Greedy Dynamic Programming Research Articles

Optimizing Parcel Package Selection Using an Enhanced Multiple Knapsack Problem Approach with Greedy Dynamic Programming

This study introduces an enhanced algorithm for solving the Multiple Knapsack Problem to optimize parcel package selection, particularly focusing on balancing value maximization with price and weight constraints. Due to Dynamic Programming requires significant memory, The proposed method employs a heuristic approach such as greedy algorithm, initially sorting items by their value-to-weight ratio and iteratively filling each knapsack to maximize total value while adhering to constraints. The algorithm demonstrates high computational efficiency, solving instances within 0.07 seconds, and achieves a total value of 122.00 across multiple knapsacks. However, the analysis reveals a significant underutilization of weight capacity (44.74%) compared to price capacity (98.92%), highlighting the need for more sophisticated constraint handling. Limitations such as the simplistic heuristic approach and single-objective focus are discussed. Future work will explore the integration of advanced optimization techniques, dynamic constraints, and multi-objective frameworks to improve solution quality and applicability in real-world scenarios. This research contributes to the field by providing a foundation for further exploration of Multiple Knapsack Problem in logistics and resource allocation contexts.

Enumerating dissimilar minimum cost perfect and error-correcting bipartite matchings for robust data matching

Matchings between objects from two datasets, domains, or ontologies have to be computed in various application scenarios. One often used meta-approach—which we call bipartite data matching—is to leverage domain knowledge for defining costs between the objects that should be matched, and to then use the classical Hungarian algorithm to compute a minimum cost bipartite matching. In this paper, we introduce and study the problem of enumerating K dissimilar minimum cost bipartite matchings. We formalize this problem, prove that it is NP-hard, and present heuristics based on greedy dynamic programming. The presented enumeration techniques are not only interesting in themselves, but also mitigate an often overlooked shortcoming of bipartite data matching, namely, that it is sensitive w.r.t. the storage order of the input data. Extensive experiments show that our enumeration heuristics clearly outperform existing algorithms in terms of dissimilarity of the obtained matchings, that they are effective at rendering bipartite data matching approaches more robust w.r.t. random storage order, and that they significantly improve the upper bounds of state-of-the art algorithms for graph edit distance computation that are based on bipartite data matching.

NEW DYNAMIC QUERY OPTIMIZATION TECHNIQUE IN RELATIONAL DATABASE MANAGEMENT SYSTEMS

Query optimizer is an important component in the architecture of relational data base management system. This component is responsible for translating user submitted query into an efficient query evolution program which can be executed against the database. The present query evolution existing algorithm tries to find the best possible plan to execute a query with a minimum amount of time using mostly semi accurate statistical information (e.g. sizes of temporary relations, selectivity factors, and availability of resources). It is a static approach for generating optimal or close to optimal execution plan. Which in turn increases the execution cost of the query to reduce the execution cost of the query; I propose a new dynamic query optimization algorithm which is based on greedy dynamic programming algorithm uses randomized strategies and reduces the execution cost of the queries and system resources and also it works efficiently with distributed and centralized databases.