Stochastic Dominance Approach to Evaluate Optimism Bias in Truck Toll Forecasts

Optimism bias is a consistent feature associated with truck toll forecasts, à la Standard & Poor’s and the NCHRP synthesis reports. Given the persistent problem, two major sources of this bias are explored. In particular, the ignorance of operating cost as a demand-side factor and lack of attention to user heterogeneity are found to contribute to this bias. To address it, stochastic dominance analysis is used to assess the risk associated with toll revenue forecasts. For a hypothetical corridor, it is shown that ignorance of operating cost savings can lead to upward bias in the threshold value of time distribution. Furthermore, dominance analysis demonstrates that there is greater risk associated with the revenue forecast when demand heterogeneity is factored in. The approach presented can be generally applied to all toll forecasts and is not restricted to trucks.Forecast Bias; Operating costs; Risk assessment; Savings; Stochastic Dominance; Tolls;Trucks

New Concept of Container Allocation at the National Level: Case Study of Export Industry in Thailand

This paper presents container allocation technique of which minimizing the total opportunity loss of an export industry in Thailand. This new allocation concept applies as a strategic management tools at the national level since it is consistent to the characteristics of the container supply chain management in Thailand. The first section of this paper presents the review of facts and problems of container supply chain management. It reveals that containerization system is significant to the international trade as it holds good characteristics of sea transportation. It can transport a lot of products while minimize the damage of goods. Supply chain management of the containerization system presents and shows that there are four main players in managing the container – principal, port, container depot, and customer. After an intensive review of containerization system’s problem, the most common problem that all parties have encountered is an imbalance between demand and supply of container. The well-known solution to the stated problem is relocation of containers between various places using optimization technique, which aims to minimize operation cost. Indeed, those solutions are unable solve the containerization system’s problem in Thailand: lacking their own fleets: having no bargaining power in relocating container between areas as needed. In the present, many of Thai exporters face with losses of sales or profit because they cannot find enough or proper containers to transport their goods to the customer. The authors, therefore, have seen that those problems need to be strategically solved by the government. The limited number of containers must be properly allocated to the exporter with regard to the minimum losses to the economics of the country. The main contributions of this paper are two folds. First, the opportunity losses of the various export industry are indicated when lack of containers, Second, the mathematical model has been formulated using linear programming technique with several constraints, such as, demand, supply, obsolete time, operating cost, lead time etc. The authors hope that the new concept presented in this paper will provide the great contribution for other countries, which face the same problem of Thailand. Keywords: Container Management, Opportunity Loss, Allocation Problem, Optimization, International Trad

Integrating operations research into green logistics:A review

Logistical activities have a significant global environmental impact, necessitating the adoption of green logistics practices to mitigate environmental effects. The COVID-19 pandemic has further emphasized the urgency to address the environmental crisis. Operations research provides a means to balance environmental concerns and costs, thereby enhancing the management of logistical activities. This paper presents a comprehensive review of studies integrating operations research into green logistics. A systematic search was conducted in the Web of Science Core Collection database, covering papers published until June 3, 2023. Six keywords (green logistics OR sustainable logistics OR cleaner logistics OR green transportation OR sustainable transportation OR cleaner transportation) were used to identify relevant papers. The reviewed studies were categorized into five main research directions: Green waste logistics, the impact of costs on green logistics, the green routing problem, green transport network design, and emerging challenges in green logistics. The review concludes by outlining suggestions for further research that combines green logistics and operations research, with particular emphasis on investigating the long-term effects of the pandemic on this field.</p

Automated System for Freight Transportation Optimization on the Transport Network

An automated system for freight traffic optimization on a transport network has been developed, which is realized in the form of a complex computer program with application of the visual design environment of Embarcadero RAD Studio. The program complex consists of the main form from which two subprograms are loaded. The search of optimum routes performed by the routing optimization subprogram is used at transportations of freight on the set transport network based on different schemes (from one vertex (the supplier) to all other vertices (consumers), serially – from each vertex to all other vertices, and from two (or several) set of vertices to all other vertices). Freight delivery between the supply and consumption points was optimized by means of the freight transportation optimization subprogram, taking into account the restrictions on the volume of freight at the points of departure and destination

Economic Modeling of Agricultural Production in North Dakota Using Transportation Analysis and Forecasting

Agricultural industry is crucial for the economy; agricultural transportation is an integrated part of that industry. Optimization of the transportation and logistics costs is an important part of the transportation economics. This study focuses on the minimization of the total cost of transportation logistics. Sugar-beet is one of the important crops in the state of North Dakota and there has been sporadic research in the sugar-beet transportation economic modeling. Therefore, this research focuses on the transportation economic modeling of the sugar-beet including yield forecasting to reduce the uncertainty in this process. This study begins with developing a yield forecasting model which is presented as a way to sustain the agricultural transportation under stochastic environments. The stochastic environment includes variation in weather conditions, precipitation, soil type, and randomness of natural disasters. The yield forecasting model developed uses Normalized Difference Vegetation Index (NDVI), Geographical Information System (GIS), and statistical analysis. The second part of this study focuses on economic model to calculate the total cost associated with the sugar-beet transportation. This model utilizes the GIS analysis to calculate the distances travelled from member coop farms during harvest and transport to processing facilities in various locations. This model sheds light on the critical cost factors associated with the total economic analysis of sugar-beet harvest, transportation, and production. Since the sugar-beet yield varies significantly based on different factors, it provides for a variable optimal harvesting time based on the plant maturity and sugar content. Sub-optimized pilers location result in the high transportation and utilization costs. The third part of this research focuses on minimizing the sum of transportation costs to and from pilers and the piler utilization cost. A two-step algorithm, based on the GIS with global optimization method, is used to solve this problem. In conclusion, this research will provide a primary stepping stone for farmers, planners, and engineers to develop a data driven analytical tool which will help to minimize the total logistics cost of the sugar-beet crop while at the same time keeping the sugar content intact and predict the sugar yield and truck volume

Intermodal Network Design and Expansion for Freight Transportation

Over the last 50 years, international trade has grown considerably, and this growth has strained the global supply chains and their underlying support infrastructures. Consequently, shippers and receivers have to look for more efficient ways to transport their goods. In recent years, intermodal transport is becoming an increasingly attractive alternative to shippers, and this trend is likely to continue as governmental agencies are considering policies to induce a freight modal shift from road to intermodal to alleviate highway congestion and emissions. Intermodal freight transport involves using more than one mode, and thus, it is a more complex transport process. The factors that affect the overall efficiency of intermodal transport include, but not limited to: 1) cost of each mode, 2) trip time of each mode, 3) transfer time to another mode, and 4) location of that transfer (intermodal terminal). One of the reasons for the inefficiencies in intermodal freight transportation is the lack of planning on where to locate intermodal facilities in the transportation network and which infrastructure to expand to accommodate growth. This dissertation focuses on the intermodal network design problem and it extends previous works in three aspects: 1) address competition among intermodal service providers, 2) incorporate uncertainty of demand and supply in the design, and 3) incorporate multi-period planning into investment decisions. The following provides an overview of the works that have been completed in this dissertation. This work formulated robust optimization models for the problem of finding near-optimal locations for new intermodal terminals and their capacities for a railroad company, which operates an intermodal network in a competitive environment with uncertain demands. To solve the robust models, a Simulated Annealing (SA) algorithm was developed. Experimental results indicated that the SA solutions (i.e. objective function values) are comparable to those obtained using GAMS, but the SA algorithm can obtain solutions faster and can solve much larger problems. Also, the results verified that solutions obtained from the robust models are more effective in dealing with uncertain demand scenarios. In a second study, a robust Mixed-Integer Linear Program (MILP) was developed to assist railroad operators with intermodal network expansion decisions. Specifically, the objective of the model was to identify critical rail links to retrofit, locations to establish new terminals, and existing terminals to expand, where the intermodal freight network is subject to demand and supply uncertainties. Addition considerations by the model included a finite overall budget for investment, limited capacities on network links and at intermodal terminals, and due dates for shipments. A hybrid genetic algorithm was developed to solve the proposed MILP. It utilized a column generation algorithm for freight flow assignment and a shortest path labeling algorithm for routing decisions. Experimental results indicated that the developed algorithm can produce optimal solutions efficiently for both small-sized and large-sized intermodal freight networks. The results also verified that the developed model outperformed the traditional network design model with no uncertainty in terms of total network cost. The last study investigated the impact of multi-period approach in intermodal network expansion and routing decisions. A multi-period network design model was proposed to find when and where to locate new terminals, expand existing terminals and retrofit weaker links of the network over an extended planning period. Unlike the traditional static model, the planning horizon was divided into multiple periods in the multi-period model with different time scales for routing and design decisions. Expansion decisions were subject to budget constraints, demand uncertainty and network disruptions. A hybrid Simulated Annealing algorithm was developed to solve this NP-hard model. Model and algorithm’s application were investigated with two numerical case studies. The results verified the superiority of the multi-period model versus the single-period one in terms of total transportation cost and capacity utilization

Strategies to increase port competitiveness

Improving the competitiveness of local businesses and their products within worldwide markets is a vital element for the long-term economic growth of a region. This paper presents a summary of ongoing research needs and outcomes formulated from a partnership between the University of Queensland and the Port of Brisbane Pty Ltd (PBPL), in order to facilitate international trade growth in Queensland and improve PBPL’s competitiveness. As part of this partnership with PBPL, we explore strategies to overcome inefficiencies in supply chain and infrastructure and discuss subsequent prospects for further investigation. The key goals of the partnership program for transport-related issues have been identified as: (i) providing a platform for freight actors trading through the port, in order to increase the performance of their logistics operations by adopting cooperative strategies; (ii) exploring modal shift opportunities to enhance the sustainability and the efficiency of the logistics operations of importers and exporters; (iii) facilitating improved inland supply chains for local export commodities through new trans-shipment points, back-loading opportunities, and logistics cost minimisation