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Panamerican University, Mexico

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6 Papers

An Airline Profit Management Model with Overbooking and No-Shows

Topics: Optimization; Linear Programming; Resource Allocation; OR in Transportation

Authors: Elias Olivares-Benitez ¹ , Ana Esparza ² , Juan Orejel ¹ and Catya Zuniga ³

Affiliations: ¹ Faculty of Engineering, Universidad Panamericana, Alvaro del Portillo 49, Zapopan 45010, Mexico , ² Faculty of Business Administration and Economics, European University Viadrina, Frankfurt (Oder), Germany , ³ Faculty of Technology, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands

Digital Object Identifier: 10.5220/0013159900003893

Proceedings of ICORES 2025, ISBN: 978-989-758-732-0

Quick Abstract

This research presents a model for airline profit optimization considering information such as demand forecasts, seat inventory, operational costs, overbooking penalties, expected no-shows, and time-dependent fare classes. The main decisions in the model are the selection of the aircraft, the number of seats sold per fare, including overbooking, and the number of denied seats. The model incorporates probabilistic information, like the expected demand and the expected proportion of no-shows. The model is constructed as a deterministic mixed-integer program. Some data was estimated using information acquired from different industry sources, and some data was set with reasonable estimations. A factorial experiment was designed to understand the importance of different parameters. The input variables were the overbooking compensation penalty, the no-show probabilities per fare and time block, and the seat demand. Using a statistical analysis, it was determined that the no-show estimation has the most significant impact on the total revenue, and the demand forecast after that. These results highlight the importance of precise estimations to increase the airline’s profit.

Exploration of a Generalized Benders Decomposition Method for Solving Project Scheduling Problems with Resource Constraints

Topics: Project Management; Optimization; Scheduling

Authors: Alfredo Ramos ¹ , Pablo Miranda-Gonzalez ² and Elias Olivares-Benitez ¹

Affiliations: ¹ Facultad de Ingeniería, Universidad Panamericana, Alvaro del Portillo 49, Zapopan, Jalisco, Mexico , ² Departamento de Ingeniería Industrial, Universidad Católica del Norte, Av. Angamos 0610, Antofagasta, Chile

Digital Object Identifier: 10.5220/0013116300003893

Proceedings of ICORES 2025, ISBN: 978-989-758-732-0

Quick Abstract

This research introduces a new Generalized Benders Decomposition-based Algorithm (GBDA) to solve the Multi-Mode Resource-Constrained Project Scheduling Problem (MRCPSP). The MRCPSP is a scheduling problem that besides precedence constraints, includes renewable and non-renewable resource constraints, as well as the selection of execution modes for the project activities. This mode selection determines the resource usage and duration of each activity. The GBDA splits the problem into a Master Problem (MP) and a Sub-Problem (SP) with a relaxation. Both problems are solved alternately, each one incorporating information from the other at each iteration, until a stopping criterion is met. Additionally, at each iteration, a non-relaxed SP is solved to obtain a solution for the original problem, and the best solution from all iterations is reported. The GBDA was tested, with three different stopping criteria, on benchmark instances from a public library and compared against solving the traditional formulation of the problem with an exact Mixed Integer Linear Programming (MILP) method. The GBDA found solutions of good quality in less than half the computing time than the exact method, with one of the stopping criteria. The analysis of the results provides valuable insights for future research.

An Analysis and Design for the Repair Process of Late Show Shipments in the Export Cargo Process at SPL HUB

Topics: Decision Support Systems; Management Sciences; Industrial Engineering; Logistics; OR in Transportation

Authors: Sjoerd van Rooden ¹ , Catya Zuniga ¹ , Bart Krol ² and Elias Olivares-Benitez ³

Affiliations: ¹ Faculty of Technology, Amsterdam University of Applied Sciences, Weesperzijde 190, Amsterdam, The Netherlands , ² KLM Cargo, Schiphol, The Netherlands , ³ Facultad de Ingenieria, Universidad Panamericana, Zapopan, Mexico

Digital Object Identifier: 10.5220/0010797500003117

Proceedings of ICORES 2022, ISBN: 978-989-758-548-7

Quick Abstract

Export shipments arriving late at the freight building of KLM Cargo at Schiphol Airport is a trigger to deviations in the standard acceptance process. These Late Shows are currently handled ad-hoc making it difficult to plan and predict these events. By conducting a data analysis to quantitatively identify the characteristics of the Late Shows, and by conducting stakeholder interviews to understand the current process and discuss the future process, this research tried to design the operational process of the Late Shows to improve the operational excellence and quality of the acceptance process. The research shows that currently, late shipments are often still tried to be build up for the planned flight. It is found that 13% of these shipments do eventually not depart on the planned flight. The research concludes that the design of the Late Show process should include a check on whether the shipment was delivered on time, before acceptance of the shipment. By only accepting the shipment once it is decided that the planned flight is achievable or when it is rebooked to another flight, it is assured that the Late Show will be on time at the build-up buffer for the booked flight.

Analysis of the Hiring Cost Impact with a Bi-objective Model for the Multi-depot Open Location Routing Problem

Topics: Optimization; Routing; Logistics; OR in Transportation

Authors: Joel-Novi Rodríguez-Escoto , Samuel Nucamendi-Guillén and Elias Olivares-Benitez

Affiliation: Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan, México

Digital Object Identifier: 10.5220/0010266604250432

Proceedings of ICORES 2021, ISBN: 978-989-758-485-5

Quick Abstract

This paper investigates the effect of the hiring cost over transportation cost and the capacity utilization for the vehicles used. This analysis is conducted on a multi-depot open location-routing problem. The problem consists of determining the optimal number of depots to open, as well as the design of the open routes in order to satisfy the demand for all of the customers while seeking the best trade-off between the total traveling and opening cost. To solve the problem, we propose a bi-objective mixed-integer linear model, which is solved using two different approaches: the augmented epsilon constraint 2 (AUGMECON2) method and the weighting revised multi-choice goal programming (WRMCGP) method. Both approaches are implemented, solving benchmark instances and comparing the quality of the Pareto fronts in terms of multi-objective metrics. Accordingly, the results indicate that AUGMECON2 performs better than WRMCGP concerning the quality of the Pareto Front and the elapsed CPU time, for instances with a homogeneous fleet. However, the WRMCGP reported the best solution time in the heterogeneous instances. In summary, considering heterogeneous fleets, the results demonstrate that the hiring cost can be reduced up to 85%, with 73% more vehicle utilization on average.

Maximization of Profit for a Problem of Location and Routing, with Price-sensitive Demands

Topics: Optimization; Industrial Engineering; Linear Programming; Network Optimization; Routing; OR in Transportation

Authors: Narda E. Ibarra-Delgado ¹ , Elias Olivares-Benitez ¹ , Samuel Nucamendi-Guillén ¹ and Omar G. Rojas ²

Affiliations: ¹ Universidad Panamericana, Facultad de Ingenieria, Prolongacion Calzada Circunvalacion Poniente 49, Zapopan, Jalisco, 45010 and Mexico , ² Universidad Panamericana, Escuela de Ciencias Económicas y Empresariales Prolongacion Calzada Circunvalacion Poniente 49, Zapopan, Jalisco, 45010 and Mexico

Digital Object Identifier: 10.5220/0007405304140421

Proceedings of ICORES 2019, ISBN: 978-989-758-352-0

Quick Abstract

This article seeks to study and solve a problem of profit maximization of a company by defining the location of an optimal number of facilities, allocation and routing of vehicles, and costs for home delivery to meet the demand of its customers. This study is based on an article in which, for the first time, a problem of location and routing and maximization of utilities with price-sensitive demands is integrated. This problem, unlike other studies that only minimize other metrics such as waiting times, route distances, and transportation costs, seeks a greater benefit by increasing profits by discriminating prices depending on the customer's location or adding an additional cost to retail sales. This paper presents a model for small instances based on the model proposed in the aforementioned article. Next, a two-phase heuristic is proposed that solves larger instances with a result close to that obtained in the previous article where a branch-and-price heuristic was used.

Related Ontology Subjects/Areas/Topics (15)

Applications	Linear Programming	OR in Transportation
Artificial Intelligence	Methodologies and Technologies	Pattern Recognition
Industrial Engineering	Network Optimization	Routing
Knowledge Discovery and Information Retrieval	Operational Research	Software Engineering
Knowledge-Based Systems	Optimization	Symbolic Systems

The Capacitated Lot Sizing Problem with Batch Ordering: A MILP and Heuristic Approach

Topics: Decision Support Systems; Supply Chain Management; Optimization; Industrial Engineering; Linear Programming; Inventory Theory

Authors: Gustavo Macedo-Barragán ¹ , Samuel Nucamendi-Guillén ¹ , Elías Olivares-Benitez ¹ and Omar G. Rojas ²

Affiliations: ¹ Universidad Panamericana, Facultad de Ingeniería, Prolongación Calzada Circunvalación Poniente 49, Zapopan, Jalisco and Mexico , ² Universidad Panamericana, Escuela de Ciencias Económicas y Empresariales, Prolongación Calzada Circunvalación Poniente 49, Zapopan, Jalisco, 45010 and Mexico

Digital Object Identifier: 10.5220/0007297402290236

Proceedings of ICORES 2019, ISBN: 978-989-758-352-0

Quick Abstract

This article presents a mixed integer linear programming method and a heuristic algorithm to deal with the problem of multi-period, multiple-product batch purchases, with a finite time horizon, considering delivery times, order placement costs and independent batch size for each product. The objective of this problem is to minimize the costs of placing purchase orders and inventory. This problem is motivated by its application in a marketing company that handles the sale of fashion products (footwear and accessories) through catalogs and for which excess inventory represents a major problem given the short life cycle of its products. Experimental results show that the heuristic algorithm is able to obtain feasible solutions that improve in cost by up to 37% the best integer solutions reported by the model when it reaches the time limit. To validate the efficiency of the algorithm, a real scenario was solved for a trading company, obtaining results that improve by 28% compared to the current company’s situation. These results show that the heuristic approach is promising in terms of the quality of the solution and the computational time required.

Related Ontology Subjects/Areas/Topics (19)

Applications	Decision Support Systems, Remote Data Analysis	Linear Programming
Artificial Intelligence	Health Engineering and Technology Applications	Methodologies and Technologies
Business Analytics	Industrial Engineering	Operational Research
Cardiovascular Technologies	Inventory Theory	Optimization
Computing and Telecommunications in Cardiology	Knowledge Discovery and Information Retrieval	Supply Chain Management
Data Engineering	Knowledge-Based Systems	Symbolic Systems
Decision Support Systems

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