On solving aquifer management problems with simulated annealing algorithms

Aquifer systems play an essential role in meeting the ever increasing use of water for different purposes. Proper design and management of such systems should therefore be a very important matter of concern, not only to ensure that water will be available in adequate quantity (and quality) to satisfy demands but also to guarantee that this would be done in an optimal manner. This paper presents a model serving to define which water supply structures (especially pumping equipment and pipes) should be installed in order to minimize the sum of set-up costs and operation costs while satisfying demands, using a heuristic approach based on simulated annealing. Annealing algorithms are random local search optimization algorithms that allow, at least in theory and in probability, the determination of a global optimum of a (possibly constrained) function.

[1]  I ScottKirkpatrick.  Optimization by Simulated Annealing: Quantitative Studies , 1984 .

[2]  Steven M. Gorelick,et al.  Groundwater Contamination Optimal Capture and Containment , 1993 .

[3]  Methods for Designing Hydraulic Aquifer Remediation Techniques , 1991 .

[4]  Huang,et al.  AN EFFICIENT GENERAL COOLING SCHEDULE FOR SIMULATED ANNEALING , 1986 .

[5]  V. Cerný.  Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .

[6]  J. Eheart,et al.  Neural network-based screening for groundwater reclamation under uncertainty , 1993 .

[7]  J. Eheart,et al.  Monitoring network design to provide initial detection of groundwater contamination , 1994 .

[8]  David P. Ahlfeld,et al.  Designing contaminated groundwater remediation systems using numerical simulation and nonlinear optimization , 1986 .

[9]  Yuh-Ming Lee,et al.  Comparison of Algorithms for Nonlinear Integer Optimization: Application to Monitoring Network Design , 1996 .

[10]  B. Wagner.  Recent advances in simulation-optimization groundwater management modeling (95RG00394) , 1995 .

[11]  Emile H. L. Aarts,et al.  Simulated annealing and Boltzmann machines - a stochastic approach to combinatorial optimization and neural computing , 1990, Wiley-Interscience series in discrete mathematics and optimization.

[12]  David E. Dougherty,et al.  Optimal groundwater management: 2. Application of simulated annealing to a field-scale contamination site , 1993 .

[13]  A. Mayer,et al.  Pump‐and‐treat optimization using well locations and pumping rates as decision variables , 1997 .

[14]  Robert A. Marryott,et al.  Optimal Groundwater Management: 1. Simulated Annealing , 1991 .

[15]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[16]  D. McKinney,et al.  Genetic algorithm solution of groundwater management models , 1994 .

[17]  Thomas Maddock,et al.  Algebraic technological function from a simulation model , 1972 .

[18]  S. Gorelick.  A review of distributed parameter groundwater management modeling methods , 1983 .

[19]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[20]  M. C. Cunha,et al.  Optimal management of a groundwater system for seasonally varying agricultural production , 1993 .

[21]  S. Ranjithan,et al.  Using genetic algorithms to solve a multiple objective groundwater pollution containment problem , 1994 .

[22]  Cecilia R. Aragon,et al.  Optimization by Simulated Annealing: An Experimental Evaluation; Part I, Graph Partitioning , 1989, Oper. Res..

[23]  Richard W. Eglese,et al.  Simulated annealing: A tool for operational research , 1990 .

[24]  L. L. Rogers,et al.  Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling , 1994 .

[25]  Ehl Emile Aarts,et al.  Statistical cooling : a general approach to combinatorial optimization problems , 1985 .