Optimizing Operational-Level Forest Biomass Logistic Costs for Storage, Chipping and Transportation through Roadside Drying

Martin Strandgard,Paul Turner,Anna Shillabeer

doi:10.3390/f13020138

Abstract

Forest biomass (FB) could supply more of Australia’s energy needs, but delivered costs must be reduced for it to be a viable energy source. Operational planning is critical to reducing delivered costs as it determines actual activities, though few operational FB supply chain (FBSC) planning tools have been published. This paper presents a “proof-of-concept” operational FBSC decision support system (DSS) to schedule FB deliveries for eight weeks from roadside storage for the least cost, taking in account moisture content changes. Four mathematical models are compared, solving a linear formulation of the FB delivery problem in terms of solution speed and delivered cost, and the practicality of implementing the solutions. The best performing model was a Greedy algorithm as it produced solutions not significantly different from those of the tested linear programming solver and was readily modified to significantly improve solution implementation through the addition of a non-linear element. FBSC planning tools typically assume accurate knowledge of stored FB quantities and that little or no rainfall occurs during storage. In practice, stored FB quantity estimates can be inaccurate due to variation in the bulk density of the piles. Improving these estimates is a critical area for future research. This study found that simulated rainfall with <20 mm during the first week of the scheduled period did not significantly effect delivered costs.

Highlights

Demand for biofuels is rising as a means to reduce global greenhouse gas emissions.Forest biomass (FB), logging residue (LR) [1], could potentially supply a major part of the energy needs of many countries, including Australia [2]
The GRG algorithm schedules involved the extraction of relatively small quantities of LR from multiple sites over multiple weeks, which was reflected in the high mean values for the number of small loads and the chipper-related test criteria (Table 4)
Their longer solution times resulted from these algorithms solving multiple problems for each solution due to the multi-start setting for the GRG algorithm and the multi-generational approach used by the Evolutionary algorithm

Summary

Introduction

Forest biomass (FB) (defined as any tree component or the whole tree), logging residue (LR) [1], could potentially supply a major part of the energy needs of many countries, including Australia [2]. Industrial-scale use of FB for biofuel is an emerging industry in Australia, where it currently supplies less than 2% of the country’s energy needs [3]. Current Australian FB harvesting for biofuel is mainly ad hoc and opportunistic, providing considerable opportunities for improvements and cost reduction. A plethora of FBSC planning models have been published, with [4,5,6] collectively reviewing over 60 published FBSC models, the majority of which dealt with strategic and tactical planning problems. A very limited number of operational level FBSC planning models have been published [8], the majority of which deal with transport scheduling and routing, for example, [9] and [10]

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