An Intelligent Decision Support System for the Empty Unit Load Device Repositioning Problem in Air Cargo Industry

Unit load devices (ULDs) are containers and pallets used in the air cargo industry to bundle freight for efficient loading and transportation. Mainly due to imbalances in global air transportation networks, deficits and surpluses of ULDs are the result and require stock balancing through the repositioning of (empty) ULDs. Following a design science research approach, we (1) elaborate the hitherto uninvestigated problem class of empty ULD repositioning (EUR) and (2) propose an intelligent decision support system (IDSS) that incorporates a heuristic for the given problem and combines artificial intelligence (i.e., rule-based expert system technology) with business analytics. We evaluate the IDSS with real-world data and demonstrate that the proposed solution is both effective and efficient. In addition, our results provide empirical evidence regarding the positive economic and ecological impact of leveraging the potential of ULD pooling in multi-carrier networks

A Lagrangian Approach for The Airfreight Consolidation Problem Under Pivot-weight

International airfreight forwarders are faced with the problem of consolidating ship- ments for efficient transportation by airline carriers. The use of standard unit loading devices (ULDs) is a solution adopted by the airfreight industry to speed up cargo loading, increase safety, and protect cargo. We study the airfreight consolidation problem from the forwarders perspective where a decision on the number of ULDs used and the assignment of shipments to ULDs is optimized. The cost of using a ULD consists of a fixed charge and depends on the weight of the cargo it contains. A ULD is charged at an under-pivot rate if the total weight is below a threshold limit, called the pivot-weight. Additional weight is charged at the over-pivot rate. We propose a solution methodology based on Lagrangian relaxation that is capable of providing high quality solutions in reasonable computational times. Besides, a high-quality lower bound, we propose three heuristics to generate feasible solutions, all based on the solution of the subproblems. The first, takes the solution of one of the subproblems and solves a restricted version of the original problem (LagHeur). The other two heuristics are a heuristic based on solving two knapsack problems (2knap) and a best-fit greedy heuristic (bestfit). Problems with up to 100 ULDs and 1000 shipments are solved to within an average of 1%, 2%, 2% of optimality in less than 51.05s, 50.57s and 589.16s by bestfit, 2knap and LagHeur, respectively

Drone location and scheduling problems in humanitarian logistics.

Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, are a promising technology for the last-mile delivery of medical and aid items in humanitarian logistics. In emergency scenarios, like disasters, where transportation networks are destroyed and people are stranded, drones can accelerate the delivery of urgently needed items, e.g., food and water, insulin shots and blood pressure pills, to those trapped in the disaster-affected areas. Drones can also provide logistics services in many non-emergency situations by delivering medical items, e.g., vaccine shots and lab specimens, to remote communities and hard-to-access locations. The contribution of using UAVs goes beyond merely having access to remote and disaster-affected areas. With inexpensive launching infrastructures and no need for on-board pilots, drones can offer an inexpensive, agile, and ready-to-use alternative to traditional last-mile delivery modes. Motivated by the challenges associated with the last-mile delivery of aid items to hard-to-access areas, this dissertation studies the problem of drone-based delivery of aid items, e.g., medical and relief packages, to hard-to-access areas in humanitarian logistics. The main goal of this dissertation is to design the logistics and orchestrate a fleet of drones to provide the timely delivery of items to hard-to-access areas in emergency and non-emergency scenarios. In this dissertation, we develop multiple extensions of a drone location and scheduling problem while taking into account the critical aspects of drone- based delivery systems in humanitarian logistics. These critical aspects include: i) limited coverage range, ii) limited payload capacity, iii) energy consumption, iv) timeliness, and v) uncertainty. Chapter II presents a general case of the drone location and scheduling (DLS) problem for the delivery of aid items in disaster-affected areas. In this chapter, we first develop a time-slot formulation to address the problem of optimally locating drone take- off platforms and concurrently scheduling and sequencing a set of trips for each drone to minimize total disutility for product delivery. We extend a two-period problem of DLS where the platforms can be relocated using useable road networks after the first period in order to provide a higher level of coverage. Chapter III proposes a multi-stop drone location and scheduling (MDLS) problem for the delivery of medical items in rural and suburban areas. In this chapter, we assume drones are allowed to stop at one or multiple charging stations, installed on existing platforms having access to electricity, e.g., streetlights, during each trip in order to improve the drones’ coverage range while considering the drones’ energy consumption. The problem is to find optimum locations for medical item providers and charging stations as well as optimally scheduling and sequencing drone trips over a long-term horizon. Chapter IV presents a stochastic extension for the drone location and scheduling (SDLS) problem. Due to the lack of information and instability of the situation, we assume the set of demand locations is not known. The main problem is to locate a set of drone take-off platforms so that with a given probability, the maximum total disutility (or cost) under all realizations of the demand locations is minimized. Finally, Chapter V presents a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas in humanitarian logistics. Our goal is to develop a simulation-based system to perform analytical/numerical studies, evaluate the performance of a drone delivery system in humanitarian logistics, and support the decision-making process in such a context while considering multiple sources of variabilities