Wood Supply

In this paper, we present a simulated annealing (SA) approach for solving the log-truck scheduling problem, which is an extension of the timber transport vehicle routing problem with time windows (TTVRPTW). The problem is characterized by a heterogeneous fleet of trucks that depart from their corresponding depots and must perform a number of daily transport tasks to move log products between wood pickup (harvest areas) locations and industrial customers, such as pulpmills and sawmills. Commencing at their depots, each truck must perform a number of successive transport tasks, each one characterized by a trip between a designated harvest area and an industrial customer, until the number of daily working hours is completed. The objective of the model is to minimize total empty travel time for the whole fleet as well as waiting times at wood pickup locations. In addition, time windows and accessibility to harvest areas and customers, must be taken into account. Our solution approach consists of three main steps: determination of a cooling scheme, search for a random feasible initial solution, and application of the simulation annealing procedure with a random neighborhood that features various types of moves, including single and multiple insertions and swaps between trucks. The efficiency of the heuristics was evaluated and validated with real-life data for two problem instances, each consisting of 30 transport tasks and a fleet of 10 trucks. Considering all the scenarios evaluated, our best SA solutions resulted in maximum deviations of 3% in comparison with the optimal solutions obtained with commercial optimization solvers.

In the coming years, Ireland will continue to face an increasing demand for wood biomass as a renewable source of energy. This will result in strained supply/demand scenarios, which will call for new planning and logistics systems capable of optimizing the efficient use of the biomass resources. In this study, a linear programming tool was developed which includes moisture content (MC) as a driving factor for the
cost optimisation of two supply chains that use short wood and whole trees from thinnings as material feedstock. The tool was designed and implemented to analyse the impact of moisture content and truck configurations (5-axle and 6-axle trucks) on supply chain costs and spatial distribution of the supply
materials. The results indicate that the inclusion of wood chips from whole trees reduces the costs of wood energy supply in comparison with only producing wood chips from short wood to satisfy the demand, with 9.8% and 10.2% cost reduction when transported with 5-axle and 6-axle trucks respectively.
Constraining the MC of the wood chips delivered to the power plant increases both transport and overall supply chain costs, due, firstly to an increase in the haulage distance and secondly, to the number of counties providing the biomass material. In terms of truck configuration, the use of 6-axle trucks resulted in a 14.8% reduction in the number of truckloads and a 12.3% reduction in haulage costs in comparison to the use of 5-axle trucks across the MC scenarios analysed.

The aim of this study was to analyse the supply of wood biomass (short wood) to the three peat power
plants in Ireland and the impacts on the competing wood-based panel industries. The methodology
includes the development of a spatial decision support tool based on LP (Linear Programming). It uses
drying curves to assess the moisture content, weight and energy content of biomass during a two year
period planning. Harvesting, chipping, storage and transportation costs are calculated based on the
biomass moisture content. The model optimally allocates woodchips and logs from thinnings and
clearfells. Results show that the planned maximum 30% co-firing rate at the three peat power station
could be met with the forecasted short wood availability from both the private and public sector. The
costs of supply increased not only with higher demands, but also with tighter constraints on the MC
demanded by power plants. Spatial distribution and operational factors such as efficiency in transportation
and truck loading showed to be sensitive to changes in MC. The analysis shows the benefits of
managing the MC when optimising supply chains in order to deliver biomass to energy plants in a cost effective
manner.

In theory, linkages between hierarchical forest management planning levels ensure coherent disaggregation of long-term wood supply allocation as input for short-term demand-driven harvest planning. In practice, these linkages may be ineffective, and solutions produced may be incoherent in terms of volume and value-creation potential of harvested timber. Systematic incoherence between planned and implemented forest management activities may induce drift of forest system state (i.e., divergence of planned and actual system state trajectories), thus compromising credibility and performance of the forest management planning process. We describe hierarchical forest management from a game-theoretic perspective and present an iterative two-phase model simulating interaction between long- and short-term planning processes. Using an illustrative case study, we confirm the existence of a systematic drift effect, which we attribute to ineffective linkages between long- and short-term planning. In several simulated scenarios, the planning process fails to ensure long-term wood supply sustainability, fails to reliably meet industrial fiber demand over time, and exacerbates incoherence between wood supply and fiber demand over several planning iterations. We show that manipulating linkages between long- and short-term planning processes can reduce incoherence and describe future work on game-theoretic planning process model formulations that may improve hierarchical planning process performance.

ABSTRACT he classic wood supply optimisation model maximises even-flow harvest levels, and implicitly assumes infinite fibre demand. In many jurisdictions, this modelling assumption is a poor fit for actual fibre consumption, which is often a species-unbalanced subset of total fibre allocation. Failure to anticipate this bias in volume and species mix of industrial wood fibre consumption has been linked to increased risk of wood supply failure. In particular, we examine the distributed wood supply planning problem, which is a variant of the general wood supply planning problem where the roles of forest owner and fibre consumer are played by independent agents (e.g. wood supply planning on public forest land in Canada, where government stewards control wood supply and forest products industry firms consume the fibre). We use agency theory to describe the source of antagonism between public forest land owners (the principal) and industrial fibre consumers (the agent). We show that the distributed wood supply planning problem can be modelled more accurately using a bilevel formulation, and present an extension of the classic wood supply optimisation model which explicitly anticipates industrial fibre consumption behaviour. The general case of the bilevel wood supply optimisation problem is NP-hard, non-linear, and non-convex - it is difficult to solve to global optimality. By imposing certain restrictions on agent network topology, we show that the general case can be decomposed into convex sub-problems. We present a solution methodology that can solve this special case to global optimality, and compare output and solution times of classic and bilevel model formulations using a computational experiment on a realistic dataset. Experimental results show that solution time for the bilevel problem is comparable to solution time for the classic single-level problem, and that the bilevel formulation can mitigate risk of wood supply failure.

The hierarchical forest management (HFM) planning process on public land may currently be failing on two levels. At the top level, HFM may not be providing credible assurance of long-term sustainability of timber supply and forest ecosystem integrity. At a lower level, HFM may be failing to fully realise the value-creation potential from timber-harvesting activities by over-constraining the harvest planning problem. These failures can be traced back to unrealistic assumptions implicitly embedded into long-term wood supply optimisation models, which may explain why this problem has received little attention in the literature. We model the hierarchical forest management planning process as a two-phase rolling-horizon iterative principal-agent problem, illustrate failure scenarios of the status quo planning process, and propose an improved wood supply model formulation. The classic wood supply optimisation model formulation (i.e. conventional even-flow wood supply maximisation model) does not explicitly consider the profit-maximising behaviour of the industrial fibre consumer, but instead implicitly assumes the complete consumption of the wood supply in every planning period, regardless of fibre type or value creation potential. We extend the status quo wood supply model to explicitly anticipate industrial fibre consumption behaviour, thereby improving the likelihood of the wood supply being entirely consumed in the first planning period, thus restoring the validity of the total-consumption assumption that is embedded in the long-term model formulation. We model the principal-agent relationship as a bilevel optimisation problem, where the top level (leader) represents the government wood supply planning process, and the lower level (follower) represents the timber consumption process (i.e. value creation network, or VCN). We show that the bilevel model formulation mitigates the risk of long-term wood supply failure and improves the credibility of the wood supply planning process. The bilevel wood supply model and solution methodology presented here constitute a technically feasible alternative to the methods currently used. Our bilevel model and iterative simulation framework represent a step forward in terms of value-driven forest management planning. Explicit integration of industrial objectives and constraints early on in the wood supply planning process could facilitate government-industry collaboration to realise the full value-creation potential of the public forest resource.