Drone-based delivery services have emerged as a promising innovation for enhancing last-mile logistics through faster deliveries, improved accessibility, and reduced environmental impacts. However, the successful implementation of this technology depends largely on consumer acceptance. Existing studies examining drone delivery adoption remain fragmented, often focusing on a limited set of determinants or relying on a single theoretical perspective. Accordingly, this study reviews and synthesizes the existing literature to identify the key factors influencing consumers’ attitudes toward drone-based delivery services. Drawing on insights from the Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology, Diffusion of Innovation Theory, Task–Technology Fit Theory, and Privacy Calculus Theory, an integrated conceptual framework is developed. The review identifies perceived usefulness, perceived ease of use, task–technology fit, relative advantage, speed expectancy, hedonic motivation, environmental friendliness expectancy, social influence, privacy risk, and safety risk as the principal determinants of consumer attitudes toward drone delivery services.

This paper studies a sustainable last-mile delivery problem, which is one of the most complex vehicle routing problems. The problem consists of customer parcel allocation and the routing of an electric vehicle and a heterogeneous fleet of drones to distribute parcels to customers. To optimize this problem, a Mixed-Integer Linear Programming (MILP) model is formulated with the objective of minimizing the total weighted tardiness. The model is solved using an exact approach with the CPLEX optimization solver on nine realistic problem instances ranging from 5 to 15 customers. Numerical results show that parcel delivery planning with multiple heterogeneous drones and a single EV provides better solutions than single drone-EV combinations.

This paper develops an integrated decision-making framework for tender evaluation and subcontractor selection in construction projects. The framework responds to the limitations of lowest-bid procurement by combining prequalification, Data Envelopment Analysis (DEA), fuzzy logic assessment, Fuzzy TOPSIS prioritization, and bi-objective Mixed-Integer Linear Programming (MILP). In the first stage, DEA screens subcontractors and retains only efficient candidates. In the second stage, risk and environmental exposure factors are evaluated using linguistic judgments and fuzzy logic. Fuzzy TOPSIS is used as a supporting prioritization check to compare ordering of efficient subcontractors under multiple criteria. In the final stage, a bi-objective MILP model selects the subcontractor or subcontractors and determines the quantity of work assigned to each selected subcontractor by minimizing total cost and total risk. A numerical UAE construction tendering case application demonstrates how the framework supports transparent award decisions and helps tender committees evaluate cost-risk trade-offs before contract award.