Road freight transport contributes to a large portion of greenhouse gas (GHG) emissions. Transitioning diesel to battery electric (BE) trucks is an attractive sustainability solution. To evaluate the BE transition in New Zealand (NZ), this study analysed the life-cycle GHG emissions and total cost of ownership (TCO) of diesel and BE trucks based on real industry data. The freight pickup and delivery (PUD) operations were simulated by a discrete-event simulation (DES) model. Spreadsheet models were constructed for life-cycle assessment (LCA) and TCO for a truck operational lifetime of 10 years (first owner), this being the typical usage of a tier-one freight company in New Zealand (NZ). The whole-of-life emissions from the diesel and BE trucks are 717,641 kg and 62,466 kg CO_2e, respectively. For the use phase (first owner), the emissions are 686,754 kg and 8714 kg CO_2e, respectively; i.e., the BE is 1.27% of the diesel truck. The TCO results are 528,124 NZ dollars (NZD) and 529,573 NZD (as of 2022), respectively. The battery price and road user charge are the most sensitive variables for the BE truck. BE truck transitions are explored for freight companies, customers, and the government. For the purchase of BE trucks, the break-even point is about 9.5 years, and straight-line depreciation increases freight costs by 8.3%. Government subsidy options are evaluated. The cost of emission credits on the emissions trading scheme (ETS) is not expected to drive the transition. An integrated model is created for DES freight logistics, LCA emissions, and TCO costs supported by real industry data. This allows a close examination of the transition economics.

The transport of freight involves numerous intermediate steps, such as freight consolidation, truck allocation, and routing, all of which exhibit high day-to-day variability. On the delivery side, drivers usually cover specific geographic regions, also known as clusters, to optimise operational efficiency. A crucial aspect of this process is the effective allocation of resources to match business requirements. The discrete-event simulation (DES) technique excels in replicating intricate real-world operations and can integrate a multitude of stochastic variables, thereby enhancing its utility for decision making. The objective of this study is to formulate a routing architecture that integrates with a DES model to capture the variability in freight operations. This integration is intended to provide robust support for informed decision-making processes. A two-tier hub-and-spoke (H&S) architecture was proposed to simulate stochastic routing for the truck fleet, which provided insights into travel distance and time for cluster-based delivery. Real industry data were employed in geographic information systems (GISs) to apply the density-based spatial clustering of applications with noise (DBSCAN) clustering method to identify customer clusters and establish a truck plan based on freight demand and truck capacity. This clustering analysis and simulation approach can serve as a planning tool for freight logistics companies and distributors to optimise their resource utilisation and operational efficiency, and the findings may be applied to develop plans for new regions with customer locations and freight demands. The original contribution of this study is the integration of variable last-mile routing and an operations model for freight decision making.

Urban pickup and delivery (PUD) activities are important for logistics operations. Real operations for general freight involve a high degree of complexity due to daily variability. Discrete-event simulation (DES) is a method that can mimic real operations and include stochastic parameters. However, realistic vehicle routing is difficult to build in DES models. The objective is to create a DES model for realistic freight routing, which considers the driver’s routing decisions. Realistic models need to predict the delivery route (including time and distance) for variable consignment address and backhaul pickup. Geographic information systems (GIS) and DES were combined to develop freight PUD models. GIS was used to process geographical data. Two DES models were developed and compared. The first was a simple suburb model, and the second an intersection-based model. Real industrial data were applied including one-year consignment data and global positioning system (GPS) data. A case study of one delivery tour is shown, with results validated with actual GPS data. The DES results were also compared with conventional GIS models. The result shows the intersection-based model is adequate to mimic actual PUD routing. This work provides a method for combining GIS and DES to build freight operation models for urban PUD. This has the potential to help industry logistics practitioners better understand their current operations and experiment with different scenarios.

Small and medium enterprises (SMEs) in the manufacturing industry normally arise as a result of product innovations. Transiting and growing into large organizations is a critical process for the sustainable growth of SMEs, and this requires developing appropriate production systems. Such development focuses on production economics and the optimization of facility layout, production capacity, and machine utilization. These optimizations are usually achieved using discrete event simulation (DES). However, the DES primarily focuses on production optimization and is not formulated to give concurrent attention to occupational health and safety risks, where the workers’ occupational health and safety are also important to production systems; hence, sustainability issues arise. Until now, the production economics and workers’ health and safety are usually treated separately, and the health and safety aspects are often managed after the system has been developed. This brings increasing hazards in the production environment since adding new facilities to the system may introduce new hazards and cause further safety issues. The present paper proposes a methodology to solve the above concerns. Its key features are the use of a quality-of-life metric for determining the occupational health and safety risks of a particular work activity and the embedding thereof as a routine in DES for manufacturing plant simulation. One of the contributions of the proposed integration method is that it helps to enhance the development of production systems that support financial due diligence, as well as occupational health and safety (OHS) due diligence. This is particularly important to SMEs in the manufacturing industries, where growth comes with particular sustainability challenges related to the need to develop more systematic operational and safety management systems.

Background: Logistics problems involve a large number of complexities, which makes the development of models challenging. While computer simulation models are developed for addressing complexities, it is essential to ensure that the necessary operational behaviours are captured, and that the architecture of the model is suitable to represent them. The early stage of simulation modelling, known as conceptual modelling (CM), is thus dependent on successfully extracting tacit operational knowledge and avoiding misunderstanding between the client (customer of the model) and simulation analyst. Objective: This paper developed a methodology for managing the knowledge-acquisition process needed to create a sufficient simulation model at the early or the CM stage to ensure the correctness of operation representation. Methods: A minimum viable model (MVM) methodology was proposed with five principles relevant to CM: iterative development, embedded communication, soliciting tacit knowledge, interactive face validity, and a sufficient model. The method was validated by a case study of freight operations, and the results were encouraging. Conclusions: The MVM method improved the architecture of the simulation model through eliciting tacit knowledge and clearing up communication misunderstandings. It also helped shape the architecture of the model towards the features most appreciated by the client, and features not needed in the model. Originality: The novel contribution of this work is the presentation of a method for eliciting tacit information from industrial clients, and building a minimally sufficient simulation model at the early modelling stage. The framework is demonstrated for logistics operations, though the principles may benefit simulation practitioners more generally.

Modelling freight logistics is challenging due to the variable consignments and diverse customers. Discrete-event Simulation (DES) is an approach that can model freight logistics and incorporate stochastic events. However, the flexible delivery routes of Pickup and Delivery (PUD) are still problematic to simulate. This research aims to develop last-mile delivery architecture in DES and evaluate the credibility of the model. A two-tier architecture was proposed and integrated with a DES model to simulate freight operations. The geographic foundation of the model was determined using Geographic Information Systems (GIS), including identifying customer locations, finding cluster centres, and implementing Travelling Salesman Problem (TSP) simulation. This complex model was simplified to the two-tier architecture with stochastic distances, which is more amenable to DES models. The model was validated with truck GPS data. The originality of the work is the development of a novel and simple methodology for developing a logistics model for highly variable last-mile delivery.