Shipment Size Research Articles

Supply chain planning: A case for Hybrid Cross-Docks

This research studies the logistics and inventory planning problem encountered at an oilfield services company whose supply chain consists of suppliers, a Hybrid Cross-Dock (HCD) facility, and production facilities. The HCD provides an option for the company to store inventory for a period of time without incurring inventory holding costs. This research aims to provide solutions that minimizes the total logistics and inventory-related costs, and obtain several managerial insights. The investigation into the computational complexity shows that the problem is NP-hard. Hence, an efficient bin-packing based heuristic is developed to provide good solutions. Furthermore, to account for the demand uncertainty, we develop an effective rolling horizon operating policy. Besides the proposed heuristics, the research also provides insights into the effects of free storage time at the HCD, which can guide the managers in their interactions and negotiations with an HCD operator. We also show that the length of free storage time at the HCD affects the outbound shipment sizes.

Wildlife trade

Global trade of wildlife is a major driver of species decline. The trade in wildlife actually plays a much larger role in our daily lives than many people realize, and its use and legality are surprisingly complex. Wildlife trade includes the trade of any organism, including fungi, plants and animals, sourced from the wild. This comprises thousands of wild species, including over 7600, or nearly one quarter, of terrestrial vertebrate species. Trade in wildlife is worth billions annually via commercial fishing at $180 billion, timber at $227 billion and fashion at $2.5 billion - in addition to largely unquantified trade for meat, medicine, ornamental use and pets. Wildlife trade, such as that of ivory, is the subject of intense public debate, international regulation and criminal prosecution, while trade of other species is more often overlooked. How wildlife trade is regulated and what is legal and illegal varies both between and within taxonomic groups and depends on where and how trade occurs. Wildlife trade across most sectors has increased since monitoring began, for example, between 1996 and 2018 the global fish market rose from $40 billion to $180 billion, wood from $65 billion to $137 billion and reptile leather for fashion trade from $140 million to $600 million. In concert, the annual number of trades legally traded through CITES has also grown, from under 5000 transactions in 1977 to peaking at over 1.3 million in 2015, with shipment size increasing in parallel and seizures of illegally traded species showing similar trends. Balancing the needs of people for livelihood generation, especially with access and benefit-sharing rights, with the impact on species survival remains difficult. Issues like the role of trophy and sports hunting within conservation remain a topic of debate in the conservation community. Finding approaches that enable long-term species survival, are equitable and do not undermine livelihoods is a constant challenge.

An integrated green supply chain model with product recovery management towards a cleaner system

In today's omnichannel environment, sustainability has become a requisite for doing clean and good business. The present paper studies a sustainable supply chain with a single vendor and a single buyer. The vendor administrates the production process and delivers the demanded lot to the buyer as per the single setup and multiple delivery policy. The buyer performs the first-hand inspection to separate the defectives from the received lot and return these secluded defectives to the vendor. Further, the vendor and buyer initiate a sustainable drive to make sure that less waste is dispatched in the environment. In this sustainable drive, the end customers can return the used products through a proper channel. Product recovery management is incorporated by performing the assembly of these defective and used products for a strong inspection that bifurcates these products into different categories viz. reworkable, reusable, recyclable, and disposable. The model also explores the effect of shortages, energy usage in production, and carbon emissions while producing, transporting, and disposing of the products. The order quantity, number of shipments, and size of shipments are optimized to maximize the total integrated turnover of the vendor and the buyer. The objective is to accomplish the 3 R's of waste management viz. Reduce, Reuse and Recycle. Such an approach ensures that less waste is being dispensed in the environment, carbon emissions are reduced, and revenue generation is increased. The study contributes by introducing a product recovery strategy to deal with defective and used merchandise. The paradigm of sustainability is highlighted via mathematical analysis concerning economic and environmental aspects. Further, to prove the viability of the present study, a comparison of the same with a traditional scenario is performed that showcases the preeminence of the developed study. Findings elucidate the positive effect of product recovery management techniques on the well-being of the environment and business.

Identifying large freight traffic generators and investigating the impacts on travel pattern: A decision tree approach for last-mile delivery management

Large urban freight traffic generators (LTGs) are large specialized buildings or landmarks housing multiple establishments and generate a significant truck trips at both disaggregate and aggregate levels. Identification of LTGs and quantifying their relationship with freight travel characteristics helps policymakers formulate necessary logistical interventions and reduce externalities from freight activity. Hence, this study proposes a methodology for identifying LTGs and exploring their interactions on freight travel, expenditure pattern, shipment pattern, and other establishment characteristics. A decision-tree approach called chi-squared automatic interaction detector (CHAID) algorithm is used to map these interactions. Results suggest that LTGs are distinctly associated with multiple variables such as shipment size, shipper expenditure, commodity classification, and business age characteristics. Business age is the best predictor across all models. These associations vary based on LTG definitions. Implications of this study would augment the efforts on interlinking LTGs with urban freight demand modeling systems and enable sustainable city logistics initiatives and last mile delivery management.

Emergency Shipment Decision in Newsvendor Model

Emergency shipments are considered as an efficacious approach for mitigating the risk of shortages and increasing resilience in inventory systems. In this study, we develop a new newsvendor model where the retailer has a single opportunity to trigger an emergency shipment during the selling season. Due to demand uncertainty, it might not be possible to satisfy customer demands during the season. Provision for requesting an emergency shipment within the season can reduce the possibility of imminent shortages. As emergency shipments affect the dynamic of the system over time, the timing of emergency shipments in addition to their sizes is of particular interest. We use the time-weighted holding cost to compute the expected holding cost and assume that any unmet demand is lost. The goal is to determine the pre-season order quantity and both size and time of the emergency shipment such that the expected profit is maximized. We find that the newsvendor problem under time-weighted holding cost has a different structure and provide a different optimal order quantity. Our numerical experiences indicate that triggering an emergency shipment during the selling season can significantly improve cost savings. The results of a set of computational experiments demonstrate the superior performance of our proposed model when compared with the classical newsvendor problem. A sensitivity analysis is conducted showing that in the classical model, the unit lost sale cost is the most effective input parameter on the expected profit value. Moreover, the selling price and unit holding cost significantly affect the size and time of the emergency shipment.

Modeling freight mode choice using machine learning classifiers: a comparative study using Commodity Flow Survey (CFS) data

ABSTRACT This study explores the usefulness of machine learning classifiers for modeling freight mode choice. We investigate eight commonly used machine learning classifiers, namely Naïve Bayes, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbors, Classification and Regression Tree, Random Forest, Boosting and Bagging, along with the classical Multinomial Logit model. US 2012 Commodity Flow Survey data are used as the primary data source; we augment it with spatial attributes from secondary data sources. The performance of the classifiers is compared based on prediction accuracy results. The current research also examines the role of sample size and training-testing data split ratios on the predictive ability of the various approaches. In addition, the importance of variables is estimated to determine how the variables influence freight mode choice. The results show that the tree-based ensemble classifiers perform the best. Specifically, Random Forest produces the most accurate predictions, closely followed by Boosting and Bagging. With regard to variable importance, shipment characteristics, such as shipment distance, industry classification of the shipper and shipment size, are the most significant factors for freight mode choice decisions.

Exploring Multiple\u2010discreteness in Freight Transport. A Multiple Discrete Extreme Value Model Application for Grain Consolidators in Argentina

There are some examples where freight choices may be of a multiple discrete nature, especially the ones at more tactical levels of planning. Nevertheless, this has not been investigated in the literature, although several discrete-continuous models for mode/vehicle type and shipment size choice have been developed in freight transport. In this work, we propose that the decision of port and mode of the grain consolidators in Argentina is of a discrete-continuous nature, where they can choose more than one alternative and how much of their production to send by each mode. The Multiple Discrete Extreme Value Model (MDCEV) framework was applied to a stated preference data set with a response variable that allowed this multiple-discreteness. To our knowledge, this is the only application of the MDCEV in regional freight context. Free alongside ship price, freight transport cost, lead-time and travel time were included in the utility function and observed and random heterogeneity was captured by the interaction with the consolidator’s characteristics and random coefficients. In addition, different discrete choice models were used to compare the forecasting performance, willingness to pay measures and structure of the utility function against.

Perish or prosper: Trade patterns for highly perishable seafood products

AbstractIn recent years trade with highly perishable agricultural products like fresh fish, berries, and cut flowers has increased substantially. The perishability of these products appears to challenge conventional wisdom when it comes to food trade, which emphasizes the importance of large shipments to reduce transportation costs. In this paper, gravity models and several margins of trade are estimated for the trade with fresh salmon, a highly perishable product. The results indicate that increased geographical distance have a larger negative effect than what is generally reported in the literature. Most interestingly, the number of exporters and the shipment frequency increase while there is little impact on shipment size when trade increase. Hence, freshness and possibly avoidance of losses by not selling products by the expiration date seem to be emphasized rather than economies of scale in transportation. [EconLit Citations: F14, Q22].

Simulation of the Impacts of a Zero-Emission Zone on Freight Delivery Patterns in Rotterdam

As part of a broader vision for emission-free city logistics, the city of Rotterdam plans to introduce a zero-emission zone in combination with urban consolidation centers (UCCs) on the outskirts of the city to generate a shift to zero-emission vehicles. For the design of this zero-emission zone, several research questions arise that require a systematic analysis of the impacts of the transition scenarios on freight demand patterns, the use and market shares of new (zero-emission) vehicles, and the impacts of truck flow and emissions. As a case study, we implemented heterogenous transition scenarios for each logistic segment into the Tactical Freight Simulator from the HARMONY project and analyzed the systemwide impacts. This model is multiagent, empirical, and shipment based and simulates long-term tactical choices (distribution channel choice, shipment size and vehicle type choice, sourcing) and short-term tactical choices (tour formation, delivery times). Results showed that the impact of UCCs is not trivial: we observed a small increase in vehicle kilometers traveled overall of +0.25%, which can be attributed to the rerouting of shipments through the UCCs. Calculations confirmed that emissions reduced dramatically, by 90%, inside the zero-emission zone. At the city scale this corresponds to a reduction of almost 10%, as most freight-related traffic is generated by the port and involves long-haul heavy goods vehicle transport that does not enter the city center. At a regional level, impact reduction was very small. More measures are needed if more ambitious reductions in emissions are to be achieved.

Distribution free approach on electrical energy supply chain using geometric shipment policy

ABSTRACT We propose an inventory based optimisation model for a system of electricity supply chain. This distribution system consists of power plant, transmission substation and distribution substations and customers. The electricity generated by the power plant is transmitted to the submission and distribution substations and finally consumed by the customers. We apply the inventory theory to the distribution system and assume the electricity demand follows a distribution-free approach. This distribution system concerns the geometric shipment policy that is the size of each shipment is a multiple of the first shipment size. In particularly, two types of lead time crashed concepts are considered in this model. The main objective of this model is to find an optimal solution of first shipment lot size, blackout cost and simultaneously minimising the joint expected total cost. An iterative technique is used to find an optimal solution numerically and a sensitivity analysis table is presented to show the impact of different parameters

Inventory and sales decisions in a vendor–buyer system with imperfect items and learning in inspection

ABSTRACT In this study, we propose a joint economic lot-sizing model to include learning process along with errors in inspection and full backordering system. We aim to study pricing and inventory decisions in a two-level supply chain involving a single vendor and a single buyer in which the demand is sensitive to price and company’s advertisement efforts. The mathematical inventory model is developed analytically and solved using a proposed algorithm. The objective of the model is to maximise joint total profit by simultaneously determining optimal shipment size, number of deliveries, backorder quantity and product selling price. A numerical example is provided to show the application of the model and to investigate the impact of key parameter’s changes on model behaviour. By comparing the integrated/centralised model to the independent/decentralised model, we note that the integrated/centralised model provides better profit to the system, along with lower selling price and smaller amount of backorder.

The economics of electric roads

In this paper we present a method for evaluating social benefits of electric roads and apply it to the Swedish highway network. Together with estimated investments costs this can be used to produce a cost benefit analysis. An electric road is characterized by high economies of scale (high investment cost and low marginal cost) and considerable economies of scope (the benefit per kilometre electric road depends on the size of the network), implying that the market will produce a smaller network of electric roads, or charge higher prices for its use, than what is welfare optimal. For this reason, it is relevant for governments to consider investing in electric roads, making the cost-benefit analysis a key decision support. We model the behaviour of the carriers using the Swedish national freight model system, SAMGODS, determining the optimal shipment sizes and optimal transport chains, including mode and vehicle type. We find that if the user charge is set as to optimize social welfare, the revenue will not fully cover the investment cost of the electric road. If they are instead set to optimize profit, we find that the revenue will cover the costs if the electric road network is large enough. Electric roads appear to provide a cost-effective means to significantly reduce carbon emissions from heavy trucks. In a scenario where the expansion connects the three biggest cities in Sweden, emissions will be cut by one-third of the overall emissions from heavy trucks in Sweden. The main argument against a commitment to electric roads is that investment and maintenance costs are uncertain and that, in the long run, battery development or hydrogen fuel cells can reduce the benefit of such roads.

Consignment stock policy in an integrated vendor-buyer model for deteriorating item with stock dependent demand under buyer’s space limitation

In this paper, a single-vendor single-buyer integrated inventory model for a deteriorating item with consignment stock policy is developed, assuming that the market demand is stock dependent and there is space limitation on the buyer’s storage capacity. Both equal and unequal shipments from the vendor to the buyer are considered. The effects of the buyer’s space capacity on the average cost, shipment size, and production batch are studied through numerical example. It is deduced that production rate is the key factor to determine whether to use equal or unequal shipment strategy. Sensitivity analysis is carried out to establish the robustness of the solutions of the models developed.

A joint production and delivery schedule for a single-vendor single-buyer system over finite horizon

In this paper, we address a joint production and delivery scheduling problem in which a single-vendor supplies goods to a single-buyer over a finite horizon. The vendor's production rate and the buyer's demand rate can vary from period to period and are known in advance. The objective is to determine a production/shipment schedule that minimises the total cost of production setup, shipment, and holding of inventory at both the vendor and the buyer. We approach this problem using a dynamic programming framework, each stage of which constitutes solutions to different types of single-period problems depending on the production phase encountered. We develop effective methods for the solutions of these single-period problems, which are then embedded within the dynamic programming framework. We show that the optimal solution in each period follows a pattern of geometric-then-equal shipment sizes except for the last shipment, which may be larger in size. Furthermore, we show that an optimal solution for the infinite horizon problem can be obtained by using a special case of our finite horizon approach. In addition, we propose two fast heuristic methods for the finite horizon problem, which, as we show, can obtain almost optimal solutions.

A joint production and delivery schedule for a single-vendor single-buyer system over finite horizon

In this paper, we address a joint production and delivery scheduling problem in which a single-vendor supplies goods to a single-buyer over a finite horizon. The vendor's production rate and the buyer's demand rate can vary from period to period and are known in advance. The objective is to determine a production/shipment schedule that minimises the total cost of production setup, shipment, and holding of inventory at both the vendor and the buyer. We approach this problem using a dynamic programming framework, each stage of which constitutes solutions to different types of single-period problems depending on the production phase encountered. We develop effective methods for the solutions of these single-period problems, which are then embedded within the dynamic programming framework. We show that the optimal solution in each period follows a pattern of geometric-then-equal shipment sizes except for the last shipment, which may be larger in size. Furthermore, we show that an optimal solution for the infinite horizon problem can be obtained by using a special case of our finite horizon approach. In addition, we propose two fast heuristic methods for the finite horizon problem, which, as we show, can obtain almost optimal solutions.

Closed-form solution for lot-sizing problem in manufacturing systems with imperfect matching

Integrated single-vendor single-buyer problem has received a lot of attention as it is the building block for wider supply chains. This paper considers a manufacturing system in which a single manufacturer procures raw materials from a single supplier, processes them to produce finished products, and delivers to a just-in-time (JIT) customer. The manufacturer's production cycle is imperfect where the production uptime and cycle time are not exact integer multiples of the shipment interval to the customer. The closed-form solution for the optimal batch size is developed to minimize the total cost of procurement and production. It is found that the total cost under imperfect matching is always lower than or equal to that under perfect matching. Different from previous works under perfect matching, it is found that the optimal total cost is a piecewise concave function of the ordering and setup costs, and also a piecewise convex function of the shipment size to the customer.

A review of recent advances in microsampling techniques of biological fluids for therapeutic drug monitoring

Conventional sampling of biological fluids often involves a bulk quantity of samples that are tedious to collect, deliver and process. Miniaturized sampling approaches have emerged as promising tools for sample collection due to numerous advantages such as minute sample size, patient friendliness and ease of shipment. This article reviews the applications and advances of microsampling techniques in therapeutic drug monitoring (TDM), covering the period January 2015 – August 2020. As whole blood is the gold standard sampling matrix for TDM, this article comprehensively highlights the most historical microsampling technique, the dried blood spot (DBS), and its development. Advanced developments of DBS, ranging from various automation DBS, paper spray mass spectrometry (PS-MS), 3D dried blood spheroids and volumetric absorptive paper disc (VAPD) and mini-disc (VAPDmini) are discussed. The volumetric absorptive microsampling (VAMS) approach, which overcomes the hematocrit effect associated with the DBS sample, has been employed in recent TDM. The sample collection and sample preparation details in DBS and VAMS are outlined and summarized. This review also delineates the involvement of other biological fluids (plasma, urine, breast milk and saliva) and their miniaturized dried matrix forms in TDM. Specific features and challenges of each microsampling technique are identified and comparison studies are reviewed.

Shipment sizing for autonomous trucks of road freight

PurposeUnprecedented endeavors have been made to take autonomous trucks to the open road. This study aims to provide relevant information on autonomous truck technology and to help logistics managers gain insight into assessing optimal shipment sizes for autonomous trucks.Design/methodology/approachEmpirical data of estimated autonomous truck costs are collected to help revise classic, conceptual models of assessing optimal shipment sizes. Numerical experiments are conducted to illustrate the optimal shipment size when varying the autonomous truck technology cost and transportation lead time reduction.FindingsAutonomous truck technology can cost as much as 70% of the price of a truck. Logistics managers using classic models that disregard the additional cost could underestimate the optimal shipment size for autonomous trucks. This study also predicts the possibility of inventory centralization in the supply chain network.Research limitations/implicationsThe findings are based on information collected from trade articles and academic journals in the domain of logistics management. Other technical or engineering discussions on autonomous trucks are not included in the literature review.Practical implicationsLogistics managers must consider the latest cost information when deciding on shipment sizes of road freight for autonomous trucks. When the economies of scale in autonomous technology prevail, the classic economic order quantity solution might again suffice as a good approximation for optimal shipment size.Originality/valueThis study shows that some models in the literature might no longer be applicable after the introduction of autonomous trucks. We also develop a new cost expression that is a function of the lead time reduction by adopting autonomous trucks.

Shipment Policy for an Economic Production Quantity Model Considering Imperfection and Transportation Cost

Determining replenishment lot size and number of shipments in a traditional production setup has been of great interest among researchers during the last decades. In order to survive modern competition, the manufacturer has to make good decisions about the lot size that is to be shipped to the retailer. Recently, several researchers have developed mathematical models for modelling different real-world situations; however, these models are lacking due to a combination of imperfection in process and shipment lot sizing. Therefore, in the proposed research, shipment policy for an imperfect production setup has been developed with transportation costs taken into consideration. The model analyzed lot sizing for manufacturers and retailers with imperfections in terms of equally sized shipments. Furthermore, an all-unit-discount policy for shipment is considered in the proposed research, and at the end, numerical computation and sensitivity analyses are carried out to gain more insight into the specifications of the model.

Replenishment policy for an economic production quantity model considering rework and multiple shipments

Determining the optimal replenishment lot size and shipment policy for a production setup has been of greater interest during the last few years. This paper derives the optimal replenishment lot size and shipment policy for an Economic Production Quantity (EPQ) model with rework of defective items. However, in a real life situation, multi-shipment policy is used in lieu of continuous issuing policy and generation of defective items is inevitable. The proposed research assume that all imperfect quality items are reworked to perfect quality items and then all perfect quality items are delivered to the customers. Mathematical modeling is used in this study and the long-run average production–inventory-delivery cost function is derived. Convexity of the cost function is proved by using the Hessian matrix equations. The closed-form optimal replenishment lot size and optimal number of shipments that minimize the long-run average costs for such an EPQ model are derived.