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
32 results
Now showing 1 - 10 of 32
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Item type:Publication, Sustainable Closed-Loop Supply Chains: A Systematic Literature Review(SCITEPRESS - Science and Technology Publications, 2026); ; - Some of the metrics are blocked by yourconsent 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); ; 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 4 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Coordination of pricing and inventory replenishment decisions in a supply chain with multiple geographically dispersed retailers(2022); Jose A. VenturaScopus© Citations 7 2 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multi-period multi-product closed loop supply chain network design: A relaxation approach(2021); Scopus© Citations 20 21 1 - Some of the metrics are blocked by yourconsent 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); ; 47 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Scopus© Citations 17 9 1 - Some of the metrics are blocked by yourconsent 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)Scopus© Citations 8 48 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem(2020); ; <jats:p>Supplier selection and order quantity allocation have a strong influence on a company’s profitability and the total cost of finished products. From an optimization perspective, the processes of selecting the right suppliers and allocating orders are modeled through a cost function that considers different elements, such as the price of raw materials, ordering costs, and holding costs. Obtaining the optimal solution for these models represents a complex problem due to their discontinuity, non-linearity, and high multi-modality. Under such conditions, it is not possible to use classical optimization methods. On the other hand, metaheuristic schemes have been extensively employed as alternative optimization techniques to solve difficult problems. Among the metaheuristic computation algorithms, the Grey Wolf Optimization (GWO) algorithm corresponds to a relatively new technique based on the hunting behavior of wolves. Even though GWO allows obtaining satisfying results, its limited exploration reduces its performance significantly when it faces high multi-modal and discontinuous cost functions. In this paper, a modified version of the GWO scheme is introduced to solve the complex optimization problems of supplier selection and order quantity allocation. The improved GWO method called iGWO includes weighted factors and a displacement vector to promote the exploration of the search strategy, avoiding the use of unfeasible solutions. In order to evaluate its performance, the proposed algorithm has been tested on a number of instances of a difficult problem found in the literature. The results show that the proposed algorithm not only obtains the optimal cost solutions, but also maintains a better search strategy, finding feasible solutions in all instances.</jats:p>Scopus© Citations 9 9 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 34 2