Optimal Fleet Size And Mix For A Rental Car Company

In this paper, a linear programming model for optimizing the fleet size and mix for a rental car company is developed and solved. Rental car companies depend on their fleet of vehicles for generating the entirety of their income. Additionally, the investments required are typically very significant due to the high cost of vehicles. Consequently, the composition of the fleet could significantly affect the company’s profitability and sustainability in a volatile demand environment. Determining the optimal fleet size and mix has been the focus of research in particular in revenue and yield management and VRP streams. However, most models focused on cost minimization without taking into account the resale value of vehicles once retired from the fleet. This paper addresses the problem from a return maximization perspective while taking into account resale values of vehicles. Sensitivity analysis is carried out to gain further insight into the problem and enable the model to support the company’s management in refining the strategic plan

Transportation-mission-based Optimization of Heterogeneous Heavy-vehicle Fleet Including Electrified Propulsion

Commercial-vehicle manufacturers design vehicles to operate over a wide range of transportation tasks and driving cycles. However, certain possibilities of reducing emissions, manufacturing and operational costs from end vehicles are neglected if the target range of transportation tasks is narrow and known in advance, especially in case of electrified propulsion. Apart from real-time energy optimization, vehicle hardware can be meticulously tailored to best fit a known transportation task. As proposed in this study, a heterogeneous fleet of heavy-vehicles can be designed in a more cost- and energy-efficient manner, if the coupling between vehicle hardware, transportation mission, and infrastructure is considered during initial conceptual-design stages. To this end, a rather large optimization problem was defined and solved to minimize the total cost of fleet ownership in an integrated manner for a real-world case study. In the said case-study, design variables of optimization problem included mission, recharging infrastructure, loading--unloading scheme, number of vehicles of each type, number of trips, vehicle-loading capacity, selection between conventional, fully electric, and hybrid powertrains, size of internal-combustion engines and electric motors, number of axles being powered, and type and size of battery packs. This study demonstrated that by means of integrated fleet customization, battery-electric heavy-vehicles could strongly compete against their conventional combustion-powered counterparts. Primary focus has been put on optimizing vehicle propulsion, transport mission, infrastructure and fleet size rather than routing

Integrated Districting, Fleet Composition, and Inventory Planning for a Multi-Retailer Distribution System

We study an integrated districting, fleet composition, and inventory planning problem for a multi-retailer distribution system. In particular, we analyze the districting decisions for a set of retailers such that the retailers within the same district share truck capacity for their shipment requirements. The number of trucks of each type dedicated to a retailer district and retailer inventory planning decisions are jointly determined in a district formation problem. We provide a mixed-integer-nonlinear programming formulation for this problem and develop a column generation based heuristic approach for its set partitioning formulation. To do so, we first characterize important properties of the optimal fleet composition and inventory planning decisions for a given retailer district. Then, we utilize these properties within a branch-and-price method to solve the integrated districting, fleet composition, and inventory planning problem. A set of numerical studies demonstrates the efficiency of the solution methods discussed for the investigated subproblems. An additional set of numerical studies compares the branch-and-price method to a commercial solver and an evolutionary heuristic method. Further numerical studies illustrate the economic as well as environmental benefits of the integrated modeling approach for various settings

Implementación del planeamiento de requerimiento de materiales (MRP) y mejora en la eficacia de la gestión de inventarios en la empresa 3C Group Módulos y Carpas S. A. C.

El presente trabajo de investigación tuvo como objetivo principal implementar la herramienta de planeación de requerimiento de materiales (MRP) para mejorar la eficacia en la gestión de inventarios de la Empresa privada 3C Group Módulos y Carpas S.A.C. 2022, basado en la problemática actual que es la falta de atenciones inmediatas o despachos de los requerimientos de los clientes por falta de stock de productos con mayor frecuencia de salida, causando así pérdidas en ventas de un promedio del 20% del total de las órdenes de compra (OC) por desestimación o cancelación del pedido de los productos ferreteros por parte del cliente por el grado de importancia o urgencia de los productos solicitados. Siendo así identificado mediante el diagrama de Ishikawa y una reunión con colaboradores de diferentes áreas de la empresa para dar a conocer sus puntos de vista de la coyuntura actual. Con la aplicación de esta herramienta MRP la compañía tendrá un impacto directo con el área de compras planificando el abastecimiento adecuado del almacén y aumentar sus atenciones inmediatas mejorando la eficacia que los clientes solicitan, dando así como resultado mayores ganancias y utilidades, así mismo se podrán reducir los costos de inventario al contar con un plan de compras al por mayor en mercado nacional o de importación, con lo que se incrementó la eficacia de la gestión de inventarios en 90.9%, para una mejora de 12.3%

Lot-Sizing of Several Multi-Product Families

Production planning problems and its variants are widely studied in operations management and optimization literature. One variation that has not garnered much attention is the presence of multiple production families in a coordinated and capacitated lot-sizing setting. While its single-family counterpart has been the subject of many advances in formulations and solution techniques, the latest published research on multiple family problems was over 25 years ago (Erenguc and Mercan, 1990; Mercan and Erenguc, 1993). Chapter 2 begins with a new formulation for this coordinated capacitated lot-sizing problem for multiple product families where demand is deterministic and time-varying. The problem considers setup and holding costs, where capacity constraints limit the number of individual item and family setup times and the amount of production in each period. We use a facility location reformulation to strengthen the lower bound of our demand-relaxed model. In addition, we combine Benders decomposition with an evolutionary algorithm to improve upper bounds on optimal solutions. To assess the performance of our approach, single-family problems are solved and results are compared to those produced by state-of-the-art heuristics by de Araujo et al. (2015) and Süral et al. (2009). For the multi-family setting, we first create a standard test bed of problems, then measure the performance of our heuristic against the SDW heuristic of Süral et al. (2009), as well as a Lagrangian approach. We show that our Benders approach combined with an evolutionary algorithm consistently achieves better bounds, reducing the duality gap compared to other single-family methods studied in the literature. Lot-sizing problems also exist within a vendor-managed-inventory setting, with production-planning, distribution and vehicle routing problems all solved simultaneously. By considering these decisions together, companies achieve reduced inventory and transportation costs compared to when these decisions are made sequentially. We present in Chapter 3 a branch-and-cut algorithm to tackle a production-routing problem (PRP) consisting of multiple products and customers served by a heterogeneous fleet of vehicles. To accelerate the performance of this algorithm, we also construct an upper bounding heuristic that quickly solves production-distribution and routing subproblems, providing a warm-start for the branch-and-cut procedure. In four scenarios, we vary the degree of flexibility in demand and transportation by considering split deliveries and backorders, two settings that are not commonly studied in the literature. We confirm that our upper bounding procedure generates high quality solutions at the root node for reasonably-sized problem instances; as time horizons grow longer, solution quality degrades slightly. Overall costs are roughly the same in these scenarios, though cost proportions vary. When backorders are not allowed (Scenarios 1 and 3), inventory holding costs account for over 90% of total costs and transportation costs contribute less than 0.01%. When backorders are allowed (Scenarios 2 and 4), most of the cost burden is shouldered by production, with transportation inching closer to 0.1% of total costs. In our fifth scenario for the PRP with multiple product families, we employ a decomposition heuristic for determining dedicated routes for distribution. Customers are clustered through k-means++ and a location-alloction subproblem based on their contribution to overall demand, and these clusters remain fixed over the entire planning horizon. A routing subproblem dictates the order in which to visit customers in each period, and we allow backorders in the production-distribution routine. While the branch-and-cut algorithm for Scenarios 1 through 4 quickly finds high quality solutions at the root node, Scenario 5's dedicated routes heuristic boasts high vehicle utilization and comparable overall costs with minimal computational effort