To What Extent Can Simulation Optimisation be Used in Wildlife Reserve Design?
Creators: name: Zhou, Shengjie
Publisher: name: Lancaster University
Year: 2025
Subjects: Simulation
Simulation optimisation
ranking and selection
reserve design
Chance constraint
Wildlife conservation

description: Establishing nature reserves is a key method for preventing biodiversity
 loss. This
 
 thesis addresses the reserve site selection (RSS) problem, which aims to
 select sites for nature reserves to ensure species survival. Specifically,
 it examines the extent to which simulation optimisation (SO) can be used
 in the RSS problem. The applicability and effectiveness of SO are
 evaluated by applying an SO method and its adaptations across three
 scenarios of a Grey Wolf (Canis lupus) RSS problem.
 
 
 The problem is formulated as a chance-constrained SO problem, with a
 deterministic objective that minimises conservation costs, subject to a
 probabilistic species survival constraint. This probability is estimated
 using a grey wolf simulation model that simulates the wolves’ birth,
 growth, dispersal and death in discrete time steps. The problem is solved
 using the sequential feasibility test procedure from Hong, Luo, and Nelson
 (2015), hereafter CCSB-F.
 
 
 Three scenarios of the RSS problem, each with different characteristics,
 are investigated in this research. Scenario 1 demonstrates how CCSB-F can
 tackle a basic problem. Several observations are made: first, since
 solution costs are trivial to obtain, computational effort (measured by
 the number of simulation runs) is required solely for establishing
 solution feasibility. Second, due to sampling error in simulation results,
 solution feasibility can only be assured subject to a ‘statistical
 guarantee’. Lastly, the computational effort required depends on how close
 the solutions are to the feasibility boundary and the required level of
 statistical guarantee.
 
 
 To address likely computational hurdles in more difficult versions of this
 problem, Scenarios 2 and 3 are designed to demonstrate and evaluate two
 solution space filtering approaches. The first approach ‘temporarily
 removes’ solutions with equivalent alternatives, identified based on the
 simulation model, without affecting CCSB-F’s statistical guarantee.
 Applying this approach to Scenario 2 (28 solutions) reduces computational
 effort by approximately 26% compared to using CCSB-F alone. When applied
 to Scenario 1 (28 solutions), it achieves an estimated savings of 40%
 while maintaining the same level of statistical guarantee.
 
 
 The second approach is a heuristic that uses expert knowledge to create
 solution dominance rules and then removes dominated solutions before
 applying CCSB-F. It reduces computational effort by approximately 80% in
 Scenario 3 (210 solutions) compared to using CCSB-F alone. When applied to
 Scenario 1 (28 solutions), the estimated computational savings is 60%.
 Even though it cannot guarantee to find the best solution in the entire
 solution space because it removes solutions, it still provides a
 statistical guarantee on the filtered solution space and the feasibility
 of the selected solutions. Although these estimates represent conservative
 lower bounds (as they do not fully account for the additional reduction in
 the number of replications per solution), they clearly demonstrate the
 potential of the proposed approaches to significantly reduce
 
 computational effort.