Fossil-free forestry transports are important to reach climate goals. In Sweden, road transports account for around 50% of the industry's CO2 emissions and almost 20% of the road freight volumes. Previous studies have shown that electrification is a cost-effective way for carbon abatement, while at the same time the requirements for flexibility in routing make electrification of forestry transport challenging. The current trend is to introduce more electrical vehicles in different sectors. However, there are challenges for the forest industry, including long distances, heavy weights, multiple shifts, and a lack of recharging stations. We propose an analytical decision tool that solves an integrated vehicle routing problem. This model includes detailed energy consumption and recharging requirements for each route generated. The solution method consists of phases where we first identify full truckloads and then generate a set of generic routes. These are later used to construct routes for individual trucks. The coordination to find the best route for each truck is found by solving a mixed integer programming model. We use a case study to analyze the impact of an increasing proportion of electrical trucks.

This study develops an integrated tactical-operational model to coordinate forestry, agricultural, and municipal solid waste biomass value chains within a single logistics system. A large-scale mixed-integer linear programming model jointly optimizes procurement, preprocessing, inventory, and multimodal transport across shared terminals while accounting for spatial detail and seasonal moisture variation. The model is analyzed in a large industrial case study in Quebec to test computational performance and practical relevance under certain conditions. Results show that collaboration among value chains reduces total system cost and improves logistics performance: rail integration shortens average truck distance and smooths seasonal flows, and terminals act as coordination hubs that decouple procurement from conversion. Agricultural residues and MSW provide short-haul and relatively stable inflows that complement forestry supply and diversify the feedstock portfolio under varying logistics and quality conditions. Sensitivity analyses examine how the logistics system responds to changes in demand, cost, and capacity. The model offers a framework for designing cost-efficient, collaborative biomass-to-bioenergy supply systems that improve feedstock security, support continuous large-scale biorefinery operation, and achieve up to 24% total system cost savings and up to 56% shorter average truck distances across the tested scenarios.

To ensure stable year-round production of second-generation bioenergy, a reliable and large supply volume of waste and residue feedstocks is essential. A collaborative supply from forestry residues (as primary source), agriculture waste, and municipal solid waste (MSW) can meet such demand and mitigate seasonal shortages and supply variations. However, these feedstocks are often spread over large geographical areas, making their transport costly and logistically challenging. Efficient transportation planning across the three sectors is therefore critical. Moreover, each sector plans transportation independently (independent fleet), using full truckload deliveries with different truck configurations. We propose a three-phase heuristics-optimization approach, using operations research tools, to integrate trucks routing across multiple sectors to supply a biorefinery efficiently (shared fleet). By sharing multi-configuration fleets while accounting for reconfiguration costs when trucks switch between specific feedstock types, transportation costs can be reduced significantly. Sharing a common fleet—where truck configurations are or can be made compatible with multiple feedstock types—furthermore enables more efficient full truckload delivery sequences and shorter travel distances. For instance, a container truck may transport agriculture residues as well as MSW or chipped wood from forest areas. We apply the proposed approach to a case study using data from Quebec, Canada. Results show that such truck sharing leads to an average 10% reduction in total costs, even after accounting for reconfiguration expenses.

Several models exist for estimating energy consumption in both diesel and electric trucks. However, there is limited research on hybrid trucks, which combine diesel and electric powertrains. We propose three energy-consumption models to estimate energy use in hybrid-electric trucks. The first model is a standard energy model grounded in fundamental physical principles. The second model estimates the overall energy consumption as a function of road gradient and speed. The third model provides a comprehensive analytical representation of energy distribution in hybrid trucks by separately modeling the diesel engine and electric motor, enhancing its adaptability to diverse driving conditions. All three models account for variations in road slope and speed limits. The models are trained on measured data from energy-consumption tests conducted on a prototype hybrid-electric truck developed by a Canadian research and development organization. The models are then used in a case study to estimate the hybrid truck's performance on roads with varying characteristics.

Transportation and route planning require accurate information about fuel consumption and travel time. This is needed to provide correct input for decision support and analytic systems. The two typical approaches are either an empirical model, e.g. linear regression based on statistical analysis or a physical model describing theoretical energy principles. In this paper, we propose a model based on a detailed bucketing (discretization) approach, where road and operating conditions are grouped into discrete categories, applied to both arcs and nodes in a network representation. The bucketing for arcs is based on available road characteristics in a road database, including road class, speed limit, road surface, curviness, and hilliness. The bucketing for nodes is used to distinguish between different types of road crossings or when consecutive arcs change characteristics, e.g. when there is a change of speed limits. To populate the buckets, we use data collected over 12 months from 21 logging trucks equipped with a CAN-bus reader and a GNSS receiver for positioning. We also apply a smoothing method to populate buckets for which no or limited data was collected and to ensure logical conditions. We compare the proposed model with a set of empirical and physical models. The results show that the proposed model provides more accurate estimates of fuel consumption and travel time and is computationally efficient and suitable for integration into decision-support systems.

Determining freight rates for heavy trucks involves a detailed analysis of multiple cost factors, including time, distance, fuel, and other operational costs, which collectively contribute to the overall compensation for transportation services. However, actual remuneration is based on more simplified agreements. Often, the standard agreement is based on the loaded driving distance. Such agreements provide an accurate description of the average cost over many transports but can be very unfair in compensation on single transports. This paper presents a pricing model for truck transportation that extends traditional models based on distance. The new model includes a measure of cost driving factors along the route, such as hills, road surface, curves, speed limits, intersections, speed changes, long ascents, and other physical difficulties. This measure is extracted from the Calibrated Route Finder, a route selection support system used for roundwood transportation in Sweden. The suggested price model that combines distance and a weighted resistance measure gives a better match between remuneration and full costing of a transport than a model that concentrates only on distance. The suggested model has been tested on a large annual transport data set and detailed and selected transportations evaluated by five large forest companies.

Integrating forest biomass into bioenergy systems poses logistical challenges due to seasonal variations in quality and the dispersed nature of supply. We develop a mixed-integer linear programming model that jointly optimizes procurement timing, multimodal transport (truck-rail-barge), chipping and drying locations, and inventory levels at supply nodes, terminals, and the biorefinery. The model embeds process-state transitions, seasonal moisture profiles, and infrastructure limits. In a large-scale Quebec case study (500 - 3000 dry metric tonne (DMT)/day), integrating rail reduces total system costs by 2.8 - 4.8% and yields mill-gate costs around CAD 119 - 121 per DMT. Terminals near the biorefinery decouple procurement from conversion and support buffer-based strategies through high-moisture periods. The optimization model is computationally tractable and provides a reusable template for planning forest biomass logistics that accounts for seasonal quality, preprocessing, and mode-choice interactions.

This paper presents a large-scale, data-driven case study using real operational and meteorological data to optimize maritime vessel routing by jointly considering fuel consumption, voyage time, and safety risks under diverse environmental conditions. A multi-objective optimization framework based on a weighted-sum approach was applied to systematically capture trade-offs among objectives. In practice, selecting appropriate objective weightings, particularly for safety, is challenging, as planners often lack quantitative guidance on their operational implications. This study addresses that gap by evaluating a broad range of weighting scenarios and identifying when safety criteria have minor, moderate, or major impacts. Five major safety issues, namely dynamic stability, bow slamming, green water on deck, and newly developed parametric rolling and surf-riding/broaching-to risk functions, were incorporated through combined critical and non-critical penalties. The case study covered multiple global routes over an entire year and across different vessel sizes and loading conditions. Results show that moderate safety weighting reduces total risk by 30–35% with only a 1–2% increase in voyage cost, while further safety prioritization yields diminishing economic returns. The framework also provides a transferable methodology for systematic parameter selection, supporting transparent and practical multi-objective decision-making in maritime routing.

Forest planning faces many uncertainties, yet existing decision support systems (DSS) seldom incorporate techniques to address them. This study explores how stochastic programming (SP) functionality could be added to forest DSSs to account for data uncertainty, aiming to investigate the added value of such functionality. An SP model was applied to a traditional long-term forest planning problem, and its quantitative performance was compared to deterministic optimisation. The user value of the DSS integration was explored in a workshop with potential users. The findings indicate that incorporating SP in a DSS is feasible both user-wise and for quantitatively improving the decisions, even if computational time and model complexity increase. Quantitatively, SP increased the total expected NPV by at least 2% compared to deterministic optimisation. Users involved in evaluating the SP integration acknowledged the benefits of using SP in forest planning, but expressed concerns about the increased complexity in problem specification and results interpretation. To enhance user adoption, the presentation of SP settings and outcomes should be done in a user-friendly manner, including intuitive visualisations and simplified summary statistics. This study underscores the potential of integrating SP in DSSs to improve long-term forest planning under data uncertainty.

Thinning operations in forestry typically involve a two-machine system (TMS) with a harvester and a forwarder. Its productivity in different forest conditions is well documented. To date, drones have been used for data collection and surveillance but not as an alternative to a TMS. Drone systems are now being developed for thinning operations for removal of smaller trees, but little is known about their productivity and configuration. Two important advantages of drone systems are reduced soil damage and CO2 emissions. We propose an evaluation approach that describes drone systems in detail and enables a direct comparison with a standard TMS. This approach is then tested on a large case study comprising 1171 harvest areas with diverse characteristics with regard to, for example, flying and forwarding distance, wetness, and steepness defined through detailed digital terrain models. We perform sensitivity analyses on a set of drone system configurations regarding the number of drones, battery system, system parameters, automation level, CO2 tax level, and soil damage cost. The results show that the drone system requires less energy, but with no automation and only four drones in a system, the TMS system is more competitive in virtually all harvest areas. However, with eight drones in a system and a higher automation level, the drone system is competitive in up to 50% of the harvest areas. This figure increases if a CO2 tax and a cost related to soil damage are included.

In Finland and Scandinavia, even-aged forest management predominates, often including mechanical site preparation and manual planting. Growing labor shortages and increased demand for sustainability have driven interest in mechanized and autonomous planting systems. This study evaluates two automated Coverage Path Planners (CPP), Pathfinder and TerraTrail, developed to optimize planting routes for mechanized forest regeneration. Their performance is compared to the routes of the manually operated mechanized planting machine, PlantMax. Three operational sites in Sweden, representing varied terrain and hydrological conditions are evaluated. The evaluation focuses on coverage, Euclidean and Dubins path lengths. Both CPPs incorporate Digital Elevation Models (DEM), Depth-to-Water (DTW) maps and vehicle-specific kinematics to generate planting routes. Two scenarios are evaluated: one where the CPPs neglect the DTW map, and another where the CPPs are constrained to avoid DTW values below 0.3 m. Results show that automated CPPs achieve 15–19% higher coverage than manual planning on average. Pathfinder showed similar normalized path lengths in an unconstrained scenario as the manual operator, but 14% shorter in the constrained environment. TerraTrail shows 7% longer normalized path lengths in an unconstrained scenario, while the constrained scenario shows similar path lengths as the manual operator. These findings emphasize the potential of deploying automated CPP systems to enhance precision, sustainability, and labor efficiency of silvicultural operations. The CPPs support both autonomous deployment and decision support tool for operators. Further refinement, including combining both CPPs to leverage the best functions of each, along with reversible path planning, could enhance their value in forestry practices.

Horizontal collaboration has emerged as a pivotal strategy in modern supply chain management, offering potential savings and improved efficiencies. However, unforeseen events often disrupt the streamlined operation of such collaborations, necessitating robust mechanisms for dynamic cost and benefit allocation. This paper proposes a dynamic approach that allocates benefits or costs based on the reasons behind the disruptions. The approach is based on adapted, well-known, equitable allocation principles. Through detailed analysis and case studies from the forest industry, we demonstrate how our proposed approach ensures fairness and adaptability, fostering stronger and more resilient collaborative relationships among stakeholders. The findings underscore the significance of adaptive allocation methods in promoting sustained collaboration, even when facing unforeseen challenges.

Electromobility plays a key role on the path toward a sustainable society, where electrification of freight transports can mitigate climate change by decreasing the use of fossil fuels, reducing noise, and improving air quality. For heavy trucks there are several challenges and aspects to consider. Among these are estimating total cost, estimating energy consumption, deciding on charging locations and capacity, fleet mix, and how to make route planning. Many companies are making investment decisions to introduce electric trucks without accurate information or any practical experience on these aspects. One reason is the lack of electric heavy trucks in actual operation and information on their use available. We present and analyze the performance from the first two years of operation of the world's first fully battery electric timber truck at the forest company SCA operating in Sweden. The analysis is based on quantitative data from the Scania battery electric timber truck with more than 65,000 kms of operation, as well as qualitative data from unstructured interviews with persons involved in developing and operating the truck, both inside and outside SCA. The analysis provides important information and experiences of the transport, energy consumption based on multiple measurement systems and estimations, total cost, and a sensitivity analysis comparing diesel and electric heavy trucks using the most important input including electric and diesel price, purchasing price, government subsidies, C0(2) emission reduction, and charging downtime. From this, it is clear how electrical trucks can be competitive.

The forest transportation sector is a significant source of greenhouse gas emissions. Industrial professionals aim to shift towards more environmentally friendly practices to help reduce emissions. Electrification is relatively new to forest transportation, as there are limited studies describing its influence because of limited practical use and a lack of relevant data on energy consumption and the behavior of electric trucks. This study investigates various opportunities and barriers to the adoption of battery electric trucks in forestry to support the emission reduction goals of Canada. This paper reviews the scientific literature relevant to studies in battery electric trucks in three planning horizons: strategic, tactical, and operational planning. It looks at the recent developments of heavy-duty electric trucks in charging infrastructure, life cycle analysis, total cost of ownership, energy consumption, emerging technology, and specific routing problems. This paper also discusses industrial initiatives in forest freight electrification. The analysis results highlight the different industrial applications in forestry where electrification brought about a watershed. The forest transportation sector has the potential to become carbon-neutral by investing in battery electric trucks, but achieving net-zero emissions might not be realistic without changes in policies and incentives.

Queuing in vehicle routing problems happens when a given node requires to be visited by several vehicles, whereas only a limited number of vehicles can perform the service simultaneously. Hence, some vehicles must wait until the node is available. We present in this paper a mathheuristic approach to solve the problem. This approach incorporates two phases. The first phase executes a rolling horizon heuristic multiple times to generate an initial set of solutions. Those generated solutions are used to initialize a pool of routes. In the second phase, a column-generation based procedure is used to generate new routes. The contribution of our paper can be summarized as follows. (1) We implemented an efficient set partitioning model that allocates pre-determined slots of time to service operations of vehicles. (2) We proposed fast pricing heuristics to generate new routes with negative reduced costs. (3) The newly generated routes are based on existing ones, keeping the same physical description but the starting times of service operations are modified to better fit the queuing aspects. Performance evaluation has been conducted using instances derived from data provided by forest companies. Experiments proved the effectiveness of the proposed approach, by recording low route duration and achieving almost zero queuing times compared to the initial pool of solutions.

The integration of theory-driven discrete choice modeling (DCM) with neural networks has demonstrated promising advances in choice behavior analysis, yet existing hybrid approaches face persistent challenges in parameter stability, overfitting, and computational efficiency. This study presents ResLogit Plus, a novel framework that enhances the synergy between residual neural networks and discrete choice models through genetic algorithm (GA) optimization. By leveraging GA for parameter initialization and hyperparameter tuning, our approach significantly improves model stability and computational performance while maintaining interpretability. We introduce a comprehensive validation framework incorporating regularization techniques and bootstrap-based validation to ensure robust parameter estimation and mitigate overfitting risks. Empirical validation is conducted using three datasets conducted in different countries: the Swiss Metro dataset (Switzerland), the Carpooling dataset (Switzerland and Germany), and the Greenhouse Gas Emissions dataset (Canada). The results reveal that ResLogit Plus achieves superior predictive accuracy compared to both the original ResLogit and traditional Multinomial Logit (MNL) models, while demonstrating enhanced parameter stability and reduced computational overhead. The framework effectively addresses key methodological challenges, including correlated alternatives and utility estimation noise, thereby advancing the field of discrete choice analysis through a balanced integration of predictive power and theoretical rigor.

Forest planning is vital for ensuring objective fulfilment for decision-makers. Forest-owning companies often organise their planning in a hierarchy of separate stages (i.e. strategic, tactical and operational planning). The objectives for the strategic stage are generally to maximise net present value and long-term harvest levels without threatening the environmental integrity of the forests. However, in the subsequent stages of the planning hierarchy, with a shorter-term focus, the objective is often to minimise costs due to budgetary constraints. These misaligned objectives introduce a dilemma, especially when considering that decisions are typically made using uncertain data. We examined the suboptimality caused by using low-quality forest data in a long-term harvesting planning problem and how this suboptimality is affected by misaligned objectives between the strategic and tactical planning stages. The low-quality forest data were simulated in a Monte Carlo simulation that maintained a real-world structure of errors. The results show that uncertainty in forest data impacts objective fulfilment more than the level of alignment of objectives. However, a high degree of objective alignment performs better than the opposite, regardless of the level of quality of data.

This study presents a novel decision support process for a pilot dispatching problem in the St. Lawrence River. It integrates a comprehensive set of time-based performance measures, including working time, waiting time, and skill level differences, to optimize fairness and operational efficiency in pilot dispatching. The proposed process employs a weighted multi-objective model and a goal programming solution method to dynamically rank pilots, continuously updating dispatch plans. A year-long case study in the St. Lawrence River, Canada with 1288 vessels and 200 pilots across four stations showed that the proposed decision support process significantly improved workload distribution, reducing waiting times by 14

Effective forest regeneration is essential for sustainable forestry practices. In Sweden, mechanical site preparation and manual planting is the dominating method, but sourcing labour for the physically demanding work is difficult. An autonomous scarifying and planting system (Autoplant) could meet the requirements of the forest industry and, for this, a tool for regeneration planning and routing is needed. The tool, Pathfinder, plans the regeneration and routes based on the harvested production (hpr) files, soil moisture and parent material maps, no-go areas (for culture or nature conservation), digital elevation models (DEM), and machine data (e.g., working width, critical slope, time taken for different turn angles). The overall planting solution is either a set of capacity constrained routes or a continuous route and could be used for any planting machine as well as for traditional scarifiers as disc trenchers or mounders pulled by forwarders. Pathfinder was tested on eleven regeneration areas throughout Sweden, both with continuous routes and routes based on a carrying capacity of 1500 seedlings. The net operation area, species and seedling density suggestions were deemed relevant by expert judgement in the field. The routes provided by Pathfinder were compared with solutions given by two experienced drivers and a third solution based on the actual soil scarification at the site. Total driving distance did not differ significantly between the suggestions, but Pathfinder included less side-slope driving on steep slopes (≥ 27

The routing of maritime vessels is a challenging optimization problem that involves finding an adequate balance between conflicting and multiple objectives. This paper proposes a methodology based on inverse optimization to find appropriate objective weights that account for conflicting objectives. To formulate the inverse optimization problem, we integrate a weighted multi-objective function in a model where duality for a network formulation and Karush-Kuhn-Tucker optimality conditions are used as key components. The objective includes route time, fuel consumption, and multiple safety considerations including dynamic stability, the probability of bow slamming, and green water occurrences. The motivation behind our choice of approach lies in the complexity of determining objective weights in multi-objective problems and the need for incorporating the preferences of multiple stakeholders. To test the proposed approach, we use "best practice routes" based on expert knowledge, real-world weather data, and domain-specific objective analysis. Within the scope of this study, these routes are generated using an optimization model with predefined objective weights applied to evaluate the efficacy of the approach. The results demonstrate that the proposed inverse optimization model identifies the weights associated with the best practice routes. A comparison with the analytic hierarchy process (AHP) shows that inverse optimization produces routing decisions more closely aligned with expert-defined best practice routes, while AHP introduces discrepancies in fuel consumption and travel time, leading to suboptimal routing.