A MIXED INTEGER LINEAR PROGRAMMING APPROACH FOR DEVELOPING SALARY ADMINISTRATION SYSTEMS

Determining salary increases of executive personnel is a challenging decision process for many companies. Salary administration policies that aid in the determination of salary increases and other compensation benefits have a wide variety of advantages for both a company and its employees. This thesis develops a mathematical programming approach to create a salary administration system that recognizes the importance of performance and potential of employees for future promotions as major components of a salary increase policy for executive personnel. A number of companies all over the world use salary administration systems that integrate work performance and potential for advancement to develop compensation packages that include benefits in addition to the base salary of their executive personnel. Some of these systems aid decision making concerning salary increase percentages, frequency of salary increases, and guidelines for promotion. The specific policy considered in this thesis assigns salary increase categories and intervals between successive salary increases based on performance and potential assessments. Two mixed integer linear programming models are formulated to assign personnel to salary increase categories and to determine intervals. Solution procedures are clearly illustrated based on a hypothetical application. The optimization toolbox of the commercial software, MATLAB, is used as the problem solver

Electric Vehicle Supply Equipment Location and Capacity Allocation for Fixed-Route Networks

Electric vehicle (EV) supply equipment location and allocation (EVSELCA) problems for freight vehicles are becoming more important because of the trending electrification shift. Some previous works address EV charger location and vehicle routing problems simultaneously by generating vehicle routes from scratch. Although such routes can be efficient, introducing new routes may violate practical constraints, such as drive schedules, and satisfying electrification requirements can require dramatically altering existing routes. To address the challenges in the prevailing adoption scheme, we approach the problem from a fixed-route perspective. We develop a mixed-integer linear program, a clustering approach, and a metaheuristic solution method using a genetic algorithm (GA) to solve the EVSELCA problem. The clustering approach simplifies the problem by grouping customers into clusters, while the GA generates solutions that are shown to be nearly optimal for small problem cases. A case study examines how charger costs, energy costs, the value of time (VOT), and battery capacity impact the cost of the EVSELCA. Charger costs were found to be the most significant component in the objective function, with an 80\% decrease resulting in a 25\% cost reduction. VOT costs decrease substantially as energy costs increase. The number of fast chargers increases as VOT doubles. Longer EV ranges decrease total costs up to a certain point, beyond which the decrease in total costs is negligible

A Time-Constrained Capacitated Vehicle Routing Problem in Urban E-Commerce Delivery

Electric vehicle routing problems can be particularly complex when recharging must be performed mid-route. In some applications such as the e-commerce parcel delivery truck routing, however, mid-route recharging may not be necessary because of constraints on vehicle capacities and maximum allowed time for delivery. In this study, we develop a mixed-integer optimization model that exactly solves such a time-constrained capacitated vehicle routing problem, especially of interest to e-commerce parcel delivery vehicles. We compare our solution method with an existing metaheuristic and carry out exhaustive case studies considering four U.S. cities -- Austin, TX; Bloomington, IL; Chicago, IL; and Detroit, MI -- and two vehicle types: conventional vehicles and battery electric vehicles (BEVs). In these studies we examine the impact of vehicle capacity, maximum allowed travel time, service time (dwelling time to physically deliver the parcel), and BEV range on system-level performance metrics including vehicle miles traveled (VMT). We find that the service time followed by the vehicle capacity plays a key role in the performance of our approach. We assume an 80-mile BEV range as a baseline without mid-route recharging. Our results show that BEV range has a minimal impact on performance metrics because the VMT per vehicle averages around 72 miles. In a case study for shared-economy parcel deliveries, we observe that VMT could be reduced by 38.8\% in Austin if service providers were to operate their distribution centers jointly

Redesigning Large-Scale Multimodal Transit Networks with Shared Autonomous Mobility Services

Public transit systems have faced challenges and opportunities from emerging Shared Autonomous Mobility Services (SAMS). This study addresses a city-scale multimodal transit network design problem, with shared autonomous vehicles as both transit feeders and a direct interzonal mode. The framework captures spatial demand and modal characteristics, considers intermodal transfers and express services, determines transit infrastructure investment and path flows, and designs transit routes. A system-optimal multimodal transit network is designed with minimum total door-to-door generalized costs of users and operators, while satisfying existing transit origin-destination demand within a pre-set infrastructure budget. Firstly, the geography, demand, and modes in each clustered zone are characterized with continuous approximation. Afterward, the decisions of network link investment and multimodal path flows in zonal connection optimization are formulated as a minimum-cost multi-commodity network flow (MCNF) problem and solved efficiently with a mixed-integer linear programming (MILP) solver. Subsequently, the route generation problem is solved by expanding the MCNF formulation to minimize intramodal transfers. To demonstrate the framework efficiency, this study uses transit demand from the Chicago metropolitan area to redesign a multimodal transit network. The computational results present savings in travelers' journey time and operators' costs, demonstrating the potential benefits of collaboration between multimodal transit systems and SAMS.Comment: 44 pages, 15 figures, under review for the 25th International Symposium on Transportation and Traffic Theory (ISTTT25

Large-Scale Dynamic Ridesharing with Iterative Assignment

Transportation network companies (TNCs) have become a highly utilized transportation mode over the past years. At their emergence, TNCs were serving ride requests one by one. However, the economic and environmental benefits of ridesharing encourages them to dynamically pool multiple ride requests to enable people to share vehicles. In a dynamic ridesharing (DRS) system, a fleet operator seeks to minimize the overall travel cost while a rider desires to experience a faster (and cheaper) service. While the DRS may provide relatively cheaper trips by pooling requests, the service speed is contingent on the objective of the vehicle-to-rider assignments. Moreover, the operator must quickly assign a vehicle to requests to prevent customer loss. In this study we develop an iterative assignment (IA) algorithm with a balanced objective to conduct assignments quickly. A greedy algorithm from the literature is also tailored to further reduce the computational time. The IA was used to measure the impact on service quality of fleet size; assignment frequency; the weight control parameter of the two objectives on vehicle occupancy -- rider wait time and vehicle hours traveled. A case study in Austin, TX, reveals that the key performance metrics are the most sensitive to the weight parameter in the objective function

Economic Feasibility of Adopting Additive Manufacturing for Low-volume Demand

Additive manufacturing (also known as 3-D printing) is in its infancy. Although 3D-printers have been long existed as an alternative manufacturing technology to the conventional manufacturing (casting, molding, etc.), they were being used to prototype products rapidly hence the technology was referred to rapid prototyping. Recently, the AM technology has been adopted by enterprises to source low-volume demand products, such as spare parts and obsolete parts. Although the economical benefits of the technology for such products have not been theoretically measured, many enterprises started to adopt the technology. Under this research framework, we partially fill the gap in the literature and propose mathematical models and their sound solution methods to address the economic feasibility of AM adoption for low-volume demand. To this end, we first focus on a single enterprise and propose a mathematical model to determine the optimal amount of investment by partitioning products into two sourcing options, namely, AM option and inventory option. Then, we extend our research to answer more questions for a multi-facility enterprise. In this extended version, we determine the optimal location to deploy AM capacity while still assessing the viability and extent of the investment. Our study contributes to the literature by providing a generally applicable solution approach to mixed integer non-linear programs involving queueing non- linearity. On the other hand, we present useful managerial insights to aid practitioners in decision-making about AM adoption