An indirect search algorithm for harvest-scheduling under adjacency constraints

Abstract

An indirect search heuristic is described for solving harvest-scheduling problems under adjacency constraints. This method works in combination with a greedy algorithm by diversifying the search through random changes in prioritized harvest queues. The indirect search is tested on a series of tactical problems and compared with published results for tabu search, simulated annealing, integer programming and linear programming. Results for large strategic problems are compared to a simulated annealing search algorithm. Objective function values are comparable to tabu search and simulated annealing, and solution times range from 38 seconds to 40 minutes, depending on the problem size and the number of iterations. Benefits of the indirect search method are: (1) objective function values can be higher than those computed through other heuristic algorithms, and (2) the algorithm produces good results without time-consuming experimentation with parameters of the search algorithm. The method also has potential for solving more complicated, multiple objective problems.