Delivery Quantity Research Articles

An inventory positioning model under vendor managed inventory system

Inventory management is one of the key functions in any supply chain. Since inventory cost occupies a significant portion of supply chain cost, it is essential to employ a good inventory policy in order to reduce the supply chain cost while improving the service level. This paper presents a vendor managed inventory (VMI) policy with emergency orders for a supply chain with a single vendor and two independent retailers. A mathematical model is developed using the concept of demand rate, assuming Poisson demand for the retailers. The formulated model is used to help determine the optimal system base stock level, delivery quantities to retailers and the cycle length such that the total expected inventory cost of the system is minimised. Numerical experiments and sensitivity analysis are then conducted to examine the effect of model parameters on total inventory cost and optimal solution.

An inventory positioning model under vendor managed inventory system

Inventory management is one of the key functions in any supply chain. Since inventory cost occupies a significant portion of supply chain cost, it is essential to employ a good inventory policy in order to reduce the supply chain cost while improving the service level. This paper presents a vendor managed inventory (VMI) policy with emergency orders for a supply chain with a single vendor and two independent retailers. A mathematical model is developed using the concept of demand rate, assuming Poisson demand for the retailers. The formulated model is used to help determine the optimal system base stock level, delivery quantities to retailers and the cycle length such that the total expected inventory cost of the system is minimised. Numerical experiments and sensitivity analysis are then conducted to examine the effect of model parameters on total inventory cost and optimal solution.

A Contract Coordination Model of Dual-Channel Delivery between UAVs and Couriers Considering the Uncertainty of Delivery for Last Mile

For the traditional last-kilometer delivery, commodities are sent from the distribution center to the distribution transfer station, which are then delivered by the couriers from the distribution transfer station to the consumers. This single distribution channel cannot prioritize services for consumers closer to the distribution center, resulting in a waste of distribution resources. To deal with the last-kilometer delivery problem, this paper attempts to build a dual-channel distribution system consisting of UAVs and couriers. And the impact of the market allocation proportion on the expected profit of the last mile service through the two different distribution channels is discussed and analyzed. It is proved that the improved revenue sharing contract can realize the coordination of the dual-channel distribution between UAVs and the couriers. The numerical examples verify the effectiveness of the model and contract coordination. According to the research, first, with the increase in the uncertainties of the delivery quantity, the profit of the UAV channel shows a downward trend, while the profit of the courier channel remains basically the same. This is because the labor cost of the courier channel is relatively stable. Second, with the increase in the market allocation proportion of the last-kilometer delivery, the optimal delivery quantity of the UAV channel and the optimal order quantity of the courier channel are both increasing, while the overall profit of the last-kilometer is first rising and then decreasing. Third, the expected profit of the UAV channel and the courier channel can achieve Pareto improvement through the improved revenue sharing contract.

A dual sourcing inventory model for modal split transport: Structural properties and optimal solution

Shifting freight volumes from road to rail transport increases the economic performances of freight logistics. However, compared to road transport, rail transport generally lacks the flexibility in delivery quantity and frequency, and exhibits economies of scale in its shipment volume. This often leads to high inventory levels in the destination after deliveries. We generalize the tailored base-surge dual sourcing inventory model by introducing a fixed cost in rail transport, adding an extra decision in its delivery frequency, and relaxing the assumption of the base stock control of road transport, to support firms’ modal split transport optimization. The objective is to optimize the controls of the two transport modes and the corresponding inventory management at the destination, which minimize the combined average transport and inventory costs per period in the steady state. Using stochastic dynamic programming, we find that when the delivery quantity and frequency of rail transport is fixed, the optimal shipment volume via the road transport indeed follows a base stock control. This allows to solve the relevant Bellman equation via an efficient policy iteration approach. We also find that the total cost is convex in the delivery quantity of rail transport, and a bi-section search can be applied. Finally, we analyze the sensitivity and robustness of our model using values suggested by a consumer goods firm.

Inventory management in supply chains with consideration of Logistics, green investment and different carbon emissions policies

This study investigates the effects that carbon policies and green technologies may have on the integrated inventory of a two-echelon supply chain with consideration of carbon emissions during the processes of product production, transportation, and storage. The three carbon emissions policies: limited total carbon emissions, carbon taxation, and cap-and-trade, are considered in the study. The proposed model can assist firms in determining their corresponding optimal production quantity, delivery quantity, and green investment amount with an aim of minimizing the costs under different carbon emissions policies. Moreover, this study also provides practical implications for the government to make appropriate policies and regulations in balancing the trade-off between environmental protection and economic growth. Finally, the results indicate that firms adopting the carbon tax policy would prefer to invest in a relatively efficient green technology. With regard to the sources of carbon emissions, the effects of unit carbon emissions during production and unit distance of transportation are the most dramatic, and the cap limit has greater effects than the carbon emissions reduction factor of the green technology. Besides, the government should set the limit of carbon emissions within a reasonable range under the cap-and-trade policy to avoid suppliers overly trading their quotas of carbon emissions.

Examining How CBT Interventions for Anxious Youth Are Delivered Across Settings

Most efforts to assess treatment integrity—the degree to which a treatment is delivered as intended—have conflated content (i.e., therapeutic interventions) and delivery (i.e., strategies for conveying the content, such as modeling). However, there may be value in measuring content and delivery separately. This study examined whether the quantity (how much) and quality (how well) of delivery strategies for individual cognitive behavioral therapy (ICBT) for youth anxiety varied when the same evidence-based treatment was implemented in research and community settings. Therapists (N = 29; 69.0% White; 13.8% male) provided ICBT to 68 youths (M age = 10.60 years, SD = 2.03; 82.4% white; 52.9% male) diagnosed with a principal anxiety disorder in research or community settings. Training and supervision protocols for therapists were comparable across settings. Two independent teams of trained coders rated 744 sessions using observational instruments designed to assess the quantity and quality of delivery of interventions found in ICBT approaches. Overall, both the quantity and quality of delivery of interventions found in ICBT approaches were significantly lower in the community settings. The extent to which didactic teaching, collaborative teaching, and rehearsal were used systematically varied over the course of treatment. In general, differences in the quantity and quality of delivery observed between settings held when differences in youth characteristics between settings were included in the model. Our findings suggest the potential relevance of measuring how therapists deliver treatment separate from the content.

Integrated Vendor–Buyer Lot-Sizing Model with Transportation and Quality Improvement Consideration under Just-in-Time Problem

This paper deals with the problem of transportation and quality within a Just-in-Time (JIT) inventory replenishment system. Formerly, transportation and quality problem are often modelled separately in most integrated inventory lot-sizing models. Hence, this paper develops an integrated vendor-buyer lot-sizing model by considering transportation and quality improvements into a JIT environment. The model is developed for minimising a total vendor–buyer system cost by optimising decisions such as delivery quantity, production batch, number of shipments, and process quality. Numerical examples and sensitivity analysis are provided to illustrate the proposed model. The developed model was also compared with an enumeration method to analyse the effectiveness of the proposed model to find the optimum solution. The results emphasise that the proposed model contributes to a new approach and obtains a near optimum solution for inventory replenishment decisions. The results are also beneficial to JIT practices as the model can improve the transport payload and reduce the chance of defective products and improving quality-related costs.

Delivery Lot Size Optimization and Schedule of Shipment in Distribution Centre

This paper describes the optimal delivery lot sizes and plan delivery schedules from distribution center to manufacturer with a Distribution Requirement Planning (DRP). A company that produces nails has a problem that the planning and scheduling of goods distribution is not coordinated well. the demand for each product is out of control resulting in shortages or excesses as well as delays in distribution at each Distribution Center (DC). The purpose of this research is to analyze the distribution needs by using the Distribution Resources Planning (DRP) method to avoid delays in distributing products to each DC. Distribution analysis using the DRP method is used to get when and how many products will be sent for each DC. This method starts with calculating demand forecasting for the next 12 months. After obtaining the request results for the next 12 months, then the EOQ is calculated and the safety stock profit of each DC. With DRP method the optimal delivery quantity in DC Aceh 51.180 kgs, DC Padang 65.830 kgs, DC Pekanbaru 54.100 kgs, DC Palembang 67.225 kgs, and DC Medan 42.950 kgs. Using the optimal quantity delivery, frequency of shipment decreased until 51,5% and inventory cost decreased until 20,4%.

An analysis of multi-item inventory model using particle swarm optimization under discrete delivery orders and limited storage space

This study explores an economic production quantity (EPQ) model designed with the assumptions of discrete delivery orders and storage capacity constraints for a multi-item production inventory system. The main purpose of this study is to determine the optimal order quantity, the optimal number of deliveries and the optimal delivery quantity. First, the developed model as part of this study was analyzed using Genetic Algorithm (GA). Numerical analysis results were compared with those of previous studies and it was found that it is possible to have better results with an increasing number of iteration. The same model was then analyzed using Particle Swarm Optimization (PSO) algorithm. A comparison of the optimization methods showed that PSO gives better results over the GA under the same number of iterations and using the same population. The effects of important model parameters such as number of iterations, population, crossover, mutation rate on the optimal solution were analyzed. The results showed that PSO performs better than the GA with respect to the total cost and the total runtime as the solution of the problem in question.

GFR-α1 Expression in Substantia Nigra Increases Bilaterally Following Unilateral Striatal GDNF in Aged Rats and Attenuates Nigral Tyrosine Hydroxylase Loss Following 6-OHDA Nigrostriatal Lesion.

Glial cell line-derived neurotrophic factor (GDNF) improved motor function in Parkinson's disease (PD) patients in Phase I clinical trials, and these effects persisted months after GDNF discontinuation. Conversely, phase II clinical trials reported no significant effects on motor improvement vs placebo. The disease duration and the quantity, infusion approach, and duration of GDNF delivery may affect GDNF efficacy in PD treatment. However, identifying mechanisms activated by GDNF that affect nigrostriatal function may reveal additional avenues to partially restore nigrostriatal function. In PD and aging models, GDNF affects tyrosine hydroxylase (TH) expression or phosphorylation in substantia nigra (SN), long after a single GDNF injection in striatum. In aged rats, the GDNF family receptor, GFR-α1, increases TH expression and phosphorylation in SN. To determine if GFR-α1 could be a mechanistic link in long-term GDNF impact, we conducted two studies; first to determine if a single unilateral striatal delivery of GDNF affected GFR-α1 and TH over time (1 day, 1 week, and 4 weeks) in the striatum or SN in aged rats, and second, to determine if soluble GFR-α1 could mitigate TH loss following 6-hydroxydopamine (6-OHDA) lesion. In aged rats, GDNF bilaterally increased ser31 TH phosphorylation and GFR-α1 expression in SN at 1 day and 4 weeks after GDNF, respectively. In striatum, GFR-α1 expression decreased 1 week after GDNF, only on the GDNF-injected side. In 6-OHDA-lesioned rats, recombinant soluble GFR-α1 mitigated nigral, but not striatal, TH protein loss following 6-OHDA. Together, these results show GDNF has immediate and long-term impact on dopamine regulation in the SN, which includes a gradual increase in GFR-α1 expression that may sustain TH expression and dopamine function therein.

Numerical modeling of icehouse and greenhouse sea-level changes on a continental margin: Sea-level modulation of deltaic avulsion processes

Abstract Sequence stratigraphic models emphasize changes in accommodation driven by relative sea level as a principal control on continental-margin development. Therefore, it has been hypothesized that there are differences in the volume and distribution of sand delivered to deep-water during the high-amplitude sea-level changes of icehouse settings versus the low-amplitude sea-level changes of greenhouse settings. Previous numerical modeling and field studies suggest an alternative hypothesis: that there would be minimal differences between each setting. Here, we used a three-dimensional numerical stratigraphic forward model to test the impact of greenhouse and icehouse sea-level changes on sand distribution in a passive margin setting. Model results were analyzed to evaluate the impacts of the rate and amplitude of relative sea-level changes on individual deep-water sand delivery events, total deep-water sand volumes, and sand distribution. Model results show that total deep-water sand volume is almost independent of the rate of relative sea-level change, which we attribute to the interaction of this sea-level change rate and the time required by the delta to reach a dynamic equilibrium state as a result of avulsion processes. However, there are major differences in the large-scale spatial distribution of sand between the icehouse and greenhouse conditions. The high amplitude sea-level changes in the icehouse model caused significant shelfal incision, which minimized deltaic avulsion processes and localized deep-water sedimentation. The relatively low amplitude sea-level changes in the greenhouse model allowed avulsion of the fluvial-deltaic system, thus causing more laterally extensive deep-water deposits. Our experimental results demonstrate that the rate of deep-water sand delivery is largest during periods of low sea level in both icehouse and greenhouse conditions, but show that there is a poor relationship between the rate of deep-water sediment delivery and the rate of sea-level change. We attribute both of these results to the nature of avulsion processes on the delta: more frequent avulsions of greenhouse deltas spread sediment across the shelf and limit sediment delivery to deep water until a dynamic equilibrium state is achieved, whereas the development of an icehouse incised valley locks the fluvial-deltaic system in place and localizes deep-water sedimentation. Moreover, our results indicate that the total volume of deep-water sand is controlled by the total sediment supply to the basin. These results provide a framework for the timing and quantity of continental-margin sedimentation and deep-water sediment delivery, with applications to sequence-stratigraphic interpretations.

Reducing Production Waste Using Lean Manufacture

Currently, in globalization era, business competition in the fertilizer industry in Indonesia is very tight. This spurred the company to continue improving production results in terms of quality, quantity, price, and timeline of delivery to its customers. PT. JM Fertilizo (PT. JMF) is a company engaged in the processing of mining products that produce fertilizer for export purposes. One of the products delivered by PT. JMF is fosfat powder and Granule fertilizer. The company data shows in 2016 the company able to produce 550 tonnes / month and in 2017 able to produce 350 tonnes / month. Furthermore, there are still many products that can't meet customers requirement and several delays in product delivery. This problem causing inefficiency and decrease in production capacity. Therefore, this study was conducted to reduce the problems that exist in PT. JMF. The study which applied tools from Lean manufacturing, named value stream mapping and root cause analysis method has a conclusion that inappropriate process and waiting are the most dominant waste.

Supply Chain Framework at the Rubber Company with a Profit Sharing approach in ensuring Material Availability

This research was conducted in rubber factory in Indonesia. Today, businesses need a more efficient supply chain that is able to resolve complex challenges in the present. This research is aimed at increasing the company’s competitive advantage by designing supply chain framework with profit sharing system to ensure material availability. This research is conducted by using data reduction method in determining quantitative data for input Stream Value Mapping and Supply Chain Management. Determination of rubber material purchase price is done by using the average trend of rubber price so that the purchase of rubber material is done flat. In order to create trusts in the supply chain, profit-sharing systems are used so that suppliers will supply the right, consistent quantity, quality, and timing of delivery. The results obtained supply chain frameworks are better able to ensure process efficiency and material availability. If applied, this framework will save up to 29.17% processing time.

Bütünleşik Üretim ve Dağıtım Planlaması: İyileştirilmiş Bir Model ve Bütünleşik Planlamanın Değeri

This study considers a supply chain management problem in which a plant produces and distributes a product to multiple retailers using a homogeneous fleet of vehicles over a finite time horizon. The aim is to decide on the production quantities at the plant, delivery quantities to retailers, the set of vehicles to use and the assignment of retailers to vehicles in each period such that the system-wide costs are minimized. A mixed integer linear programming formulation of the problem outperforming the existing ones in the literature is proposed. This study also compares integrated production and distribution planning with the sequential planning in which retailers place their own orders and the plant makes its plan based on these orders, and assesses the value of integrated planning. The computational results indicate that average cost savings of 8.9% and maximum cost savings of 28% can be obtained with the integrated planning over the sequential planning.

Retailer order commitments improve distribution supply chain performance

Manufacturing, retail, and healthcare organizations are improving their distribution supply chains by implementing order commitment – an agreement under which some delivery quantities are fixed a priori while some others are freely chosen by the recipients. We show that these strategies are effective whenever the supplier’s cost is at least 29.3% of the total system cost. This result is robust to supply chain operating structure, and savings increase as the supplier’s share of the total system cost increases.

An integration of algal biofuel production planning, scheduling, and order‐based inventory distribution control systems

AbstractThis paper studies an integration model of production planning, scheduling, and order‐ based inventory distribution control systems. It models supply‐chain and production dynamics and employs process control to supply‐chain operations and the production process and scheduling. It embeds generalized disjunctive programming (GDP) and branch‐and‐reduce algorithms to obtain an optimal solution. Generalized disjunctive programming reformulates the original mixed integer nonlinear programming (MINLP) model and solves the new model with a lower number of integer variables. Afterwards, the branch and reduce algorithm, which is strengthened with outer approximation (OA) cutting planes, is able to find optimal solutions with permissible optimality gaps. The results show that supply network size does not affect forecast accuracy; there is a forecast error rate of 12.9%. Similar figures are also shown for the inventory level, which is expected to be 32% of demand level, production changeover time at 0.58 days, inventory ramp‐up period to meet the expected raw materials and final products inventory at 0.02 and 0.04 days respectively, and extra delivery time to meet a certain level of demand change at 1.1 days. The results show that the model is capable of controlling demand forecast, inventory level, and delivery quantity, so that the supply chain can avoid stock out at a minimal level of inventory, regardless of the size of supply‐chain networks. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.

A stochastic reverse logistics production routing model with emissions control policy selection

Carbon emission control policies have emerged in response to the growing global concerns of environmental problems. These regulations should be considered when operational decisions on production, inventory, and routing are being made as emission costs are incurred. In this study, we introduce a reverse logistics supply chain model with remanufacturing and simultaneous pickup and delivery. The problem considers different carbon emission control policies with heterogeneous transportation fleets and allows for lost sales. The aim is to select the optimal carbon control policy with optimal production, inventory, and delivery quantities. The proposed model is formulated as both a deterministic and a two-stage stochastic mixed-integer programming problem. The proposed formulations are demonstrated through two case studies, a simulated reverse logistics supply chain and an actual home appliances production supply chain with remanufacturing options. Sensitivity analyses are conducted to test for the effect of different parameters on the optimal solution. Results indicate that the carbon policy selected has significant effect on the supply chain performance with carbon price being the most significant parameter.

A column‐generation‐based approach to fleet design problems mixing owned and hired vehicles

AbstractWe look at the problem of choosing a fleet of vehicles to carry out delivery tasks across a long time horizon. The delivery quantities may vary significantly from day to day, and from season to season, and the underlying routing problem may have rich constraints, for example, time windows, multiple compartments, multiple commodities, and compatibility constraints. We consider the option of hiring extra vehicles from external carriers in order to efficiently carry out the day‐to‐day operations while containing the costs of owning the fleet. The goal is to design a fleet so as to minimize the sum of costs of routing the fleet every day of the horizon, the acquisition costs, the maintenance costs, and the costs of hiring external vehicles. In the literature, there is no previous work on fleet design for a long time horizon, which also considers the hiring options. We propose to tackle the problem using column generation and develop three different heuristics. The methods proposed are tested and compared on a set of real‐world problems. It is also shown how introducing the possibility of hiring helps reducing the overall cost and the number of idle vehicles over the planning horizon.

Low Carbon Supply Chain Coordination for Imperfect Quality Deteriorating Items

Nowadays, many countries have implemented carbon pricing policies. Hence, the industry adapts to this policy while striving for its main goal of maximizing financial benefits. Here, we study a single manufacturer–retailer inventory decision considering carbon emission cost and item deterioration for an imperfect production system. This study examines two models considering two cases of quality inspection. The first is when the buyer performs the quality inspection, and the second is when the quality inspection becomes the vendor’s responsibility so that no defective products are passed to the buyer. Carbon emission costs are incorporated under a carbon tax policy, and we consider the carbon footprint from transporting and warehousing the items. The objective is to jointly optimize the delivery quantity and number of deliveries per production cycle that minimize the total cost and reduce the total carbon emissions. This study provides solution procedures to solve the models, as well as two numerical examples.

Branch-and-Cut for the Split Delivery Vehicle Routing Problem with Time Windows

The split delivery vehicle routing problem with time windows (SDVRPTW) is a notoriously hard combinatorial optimization problem. First, it is hard to find a useful compact mixed-integer programming (MIP) formulation for the SDVRPTW. Standard modeling approaches either suffer from inherent symmetries (mixed-integer programs with a vehicle index) or cannot exactly capture all aspects of feasibility. Because of the possibility to visit customers more than once, the standard mechanisms to propagate load and time along the routes fail. Second, the lack of useful formulations has rendered any direct MIP-based approach impossible. Up to now, the most effective exact algorithms for the SDVRPTW have been branch-and-price-and-cut approaches using path-based formulations. In this paper, we propose a new and tailored branch-and-cut algorithm to solve the SDVRPTW. It is based on a new, relaxed compact model, in which some integer solutions are infeasible for the SDVRPTW. We use known and introduce some new classes of valid inequalities to cut off such infeasible solutions. One new class is path-matching constraints that generalize infeasible-path constraints. However, even with the valid inequalities, some integer solutions to the new compact formulation remain to be tested for feasibility. For a given integer solution, we build a generally sparse subnetwork of the original instance. On this subnetwork, all time-window-feasible routes can be enumerated, and a path-based residual problem then solved to decide on the selection of routes, the delivery quantities, and thereby the overall feasibility. All infeasible solutions need to be cut off. For this reason, we derive some strengthened feasibility cuts exploiting the fact that solutions often decompose into clusters. Computational experiments show that the new approach is able to prove optimality for several previously unsolved instances from the literature. The e-companion is available at https://doi.org/10.1287/trsc.2018.0825 .