Optimised schedules for sequential agricultural operations using a Tabu Search method

Abstract

Due to the increasing demands of a growing world population, farmers are tasked with increasing productivity without increasing resource use. This can be achieved by deploying resources in a more efficient manner and by ensuring operations will produce maximum results. However when contemplating the problem of scheduling multiple machines, executing multiple operations at multiple fields, it is a cumbersome task to find an optimal solution without the aid of a decision support tool. A field’s readiness, in terms of its trafficability and workability, is an important factor to consider when scheduling operations. Executing operations on an unready field can lead to additional operational costs, such as drying grain harvested at an unready moisture level or removing subsoil compaction caused by machinery driving on unready soils. To ensure a schedule is actionable, the fields’ readiness must be accounted for by the scheduling process, further increasing the complexity of the problem to be solved.In this paper, a novel scheduling algorithm is presented which creates individual machine work plans for multiple machines to execute multiple consecutive operations at multiple fields, accounting for the field’s readiness for the specified operation. Two optimisation algorithms are utilised to find near optimal solutions of predefined scenarios, a standard Tabu Search and a modified Tabu Search, producing optimised work schedules. The functionality of the optimisation algorithms are tested both for a real world scenario and a large set of numerical examples. The results of the optimisation methods are assessed by their computational time and their relative error compared to the optimal solution, where available. The combination of the presented scheduling algorithm and optimisation algorithms could be used either as a stand-alone tool for farm managers and agricultural contractors as part of their logistic planning, or as a component of an overall Farm Management Information System.