Search Results (6)

This study addresses a specialized variant of the full-truckload delivery problem inspired by a Turkish logistics firm that operates in the liquid transportation sector. An exact algorithm is proposed for the relevant problem, to which no exact approach has been applied before. Multiple customer and trailer types, as well as washing operations, are introduced simultaneously during the exact solution process, bringing new aspects to the exact algorithm approach among full-truckload systems in the literature. The objective is to minimize transportation costs while addressing constraints related to multiple time windows, trailer types, customer types, product types, a heterogeneous fleet with limited capacity, multiple departure points, and various actions such as loading, unloading, and washing. Additionally, the elimination or reduction of waiting times is provided along transportation routes. In order to achieve optimal solutions, an exact algorithm based on the column generation method is proposed. A route-based insertion algorithm is also employed for initial routes/columns. Regarding the acquisition of integral solutions in the exact algorithm, both dynamic and static sets of valid inequalities are incorporated. A label-setting algorithm is used to generate columns within the exact algorithm by being accelerated through bi-directional search, ng-route relaxation, subproblem selection, and heuristic column generation. Due to the problem-dependent structure of the column generation method and acceleration techniques, a tailored version of them is included in the solution process. Performance analysis, which was conducted using artificial input sets based on the real-life operations of the logistics firm, demonstrates that optimality gaps of less than 1% can be attained within reasonable times even for large-scale instances relevant to the industry, such as 120 customers, 8 product and 8 trailer types, 4 daily time windows, and 40 departure points. Full article

In today’s complex and dynamic transportation networks, increasing energy costs and adverse environmental impacts necessitate the efficient transport of goods or raw materials across a network to minimize all related costs through vehicle assignment and routing decisions. Vehicle routing problems under dynamic and stochastic conditions are known to be very challenging in both mathematical modeling and computational complexity. In this study, a special variant of the full-truckload vehicle assignment and routing problem was investigated. First, a detailed analysis of the processes in a liquid transportation logistics firm with a large fleet of tanker trucks was conducted. Then, a new original problem with distinctive features compared with similar studies in the literature was formulated, including pickup/delivery time windows, nodes with different functions (pickup/delivery, washing facilities, and parking), a heterogeneous truck fleet, multiple trips per truck, multiple trailer types, multiple freight types, and setup times between changing freight types. This dynamic optimization problem was solved using an intelligent multi-agent model with agent designs that run on vehicle assignment and routing algorithms. To assess the performance of the proposed approach under varying environmental conditions (e.g., congestion factors and the ratio of orders with multiple trips) and different algorithmic parameter levels (e.g., the latest response time to orders and activating the interchange of trip assignments between vehicles), a detailed scenario analysis was conducted based on a set of designed simulation experiments. The simulation results indicate that the proposed dynamic approach is capable of providing good and efficient solutions in response to dynamic conditions. Furthermore, using longer latest response times and activating the interchange mechanism have significant positive impacts on the relevant costs, profitability, ratios of loaded trips over the total distance traveled, and the acceptance ratios of customer orders. Full article

The production of wood chips can be achieved using different types of wood chippers whose productivity can be influenced by many factors including proper knife management. Research was conducted to determine the productivity of the new Diamant chipper in chipping air-dried tops stacked at a roadside landing and to compare the efficiency of dry sharpening and wet sharpening in restoring chipper productivity, the time required by dry sharpening with that of knife replacement, and the cost of dry sharpening to knife change in real-life conditions. To clearly define the influence of knife management, a model of the effect of knife wear on chipper productivity was produced. Analysis of variance was used to check the significance of any differences in chipping and total time consumption per cycle. Multiple regression was used to express the relationship between chipping time consumption per cycle and the cumulated mass processed by a set of knives—the latter taken as an indicator of knife wear. The study lasted 10 full workdays, included a total of 136 truckloads or 3560 t of fresh wood chips (or green tons = gt), and resulted in the average productivity of 59.0 gt per productive chipping hour (excluding all delays) or 39.4 gt per machine scheduled hour (including all delays). Delays represented 37% of total worksite time. Knife management (dry sharpening or change) accounted for 30% of the total delay time due to raw material contamination. Dry sharpening took 30% less time than a full knife change. As wear accumulated and knives lost their edge, the chipping time per cycle increased from 25 in the first cycle (full truck load) to 38 min in the third cycle. The presented study offers robust productivity figures, together with a reliable estimate of the productivity losses caused by knife wear, and could help improve knife management in order to increase chipper productivity as well as reduce unnecessary delays. Full article

Freight forwarding and transportation are the backbone of the modern economy. There are thousands of transportation companies on the market whose sole purpose is to deliver ordered goods from pickup to delivery. Transportation can be carried out by two types of fleets. A company can have its own trucks, or it can use third-party companies. This transportation can be carried out in a variety of formulas, with full truckload being the most common for long routes. The shipper must be aware of the potential cost of such a service during the process of selecting a particular transport. The presented solution addresses this exact issue. There are many approaches, ranging from detailed cost calculators to machine learning solutions. The present study uses a dedicated hybrid algorithm that combines different techniques, spanning clustering algorithms, regression and kNN (k Nearest Neighbors) estimators. The resulting solution was tested on real shipping data covering multi-year contract data from several shipping companies operating in the European market. The obtained results proved so successful that they were implemented in a commercial solution used by freight forwarding companies on a daily basis. Full article

There are two key parameters in short-haul truck operations to deliver biomass to a biorefinery: (1) mass of the load and (2) cycle time (load, travel, unload, and return). A plan to optimize both these parameters is outlined in this study. Operation of a logistics system to deliver 20-bale racks to a biorefinery for continuous 24/7 operation, 48 weeks/year is described. Round bales are stored in satellite storage locations (SSLs) by feedstock producers. A truckload consists of two tandem trailers (40, 0.4 Mg bales), a specification that maximizes load mass. Load-out at the SSL (loading bales into racks) is performed by a contractor and paid by the biorefinery. Subsequent hauling (truck tractor to pull the trailers) is also contracted for by the biorefinery. Central control is specified; the “feedstock manager” at the biorefinery decides the order SSLs are loaded out and can route a truck to any SSL where a load is ready. Tandem trailers with empty racks are dropped at the SSL, and the trailers with full racks are towed to the biorefinery. Uncoupling the loading and hauling in this manner reduces the time the truck waits for loading and the SSL load-out waits for a truck; thus, productivity of both operations is increased. At the biorefinery receiving facility, full racks are removed from the trailers and replaced with empty racks. The objective for this transfer is a 10 min unload time, which completes a logistics design that minimizes cycle time. A delivered rack is placed in a rack unloader to supply bales for immediate processing, or it is placed in central storage to supply bales for nighttime and weekend operations. Three biorefinery capacities were studied: 0.5, 1.0, and 1.5 bale/min. The analysis shows that rack cost to supply a biorefinery processing a bale/min for 24/7 operation is ~3.00 USD/Mg of annual biorefinery capacity, and the rack trailer cost is ~3.25 USD/Mg. Total delivery cost, beginning with bales in SSL storage and ending with a rack being placed in an unloader to deliver individual bales for processing, is 31.51, 28.42, and 26.92 USD/Mg for a biorefinery processing rates of 0.5, 1.0, and 1.5 bale/min, respectively. Full article

The trucking sector in the United States is a $700 billion plus a year industry and represents a large percentage of many firms’ logistics spend. Consequently, there is interest in accurately forecasting prices for truck transportation. This manuscript utilizes the autoregressive integrated moving average (ARIMA) methodology to develop forecasts for three time series of monthly archival trucking prices obtained from two public sources—the Bureau of Labor Statistics (BLS) and Truckstop.com. BLS data cover January 2005 through August 2018; Truckstop.com data cover January 2015 through August 2018. Different ARIMA models closely approximate the observed data, with coefficients of variation of the root mean-square deviations being 0.007, 0.040, and 0.048. Furthermore, the estimated parameters map well onto dynamics known to operate in the industry, especially for data collected by the BLS. Theoretical and practical implications of these findings are discussed. Full article