An Operational Analysis Of Shell Planting Strategies For Improving The Survival Of Oyster Larvae In The Chesapeake Bay

Abstract

Maryland’s oyster fishery has declined dramatically since 1930. Meanwhile, recent studies have shown that careful management of the shell planting process can lead to increased oyster yields. This is due to the fact that oyster larvae that attach to fossil shells at the bottom of the Chesapeake Bay have a greatly enhanced likelihood of survival. In this paper, we indicate how linear programming and a rule-based heuristic can be used in tandem to determine effective sites and schedules for shell planting. In particular, linear programming is used to address the macro problem of deciding where to plant the shells, subject to high-level constraints. The rule-based heuristic addresses the micro problem of scheduling tugboats, barges, and the planting crew, subject to more detailed constraints.

Résumé

L’industrie de la pêche à l’huître au Maryland a considérablement diminué depuis 1930. Cependant des études récentes ont démontré qu’une administration avisée de la plantation des coquilles peut accroître la production d’huitres. Ceci est dû au fait que les larves d’huître qui s’attachent aux coquilles fossilles au fond de la baie de Chesapeake ont plus de chances de survie. Dans ce mémoire, nous indiquons comment la programmation linéaire et un heuristique fondé sur des règles peuvent être utilisés en tandem pour déterminer de bons sites et de bons calendriers de plantation de coquilles. Plus particulièrement, la programmation linéaire est utilisée pour résoudre le problème macro de décider où planter les coquilles, sujet à des contraintes de haut-niveau. L’heuristique fondé sur des règles s’attaque au problème micro d’établir l’ordonnancement des remorqueurs, barges et l’équipe de plantation, sujet à des contraintes plus détaillées.

Additional information

Notes on contributors

Qiwen Wang

Qiwen Wang graduated from the Department of Mathematics at Peking University in China in 1968. In 1990, he received his Ph.D. degree from the College of Business and Management at the University of Maryland at College Park with a double major in management science and statistics. Currently, he is a Professor and an Associate Dean in the Guanghua School of Management at Peking University in China. He is also a Vice Chairman of the Center for Management Science and a Vice Chairman of the Center for Sustainable Development of China at Peking University. His research interests include network optimization, artificial neural networks, and resource and environmental management.

Bruce Golden

Bruce Golden is a Professor at the University of Maryland in the Department of Management Science and Statistics. His research interests include network optimization, distribution management, natural resource management, and applied operations research, and he has published many articles in these and related fields. He is currently Editor-in-Chief of the INFORMS Journal of Computing.

Edward Wasil

Edward Wasil is a Professor of Management Science in the Kogod College of Business Administration at American University in Washington, DC, where he has taught graduate courses in operations research and applied statistics for the last 10 years. His research interests include the application of neural networks to business decision problems, network optimization, and the process of implementing management science models.

Sridhar Bashyam

Sridhar Bashyam is a Director at Resource Planning Consultants, of KPMG Peat Marwick LLP. His academic background consists of a B.S. in Mechanical Engineering, an M.B.A. and a Ph.D. in Management Science from the University of Maryland at College Park. His primary research interests are in simulation-based gradient estimation and optimization methods with a specific focus on stochastic inventory models. He also has interests and experience in the application of mathematical models to retail site selection, engineering design, and manufacturing.