Investigating the enhanced Best Performance Algorithm for Annual Crop Planning problem based on economic factors

Area of Science:

• Operations Research
• Agricultural Economics
• Mathematical Optimization

Background:

• The Annual Crop Planning (ACP) problem is a recent addition to optimization literature.
• Existing ACP models may not fully capture market economic influences.
• Continuous optimization techniques are crucial for dynamic planning scenarios.

Purpose of the Study:

• To present a novel mathematical formulation for the Annual Crop Planning problem.
• To incorporate market economic factors into the ACP formulation.
• To investigate a new metaheuristic algorithm for solving the enhanced ACP problem.

Main Methods:

• Developed a new mathematical formulation for the Annual Crop Planning problem based on market economic factors.
• Investigated a new local search metaheuristic algorithm: the enhanced Best Performance Algorithm (eBPA).
• Compared the performance of eBPA against Tabu Search and Simulated Annealing using benchmark instances.

Main Results:

• The enhanced Best Performance Algorithm (eBPA) demonstrated effectiveness in solving the formulated Annual Crop Planning problem.
• eBPA's performance was competitive with established metaheuristic algorithms like Tabu Search and Simulated Annealing.
• The study highlights the potential of eBPA for addressing continuous optimization problems in agricultural planning.

Conclusions:

• The new mathematical formulation provides a more economically relevant approach to Annual Crop Planning.
• The enhanced Best Performance Algorithm (eBPA) is a viable and promising method for solving complex ACP instances.
• This research contributes to the advancement of optimization techniques in agricultural management and economic planning.