Full Truckload Research Articles

Dynamic Supplier Selection and Its Optimal Strategy Considering Full Truck Load and Fuzzy Demand using Fuzzy Expected Value-Based Programming

This article proposes a linear integer optimization model incorporating fuzzy parameters to find the optimal solution for a dynamic supplier selection problem with uncertain demand. The uncertain demand value is represented using a fuzzy variable. A fuzzy expected value-based linear optimization solver is used to address the optimization problem, to minimize the total cost under fuzzy demand values. Several computational experiments were conducted to evaluate and analyze the model. The results show that the proposed model effectively identifies the optimal suppliers for each product. Additionally, the model determines the optimal purchase volumes for each product type from the selected suppliers, leading to the minimal total expected cost.

Exploring cost performance tradeoffs and uncertainties for electric- and autonomous electric trucks using computational experiments

The recent development of battery electric trucks (BETs) suggests that they could play a vital role in transitioning to zero-emission road freight. To facilitate this transition, it is important to understand under which conditions BETs can be a viable alternative to internal combustion engine trucks (ICETs). Concurrently, the advancement of autonomous driving technology adds uncertainty and complexity to analyzing how the cost competitiveness of future zero-emissions trucks, such as autonomous electric trucks (AETs) may develop. This study examines the cost performance of BETs and AETs compared to ICETs, and how it varies over different market and technology conditions, charging strategies, and transport applications. Focus is on heavy-duty tractor-trailer trucks operating full truckload shuttle-flows in Sweden. Due to the inherent uncertainty and interactions among the analyzed factors, the analysis is performed as computational experiments using a simulation model of BET, AET, and ICET shuttle flow operations and associated costs. In total, 19,200 experiments are performed by sampling the model across 1200 scenarios representing various transport applications and technical and economic conditions for sixteen charging strategies with different combinations of depot, destination, and en route charging. The results indicate that both BETs and AETs are cost competitive compared to ICETs in a large share of scenarios. High asset utilization is important for offsetting additional investment costs in vehicles and chargers, highlighting the importance of deploying these vehicles in applications that enable high productivity. The cost performance for BETs is primarily influenced by energy related costs, charging strategy, and charging infrastructure utilization. The AET cost performance is in addition heavily affected by remote operations cost, and costs for the automated driving system. When feasible, relying only on depot charging is in many scenarios the most cost-effective charging strategy, with the primary exceptions being highly energy-demanding scenarios with long distances and heavy goods in which the required battery is too heavy to operate the truck within vehicle weight regulations if not complemented by destination, or en route charging. However, many experiments do not lead to a reduced payload capacity for BETs and AETs compared to ICETs, and a large majority of the considered scenarios are feasible to operate with a BET or AET within current gross vehicle weight regulations.

The Effect of Knife Wear and Sharpening Mode on Chipper Productivity and Delays

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.

Multicriteria Machine Learning Model Assessment—Residuum Analysis Review

The use of machine learning (ML) and its applications is one of the leading research areas nowadays. Neural networks have recently gained enormous popularity and many works in various fields use them in the hope of improving previous results. The application of the artificial intelligence (AI) methods and the rationale for this decision is one issue, but the assessment of such a model is a completely different matter. People mostly use mean square error or less often mean absolute error in the absolute or percentage versions. One should remember that an error does not equal an error and a single value does not provide enough knowledge about the causes of some behavior. Proper interpretation of the results is crucial. It leads to further model improvement. It might be challenging, but allows us to obtain better and more robust solutions, which ultimately solve real-life problems. The ML model assessment is the multicriteria task. A single measure delivers only a fraction of the picture. This paper aims at filling that research gap. Commonly used integral measures are compared with alternative measures like factors of the Gaussian and non-Gaussian statistics, robust statistical estimators, tail index and the fractional order. The proposed methodology delivers new single-criteria indexes or the multicriteria approach, which extend the statistical concept of the moment ratio diagram (MRD) into the index ratio diagram (IRD). The proposed approach is validated using real data from the Full Truck Load cost estimation example. It compares 35 different ML regression algorithms applied to that task. The analysis gives an insight into the properties of the selected methods, enables their comparison and homogeneity analysis and ultimately leads towards constructive suggestions for their eventual proper use. The paper proposes new indexes and concludes that correct selection of the residuum analysis methodology makes the assessment and the ML regression credible.

Custom Approach to the Cost Estimation of the Full Truckload Contracts for Short Routes

Custom Approach to the Cost Estimation of the Full Truckload Contracts for Short Routes

A simulation study of single-vendor, single and multiple-manufacturers supply chain system, with stochastic demand and two distribution policies

This paper addresses the long-standing stochastic single-vendor, multi-manufacturer inventory control problem, using simulation optimization. It is assumed that Manufacturers producing similar goods experience very high random demand; hence, safety stock of raw materials is held in reserve in their warehouses. The vendor supplying this raw material (principal ingredients) as a policy restricts shipments to multiples of full truck load. Thus, it is necessary to take replenishment decision and coordinate delivery among these manufacturers. To solve this problem, we modeled the single vendor, single manufacturer version of the problem (AlDurgam et al., 2017) using simulation optimization techniques, which was validated numerically using parameters and results from AlDurgam et al. (2017). The simulation model was modified systematically to relax the single manufacturer assumption under two distribution policies namely, joint reorder point and vendor managed inventory. These policies were evidently modeled with stringent conditions in literature. A numerical example was provided to compare the performances of the two proposed policies, and the VMI policy was found to performed better in terms of financial savings. Lastly, we investigate the robustness of the famous continuous review (Q, R) inventory policy which is widely used in the mathematical modeling of this problem, against the common cycle assumption. The coefficient of variation is thus suggested as a judgment criterion of when to embrace simulation modeling ahead of other modeling techniques.

Reverse milk run – the logistics retrenchment strategy

Learning outcomes After completion of the case study, students will be able to understand the concept of clustering and identify clusters for improving capacity utilization, analyse transport routes to optimize logistics resources, analyse the impact of a full truckload on resource optimization, evaluate unused capacity and ascertain the impact of reverse milk run to reduce the same and apply clustering and reverse milk run to optimize the logistics resources. Case overview/synopsis The case study is about a freight forwarding company that offered end-to-end logistics solutions for the exporters based in India. Within a short time span, the company became one of the sought-after service providers for its clients. However, when the company planned to expand its business by expanding its client base, the efficiencies reduced and hurt the profitability of the company. It was all excellent with the limited number of clients, but as the number of distantly located clients surged, the operating costs increased. Trucks were running with partial loads, thus reducing efficiency. The rate of increase in cost surpassed the rate of revenue every time. The cost per mile of transportation was on the rise. The surging fuel prices were adding to the heat. In spite of being one of the first choices for clients, the company could not generate good profit margins. If they chose to increase prices, the company would have lost customers to the cheaper unorganized players in the market. It was time to choose between growth and survival. The company could not sustain itself without devising a mechanism to reduce costs. The company would not have sustained itself without devising a mechanism to reduce costs. To sustain in the business, the company had to device a mechanism to reduce costs. Whether to continue operating the conventional way or to transform? Was there a logistics strategy that would have improved transportation efficiency and reduced the costs for the company? Complexity academic level The case study is suitable for teaching post-graduate management courses in operations and logistics, supply chain management and supply chain analytics, as well as entrepreneurship-related courses. Supplementary material Teaching notes are available for educators only. Subject code CCS 9: Operations and logistics.

Hybrid Approach to the Cost Estimation of External-Fleet Full Truckload Contracts

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.

Exact analysis of One-Warehouse-Multiple-Retailer inventory systems with quantity restricted deliveries

Joint consideration and coordination of inventory and shipment decisions is an important challenge in the quest for sustainable distribution systems. This paper addresses this issue by presenting a method for exact analysis of the inventory level distributions in a centralized One-Warehouse-Multiple-Retailer (OWMR) inventory system with quantity restricted deliveries from the warehouse. The system is characterized by continuous review (R,nQ) policies, complete backordering, and compound Poisson demand. The class of quantity restricted delivery policies we consider is motivated by a common desire in practice to ship full load carriers. It generalizes the assumption of unrestricted partial deliveries often used in the literature, and allows the warehouse to only ship retailer specific delivery batch quantities to the different retailers. The delivery batches typically represent package or pallet sizes, containers, or full truckloads. An attractive feature of our approach is that it does not add to the computational effort of analyzing the special case of unrestricted partial deliveries available in the existing literature. A numerical study shows that accounting for quantitative delivery restrictions used in transportation and handling operations when optimizing the reorder points in multi-echelon systems can be very important. Failure to do so can lead to high backorder costs and inadequate customer service.

An MILP-based Lexicographic Approach for Robust Selective Full Truckload Vehicle Routing Problem

An MILP-based Lexicographic Approach for Robust Selective Full Truckload Vehicle Routing Problem

Resources synchronization in a full truckload pickup and delivery problem: An exact approach

In this work, the Unpaired Full Truckload Pickup and Delivery Problem with Resource Synchronization is modeled and solved, where routes must be determined to transport commodities from pickup to delivery locations by a set of vehicles, subject to timing and resource synchronization constraints, to satisfy demands at minimum cost. Unlike previous works, the use of multiple resources for loading and unloading tasks at each location are considered and appropriately managed through a representation using discrete shifts. An integer linear programming model is proposed to simultaneously solve allocation, routing and resources synchronization optimization problems. In order to improve the model performance for large size instances, diverse reformulations, including additional inequalities and symmetric-breaking constraints, are implemented and tested. Moreover, a heuristic procedure is proposed to provide good initial feasible solutions. The capabilities of the proposed approach are assessed through several examples.

Optimization of Platoon Formation Center Location for Truck Platooning in Turkey

In Road Transportation, truck transportation is commonly being categorized into Less Than Truckload (LTL), Partial Truckload and Full Truckload (TL). The standard LTL transportation is carried out by means of consolidated freight at optimized depots of designation, in the form of single or multiple assignment. Nowadays, freight transportation industry is now facing a serious problem of scarce labor force and environmental concerns. One solution for that is truck platooning. Truck Platooning is a grouping of freight vehicles into connected vehicle convoys using electronic coupling as an application in automated driving technology with the aim of saving fuel, reducing travel costs, and improving infrastructure efficiency. Platoon planning is required to obtain the best results of platooning. Therefore, the objective of this study is to find the optimal locations of PFC in Turkey for (de)formation truck platoons by using discrete mathematical optimization.

Agent-Based Modeling to Improve Beef Production from Dairy Cattle: Model Description and Evaluation

Agent-based modeling (ABM) enables an in silico representation of complex systems and captures agent behavior resulting from interaction with other agents and their environment. This study developed an ABM to represent a pasture-based beef cattle finishing systems in New Zealand (NZ) using attributes of the rearer, finisher, and processor, as well as specific attributes of dairy-origin beef cattle. The model was parameterized using values representing 1% of NZ dairy-origin cattle, and 10% of rearers and finishers in NZ. The cattle agent consisted of 32% Holstein-Friesian, 50% Holstein-Friesian–Jersey crossbred, and 8% Jersey, with the remainder being other breeds. Rearers and finishers repetitively and simultaneously interacted to determine the type and number of cattle populating the finishing system. Rearers brought in four-day-old spring-born calves and reared them until 60 calves (representing a full truck load) on average had a live weight of 100 kg before selling them on to finishers. Finishers mainly attained weaners from rearers, or directly from dairy farmers when weaner demand was higher than the supply from rearers. Fast-growing cattle were sent for slaughter before the second winter, and the remainder were sent before their third winter. The model finished a higher number of bulls than heifers and steers, although it was 4% lower than the industry reported value. Holstein-Friesian and Holstein-Friesian–Jersey-crossbred cattle dominated the dairy-origin beef finishing system. Jersey cattle account for less than 5% of total processed beef cattle. Further studies to include retailer and consumer perspectives and other decision alternatives for finishing farms would improve the applicability of the model for decision-making processes.

A decision support system for consolidated distribution of a ceramic sanitary ware company

This study proposes a model-driven decision support system (DSS) for a consolidated distribution problem of a ceramic sanitary ware company with two factories producing different types of products. The primary motivation for consolidating loads of the these factories is to better use the empty capacity of the vehicles in terms of weight and volume. For transportation, the company has made contracts for each factory with a third-party logistics provider. In these contracts, there are special conditions according to the characteristics of the products. We suggest two kinds of transportation approaches that determine how to combine loads of two factories and how to solve the routing problem that arises to distribute the remaining loads. The DSS includes a mathematical model that determines full-truck loads and a hybrid genetic algorithm that solves the open vehicle routing problem. Both of these approaches have been developed specifically for the problem within the scope of the study. The mathematical model provides the best use of vehicles in terms of volume and weight. At the same time, the meta-heuristic approach offers the opportunity to address the complex cost structure of the emerging routing problem. The proposed DSS receives all order data from the enterprise resource planning system and constructs a dynamic distribution problem. Then it generates solution alternatives by using the models developed in this study and presents cost-effective and visualizable distribution plans.

Carbon Dioxide Neutralization Across the Global Supply Chain

The increased impacts of climatic change and global warming has led many organizations to adopt green initiatives in several areas of their business processes. Many multinational companies are moving towards reduction of carbon emission across its various operations. Carbon neutrality is the process where steps are taken to achieve net zero carbon dioxide emissions. This article proposes measures to achieve carbon neutrality across the supply chain globally. As part of its sustainability initiative, organizations have decided to reduce carbon consumption across their plants. This calls for estimation of carbon dioxide emissions and reducing the carbon footprint in the entire supply chain process. It also involves gauging greenhouse CO2 emissions during the transportation process for all TMC regions and transportation models used by various companies. The main calculations include total CO2 emissions, CO2 emissions per ton of goods transported, CO2 emissions per transport Km. These calculations are done based on factors such as full truck load, less truck load, sea mode of transportation, and air mode of transportation. An analysis is performed on the resulting calculation figures for different modes of transportation such as road, air, and sea. The analysis shows that there is an increase in overall CO2 for air mode of transportation. The least increase in overall Co2 is sea mode of transportation. Through this analysis, it helps the company to take better decisions regarding the mode of transportation that they need to adopt to achieve carbon neutrality.

Spot market versus full charter fleet: Decision support for full truck load tenders

Given the dynamics of today’s markets, decision support based on advanced analytics is required to help market players keep their top position. This paper presents an approach to help business decision-makers gain market share by providing competitive tender offers for Full Truck Load (FTL) services. In particular, we compare operating a fleet of full charter trucks (FCT), using Spot-Market (SM) capacity and a mixture of both options against each other. A Pickup and Delivery Problem is modeled, and solved using an Adaptive Large Neighborhood Search heuristic. Computational results indicate strong service benefits combining FCT and SM usage. Numerical experiments are presented in detail to support the findings. Additionally, a real-life case study originating from DB Schenker is presented.

Minimizing Total Logistics Cost for Long-Haul Multi-Stop Truck Transportation

Whenever there is sufficient demand, companies generally prefer the full truckload (TL) option for long-distance transport, resulting in large and less frequent shipment operations that can be costly if inventory carrying costs are high. Less than truckload (LTL) is another option for transport when carrying costs are high and/or there is insufficient demand. Shipment consolidation provides another option that combines many of the benefits of both TL and LTL. Shipment consolidation is a cost-effective transport solution that combines different size shipments into a single truckload. Combining many loads as a single load brings together economies of scale and potential savings. Traditional routing techniques that minimize distance are not suitable for shipments that have different origins and destinations because it can be beneficial to travel further to minimize overall transport and inventory cost, or what is termed total logistics cost (TLC). Effective consolidation of multi-stop routes to minimize TLC requires routing procedures that are more computationally intensive to find beneficial combinations of loads into consolidated shipments. In this study, we have developed a saving-based procedure to determine consolidated route sequences that minimize the TLC of shipments. Twenty-one data sets were produced using real city coordinates and population densities in North Carolina to demonstrate the effectiveness of the procedure. The solutions of the proposed method are compared with the solutions of the traditional Clarke and Wright (C-W) algorithm. Although the traditional C-W algorithm provides very fast solution times, the proposed method has produced much better solution values.

Core stable coalition selection in collaborative truckload transportation procurement

We study the problem of forming a core stable coalition that minimizes system-wide cost, given a set of candidate full truckload shipment lanes, which we model as players, and a characteristic function that corresponds to solving an NP-Hard cost minimizing lane covering problem. Since not every coalition has a non-empty core in this setting, we formulate a mixed integer linear program that can identify a core stable coalition with minimal system-wide cost along with a cost allocation in the core. We propose a solution method that embeds row generation, column generation, and an upper bounding heuristic into branch-and-bound, which can be considered as a form of branch-and-cut-and-price. We evaluate the performance of our solution method through extensive numerical experiments on randomly generated problem instances.

Multi-Product Shipment and Production Scheduling Mathematical Model under Different Distribution Policies

Nowadays, integration of production and distribution has been important in the supply chain. Besides, due to their limited lifespan, integration in the supply chain of perishable items has led to more studies. This study aims to present integrated production and shipment models under full truckload capacity (TL) and less than truckload (LTL) policies combining perishable and non-perishable products in a supply chain, simultaneously. The optimization objective is to minimize the total relevant cost of manufacturer such as the production setup, inventory holding, and transportation costs per time unit. After presenting the models, different numerical example has been used to evaluate based on the change of truck capacity, lifespan as well as costs, and increasing the number of perishable products via Gams software. The results obtained illustrate that the priority of policies TL and LTL depends on the capacity of the truck and shipping costs.

Mixed-integer linear optimization for full truckload pickup and delivery

This paper is focused on solving an industrially-motivated, rich routing variant of the so-called full truckload pickup and delivery problem. It addresses a setting where the distributor has to transport full truckload shipments between distribution centers and customer locations, yet the distributor’s owned fleet is inadequate to perform the totality of the required deliveries and thus a subset of the deliveries has to be outsourced to third-party carriers. In this work, we propose a novel mixed-integer linear programming formulation to model this problem. Using datasets inspired from industrial practice, we evaluate the computational tractability of this model and demonstrate its potential to serve as a decision-support system for real-life operations. Furthermore,we hypothesize that the distributor may realize cost savings when the later portion of the distribution period is utilized to pre-load cargo for delivery during the following period. To that end, we augment the original model to allow for such cargo pre-loading, and we conduct a rolling horizon-based simulation study to quantify its overall economic effect.