Simulating Combined Self-Loading Truck and Semitrailer Truck Transport in the Wood Supply Chain

Abstract

Forestry faces frequent and severe natural calamities causing high amounts of salvage wood. Especially under mountainous conditions, regional available self-loading truck capacity is often the main limiting factor causing transport capacity bottlenecks. Therefore, innovative logistics strategies are needed to ensure quick transport of high amounts of salvage wood. Consequently, a multi-echelon unimodal transport concept, where timber is synchronously transshipped at a truck terminal with four transshipment lots from self-loading trucks to semitrailers, was modeled by means of a discrete event simulation. The simulation model calculates key performance indicators such as transshipped volumes and costs and support estimations of optimal truck fleet configuration. The results provide cost-optimal truck fleet configurations in terms of the number of self-loading trucks, semitrailers and prime mover trucks for varying transshipment volumes, delivery time to terminal and legal truck payload scenarios. Applying the truck terminal concept considerably decreases the number of self-loading trucks needed to transport the same volume when compared to unimodal wood transport, which is most common under mountainous conditions in Europe. In the majority of delivery time to terminal and terminal transshipment volume scenarios, the number of self-loading trucks was reduced by more than 50%. Increasing the legal gross vehicle weight for timber transport from 44 t up to 50 t reduces the number of self-loading trucks needed by 20% to 38%, depending on the scenario setting. Additionally, less self-loading trucks arriving at the terminal also cuts queuing times and system efficiency increases as transport cost/t is reduced by 6% to 11% depending on the scenario setting. Expanding the truck terminal concept by adding storage capacity as well as varying the number of transshipping lots and also including costs for terminal construction and operations in the economic analyses are promising topics for future studies.

Benchmarking Procurement Cost Saving Strategies for Wood Supply Chains

“…Truck and train terminals as well as the effects of higher truck payloads (i.e., up to 50 t) on critical key performance indicators were analyzed in Discrete Event Simulation models for Austrian case study settings of multi-echelon [26] and multimodal [27] wood supply chains. These built on an earlier model [28] enabling game-based stakeholder workshops [29].…”

Assessment of the Relevance of Use in the Construction of a Forest Tractor With a Semi-Trailer of a Recuperative Pneumatic Hydraulic Saddle Device

“…Christoph Kogler и др. [6] в своей научной работе исследуют многоступенчатую транспортную имитационную модель, описываемую синхронную перегрузку лесоматериалов на автомобильном терминале с четырьмя перевалочными пунктами из самозагружающихся лесовоз-ных автомобилей в полуприцепы. Имитационная модель позволяет определить такие показатели эффективности, как объемы вывозимых лесоматериалов и затраты на вывозку.…”

A Systematic Review on Technologies and Industry 4.0 in the Forest Supply Chain: A Framework Identifying Challenges and Opportunities

“…Simulations used simulation software as a tool to simulate the optimal options for the research problems. Three simulation software appeared more than once in the results: Witness ® [32][33][34][35], AnyLogic ® [36][37][38][39] and ExtendSim ® [40,41]. The simulation software are able to evaluate, compare and optimize their designed model.…”

“…Vaatainen et al [33] used a discrete-event simulation method to compare the truck performance indicators with different gross vehicle weights and payloads. Simulation software can calculate the processing time, weights, costs and working times of transportation methods in the supply chain [37]. Some studies considered using GIS data as simulation data to solve problems in transportation.…”

“…Efficiency [33,[35][36][37]39,45,63,65,73,75] Cost reduction or profitability [28,33,35,37,[39][40][41][43][44][45][47][48][49]58,[61][62][63]65,73,75] Reduce Complexity [55,59] Transparency [43,44,51,70] Environmental level Reduce greenhouse gas (GHG) emissions [33,38,44,61,65] Energy consumption [44,61] Social level Increase job opportunity [50] Reduce illegal activity [51,71,74]…”