Maryam Torabbeigi

Bio: Maryam Torabbeigi is an academic researcher from University of Houston. The author has contributed to research in topics: Drone & Job shop scheduling. The author has an hindex of 4, co-authored 5 publications receiving 61 citations.

Drone Delivery Scheduling Optimization Considering Payload-induced Battery Consumption Rates

Abstract: This paper addresses the design of a parcel delivery system using drones, which includes the strategic planning of the system and operational planning for a given region. The amount of payload affects the battery consumption rate (BCR), which can cause a disruption in delivery of goods if the BCR was under-estimated in the planning stage or cause unnecessarily higher expenses if it was over-estimated. Hence, a reliable parcel delivery schedule using drones is proposed to consider the BCR as a function of payload in the operational planning optimization. A minimum set covering approach is used to model the strategic planning and a mixed integer linear programming problem (MILP) is used for operational planning. A variable preprocessing algorithm and primal and dual bound generation methods are developed to improve the computational time for solving the operational planning model. The optimal solution provides the least number of drones and their flight paths to deliver parcels while ensuring the safe return of the drones with respect to the battery charge level. Experimental data show that the BCR is a linear function of the payload amount. The results indicate the impact of including the BCR in drone scheduling, 3 out of 5 (60%) flight paths are not feasible if the BCR is not considered. The numerical results show that the sequence of visiting customers impacts the remaining charge.

Drone delivery schedule optimization considering the reliability of drones

Abstract: We consider a delivery network using drones Drones can fail during the flight and such drone failures can result in loss of demand and consumer satisfaction Therefore, we consider the reliability of drones in a delivery network to minimize expected loss of demand (ELOD) The more reliable the network is, the less amount of demand will be lost We assume drone failures follow an exponential distribution A two-stage programming is proposed: the first stage provides a pool solution of feasible paths and the second stage provides the most reliable scheduling The proposed procedure is implemented for a case study and the results show the impact of including reliability in drone scheduling

A Rescheduling Method of Drone Flights under Insufficient Remaining Battery Duration

Abstract: A rescheduling method is proposed for drone flights to handle insufficient remaining battery duration during flights. The flight of drones is usually affected by strong winds and/or moving obstacles. To keep a planned flight path, drones are imposed to consume more battery to overcome these unexpected external events. It results in insufficient battery duration, which cannot guarantee the safe return of drones and/or the fulfillment of the assigned mission (demands). Hence, the rescheduling method is presented to ensure the safe return of drones and minimize the unmet demand.

Collision-Free Multi-UAV Flight Scheduling for Power Network Damage Assessment

Abstract: This paper discusses the unmanned aerial vehicles (UAVs) flight scheduling problem in the context of power networks damage assessment. We propose a mixed-integer programming model to determine the optimal collision-free schedule for multiple UAVs. It is essential to perform the damage assessment procedure in the least amount of time in order to quickly repair the network. Hence, our goal is to minimize the makespan which equals the total operation time of the UAVs until the last task (i.e., the lengthiest) is complete. As the power networks are complex structures, there is a high probability of collision among the inspecting UAVs. To address this issue, we separate the arrival times of two UAVs so that no more than one UAV can be around each node at a given time interval. Using the proposed model, the optimal flight scheduling is provided for a randomly generated graph instance. The results of the numerical instance indicate that our approach reduces the possibility of collision between UAVs by creating a gap between their arrival time to each node.

An Optimization Approach to Minimize the Expected Loss of Demand Considering Drone Failures in Drone Delivery Scheduling

Abstract: This study proposes a drone-based delivery schedul- ing method considering drone failures to minimize the expected loss of demand (ELOD). An optimization model (DDS-F) is developed to determine the assignment of each drone to a subset of customers and the corresponding delivery sequence. Because solving the optimization model is computationally challenging, a Simulated Annealing (SA) heuristic algorithm is developed to reduce the computational time. The proposed SA features a fast initial solution generation based on the Petal algorithm, a binary integer programming model for path selection, and a local neigh- borhood search algorithm to find better solutions. Numerical results showed that the proposed approach outperformed the well-known Makespan problem in reducing the ELOD by 23.6% on a test case. Several case studies are conducted to illustrate the impact of the failure distribution function on the optimal flight schedules. Furthermore, the proposed approach was able to obtain the exact solutions for the test cases studied in this paper. Numerical results also showed the efficiency of the proposed algorithm in reducing the computational time by 44.35%, on average, compared with the exact algorithm.

Last-mile delivery concepts: a survey from an operational research perspective

Abstract: In the wake of e-commerce and its successful diffusion in most commercial activities, last-mile distribution causes more and more trouble in urban areas all around the globe. Growing parcel volumes to be delivered toward customer homes increase the number of delivery vans entering the city centers and thus add to congestion, pollution, and negative health impact. Therefore, it is anything but surprising that in recent years many novel delivery concepts on the last mile have been innovated. Among the most prominent are unmanned aerial vehicles (drones) and autonomous delivery robots taking over parcel delivery. This paper surveys established and novel last-mile concepts and puts special emphasis on the decision problems to be solved when setting up and operating each concept. To do so, we systematically record the alternative delivery concepts in a compact notation scheme, discuss the most important decision problems, and survey existing research on operations research methods solving these problems. Furthermore, we elaborate promising future research avenues.

Applications and Research avenues for drone-based models in logistics: A classification and review

Abstract: The operational design and planning of drone-based logistics models is a rapidly growing area of scientific research. In this paper, we present a structured, comprehensive, and scalable framework for classifying drone-based delivery systems and their associated routing problems along with a comprehensive review and synthesis of the extant academic literature in this domain. While our proposed classification defines the boundaries and facilitates the comparison between a wide variety of possible drone-based logistics systems, our comprehensive literature review helps to identify and prioritize research gaps that need to be addressed by future work. Our review shows that the extant research reasonably considers some relevant real-world operational constraints. Although the multi-visit multi-drone Pure-play Drone-based (PD) delivery models are popular, the majority of the Synchronized Multi-modal (SM) delivery models focus on formulating and evaluating single-truck, single-drone models. Moreover, the Resupply Multi-modal (RM) models have not received the due attention for research compared to other drone-based delivery models. Our comprehensive review of use cases of drones for delivery indicates that most of the reviewed models are designed for applications in e-commerce and healthcare/emergency services. Other applications, such as food and mail deliveries are still underrepresented in the academic discussion.

Drone Delivery Scheduling Optimization Considering Payload-induced Battery Consumption Rates

Abstract: This paper addresses the design of a parcel delivery system using drones, which includes the strategic planning of the system and operational planning for a given region. The amount of payload affects the battery consumption rate (BCR), which can cause a disruption in delivery of goods if the BCR was under-estimated in the planning stage or cause unnecessarily higher expenses if it was over-estimated. Hence, a reliable parcel delivery schedule using drones is proposed to consider the BCR as a function of payload in the operational planning optimization. A minimum set covering approach is used to model the strategic planning and a mixed integer linear programming problem (MILP) is used for operational planning. A variable preprocessing algorithm and primal and dual bound generation methods are developed to improve the computational time for solving the operational planning model. The optimal solution provides the least number of drones and their flight paths to deliver parcels while ensuring the safe return of the drones with respect to the battery charge level. Experimental data show that the BCR is a linear function of the payload amount. The results indicate the impact of including the BCR in drone scheduling, 3 out of 5 (60%) flight paths are not feasible if the BCR is not considered. The numerical results show that the sequence of visiting customers impacts the remaining charge.

Reliable Path Planning for Drone Delivery Using a Stochastic Time-Dependent Public Transportation Network

Abstract: Drones have been regarded as a promising means for future delivery industry by many logistics companies. Several drone-based delivery systems have been proposed but they generally have a drawback in delivering customers locating far from warehouses. This paper proposes an alternative system based on a public transportation network. This system has the merit of enlarging the delivery range. As the public transportation network is actually a stochastic time-dependent network, we focus on the reliable drone path planning problem (RDPP). We present a stochastic model to characterize the path traversal time and develop a label setting algorithm to construct the reliable drone path. Furthermore, we consider the limited battery lifetime of the drone to determine whether a path is feasible, and we account this as a constraint in the optimization model. To accommodate the feasibility, the developed label setting algorithm is extended by adding a simple operation. The complexity of the developed algorithm is analyzed and how it works is demonstrated via a case study.