Multi-objective models for lot-sizing with supplier selection

In this paper, two multi-objective mixed integer non-linear models are developed for multi-period lot-sizing problems involving multiple products and multiple suppliers. Each model is constructed on the basis of three objective functions (cost, quality and service level) and a set of constraints. The total costs consist of purchasing, ordering, holding (and backordering) and transportation costs. Ordering cost is seen as an 'ordering frequency'-dependent function, whereas total quality and service level are seen as time-dependent functions. The first model represents this problem in situations where shortage is not allowed while in the second model, all the demand during the stock-out period is backordered. Considering the complexity of these models on the one hand, and the ability of genetic algorithms to obtain a set of Pareto-optimal solutions, we apply a genetic algorithm in an innovative approach to solve the models. Comparison results indicate that, in a backordering situation, buyers are better able to optimize their objectives compared to situations where there is no shortage. If we take ordering frequency into account, the total costs are reduced significantly.Lot-sizing Supplier selection Inventory Multi-objective mixed integer non-linear programming Genetic algorithm

Test Center Location Problem: A Bi-Objective Model and Algorithms

The optimal placement of healthcare facilities, including the placement of diagnostic test centers, plays a pivotal role in ensuring efficient and equitable access to healthcare services. However, the emergence of unique complexities in the context of a pandemic, exemplified by the COVID-19 crisis, has necessitated the development of customized solutions. This paper introduces a bi-objective integer linear programming model designed to achieve two key objectives: minimizing average travel time for individuals visiting testing centers and maximizing an equitable workload distribution among testing centers. This problem is NP-hard and we propose a customized local search algorithm based on the Voronoi diagram. Additionally, we employ an ϵ-constraint approach, which leverages the Gurobi solver. We rigorously examine the effectiveness of the model and the algorithms through numerical experiments and demonstrate their capability to identify Pareto-optimal solutions. We show that while the Gurobi performs efficiently in small-size instances, our proposed algorithm outperforms it in large-size instances of the problem

Data publication: Learning-based systems for assessing hazard places of contagious diseases and diagnosing patient possibility

The codes and data for the paper "Learning-based systems for assessing hazard places of contagious diseases and diagnosing patient possibility

On the Partial Decoding Delay of Sparse Network Coding

Sparse network coding (SNC) is a promising technique for reducing the complexity of random linear network coding (RLNC), by selecting a sparse coefficient matrix to code the packets. However, the performance of SNC for the average decoding delay (ADD) of the packets is still unknown. We study the performance of ADD and propose a Markov chain model to analyze this SNC metric. This model provides a lower bound for decoding delay of a generation as well as a lower bound for decoding delay of a portion of a generation. Results show that although RLNC provides a better decoding delay of an entire generation, SNC outperforms RLNC in terms of ADD per packet. Sparsity of the coefficient matrix is a key parameter for ADD per packet to transmit stream data. The proposed model enables us to select the appropriate degree of sparsity based on the required ADD. Numerical results validate that the proposed model would enable a precise evaluation of SNC technique behavior

On the Partial Decoding Delay of Sparse Network Coding

Sparse network coding (SNC) is a promising technique for reducing the complexity of random linear network coding (RLNC), by selecting a sparse coefficient matrix to code the packets. However, the performance of SNC for the average decoding delay (ADD) of the packets is still unknown. We study the performance of ADD and propose a Markov chain model to analyze this SNC metric. This model provides a lower bound for decoding delay of a generation as well as a lower bound for decoding delay of a portion of a generation. Results show that although RLNC provides a better decoding delay of an entire generation, SNC outperforms RLNC in terms of ADD per packet. Sparsity of the coefficient matrix is a key parameter for ADD per packet to transmit stream data. The proposed model enables us to select the appropriate degree of sparsity based on the required ADD. Numerical results validate that the proposed model would enable a precise evaluation of SNC technique behavior

Finding Monochromatic L-Shapes in Bichromatic Point Sets

Given a set R of red points and a set B of blue points in the plane of total size n, we study the problem of determining all angles for which there exists an L-shape containing all points from B without containing any points from R. We propose an algorithm to solve the problem in O(n 2 log n) time and O(n) storage. We also describe an output-sensitive algorithm that reports all angles in O(n 5/3+ε + k log k) time and O(n 5/3+ε) storage, where k is the number of reported angular intervals.