A bi-level model for the design of dynamic electricity tariffs with demand-side flexibility

This paper addresses the electricity pricing problem with demand-side flexibility. The interaction between an aggregator and the prosumers within a coalition is modeled by a Stackelberg game and formulated as a mathematical bi-level program where the aggregator and the prosumer, respectively, play the role of upper and lower decision makers with conflicting goals. The aggregator establishes the pricing scheme by optimizing the supply strategy with the aim of maximizing the profit, prosumers react to the price signals by scheduling the flexible loads and managing the home energy system to minimize the electricity bill. The problem is solved by a heuristic approach which exploits the specific model structure. Some numerical experiments have been carried out on a real test case. The results provide the stakeholders with informative managerial insights underlining the prominent roles of aggregator and prosumers

Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

In contemporary urban logistics, drones will become a preferred transportation mode for last-mile deliveries, as they have shown commercial potential and triple-bottom-line performance. Drones, in fact, address many challenges related to congestion and emissions and can streamline the last leg of the supply chain, while maintaining economic performance. Despite the common conviction that drones will reshape the future of deliveries, numerous hurdles prevent practical implementation of this futuristic vision. The sharing economy, referred to as a collaborative business model that foster sharing, exchanging and renting resources, could lead to operational improvements and enhance the cost control ability and the flexibility of companies using drones. For instance, the Amazon patent for drone beehives, which are fulfilment centers where drones can be restocked before flying out again for another delivery, could be established as a shared delivery systems where different freight carriers jointly deliver goods to customers. Only a few studies have addressed the problem of operating such facilities providing services to retail companies. In this paper, we formulate the problem as a deterministic location-routing model and derive its robust counterpart under the travel time uncertainty. To tackle the computational complexity of the model caused by the non-linear energy consumption rates in drone battery, we propose a tailored matheuristic combining variable neighborhood descent with a cut generation approach. The computational experiments show the efficiency of the solution approach especially compared to the Gurobi solver

A Selective Scheduling Problem with Sequence-dependent Setup Times: A Risk-averse Approach

This paper addresses a scheduling problem with parallel identical machines and sequence-dependent setup times in which the setup and the processing times are random parameters. The model aims at minimizing the total completion time while the total revenue gained by the processed jobs satisfies the manufacturer’s threshold. To handle the uncertainty of random parameters, we adopt a risk-averse distributionally robust approach developed based on the Conditional Value-at-Risk measure hedging against the worst-case performance. The proposed model is tested via extensive experimental results performed on a set of benchmark instances. We also show the efficiency of the deterministic counterpart of our model, in comparison with the state-of-the-art model proposed for a similar problem in a deterministic context

Energy Efficient UAV-Based Last-Mile Delivery: A Tactical-Operational Model With Shared Depots and Non-Linear Energy Consumption

In this paper, we have investigated a drone delivery problem to address the tactical decisions arising in last-mile applications where the connection with operational plans is taken into account. The problem deals with the tactical selection of a subset of FCs to launch and retrieve the drones, and the fleet sizing decisions on the optimal number of drones to be employed. We have incorporated the non-linear and load-dependent energy consumption function into the definition of a load-indexed layered network, leading to the definition of a MILP that can be efficiently solved for instances with 50 and 75 customers. There are several fruitful directions for future research. The use of shared depots implies for the drones the freedom to choose different FCs for departure and arrival. Anyway, a drawback may exist in the considered scenario, since we should have enough drones in each FC for the next period. The extension of the present model to the multi-period location routing case, where the location decisions are taken once and the routing plans are addressed within each period, is an interesting issue for future research. Moreover, the design of heuristic and self-adaptive approaches to alleviate the computational burden for larger instances deserves further attention, as well as the extension of the present model to en-route drone charging

Correction: Bruni, M.E.; Khodaparasti, S. A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing. Sustainability 2022, 14, 9978

There was an error in the original publication [...

A new combined dynamic location model for emergency medical services in fuzzy environment

In this paper, a new combined dynamic model for locating emergency vehicles and ambulance stations in an emergency medical service (EMS) system is presented. The proposed model can deal with the uncertainty in problem parameters. Meanwhile, the dynamic structure of the model enables EMS managers to adopt a horizon time planning approach. The model can also investigate the efficiency of EMS facilities, whereas the input and output parameters of stations are expressed as fuzzy numbers. To show the validity of the present work, a numerical example is also reported

A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

In contemporary urban logistics, drones will become a preferred transportation mode for last-mile deliveries, as they have shown commercial potential and triple-bottom-line performance. Drones, in fact, address many challenges related to congestion and emissions and can streamline the last leg of the supply chain, while maintaining economic performance. Despite the common conviction that drones will reshape the future of deliveries, numerous hurdles prevent practical implementation of this futuristic vision. The sharing economy, referred to as a collaborative business model that foster sharing, exchanging and renting resources, could lead to operational improvements and enhance the cost control ability and the flexibility of companies using drones. For instance, the Amazon patent for drone beehives, which are fulfilment centers where drones can be restocked before flying out again for another delivery, could be established as a shared delivery systems where different freight carriers jointly deliver goods to customers. Only a few studies have addressed the problem of operating such facilities providing services to retail companies. In this paper, we formulate the problem as a deterministic location-routing model and derive its robust counterpart under the travel time uncertainty. To tackle the computational complexity of the model caused by the non-linear energy consumption rates in drone battery, we propose a tailored matheuristic combining variable neighborhood descent with a cut generation approach. The computational experiments show the efficiency of the solution approach especially compared to the Gurobi solver

Correction: Bruni, M.E.; Khodaparasti, S. A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing. <i>Sustainability</i> 2022, <i>14</i>, 9978

There was an error in the original publication [...

A bi-level approach for last-mile delivery with multiple satellites

Last-mile delivery is regarded as an essential, yet challenging problem in city logistics. One of the most common initiatives, implemented to streamline and support last-mile activities, are satellite depots. These intermediate logistics facilities are used by companies in urban areas to decouple last-mile activities from the rest of the distribution chain. Establishing a business model that considers different stakeholders’ interests and balances the economic and operational dimensions, is still a challenge. In this paper, we introduce a novel problem that broadly covers such a setting, where the delivery to customers is managed through satellite depots. The interplay and the hierarchical relation between the problem agents are modeled in a bi-level framework. Two mathematical models and an exact solution approach, properly customized for our problem, are presented. To assess the validity of the proposed formulations and the efficiency of the solution approach, we conduct an extensive set of computational experiments on benchmark instances. In addition, we present managerial insights for a case study on parcel delivery in Turin, Italy