small scale instances; then according to the specific feature of the problem a tailored nonlinear branch and bound framework is developed for larger scale problems through the introduction of triangular approximation approach and an iterative algorithm. Both open source solvers and commercial solvers are employed to solve the inner problem, and the results to larger scale problems demonstrate the competency of introduced approaches. In addition, two small heuristics are also introduced and the selected results are reported

NONLINEAR PANEL ESTIMATION OF TIME-VARYING EFFECTS OF IMPORT QUOTAS

We develop a panel-based ICLS framework for estimating the export tax equivalent (ETE) of quotas where the ETEs vary over time. Working with a panel of bilateral data on textile and clothing trade, underlying bilateral tari?s, and the country-pair coverage of quotas under the WTOÕs Agreement on Textiles and Clothing (ATC), we use this framework to examine the evolution of market access conditions in the textile and clothing sectors. Our estimating framework takes advantage of the panel nature of trade data when calculating export tax equivalents while allowing for inequality constraints on the quota premium estimates. We also implement quadrature methods for calculating conÞdence intervals for our regression-based NTB measures.NTB, ICLS, quadrature, quotas, ATC, MFA, MPEC, gravity model

A Polynomial Optimization Approach to Constant Rebalanced Portfolio Selection

We address the multi-period portfolio optimization problem with the constant rebalancing strategy. This problem is formulated as a polynomial optimization problem (POP) by using a mean-variance criterion. In order to solve the POPs of high degree, we develop a cutting-plane algorithm based on semidefinite programming. Our algorithm can solve problems that can not be handled by any of known polynomial optimization solvers.Multi-period portfolio optimization;Polynomial optimization problem;Constant rebalancing;Semidefinite programming;Mean-variance criterion

Optimal departure aircraft trajectories minimising population annoyance

Best Paper in Track Award de l’International Congress on Research in Air TransportThis paper presents a strategy for designing noise abatement procedures aimed at reducing the global annoyance perceived for the population living around the airports. By using fuzzy logic techniques it is shown how annoyance can be modelled in function of the maximum perceived noise level at a specific noise sensitive location and the time of the day when the departure takes place. Thus, the annoyance is computed for different kinds of sensibility areas, such as residential zones, industrial zones, schools or hospitals and an annoyance figure is obtained for each possible trajectory. Then, a non-linear multiobjective optimal control problem is presented in order to obtain the minimum annoyance trajectory for all sensitive locations. Lexicographic optimisation is used to cope with the difficulties that arise when several criteria appear in the optimisation process. Finally, a practical example is given for an hypothetical scenario where different optimal trajectories are obtained at different day periods.Peer ReviewedAward-winnin

Online Simplification of Water Distribution Network Models

This paper presents an implementation of the simplification algorithm of water distribution network (WDN) models for the purpose of inclusion to the online optimisation strategy for the energy and leakage management in WDN, formulated within a model predictive control framework. The advantage of the online model reduction is adaptation to abnormal situations and structural changes in a network. The implementation was carried out with the utilisation of nowadays parallel programing techniques to distribute the simplification tasks across multiple CPU treads. This resulted in significant reduction of the computational time required for the simplification process of the large–scale WDN models. The authors also highlighted a problem of the energy distribution when the reduced and original models were compared

A Polynomial Optimization Approach to Constant Rebalanced Portfolio Selection

We address the multi-period portfolio optimization problem with the constant rebalancing strategy. This problem is formulated as a polynomial optimization problem (POP) by using a mean-variance criterion. In order to solve the POPs of high degree, we develop a cutting-plane algorithm based on semidefinite programming. Our algorithm can solve problems that can not be handled by any of known polynomial optimization solvers.