Operations research

Priority service and multilevel demand subscription have been proposed as two alternative methods for the mobilization of residential demand response. Whereas priority service relies on the differentiation of electricity service according to reliability, multilevel demand subscription further differentiates electricity service according to duration. Despite its increased complexity, multilevel demand subscription promises increased operational efficiency, as it permits a finer differentiation of consumer classes by the utility. It also allows households to reduce their electricity bills relative to priority service. This paper proposes a framework for quantifying these effects. We design a modeling approach for evaluating the performance of these different aggregator service offerings in a system with utility-scale renewable supply, residential renewable supply, and residential storage. We compare priority service to multilevel demand subscription, and discuss the implications of these different residential demand response options on operational efficiency and consumer expenditures for electricity service on a realistic model of the Belgian power market. We show how the comparison between the two schemes is affected by the adoption of a different time resolution in a detailed case study.

Horizontal cooperation is a promising avenue to improve the efficiency of logistical operations, as shown in the literature with real-life cases and experimental studies. In this paper, we analyse the benefits of a joint supply network for the various stages of the supply chain, parse the contribution of the various cost components and assess the impact of the markets and partners characteristics. For this, we present a collaborative location-inventory model which minimizes facility opening and transportation costs, as well as inventory costs at distribution centers and at retailers. It allows us to investigate the impact of inventory decisions and of the vehicle loading rate on the collaboration benefits. Our extensive set of experiments shows that horizontal cooperation leads to significant savings for all the supply chain stages, with an average total cost reduction of 22.4%. The benefits for the retailers, which are rarely discussed in the current literature, are significant with a reduction of the cycle inventory of 43%, deliveries almost twice more frequent and safety stock reduced by 7% on average. Collaboration shows to be particularly profitable for companies with high facility opening costs and low order costs, carrying small products (compared to the vehicle capacity) with a low unit holding cost, in a market with a low demand variability.

Horizontal cooperation in logistics has gathered momentum in the last decade as a way to reach economic as well as environmental benefits. In the literature, these benefits are most often assessed by aggregating all demand and then optimizing the supply chain at the level of the coalition. However, such an approach ignores the individual preferences of the participating companies and forces them to agree on a unique coalition objective. Companies with different (potentially conflicting) preferences could improve their individual outcome by diverging from this joint solution. In order to prevent such individualistic behavior, we propose an optimization framework that explicitly accounts for the individual partners’ interests. In the models presented in this paper, all partners are allowed to specify their preferences regarding the decrease in logistical costs versus reduced CO2 emissions. Consequently, all stakeholders are more likely to accept the solution, and the long-term viability of the collaboration is improved. The contribution of our work is threefold. First, we formulate a multi-partner multi-objective location-inventory model. Second, we distinguish two approaches to solve such a multi-partner multi-objective optimization problem, each focusing primarily on a single dimension. The result is a set of Pareto-optimal solutions that support the decision and negotiation process. Third, we propose and compare three different solution techniques to construct a unique solution which is fair and efficient for the coalition. Our numerical experiments not only confirm the potential of collaboration but—more importantly—also reveal valuable managerial insights on the effect of dissimilarities between partners with respect to size, geographical overlap and operational preferences.

The Express Shipment Service Network Design (ESSND) problem consists in defining a network of flights that enables the overnight flow of express packages from their origins to their destinations at minimum cost. This problem is normally solved considering only one-leg, multi-leg and ferry routes. Assessing the value of more complex route types is an open question of academic and practical importance. In this article, we present a mixed integer programming model that includes five types of complex routes: two-hub, transload, direct, inter-hub and early routes. We assess their economic impact by performing many experiments built from an instance provided by FedEx Express Europe. Inter-hub and early routes have the best performance, with significant average savings (from 0.5% to 3.5%).