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e‐Commerce activity has been increasing during recent years, and this trend is expected to continue in the near future. e‐Commerce practices are subject to uncertainty conditions and high variability in customers’ demands. Considering these characteristics, we propose two facility–location models that represent alternative distribution policies in e‐commerce (one based on outsourcing and another based on in‐house distribution). These models take into account stochastic demands as well as more than one regular supplier per customer. Two methodologies are then introduced to solve these stochastic versions of the well‐known capacitated facility–location problem. The first is a two‐stage stochastic‐programming approach that uses an exact solver. However, we show that this approach is not appropriate for tackle large‐scale instances due to the computational effort required. Accordingly, we also introduce a “simheuristic” approach that is able to deal with large‐scale instances in short computing times. An extensive set of benchmark instances contribute to illustrate the efficiency of our approach, as well as its potential utility in modern e‐commerce practices.

Many of the existing electricity markets are of the mixed type, which has pool auction and bilateral contracts between producers and distributors. In this case, the problem faced by a Generation Company (GenCo) is that of maximizing the revenues from participating in the market through the pool auction while honoring the bilateral contracts agreed, for which the revenue is fixed.The extension to mixed markets of a medium-term model, successfully employed for auction-only markets, is presented. It results in a non-convex expected revenue function to be maximized subject to constraints, for which the currently available direct global-optimization solvers prove not to be efficient enough. A heuristic procedure based on a sequence of solutions by a nonlinear solver is presented, and numerical results obtained with several realistic cases show satisfactory results. The test cases presented have dispatchable and non-dispatchable renewables and consider medium-term pumping together with conventional units by all GenCos participating in the mixed market.The advantages for GenCos of employing medium-term results as those produced by the model presented, include, among others, the evaluation of the expected profitability of their bilateral contracts.

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This paper presents a bi-objective model for optimizing pig deliveries to the abattoir accounting for total revenue and CO 2 emissions. Fattening farms house the most important stage in pig production, and operations on farms must be coordinated with the rest of the pig supply chain when batch management is generally applied. The novelty of the model lies in the change of attitude in producers towards a greener production, which is becoming one of the major concerns in our society. In this context, we enrich the classical approach focused on revenues with the addition of the CO 2 emissions from the pigs on the fattening farms. Emissions derived from feeding and transportation are considered since they are the most important sources of CO 2 . The model is tested using parameters representing a typical integrated Spanish fattening farm. Our findings reveal the impact and the relationship between revenues and emissions, highlight that the break-even is reached achieving 459 kg of CO 2 per pig, which corresponds to a reduction of 6.05%. On the other hand, the profit is slightly reduced by 4.48% in favor of the environment.

Carbon capture and sequestration is a possible technology for abating carbon dioxide emissions. This is costly and requires investment in capture, transportation and storage facilities, and compensation for possibly substantial operational cost at these facilities. On the other hand, this option avoids buying carbon offsets, and the CO 2 may in some cases be used for enhanced oil recovery. Stochastic dynamic programming is applied to perform the underlying investment analysis, that is, to decide whether investment on a CO 2 value chain is profitable, and if so, then when the decisions should be taken. The oil and CO 2 prices are modelled as stochastic processes. As a case study we consider possible CO 2 value chain investments on the Norwegian Continental Shelf.