CEGI

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This service focuses its activity on the frontier between Engineering, Management and Social Sciences, in order to identify processes, techniques and efficiency indicators of the institutions. At the heart of this center's strategy is the "problem-driven research" concept, which implies the development of solutions tailored to the needs of each company / institution.

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Browsing CEGI by Author "6333"
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    A co-evolutionary matheuristic for the car rental capacity-pricing stochastic problem
    ( 2019) Maria Antónia Carravilla ; Costa,AM ; José Fernando Oliveira ; Beatriz Brito Oliveira ; 265 ; 6333 ; 1297
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    A dynamic programming approach for integrating dynamic pricing and capacity decisions in a rental context
    ( 2018) José Fernando Oliveira ; Beatriz Brito Oliveira ; Maria Antónia Carravilla ; 1297 ; 265 ; 6333
    Car rental companies have the ability and potential to integrate their dynamic pricing decisions with their capacity decisions. Pricing has a significant impact on demand, while capacity, which translates fleet size, acquisition planning and fleet deployment throughout the network, can be used to meet this price-sensitive demand. Dynamic programming has been often used to tackle dynamic pricing problems and also to deal with similar integrated problems, yet with some significant differences as far as the inventory depletion and replenishment are considered. The goal of this work is to understand what makes the car rental problem different and hinders the application of more common methods. To do so, a discrete dynamic programming framework is proposed, with two different approaches to calculate the optimal-value function: one based on a Mixed Integer Non Linear Program (MINLP) and one based on a Constraint Programming (CP) model. These two approaches are analyzed and relevant insights are derived regarding the (in)ability of discrete dynamic programming to effectively tackle this problem within a rental context when realistically sized instances are considered. © Springer International Publishing AG 2018.
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    Integrating pricing and capacity decisions in car rental: A matheuristic approach
    ( 2018) Beatriz Brito Oliveira ; Maria Antónia Carravilla ; José Fernando Oliveira ; 1297 ; 6333 ; 265
    Pricing and capacity decisions in car rental companies are characterized by high flexibility and interdependence. When planning a selling season, tackling these two types of decisions in an integrated way has a significant impact. This paper tackles the integration of capacity and pricing problems for car rental companies. These problems include decisions on fleet size and mix, acquisitions and removals, fleet deployment and repositioning, as well as pricing strategies for the different rental requests. A novel mathematical model is proposed, which considers the specific dynamics of rentals on the relationship between inventory and pricing as well as realistic requirements from the flexible car rental business, such as upgrades. Moreover, a solution procedure that is able to solve real-sized instances within a reasonable time frame is developed. The solution procedure is a matheuristic based on the decomposition of the model, guided by a biased random-key genetic algorithm (BRKGA) boosted by heuristically generated initial solutions. The positive impact on profit, of integrating capacity and pricing decisions versus a hierarchical/sequential approach, is validated. © 2018 The Authors
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    Understanding complexity in a practical combinatorial problem using mathematical programming and constraint programming
    ( 2018) Maria Antónia Carravilla ; Beatriz Brito Oliveira ; 6333 ; 1297
    Optimization problems that are motivated by real-world settings are often complex to solve. Bridging the gap between theory and practice in this field starts by understanding the causes of complexity of each problem and measuring its impact in order to make better decisions on approaches and methods. The Job-Shop Scheduling Problem (JSSP) is a well-known complex combinatorial problem with several industrial applications. This problem is used to analyse what makes some instances difficult to solve for a commonly used solution approach – Mathematical Integer Programming (MIP) – and to compare the power of an alternative approach: Constraint Programming (CP). The causes of complexity are analysed and compared for both approaches and a measure of MIP complexity is proposed, based on the concept of load per machine. Also, the impact of problem-specific global constraints in CP modelling is analysed, making proof of the industrial practical interest of commercially available CP models for the JSSP. © Springer International Publishing AG 2018.