Operations research

Inspired by recent applications, this article considers the problem of an on-demand service platform (e.g., Uber, Lyft, Deliveroo) that differentiates the service of customers according to their willingness to wait and to pay. Upon arrival, customers can be admitted to the system or rejected and, at any time, service providers can be allocated to admitted customers or reserved for future customers. We formulate this problem as a continuous-time Markov decision process, which is used to find the optimal policy. Exploiting properties of the value function, we first show that the optimal policy is described by state-dependent admission thresholds and a strict priority rule with respect to service provider allocation. In a numerical study, we then compare the performance of the optimal policy with simpler policies. We find that the platform, the customers, and the service providers can benefit from a policy that controls customer admission and service provider reservation. Finally, some evidence of the robustness of the policy is provided in two extensions. 

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 collaboration is a promising avenue to improve the efficiency of logistical operations. However, the benefits strongly depend on the degree of fit between partners. In this paper, we analyze the impact of the partners’ product characteristics on those benefits, focusing on their innovativeness. Companies supplying functional versus innovative products have different requirements in supply chain efficiency and responsiveness, which impacts the benefits that can be reached with a given partner. To assess the collaborative benefits, we use a location–inventory model accounting for the partners’ individual interests and the costs revealing the responsiveness level of the supply chain (facilities, transportation, cycle inventory, safety stocks and stock-outs). The model offers a set of Pareto-optimal solutions balancing the partners’ costs to support the selection and negotiation process. Finally, we perform numerical experiments in which the partners supply products with identical or different levels of innovativeness and with various demand volumes, leading to valuable managerial insights on the impact of product characteristics on collaborative benefits.

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.

As customers are aware of the climate change, eco-friendly strategies have become a competitive advantage for companies. In particular, they are aiming to reduce their carbon footprint along their supply chain. In this context, substantial CO2  emissions reductions can be reached by horizontal cooperation, i.e. the collaboration of companies that work at the same level of the supply chain. In this paper, we evaluate these reductions using a location-inventory model which minimizes facility opening, transportation, cycle inventory, ordering and safety stock costs. To understand the impact of different market and partners characteristics on the CO2  emissions reductions, we compute a large set of numerical experiments, varying several key parameters (vehicles capacity, facility opening cost, inventory holding cost, order cost, demand variability and distances). Results show that horizontal cooperation reduces CO2  emissions by 16% on average. Moreover, horizontal cooperation is more effective in decreasing the carbon footprint of companies with low facility opening costs and low order costs, carrying expensive products (high unit holding cost) on a market with a high demand variability and a vast market area.