Full Truckload Research Articles

Criteria analysis for deciding the LTL and FTL modes of transport

Abstract: Choosing between Less Than Truckload (LTL) and Full Truckload (FTL) is based on a multicriteria decision because it takes into account aspects such as costs and operational efficiency in transportation, handling and stock of goods. The aim of this article is to provide a detailed analysis of the criteria that comprise the LTL and FTL transport decision using a multicriteria methodology that considers decision makers´ preferences. The SMARTS method was applied in a telecommunications company that outsources its logistics operation. Results show that the main criteria for choosing LTL shipment are related to the relationship with the customer and the efficiency in the delivery of the freight (reliability of the delivery time). On the other hand, choosing FTL transport requires a high weight given to the criteria regarding benefits and cost of transit inventory. The contribution of the research is to provide a structured and flexible analysis to choose the types of shipment (FTL and FLT) when taking into account the risk aversion of decision makers using a non-optimal approach, but with approximate solutions encompassing several qualitative and quantitative criteria in this choice.

Single-item lot-based supplying and batch production under a bilateral capacity reservation: A partnership structure

This paper studies a single-item lot-based supplying and batch production under a bilateral capacity reservation contract based on a partnership structure in which some suppliers, a manufacturer, and a third-party logistics provider are connected through long-term cooperation agreements. This configures a partnership supply chain and promotes the manufacturer’s performance in a continuous improvement cycle. Since in this paper, it is assumed that the products are perishable, there is not any inventory storage. Moreover, due to a settled contract, the final products are delivered by third-party logistics corporate providing vehicles according to the products’ features, their expiration time lengths, customers’ demands and other considerations in each period. Thus, the delivery batch size, which is equal to the vehicle capacity in a full truckload mode, is targeted for the manufacturer operations resulting in a dynamic improvement environment. In this problem, respect to the performance level of the manufacturer, in each period, a certain capacity is reserved at each selected supplier level according to the appointed batch deliveries. Here, in addition to the ordinary costs incurred due to ordering and delivery processes, some penalties are imposed for orders/delivers exceeding or falling behind the reserved capacity. After investigating the computational complexity of the problem, we propose three dynamic programming algorithm as well. Finally, numerical results and some potential future researches are presented.

MEASURING LOGISTICS PERFORMANCE FOR COMPETITIVENESS- A CASE STUDY OF AUTOMOTIVE CONNECTION SYSTEMS COMPANY, KERALA, INDIA

A company having implemented a logistics system must continually monitor and evaluate its effectiveness. Similarly, a company may utilize several different methods to measure logistics performance. This study was conducted at Automotive System Connections Ltd., in Kerala, India. This company’s warehouse handles 720 traded parts out of which 96% comes from intercompany basis and the remaining 4 % of items comes from their local suppliers located in India. It was found that the company moves 98% of traded parts by road / surface transportation mode and the remaining only by air cargo mode. These parts are supplied to original equipment manufacturers in India. So, logistics performance measurement was considered an important area for this research. Twelve important metrics were selected for measurements and three years data collected from their main information report (MIR), returns, and reports followed by interview method with senior executives of the company. This company is in the process of implementing lean approach in their warehouse operations. This company should more focus on some of the metrics like inventory turnover ratio, overall customer satisfaction, total logistics cost, and capacity of warehouse vs space utilization. This company should focus on Full Truck Load (FTL) rather than Less Truck Load (LTL) by consolidation and collaboration with Third Party Logistics Service providers (3PL). This company should also change incoterm to Freight on Board (FOB) / Free along Ship side (FAS) from Ex-Works.

The environmental impact of fast shipping ecommerce in inbound logistics operations: A case study in Mexico

Consumer demand for short delivery windows has driven companies to compete for faster shipping to clients. Recently, Amazon and Walmart along with other major companies have started offering same-day delivery. That translates into not using full truckload capacities, requiring more frequent dispatch, and in turn, increases transportation cost. However, inefficient routes are not only more expensive for the shipper but they are also more carbon-intensive. In this context, and from an operative level, we analyze the environmental impact of inbound logistics caused by fast shipping. We create a discrete-event simulation model to understand and compare the effect that certain parameters (i.e., delivery windows, inventory management policies, truck type) have on sustainability inbound logistics. We validate this model with the largest retailer in Mexico in order to analyze how sustainable fast shipping actually is and how to reduce its environmental impact. Results from the simulation model show that fast shipping produces significantly higher CO2 emissions since it imposes a challenge for cargo consolidation. We found that fast-shipping increases both total CO2 emissions and costs up to 15% and 68%, respectively.

A hybrid pricing and cutting approach for the multi-shift full truckload vehicle routing problem

Full truckload transportation (FTL) in the form of freight containers represents one of the most important transportation modes in international trade. Due to large volume and scale, in FTL, delivery time is often less critical but cost and service quality are crucial. Therefore, efficiently solving large scale multiple shift FTL problems is becoming more and more important and requires further research. In one of our earlier studies, a set covering model and a three-stage solution method were developed for a multi-shift FTL problem. This paper extends the previous work and presents a significantly more efficient approach by hybridising pricing and cutting strategies with metaheuristics (a variable neighbourhood search and a genetic algorithm). The metaheuristics were adopted to find promising columns (vehicle routes) guided by pricing and cuts are dynamically generated to eliminate infeasible flow assignments caused by incompatible commodities. Computational experiments on real-life and artificial benchmark FTL problems showed superior performance both in terms of computational time and solution quality, when compared with previous MIP based three-stage methods and two existing metaheuristics. The proposed cutting and heuristic pricing approach can efficiently solve large scale real-life FTL problems.

The Effect of Truckload Driver Turnover on Truckload Freight Pricing

High rates of truck driver turnover have long plagued the full truckload (TL) sector. While greater driver turnover raises TL carriers’ costs, no research has examined how changes in industry‐wide TL driver turnover rates affect industry‐wide prices shippers pay for TL transportation. Drawing on economic theory regarding firms’ asymmetric adjustment of prices in response to changing costs, we explain why increases in driver turnover are expected to increase the prices carriers charge for their services, whereas decreases in driver turnover are expected to have limited effect on the prices carriers charge for their services. We further explain why the positive impact of increases in industry‐wide TL driver turnover rates on prices will be more pronounced when industry employment is rising more rapidly. To test our theory, we assemble a time series data set combining proprietary driver turnover data with publicly available data regarding TL pricing, trucking employment, diesel prices, and aggregate industry activity. Results from fitting a set of time series econometric models corroborate our theorized predictions. We explain the implications of these findings for theory and practice.

Relationship among Various Performance Measures and Outsourcing Services in the Third-Party Logistics Industry

In the recent years third-party logistics market is an emerging trend all over the world and most of the companies want to reduce the logistic costs, increase the market share, and focus on competency through the use of 3PLs. Due to the diversity of economic development, industrial segments and core competency between the markets, the third-party logistic market is growing very fast in the United Arab Emirates. The empirical study was carried out based on 102 responses received from 200 respondents for the third-party logistics market in Abu Dhabi, UAE. These data are analyzed by the SPSS software. Accordingly, the Friedman’s test shows that the full truck load & transportation management and freight forwarding are the major outsourced activities from the 3PL firms having the “P” value which is lower than 0.01, with the null hypothesis being rejected at 1% level of significance. The detailed study explains the Pearson correlation coefficient, the regression analysis for the various business measures and factor analysis of the influencing the success factors in the 3PL market and necessity of outsourcing of 3PL services in Abu Dhabi, UAE.

Integrated planning of inbound and outbound logistics with a Rich Vehicle Routing Problem with backhauls

This paper addresses the integration of the planning decisions concerning inbound logistics in an industrial setting (from the suppliers to the mill) and outbound logistics (from the mill to customers). The goal is to find the minimum cost routing plan, which includes the cost-effective outbound and inbound daily routes (OIRs), consisting of a sequence of deliveries of customer orders, pickup of a full truck-load at a supplier, and its delivery to the mill. This study distinguishes between three planning strategies: opportunistic backhauling planning (OBP), integrated inbound and outbound planning (IIOP) and decoupled planning (DIOP), the latter being the commonly used, particularly in the case of the wood-based panel industry under study. From the point of view of process integration, OBP can be considered as an intermediate stage from DIOP to IIOP. The problem is modelled as a Vehicle Routing Problem with Backhauls, enriched with case-specific rules for visiting the backhaul, split deliveries to customers and the use of a heterogeneous fleet. A new fix-and-optimise matheuristic is proposed for this problem, seeking to obtain good quality solutions within a reasonable computational time. The results from its application to the wood-based panel industry in Portugal show that IIOP can help to reduce total costs in about 2.7%, when compared with DIOP, due to better use of the delivery truck and a reduction of the number of dedicated inbound routes. Regarding OBP, fostering the use of OIRs does not necessarily lead to better routing plans than DIOP, as it depends upon a favourable geographical configuration of the set of customers to be visited in a day, specifically, the relative distance between a linehaul that can be visited last in a route, a neighboring backhaul, and a mill. The paper further provides valuable managerial insights on how the routing plan is impacted by the values of business-related model parameters which are set by the planner with some degree of uncertainty. Results suggest that increasing the maximum length of the route will likely have the largest impact in reducing transportation costs. Moreover, increasing the value of a reward paid for visiting a backhaul can foster the percentage of OIR in the optimal routing plan.

Order first split second heuristic for alternative routing strategy for freight railways

The rake movements in rail freight transportation are executed by dynamically allocating empty rakes to observed demands. This work proposes an alternate cycle planning strategy based on the average demand data. The system is modeled as the Pickup and Delivery Problem with customer specified origin and destination and multiple full truckload demands by each customer. Initially, an Exact Formulation of the problem that gives optimal cycles is presented. A modified Order First Split Second Heuristic (OFSS) is proposed to solve the deterministic version of this problem where a giant tour is constructed by assigning a value to each node based on the greedy path from that location. The splitting algorithm forms multiple cycles satisfying maximum cycle time constraint from the Giant Tour. A heuristic solution obtained by splitting optimal giant tour is also determined to study the performance of proposed heuristic. Considering the possibility of deviation of actual demands from average, an adaptive algorithm that modifies the planned cycles to accommodate the changes in demand is presented. Computations show that the heuristic gives near-optimal solution in terms of net revenue and the number of vehicles required. Testing this algorithm on dynamic demands show that the initial cycle plan can be followed with minor modification if the Degree of Dynamism of the system is small. The proposed algorithm is tested on Indian freight rail data and the improvements in rake utilization are noted.

Optimizing the sustainable decisions in a multi-echelon closed-loop supply chain of the manufacturing/remanufacturing products with a competitive environment

Along with raised public awareness about environmental issues, some supply chains have begun to develop eco-friendly strategies aiming to manufacture and (or) remanufacture green products. This research presents a new preferable gaming structure in a multi-echelon closed-loop supply chain including a manufacturer, a retailer/remanufacturer, and an intermediate collection center (which is a governmental entity). The supply chain is structured to sell the products in a first and a secondary market. The significance of the research ahead is enhanced due to the use of game theory in sustainability in a new competitive supply chain structure. Toward addressing sustainability requirements, emission reductions within manufacturing, remanufacturing, and delivery functions are considered in a two-period game-theoretic-based model. Regarding the customer’s low-carbon preferences in production and delivery, the manufacturer wholesales new products to the retailer, and then, the retailer forwards them to the primary market via a full truckload policy in the first period. Even more striking, considering the environmental commitments in the second period, used products are gathered by the collection center in a reverse flow and sent to the retailer/remanufacturer for remanufacturing processes. In this regard, a Stackelberg, a Nash game, and also a novel bargaining structure are developed in the first and second periods. Then, the resulting decentralized approach is compared with a centralized model, illustrating a better performance of the decentralized scenario. Finally, through numerical analyses, it is observed that there are ascending relations between the triple dimensions of reduction rates (in manufacturing, remanufacturing, and delivering) and the corresponding profits, in which incorporating low-carbon considerations in remanufacturing has a greater efficacy.

Optimal green supply-chain model design considering full truckload

PurposeSupply chain management (SCM) focuses smart logistics and quality service. Diverse elements such as design, procurement, production and sale policies are the keys to SCM efficiency. Due to worsening environmental pollution in recent years, many businesses, government agencies and consumers have become more aware of greenhouse gases (GHG) emissions. In response, the government has established new environmental regulations to control various GHG, such as CO2 and sulfur dioxide. Therefore, to reduce pollution and its adverse effects, the authors have promoted environmental concerns by developing environmental friendly policies. The purpose of this paper is to develop a multi-objective decision making model that integrates both forward and reverse logistics to determine how best to incorporate recycling and reduce manufacturing costs.Design/methodology/approachIn this study, the authors developed a multi-objective decision-making model that integrates both forward and reverse logistics to determine how best to incorporate recycling and reduce manufacturing costs. They used the normalized normal constraint method as proposed by Messac et al. (2003) to generate a series of uniform lines on a Pareto Frontier chart.FindingsBased on the results of this study, the authors can determine the trade-off between costs and emissions and design the most environmental-friendly and economical strategy for production.Research limitations/implicationsThis study is limited to a case study on paper manufacturing.Practical implicationsThe authors considered the full truckload discount policy in which buyers can reduce their purchase costs by increasing the number of full truckload product orders; this will reduce transportation costs and also minimize overall carbon emissions.Social implicationsThis study encourages industries to focus on environmental friend policies and social responsibilities.Originality/valueThe authors investigated the impacts of the paper making industry on economy and environment. An increase in demand will negatively impact the environment by causing CO2 emissions to increase from higher production and the felling of more trees to provide raw materials for manufacturers (paper mills).

Batch replenishment planning under capacity reservation contract

We study the single-item Lot Sizing Problem (LSP) in a supply chain composed of a retailer and a supplier by integrating the buyback contract and the batch ordering. The purpose is to determine a replenishment planning for the retailer to satisfy his deterministic demands over a finite horizon, while minimizing the procurement and inventory costs. Regarding the procurement cost, we assume two different structures: FTL (Full Truck Load) and OFB (Only Full Batch). We consider three types of buyback contract: with fixed return periods, with a time limit on returns, and with returns permitted only in procurement periods. Each contract is characterized by the maximum return percentage being either equal to 100% (full return) or less than 100% (partial return). For the LSP under the buyback contract with fixed return periods, we assume the concept of lost sales. Another concept considered in the LSP's under the three types of buyback contract is the disposal of the unsold and unreturned quantities. We model these different LSP extensions as a Mixed Integer Linear Program (MILP). Thereafter, we develop exact polynomial time dynamic programming algorithms for some extensions and show the NP-hardness of others. For each problem solved in polynomial time, we compare the efficiency and the limits of the proposed algorithm with those of four MILP formulations by performing different tests. Finally, we propose mathematical models for the LSP's under other types of the capacity reservation contract in the deterministic and multi-period case.

Strategic Purity and Efficiency in the Motor Carrier Industry: A Multiyear Panel Investigation

Two questions facing motor carrier managers are (1) whether carriers should specialize in providing full truckload (TL) or less‐than‐truckload (LTL) services vis‐à‐vis offering mix of both and (2) whether this decision is contingent on carrier size. Yet, the literature provides little guidance because research to date has offered contradictory theoretical predictions and inconsistent empirical findings. Drawing on the theory of strategic purity and information processing theory, we explain why service specialization is likely to increase carriers' technical efficiency and why size will have a more pronounced effect on technical efficiency for carriers specializing inLTLservices versusTLservices. To test our theory, we assemble a panel data set from archival government sources regarding general freight motor carriers' provision ofLTLandTLservices. We measure carriers' technical efficiency using data envelopment analysis and test our hypotheses by fitting a series of panel data mixed‐effects models. Our results indicate that carriers are most technically efficient when they specialize in one service type. We also find that size positively affects technical efficiency but only for carriers specializing inLTLservices; no returns to scale with regard to technical efficiency exist for carriers specializing inTLservices.

An integrated production and distribution problem with direct shipment: a case from Moroccan bottled-water market

We consider an industrial application encountered in the Moroccan context that involves the production and distribution of bottled water. Our industrial partner owns one plant from which all requests are delivered in direct shipments and usually full truckloads to the customers (a set of regional depots and wholesalers). We must take into account the production, the delivery requirements, multiple products, and inventory levels. The objective is to minimise the sum of the production, transportation, and inventory costs. We propose a mixed integer linear program for a variant of the multi-vehicle, multi-product production routing problem. Experiments have been conducted using CPLEX 12.3.0, and almost all instances were solved with a reasonable optimality gap. The results show that significant savings can be obtained by using our approach with respect to the current company practice.

A Genetic Algorithm Approach for Multi Objective Cross Dock Scheduling in Supply Chains

In supply chain, cross docking terminal aims to move products directly from inbound trucks to outbound trucks; however, value or non-value added operations might be needed before loading into outbound trucks, for instance, repacking, sorting, and labeling. Also, cross docking terminal might be the place for consolidated shipment intended for full truckload freight or less-than-truckload freight and can reduce the inventory cost of supply chain network. Further, cross docking helps reducing space required and reducing transportation costs. Although the literature shows several methods for improving operations in cross docking terminal, there is a gap in finding the inventory level of temporary storage considering truck scheduling and dock assignment simultaneously. In this paper, Genetic Algorithm (GA) is used for solving the truck scheduling and dock assignment for the inbound and outbound trucks with minimizing three objectives namely makespan, maximum inventory level, and total traveling distance of material handling. Also, the performance of the four crossover strategies is compared in finding the satisfaction level of the three objectives combined. The results show that, among the four crossover strategies, none of the crossover strategies is dominant in finding the best satisfaction level according the dataset used.

Lot sizing problem with batch ordering under periodic buyback contract and lost sales

We study the deterministic single-item lot sizing problem with batch ordering under the buyback contract (LSP-BB) in a system with one retailer and one supplier. We consider a fixed cost per batch replenished in addition to the classical lot sizing costs, making the procurement cost structure stepwise. We also consider lost sales option with a lost sales cost incurred for each unit of demand not satisfied. The buyback contract considered here consists in returning unused units at the end of every w periods, with a buyback revenue for each unit returned back. We study this problem under both FTL (full truck load) cost structure and only full batch replenishment assumption. We propose efficient and exact polynomial time algorithms for different extensions of this problem which is known to be NP-hard in the general case. To the best of our knowledge, it is the first time that the buyback contract is introduced in a multi-period lot sizing problem (LSP) in the literature. Moreover, batch ordering and lost sales issues have never been tackled in the same LSP model before our study. Hence, this paper provides efficient algorithms to solve the LSP with batch ordering and lost sales under the buyback contract not yet addressed in the literature.

A bi-level programming for transportation services procurement based on combinatorial auction with fuzzy random parameters

Purpose The purpose of this paper is to study a transportation service procurement bid construction problem from a less than a full truckload perspective. It seeks to establish stochastic mixed integer programming to allow for the proper bundle of loads to be chosen based on price, which could improve the likelihood that carrier can earn its maximum utility. Design/methodology/approach The authors proposes a bi-level programming that integrates the bid selection and winner determination and a discrete particle swarm optimization (PSO) solution algorithm is then developed, and a numerical simulation is used to make model and algorithm analysis. Findings The algorithm comparison shows that although GA could find a little more Pareto solutions than PSO, it takes a longer time and the quality of these solutions is not dominant. The model analysis shows that compared with traditional approach, our model could promote the likelihood of winning bids and the decision effectiveness of the whole system because it considers the reaction of the shipper. Originality/value The highlights of this paper are considering the likelihood of winning the business and describing the conflicting and cooperative relationship between the carrier and the shipper by using a stochastic mixed integer programming, which has been rarely examined in previous research.

An effective evolutionary algorithm for the biobjective full truckload transportation service procurement problem

Abstract In truckload transportation service procurement, transit time is an important factor considered by shippers. In addition to transportation costs, transit time is also expected to be minimized so as to improve service quality and transportation efficiency. In this work, we study the full truckload transportation service procurement problem with transit time and develop a two-phase multiobjective evolutionary algorithm to explore the Pareto front of the problem. The algorithm encodes a chromosome of an individual using a set of winning carriers to reduce decision space. Each individual corresponds to a biobjective subproblem that has a set of solutions on its local Pareto front. The individuals are explored using fitness-based crossover and mutation operators in the first phase of the algorithm. The promising individuals that could produce nondominated solutions, and their local Pareto fronts are further explored by a sophisticated method in the second phase. Computational results show that the new fitness-based operators are effective and the solutions found by the algorithm can well approach the Pareto front.

ARIMA Time Series Models for Full Truckload Transportation Prices

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.

A machine learning approach for the operationalization of latent classes in a discrete shipment size choice model

This paper elaborates a novel approach for implementation of latent segments concerning behaviorally sensitive shipment size choice in strategic interregional freight transport models. Discrete shipment size choice models are estimated for different homogenous segments formed by latent class analysis. A machine learning technique called Bayesian classifier is applied to link segments obtained from a sample to data of commodity flows being available on a national level. Finally, in an exemplary scenario, the impact of information and communication technologies on shipment size distributions is calculated, revealing moderate elasticities and a predominant substitution of less than truck loads by full truck loads.