In this work we focus on efficient heuristics for solving a class of stochastic planning problems that arise in a variety of business, investment, and industrial applications. The problem is best described in terms of future buy and sell contracts. By buying less reliable, but less expensive, buy (supply) contracts, a company or a trader can cover a position of more reliable and more expensive sell contracts. The goal is to maximize the expected net gain (profit) by constructing a dose to optimum portfolio out of the available buy and sell contracts. This stochastic planning problem can be formulated as a two-stage stochastic linear programming problem with recourse. However, this formalization leads to solutions that are exponential in the number of possible failure combinations. Thus, this approach is not feasible for large scale problems. In this work we investigate heuristic approximation techniques alleviating the efficiency problem. We primarily focus on the clustering approach and devise heuristics for finding clusterings leading to good approximations. We illustrate the quality and feasibility of the approach through experimental data.
A Hybrid Algorithm Based Robust Big Data Clustering for Solving Unhealthy Initialization, Dynamic Centroid Selection and Empty clustering Problems with Analysis
SMART: Semantic Matching Contrastive Learning for Partially View-Aligned Clustering
College of Computer Science and Technology, Huaqiao University（华侨大学计算机科学与技术学院）；Department of Computer Science, Shantou University（汕头大学计算机科学系）；Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University（西南大学电子信息工程学院非线性电路与智能信息处理重点实验室）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
A Hybrid Chimp Optimization Algorithm and Generalized Normal Distribution Algorithm with Opposition-Based Learning Strategy for Solving Data Clustering Problems
Solving Inverse Problems of Chaotic Systems with Bidirectional Conditional Flow Matching
Westlake University（西湖大学）；Georgia Institute of Technology（佐治亚理工学院）；Chinese Academy of Sciences（中国科学院）；Max-Planck-Institut für Astronomie（马克斯·普朗克天文学研究所）；The Ohio State University（俄亥俄州立大学）
Spatial-temporal-demand clustering for solving large-scale vehicle routing problems with time windows
Logistics and Supply Chain Management, School of Management, Technical University of Munich（物流与供应链管理系，管理学院，慕尼黑技术大学）；Munich Data Science Institute (MDSI), Technical University of Munich（慕尼黑数据科学研究所（MDSI），慕尼黑技术大学）
