The situation on the log yard changes seasonally and also over the years. The quantities of assortments to be stored, their number and also the type of wood can change. To respond to this, we have developed a dynamic log yard planning model for assigning roundwood to specific ejection boxes and storage areas in order to minimise the overall transport distances of the loaded transportation vehicles on the log yard, including any possible re-allocation of assortments. The study centres on the log yard of a medium-sized hardwood sawmill in Europe, with actual cutting data from a six-month period. We are comparing a multi-period binary integer program with a model that operates on a period per period basis and a solution approach that splits the problem into two subproblems and solves them sequentially. The models undergo testing with decreasing space capacities at the storage boxes on the log yard and are compared. If capacity is continuously decreasing from 100% to 80%, then period per period planning is on average 13% worse than multi-period planning. We also investigate how the solutions change when twice as many or half as many assortments are stored at the log yard. In addition, we study how much the solutions improve when logs can be removed from the storage boxes to clear them and release them for other material in the following period.
For sawmills, paper mills, particleboard, oriented strand board (OSB), fiberboard and other wood production factories, the log yard is the first step, where raw materials are sorted and stored before production begins. Due to the size of these production sites great potential exists for the optimisation of internal logistics. In this paper the different planning problems of the log yard are introduced and existing literature examined. Beginning with the tactical problems of structure, such as assessing material flow, planning facility layout and assigning storage areas, it continues with operational problems such as vehicle movement planning within the log yard, empty trip minimisation and the seasonality of raw material availability. Data derived from this study reveals a variety of possible solution methods, the applicability of which depends on the precise nature of the log yard operations. Additionally, several real life examples are provided which illustrate the potential for operational improvement.