Shipment Size Research Articles

Integrating route optimisation with vehicle and unloading dock scheduling in LCL cargo collection

Less container load (LCL) has become an increasingly important element in containerised cargo export, due to the involvement of numerous small and medium size enterprises. Traditional cargo collection and consolidation processes are extremely complex and inefficient, which provides an excellent opportunity for improvement through integration. In this paper, we design a two-stage model comprising vehicle route optimisation for cargo collection and vehicle and unloading dock scheduling. In the first stage, namely, the route optimisation model, the Clarke-Wright saving algorithm is used, with the objective of minimising the total transport cost for a given shipment size, weight, and capacity constraint of cargo collection vehicles. In the second stage, the scheduling of both collection vehicles and unloading dock are modelled, using two sub-models for given constraints on the time window of the unloading docks and cargo collection routes. An application of this integrated model is illustrated based on the cargo collection problems in the hinterland of Shanghai port.

Integrating route optimisation with vehicle and unloading dock scheduling in LCL cargo collection

Less container load (LCL) has become an increasingly important element in containerised cargo export, due to the involvement of numerous small and medium size enterprises. Traditional cargo collection and consolidation processes are extremely complex and inefficient, which provides an excellent opportunity for improvement through integration. In this paper, we design a two-stage model comprising vehicle route optimisation for cargo collection and vehicle and unloading dock scheduling. In the first stage, namely, the route optimisation model, the Clarke-Wright saving algorithm is used, with the objective of minimising the total transport cost for a given shipment size, weight, and capacity constraint of cargo collection vehicles. In the second stage, the scheduling of both collection vehicles and unloading dock are modelled, using two sub-models for given constraints on the time window of the unloading docks and cargo collection routes. An application of this integrated model is illustrated based on the cargo collection problems in the hinterland of Shanghai port.

Location-distribution of cruise ship supply logistics distribution centre considering time window

According to the characteristics of cruise ship supply logistics, using a mixed-integer linear programming model (MILP), we describe the location-distribution and time utility requirements, in which the capacity constraints of the distribution centres and time window are considered, in order to minimize the sum of fixed cost, variable cost, distribution cost between nodes and time penalty cost. Further, a ‘two-stage’ method is suggested to solve the MILP problem. In the first stage, the Benders decomposition algorithm is adopted to determine the location of the distribution centres, flow allocation and shipment sizes on each link. Then IBM ILOG CPLEX optimization studio software is used to get the optimal value of the distribution schedule of ship suppliers in the second stage. Finally, the proposed method is verified by a study simulation.

An optimal policy for three-tier supply chain for processing an agricultural product with a stochastic demand

Agricultural products are very common in our daily life. A few works limited to a two-tier supply chain (SC) of these products, have been carried out by controlling their management cost. This study develops a threetier SC model of processing an agricultural product with a stochastic demand, consisting of a single seasonal supplier, a single manufacturer and multiple retailers. A safety stock is maintained to meet extra demand. The objective is to identify the number of shipments and the shipment sizes in a cycle to be delivered to the manufacturer and the retailers in order to minimise the chain-wide cost. We develop two optimal solution methods to the model. These methods and the parallel multiple jumps technique used to find integer solutions are summarised in an algorithm to obtain global optimal solutions to the concerned problems. Managerial insight of this model is highlighted with a numerical illustration.

Estimation of Shipment Size in Seaborne Iron Ore Trade

Estimation of Shipment Size in Seaborne Iron Ore Trade

A new approach to deal with learning in inspection in an integrated vendor-buyer model with imperfect production process

This article considers learning in inspection in an integrated vendor-buyer model in which the vendor delivers the buyer’s order quantity in a number of equal-sized shipments. The vendor’s production process is imperfect and it produces a fraction of defective items. The buyer performs screening upon delivery of each batch but the screening process is erroneous and suffers from misclassification errors (Type I and Type II). As the buyer learns from the experience of screening, the probabilities of misclassification errors decrease with the increase in the cumulative number of batches inspected. The objective of this study is to incorporate the effect of learning in inspection when items are inspected in a batch-wise manner. The expected total cost per unit time of the integrated system is derived and a solution procedure is suggested to determine the optimal number of shipments and shipment size of the vendor. A numerical example is taken to illustrate the impact of learning through batch-wise inspection in terms of cost reduction and provide some managerial insights.

Solutions for More Sustainable Distribution in the Short Food Supply Chains

The largest part of food sales is managed by large food supply chains. However, an alternative system of food distribution focuses on locally produced and sold food that has gotten great attention in the last two decades. The challenges of those new systems, called short food supply chains (SFSC), represent tough market competitions, high distribution and logistics costs, small shipment sizes and so forth. Hence, the SFSC requires corresponding solutions in food distribution that are aligned with the contemporary logistics trends, sustainability and aspects of the new digital era. Using specially developed methodology, based on two different conceptual models, we showed how the SFSC could be designed from the aspects of innovative logistics modes and contemporary information and communication technologies, with the final aim to outline and evaluate different food distribution scenarios towards greater sustainability. The first conceptual model was aimed at the creation of innovative forms of SFSC, in which business process modelling was used in order to design and explore the given situation more thoroughly. For the purposes of conducting a comparative assessment of the distribution models developed in the previous part, the second conceptual model is developed. By using a qualitative approach, this is how the major advantages and challenges of practical implementations in creating sustainable distribution solutions are stated for each scenario.

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.

A disaggregate stochastic freight transport model for Sweden

This paper presents estimation results for models of transport chain and shipment size choice, as well as an implementation of the estimated disaggregate models (for two commodity groups), in the context of the national freight transport model for Sweden. The new model is a disaggregate and stochastic (logit) model, whereas the existing Swedish national model is deterministic. One advantage of the new approach is that it bases the underlying behavior of shippers on a stronger empirical foundation (that is micro-data from the Swedish Commodity Flow Survey, CFS). Another advantage is that it overcomes a well-known disadvantage of deterministic models that lead to implausibly large responses to changes in scenario or policy variables. Although estimation and implementation of aggregate stochastic models were done before, in the context of a national freight transport forecasting model, we think this is the first implementation of disaggregate freight transport chain and shipment size models estimated on choice data for individual shipments, certainly in Europe. We carried out a number of model runs with both versions of the implemented model to compare elasticities and found that transport cost and time elasticities for tonne-km are smaller (in absolute values) in the disaggregate stochastic model than in their deterministic counterparts.

Analysis on the determinants of shipment size and type-of-truck choices using a discrete-continuous hybrid model

When modelling freight demand, a common assumption is that decision makers decide on the commercial vehicle that best suits their transportation needs. Even though the discrete choice of vehicle type depends on the shipment size, which is a continuous variable, vehicle type choice models usually do not include any shipment size factor. The origin and destination of the cargo, the type of product, attributes related to the level of service (e.g., travel time and cost), trucking operational patterns and shipper's characteristics are also factors that determinate both choices. This paper proposes a joint estimation procedure for the discrete-continuous model based on the integrated choice and latent variable model. Our approach explicitly includes the shipment size as a continuous latent variable within the discrete type-of-truck choice. The application of the model in the Colombian context highlights the importance of considering the interrelationship between freight mode and shipment size decisions. Modelling results also suggest the presence of unobserved common factors influencing both decisions. The model was used to evaluate policies oriented to promote cargo consolidation. The evaluation suggests that increasing the tolls for small vehicles is feasible because of their higher cost sensitivity.

Optimal selling price, replenishment lot size and number of shipments for two-echelon supply chain model with deteriorating items

Optimal selling price, replenishment lot size and number of shipments for two-echelon supply chain model with deteriorating items

A collaborative inventory model for vendor-buyer system with stochastic demand, defective items and carbon emission cost

This paper develops a joint economic lot-sizing problem (JELP) involving of a vendor and a buyer under defective products. The vendor's production process is assumed to be imperfect, hence the lot delivered to the buyer may contain a certain number of imperfect products with known probability density function. The carbon emissions are considered in the model and are calculated from both transportation and production activities. The model also considers a situation in which the vendor has an opportunity to adjust the production rate. The objective of the model is to minimise total cost by simultaneously determining the shipment size, number of deliveries, production rate and safety factor. An iterative procedure is suggested for solving the model. Numerical examples are used to illustrate the application of the model.

A collaborative inventory model for vendor-buyer system with stochastic demand, defective items and carbon emission cost

This paper develops a joint economic lot-sizing problem (JELP) involving of a vendor and a buyer under defective products. The vendor's production process is assumed to be imperfect, hence the lot delivered to the buyer may contain a certain number of imperfect products with known probability density function. The carbon emissions are considered in the model and are calculated from both transportation and production activities. The model also considers a situation in which the vendor has an opportunity to adjust the production rate. The objective of the model is to minimise total cost by simultaneously determining the shipment size, number of deliveries, production rate and safety factor. An iterative procedure is suggested for solving the model. Numerical examples are used to illustrate the application of the model.

Analysis on the determinants of shipment size and type-of-truck choices using a discrete-continuous hybrid model

Analysis on the determinants of shipment size and type-of-truck choices using a discrete-continuous hybrid model

A Joint Optimal Decision on Shipment Size and Carbon Reduction under Direct Shipment and Peddling Distribution Strategies

Recently, much research has focused on lowering carbon emissions in logistics. This paper attempts to contribute to the literature on the joint shipment size and carbon reduction decisions by developing novel models for distribution systems under direct shipment and peddling distribution strategies. Unlike the literature that has simply investigated the effects of carbon costs on operational decisions, we address how to reduce carbon emissions and logistics costs by adjusting shipment size and making an optimal decision on carbon reduction investment. An optimal decision is made by analyzing the distribution cost including not only logistics and carbon trading costs but also the cost for adjusting carbon emission factors. No research has explicitly considered the two sources of carbon emissions, but we develop a model covering the difference in managing carbon emissions from transportation and storage. Structural analysis guides how to determine an optimal shipment size and emission factors in a closed form. Moreover, we analytically prove the possibility of reducing the distribution cost and carbon emissions at the same time. Numerical analysis follows validation of the results and demonstrates some interesting findings on carbon and distribution cost reduction.

A hybrid agent-based computational economics and optimization approach for supplier selection problem

Abstract Supplier evaluation and selection problem is among the most important of logistics decisions that have been addressed extensively in supply chain management. The same logistics decision is also important in freight transportation since it identifies trade relationships between business establishments and determines commodity flows between production and consumption points. The commodity flows are then used as input to freight transportation models to determine cargo movements and their characteristics including mode choice and shipment size. Various approaches have been proposed to explore this latter problem in previous studies. Traditionally, potential suppliers are evaluated and selected using only price/cost as the influential criteria and the state-of-practice methods. This paper introduces a hybrid agent-based computational economics and optimization approach for supplier selection. The proposed model combines an agent-based multi-criteria supplier evaluation approach with a multi-objective optimization model to capture both behavioral and economical aspects of the supplier selection process. The model uses a system of ordered response models to determine importance weights of the different criteria in supplier evaluation from a buyers’ point of view. The estimated weights are then used to calculate a utility for each potential supplier in the market and rank them. The calculated utilities are then entered into a mathematical programming model in which best suppliers are selected by maximizing the total accrued utility for all buyers and minimizing total shipping costs while balancing the capacity of potential suppliers to ensure market clearing mechanisms. The proposed model, herein, was implemented under an operational agent-based supply chain and freight transportation framework for the Chicago Metropolitan Area.

Emissions allocation in transportation routes

This article studies the allocation of CO2 emissions to a specific shipment in routing transportation. The authors show that this problem differs from a cost allocation problem specifically because the concavity condition does not hold necessarily in the CO2 allocation problem. This implies that a traditional cost allocation method cannot be straightforwardly translated into a CO2 allocation problem, and thus, new methods need to be developed. This study proposes four allocation mechanisms that are extensions from the literature and the common practice in industries. In doing so, the authors introduce the concept of carbon efficiency to assess if a particular allocation rule drives companies to carbon emissions reduction. They present analytical properties that show that the current practice of allocating CO2 emissions based on the greenhouse gas protocol, fails to be sensitive to drive companies to the sustainable practice of placing consolidated orders. The authors also conduct an experimental analysis using data of a logistics service provider that operates in Europe. Using the results of the experiments, they show that simple allocation methods can lead to a fair and carbon efficient allocation. In addition, the study provides insights by conducting a sensitivity analysis, and it shows that the CO2 allocations are not substantially susceptible to shipment size estimation errors.

A joint model of mode and shipment size choice using the first generation of Commodity Flow Survey Public Use Microdata

Abstract A behavior-based supply chain and freight transportation model was developed and implemented for the Maricopa Association of Governments (MAG) and Pima Association of Governments (PAG). This innovative, data-driven modeling system simulates commodity flows to, from and within Phoenix and Tucson Megaregion and is used for regional planning purposes. This paper details the logistics choice component of the system and describes the position and functioning of this component in the overall framework. The logistics choice model uses a nested logit formulation to evaluate mode choice and shipment size jointly. Modeling decisions related to integrating this component within the overall framework are discussed. This paper also describes practical insights gained from using the 2012 Commodity Flow Survey Public Use Microdata (released in 2015), which was the principal data source used to estimate the joint shipment size-mode choice nested logit model. Finally, the validation effort and related lessons learned are described.

A closed-loop supply chain with stochastic product returns and worker experience under learning and forgetting

This paper addresses a single-manufacturer single-retailer closed-loop supply chain with stochastic product returns considering worker experience under learning and forgetting in production and inspection of returned items at the manufacturer. Customer demand is assumed to be dependent linearly on the retail price, and it is fulfilled by using both manufactured and remanufactured products. The manufacturer delivers the buyer’s order quantity in a number of equal-sized batches. The optimal number of shipments, the shipment size and the retail price are determined by maximising the average expected profit of the closed-loop supply chain. It is observed from the numerical study that high learning effects in production and inspection lead to high recovery rates of used products, which, besides an economic advantage, may have a positive effect on the environment. Even though forgetting has an adverse effect, the average expected profit of the closed-loop supply chain is much higher than that of the basic model which ignores worker learning.

Modelling contamination of trucks used in the shipment of pigs infected with porcine reproductive and respiratory syndrome virus

Objectives: To quantify the likelihood that a shared truck used to ship pigs will be contaminated with porcine reproductive and respiratory syndrome (PRRS) virus at the end of a given day, and to evaluate the efficacy of cleaning and washing protocols for trucks, using a Bayesian approach. Materials and methods: PRRS virus-infected farms, from which trucks had shipped pigs, were deemed to be the source of contamination. A quantitative stochastic model was built using farm- and animal-level PRRS prevalence data, the number of times a truck is typically shared on any given day, shipment size, travel time between farms, and the efficacy of three different cleaning and disinfection procedures. Results: The model predicted a median probability of 0.525 that a truck would be contaminated at the end of any given day, without considering the number of previous shipments made by that truck or whether or not it had been washed and disinfected between shipments. Truck washing alone resulted in a negligible decrease in probability that a truck would be contaminated, while washing and disinfection followed by drying had the highest impact, with a greater than 99% reduction in probability of contamination. Implications: Findings of this study suggest that under current biosecurity practices, a substantial risk exists for the spread of PRRS virus due to truck sharing. This model could also be utilized in understanding the risk of truck sharing on the spread of other swine diseases (such as porcine epidemic diarrhea) where transportation is believed to spread the virus.