Heuristic approaches to some intralogistics layout and routing problems

Abstract

Intralogistics plays a vital role in efficiently managing and optimizing the movement of goods and materials within warehouses, distribution centers, and manufacturing facilities. This thesis focuses on addressing optimization challenges in intralogistics with the aim of enhancing operational efficiency, productivity, and worker well-being in intralogistics warehouse management. The thesis consists of three essays that address layout and routing problems in intralogistics, specifically the tool indexing problem in manufacturing facilities and the picker routing problem in warehousing facilities. The first two essays investigate the tool indexing problem in automated machining centers. These centers are crucial in modern manufacturing facilities and rely on automatic tool changers for efficient operations. In the first essay, we specifically tackle the tool indexing problem without tool duplication. By eveloping local search and tabu search algorithms that incorporate innovative bookkeeping techniques, we achieve significant reductions in search time and obtain high-quality solutions. Computational results on benchmark instances demonstrate the competitiveness and effectiveness of our proposed algorithms. In the second essay, we extend our study to the tool indexing problem with tool duplication. The inclusion of multiple copies of each tool in the tool magazine introduces additional computational complexities in evaluating the objective function. To overcome these challenges, we introduce an efficient approach for updating the objective function value during the neighborhood search. We then develop tailored neighbourhood search algorithms that improve computational efficiency and solution quality for this problem. In the third essay, we shift our focus to the issue of worker safety in order picking processes within warehouses. Research in this field has primarily focused on cost and order delay minimization, neglecting worker safety considerations. However, given the importance of ensuring a safe working environment in the backdrop of infectious diseases like the COVID-19 pandemic, we address this concern by optimizing picker routes using a mixed-integer linear programming model and deploying four well-known routing policies. By optimally assigning wait times to pickers, we effectively reduce picker overlap without compromising order completion time. Our findings demonstrate the potential to achieve significant reductions in picker overlap by allowing a slight increase in the maximum picking time for pickers.