The proximal point method for locally Lipschitz functions in multiobjective optimization

This paper studies the constrained multiobjective optimization problem of finding Pareto critical points of vector-valued functions. The proximal point method considered by Bonnel et al. (SIAM J. Optim., 4 (2005), pp. 953-970) is extended to locally Lipschitz functions in the finite dimensional multiobjective setting. To this end, a new approach for convergence analysis of the … Read more

An exact hybrid method for the vehicle routing problem with time windows and multiple deliverymen

The vehicle routing problem with time windows and multiple deliverymen (VRPTWMD) is a variant of the vehicle routing problem with time windows in which service times at customers depend on the number of deliverymen assigned to the route that serves them. Hence, in addition to the usual routing and scheduling decisions, the crew size for … Read more

Scalable Robust and Adaptive Inventory Routing

We consider the finite horizon inventory routing problem with uncertain demand, where a supplier must deliver a particular commodity to its customers periodically, such that even under uncertain demand the customers do not stock out, e.g. supplying residential heating oil to customers. Current techniques that solve this problem with stochastic demand, robust or adaptive optimization … Read more

A GENERALIZED PROXIMAL LINEARIZED ALGORITHM FOR DC FUNCTIONS WITH APPLICATION TO THE OPTIMAL SIZE OF THE FIRM PROBLEM

A proximal linearized algorithm with a quasi distance as regularization term for minimizing a DC function (difference of two convex functions) is proposed. If the sequence generated by our algorithm is bounded, it is proved that every cluster point is a critical point of the function under consideration, even if minimizations are performed inexactly at … Read more

Pricing wind: a revenue adequate, cost recovering uniform auction for electricity markets with intermittent generation

With greater penetration of renewable generation, the uncertainty faced in electricity markets has increased substantially. Conventionally, generators are assigned a pre-dispatch quantity in advance of real time, based on estimates of uncertain quantities. Expensive real time adjustments then need to be made to ensure demand is met, as uncertainty takes on a realization. We propose … Read more

Evaluating the effect of environmental regulations on a closed-loop supply chain network: a variational inequality approach

Global climate change has encouraged international and regional adoption of pollution taxes and carbon emission reduction policies. Europe has taken the leadership in environmental regulations by introducing the European Union Emissions Trading System (EU-ETS) in 2005 and by developing and promoting a set of policies destined to lower carbon emissions from transport sectors. These environmental … Read more

Mixed-integer Programming Based Approaches for the Movement Planner Problem: Model, Heuristics and Decomposition

This is the first prize winning report for the 2012 INFORMS Railway Application Section Problem Solving Competition (https://www.informs.org/Community/RAS/Problem-Solving-Competition/2012-RAS-Problem-Solving-Competition). ArticleDownload View PDF

Complexity of Routing Problems with Release Dates and Deadlines

The desire of companies to offer same-day delivery leads to interesting new routing problems. We study the complexity of a setting in which a delivery to a customer is guaranteed to take place within a pre-specified time after the customer places the order. Thus, an order has a release date (when the order is placed) … Read more

A CVaR Scenario-based Framework: Minimizing Downside Risk of Multi-asset Class Portfolios

Multi-asset class (MAC) portfolios can be comprised of investments in equities, fixed-income, commodities, foreign-exchange, credit, derivatives, and alternatives such as real-estate and private equity. The return for such {\em non-linear} portfolios is {\em asymmetric} with significant tail risk. The traditional Markowitz Mean-Variance Optimization (MVO) framework, that linearizes all the assets in the portfolio and uses … Read more

Exact Algorithms for the Chance-Constrained Vehicle Routing Problem

We study the chance-constrained vehicle routing problem (CCVRP), a version of the vehicle routing problem (VRP) with stochastic demands, where a limit is imposed on the probability that each vehicle’s capacity is exceeded. A distinguishing feature of our proposed methodologies is that they allow correlation between random demands, whereas nearly all existing exact methods for … Read more