An integrated multi-stage supply chain inventory model under an infinite planning horizon and continuous price decrease

This paper studies an integrated inventory model in a supply chain that involves procurement, production and delivery activities. The model is studied in an environment where products experience continuous price decrease and planning is performed in an infinite time horizon. In this model, a manufacturing facility purchases a fixed-quantity of raw materials from an outside supplier, processes the materials, and delivers a fixed-quantity of finished products to a customer periodically. In order to take advantage of the decreasing price trend, customers demand frequent deliveries of small lots of finished products, and this inventory management strategy has been used by many successful companies in technology-related industries. Therefore, the ultimate intention of this research is to study and model the inventory system for high-tech companies whose products are experiencing continuous price decrease. This model is used to determine an optimal economic lot size model for raw material procurement, production setup and finished goods delivering under an infinite planning horizon. Two efficient algorithms are developed in this paper to solve this nonlinear model and the test results consistently indicate that ordering of raw materials and delivery of finished goods should be frequent in small lots for low ordering and shipment costs. Finally an operational schedule is provided to show the implementation procedure of the model.     

A deteriorating multi-item inventory model with price discount and variable demands via fuzzy logic under resource constraints

An inventory model of deteriorating seasonal products with Maximum Retail Price (MRP) for a wholesaler having showrooms at different places under a single management system is considered under random business periods with fuzzy resource constraints. The wholesaler replenishes the products instantaneously and earns commissions on MRP which vary with the ordered quantities following All Unit Discount (AUD), Incremental Quantity Discount (IQD) or IQD in AUD policy. Demand at showrooms are imprecise and related to selling prices by ‘verbal words’ following fuzzy logic. The wholesaler shares a part of commission with customers. The business periods follows normal distribution and converted to deterministic ones through chance constraint technique. The fuzzy space and budget constraints and fuzzy relations are defuzzified using possibility measures, surprise function and Mumdani fuzzy inference technique. The model is formulated as profit maximization for the wholesaler and solved using a real coded Genetic Algorithm (GA) and illustrated through some numerical examples and some sensitivity analysis. A real-life problem of a developing country is presented, solved using the above mentioned procedures and an appropriate inventory policy is suggested.     

An operational policy for an integrated inventory system under consignment stock policy with controllable lead time and buyers’ space limitation

This paper aims to enable the decision maker of an integrated vendor–buyer system, under Consignment Stock (CS) policy, to make the optimal/sub-optimal production/replenishment decisions where the buyer places a space limitation to the vendor and the lead-time is controllable with an extra investment. Within any production cycle, the vendor produces at a finite rate and ships the outputs to the buyer with a number of equal-sized lots. With a long-term consignment stock agreement, the vendor takes responsibility to maintain a certain inventory level in the buyer's warehouse. Some of the shipments are delayed so that the buyer's inventory does not go beyond the capacity limitation. The buyer compensates the vendor after the complete consumption of the products. The holding cost consists of a storage component and a financial component. Two constraint four-variable non-linear integer optimization models are established wherein the buyer space limitation is considered. Because the developed models are mathematically very difficult to solve, three doubly hybrid meta-heuristic algorithms are employed to solve the models. The computational results show that one of these three algorithms works very well both in the sense of the success rate and the mean CPU time. The analysis of the computational example also reveals the quantitative effects the buyer space limitation may have to the annual joint total expected cost (JTEC) of the integrated system.     

An optimization approach for joint pricing and ordering problem in an integrated inventory system with order-size dependent trade credit

Under a business trading environment, it is common for the trade credit to depend on the order size. Therefore, it is important to discuss the single-supplier and single-buyer supply chain problem which includes order-size dependent trade credit. In this study, an integrated inventory model with a price sensitive demand rate, determining jointly economic lot size of the buyer’s ordering and the supplier’s production batch, are developed to maximize the total profit per unit time. An efficient algorithm is provided to obtain the optimal solution, and then numerical examples are presented to illustrate the theoretical results. Finally, the comparison between whether an optimal solution is jointly or independently determined is also provided.     

An integrated production inventory model under interactive fuzzy credit period for deteriorating item with several markets

The presence of multiple markets create profitable opportunities to the supply chain system. In this regard, this paper consists of the joint relationship between a manufacturer and multiple markets in which manufacturer offers part-payment to the markets due to their collection of finished products during the production run time. Here it is also considered that manufacturer is facilitated with credit period by raw material supplier where credit period has been presented as an interactive fuzzy fashion. In this paper, two types of deterioration have been assumed such as one for finished products and the other for raw materials. A solution algorithm is presented to get fuzzy optimal profit for the proposed integrated production inventory system optimizing production run time. A numerical example is used to illustrate the proposed model. Finally, sensitivity analysis has been carried out with respect to the major parameters to demonstrate the feasibility of the proposed model.     

Two-echelon competitive integrated supply chain model with price and credit period dependent demand

This study considers a two-echelon competitive supply chain consisting of two rivaling retailers and one common supplier with trade credit policy. The retailers hope that they can enhance their market demand by offering a credit period to the customers and the supplier also offers a credit period to the retailers. We assume that the market demand of the products of one retailer depends not only on their own market price and offering a credit period to the customers, but also on the market price and offering a credit period of the other retailer. The supplier supplies the product with a common wholesale price and offers the same credit period to the retailers. We study the model under a centralised (integrated) case and a decentralised (Vertical Nash) case and compare them numerically. Finally, we investigate the model by the collected numerical data.     

Satisfaction with virtual worlds: An integrated model of experiential value

Although virtual worlds increasingly attract users today, few studies have addressed what satisfies virtual world users. We therefore defined and tested an integrated model of experiential system value and virtual world satisfaction. Drawing upon expectancy-value and cognitive evaluation theories, four important facets of experiential system value were hypothesized as determinants of virtual world satisfaction. Structural equation modeling was employed on a sample of 567 users of the virtual world Second Life. Direct and indirect effects were tested and are reported. Theoretical and practical implications are discussed.     

Exploring Koreans’ smartphone usage: An integrated model of the technology acceptance model and uses and gratifications theory

This study investigates factors that influence adoption and use of smartphones among Koreans and seeks to integrate two theoretical approaches: the technology acceptance model (TAM) and the uses and gratifications (U&G) approach. To that end, the study used data from a self-reported survey of 491 Korean adults who use Apple’s iPhone. A structural equation model employed in the current study demonstrates that Koreans’ smartphone use is affected more by motivations based on instrumental and goal-oriented use than by ritualized and less-goal oriented use. The findings suggest that to spread information system with innovative and active features, developers should pay attention to users’ intrinsic motivations as well as to their extrinsic perceptions.     

The joint model of the logistic model and linear random effect model — An application to predict orthostatic hypertension for subacute stroke patients

Stroke is a common acute neurologic and disabling disease. Orthostatic hypertension (OH) is one of the catastrophic cardiovascular conditions. If a stroke patient has OH, he/she has higher chance to fall or syncope during the following courses of treatment. This can result in possible bone fracture and the burden of medical cost therefore increases. How to early diagnose OH is clinically important. However, there is no obvious time-saving method for clinical evaluation except to check the postural blood pressure.This paper uses clinical data to identify potential clinical factors that are associated with OH. The data include repeatedly observed blood pressure, and the patient’s basic characteristics and clinical symptoms. A traditional logistic regression is not appropriate for such data. The paper modifies the two-stage model proposed by Tsiatis et al. (1995) and the joint model proposed by Wulfsohn and Tsiatis (1997) to take into account of a sequence of repeated measures to predict OH. The large sample properties of estimators of modified models are derived. Monte Carlo simulations are performed to evaluate the accuracy of these estimators. A case study is presented.     

An improved state-space model structure and a corresponding predictive functional control design with improved control performance

Conventional state-space model predictive control requires a state estimator/observer to access the state information for feedback controller design. Its drawbacks are the numerical convergence stability of the observer and closed-loop control performance deterioration with activated plant input/output constraints. The recent direct use of measured input and output variables to formulate a non-minimal state-space (NMSS) model overcomes these problems, but the subsequent controller is too sensitive to model mismatch. In this article, an improved structure of NMSS model that incorporates the output-tracking error is first formulated and then a subsequent predictive functional control design is proposed. The proposed controller is tested on both model match and model mismatch cases for comparison with previous controllers. Results show that control performance is improved. In addition, a linear programming method for constraints dealing and a closed form of transfer function representation of the control system are provided for further insight into the proposed method.