Mendoza, Abraham

Main Affiliation
Preferred name
Mendoza, Abraham
Official Name
Mendoza Andrade, Abraham
ORCID
0000-0001-8496-4779
Researcher ID
V-4102-2018
Scopus Author ID
56002690300

Settings

Now showing 1 - 10 of 31
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Generalized Benders decomposition-based matheuristics for the multi-mode resource-constrained project scheduling problem
    (Springer Science and Business Media LLC, 2025-03-13)
    ;
    Pablo A. Miranda-Gonzalez
    ;
    ;
    The multi-mode resource-constrained project scheduling problem is an NP-hard optimization problem with practical applications in construction, software development, manufacturing, and other industrial and business situations. It involves a set of activities that need to be sequenced while considering precedence and resource constraints as well as different alternative execution modes, which determine each activity’s duration and resource consumption. This research proposes three matheuristic strategies based on a reformulation and partial relaxation of the problem, including a generalized Benders decomposition (GBD)-based algorithm to solve the relaxed problem and three different procedures to find a solution to the original problem. The strategies were tested using benchmark instances of various sizes obtained from published libraries. These strategies showed a significant improvement in speed, achieving up to 92.77% faster performance than the exact method for finding high-quality sub-optimal solutions. This offers a valuable trade-off between computation time and solution quality. Additionally, the GBD-based algorithm generated tighter lower bounds than other existing methods in the literature for a substantial number of the tested instances, all within a very short computing time.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
      4  2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Multi-period multi-product closed loop supply chain network design: A relaxation approach
    (2021)
    Subramanian Pazhani
    ;
    ;
    Ramkumar Nambirajan
    ;
    T.T. Narendran
    ;
    K. Ganesh
    Scopus© Citations 20  21  1
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Scopus© Citations 7  2  2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Scopus© Citations 17  9  1
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A Mathematical Model for an Inventory Management and Order Quantity Allocation Problem with Nonlinear Quantity Discounts and Nonlinear Price-Dependent Demand
    (2023)
    ;
    ;
    Erik Cuevas
    ;
    <jats:p>This article focuses on solving the order quantity allocation problem for retailers. It considers factors such as quality constraints, nonlinear quantity discounts, and price-dependent demand. By formulating it as a nonlinear maximization problem, the article aims to find the best combination of suppliers and order quantity out of infinite solutions to maximize the retailer’s profit. The main contribution of this research is a new mathematical model that can solve the problem of quality constraint and demand in a single step. This problem is complex due to the number of equations, their nonlinear nature, and the various trade-offs given by the market. Additionally, this research considers demand as output and includes price-dependent demand, which is more realistic for retailers. The proposed model was tested using an example from the recent literature and showed better results than the previously published best solution regarding profit maximization.</jats:p>
      47  1
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Scopus© Citations 4  6  2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
      5  1
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A Modified Simulated Annealing (MSA) Algorithm to Solve the Supplier Selection and Order Quantity Allocation Problem with Non-Linear Freight Rates
    (2023)
    González Cabrera, Nora Paulina
    ;
    Mayo Maldonado, Jonathan
    ;
    Erik Cuevas
    ;
    <jats:p>Economic Order Quantity (EOQ) is an important optimization problem for inventory management with an impact on various industries; however, their mathematical models may be complex with non-convex, non-linear, and non-differentiable objective functions. Metaheuristic algorithms have emerged as powerful tools for solving complex optimization problems (including EOQ). They are iterative search techniques that can efficiently explore large solution spaces and obtain near-optimal solutions. Simulated Annealing (SA) is a widely used metaheuristic method able to avoid local suboptimal solutions. The traditional SA algorithm is based on a single agent, which may result in a low convergence rate for complex problems. This article proposes a modified multiple-agent (population-based) adaptive SA algorithm; the adaptive algorithm imposes a slight attraction of all agents to the current best solution. As a proof of concept, the proposed algorithm was tested on a particular EOQ problem (recently studied in the literature and interesting by itself) in which the objective function is non-linear, non-convex, and non-differentiable. With these new mechanisms, the algorithm allows for the exploration of different regions of the solution space and determines the global optimum in a faster manner. The analysis showed that the proposed algorithm performed well in finding good solutions in a reasonably short amount of time.</jats:p>
    Scopus© Citations 8  48  1
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A Memetic Algorithm for the Cumulative Capacitated Vehicle Routing Problem Including Priority Indexes
    (2020)
    ;
    Gutiérrez, Sebastián
    ;
    ;
    <jats:p>This paper studies the Cumulative Capacitated Vehicle Routing Problem, including Priority Indexes, a variant of the classical Capacitated Vehicle Routing Problem, which serves the customers according to a certain level of preference. This problem can be effectively implemented in commercial and public environments where customer service is essential, for instance, in the delivery of humanitarian aid or in waste collection systems. For this problem, we aim to minimize two objectives simultaneously, the total latency and the total tardiness of the system. A Mixed Integer formulation is developed and solved using the AUGMECON2 approach to obtain true efficient Pareto fronts. However, as expected, the use of commercial software was able to solve only small instances, up to 15 customers. Therefore, two versions of a Memetic Algorithm with Random Keys (MA-RK) were developed to solve the problem. The computational results show that both algorithms provided good solutions, although the second version obtained denser and higher quality Pareto fronts. Later, both algorithms were used to solve larger instances (20–100 customers). The results were mixed in terms of quality but, in general, the MA-RK v2 consistently outperforms the first version. The models and algorithms proposed in this research provide useful insights for the decision-making process and can be applied to solve a wide variety of business situations where economic, customer service, environmental, and social concerns are involved.</jats:p>
    Scopus© Citations 14  11  1