Learning EOQ Model with Trade-Credit Financing Policy for Imperfect Quality Items under Cloudy Fuzzy Environment

An imprecise demand rate creates problems in profit optimization in business scenarios. The aim is to nullify the imprecise nature of the demand rate with the help of the cloudy fuzzy method. Traditionally, all items in an ordered lot are presumed to be of good quality. However, the delivered lot may contain some defective items, which may occur during production or maintenance. Inspection of an ordered lot is indispensable in most organizations and can be treated as a type of learning. The learning demonstration, a statistical development expressing declining cost, is necessary to achieve any cyclical process. Further, defective items are sold immediately after the screening process as a single lot at a discounted price, and the fraction of defective items follows an S-shaped learning curve. The trade-credit policy is adequate for suppliers and retailers to maximize their profit during business. In this paper, an inventory model is developed with learning and trade-credit policy under the cloudy fuzzy environment where the demand rate is treated as a cloudy fuzzy number. Finally, the retailer’s total profit is maximized with respect to order quantity. Sensitivity analysis is presented to estimate the robustness of the model.

The Role of the Discount Policy of Prepayment on Environmentally Friendly Inventory Management

Nowadays, more and more consumers consider environmentally friendly products in their purchasing decisions. Companies need to adapt to these changes while paying attention to standard business systems such as payment terms. The purpose of this study is to optimize the entire profit function of a retailer and to find the optimal selling price and replenishment cycle when the demand rate depends on the price and carbon emission reduction level. This study investigates an economic order quantity model that has a demand function with a positive impact of carbon emission reduction besides the selling price. In this model, the supplier requests payment in advance on the purchased cost while offering a discount according to the payment in the advanced decision. Three different types of payment-in-advance cases are applied: (1) payment in advance with equal numbers of instalments, (2) payment in advance with a single instalment, and (3) the absence of payment in advance. Numerical examples and sensitivity analysis illustrate the proposed model. Here, the total profit increases for all three cases with higher values of carbon emission reduction level. Further, the study finds that the profit becomes maximum for case 2, whereas the selling price and cycle length become minimum. This study considers the sustainable inventory model with payment-in-advance settings when the demand rate depends on the price and carbon emission reduction level. From the literature review, no researcher has undergone this kind of study in the authors’ knowledge.

Inventory models with stock - and price-dependent demand for deteriotating items based on limited space

This paper deals with the problem of determining the optimal selling price and order quantity simultaneously under EOQ model for deteriorating items. It is assumed that the demand rate depends not only on the on-display stock level but also the selling price per unit, as well as the amount of shelf/display space is limited. We formulate two types of mathematical models to manifest the extended EOQ models for maximizing profits and derive the algorithms to find the optimal solution. Numerical examples are presented to illustrate the models developed and sensitivity analysis is reported.

Vehicle routing problem for humanitarian relief distribution under hybrid uncertainty

PurposeGiven the importance of estimating the demand for relief items in earthquake disaster, this research studies the complex nature of demand uncertainty in a vehicle routing problem in order to distribute first aid relief items in the post disaster phase, where routes are subject to disruption.Design/methodology/approachTo cope with such kind of uncertainty, the demand rate of relief items is considered as a random fuzzy variable and a robust scenario-based possibilistic-stochastic programming model is elaborated. The results are presented and reported on a real case study of earthquake, along with sensitivity analysis through some important parameters.FindingsThe results show that the demand satisfaction level in the proposed model is significantly higher than the traditional scenario-based stochastic programming model.Originality/valueIn reality, in the occurrence of a disaster, demand rate has a mixture nature of objective and subjective and should be represented through possibility and probability theories simultaneously. But so far, in studies related to this domain, demand parameter is not considered in hybrid uncertainty. The worth of considering hybrid uncertainty in this study is clarified by supplementing the contribution with presenting a robust possibilistic programming approach and disruption assumption on roads.

Reliability Assessment of RC Bridges Subjected to Seismic Loadings

An approach to estimate both the reliability index β and its complement, the probability of failure, through closed-form expressions that consider aleatory and epistemic uncertainties, is proposed. Alternatively, exceedance demand rates are obtained based on simplified expressions and numerical integration. Reliability indicators are calculated, considering the uncertainties in the compressive strength of concrete, steel yield, and section geometry, together with the aleatory uncertainties related to seismic loadings. Such indicators are estimated in a continuous RC bridge located in Acapulco, Guerrero, Mexico. The bridge was designed to comply with a drift of 0.004. Exceedance demand rates for drift thresholds from 0.001 to 0.012 are estimated, and maximum differences of 5.5% are found between the closed-form expression and numerical integration. The exceedance demand rate expressed by means of its inverse, the return period, indicates that the serviceability limit state is exceeded after 58 years of the bridge construction. The reliability index decreases by about 1.66%, and the probability of failure increases by about 16.1% when the epistemic uncertainties are considered. The approach shows the importance of epistemic uncertainties in the estimation of reliability indicators.

Manufacturing/remanufacturing based supply chain management under advertisements and carbon emission process

One of the most successful ways to get the word out about a product's popularity across all types of customers is through advertising. It has a valuable direct influence on increasing product demand. The supply chain model is developed for manufacturer and retailer, where advertisements are dependent on demand. The advertisement rate has been considered a function that has enhanced at a diminishing rate concerning time, although the growth rate slowed. During the manufacturing cycle, the market's demand is a function of advertisement, and the customer's demand is a linear function of time. The production rate exceeds the demand rate during manufacturing and remanufacturing; shortages are not faced. It involves a manufacturing/remanufacturing process that quickly delivers consumer products and less waste. To keep the environment clean, the cost of carbon emissions is incorporated into the manufacturer's and supplier's holding and degrading costs. The model's primary purpose is to minimize the overall cost of manufacturing and remanufacturing. The overall cost during the manufacturing cycle is higher than that during the remanufacturing cycle. This study confirms that the increasing cost of advertising provides the continuous increasing value of the total cost. A numerical example is provided, graphical representation and sensitivity analysis determine the function's behavior and test the model.

Sortie-based aircraft component demand rate to predict requirements

PurposeForecasting techniques improve supply chain resilience by ensuring that the correct parts are available when required. In addition, accurate forecasts conserve precious resources and money by avoiding new start contracts to produce unforeseen part requests, reducing labor intensive cannibalization actions and ensuring consistent transportation modality streams where changes incur cost. This study explores the effectiveness of the United States Air Force’s current flying hour-based demand forecast by comparing it with a sortie-based demand forecast to predict future spare part needs.Design/methodology/approachThis study employs a correlation analysis to show that demand for reparable parts on certain aircraft has a stronger correlation to the number of sorties flown than the number of flying hours. The effect of using the number of sorties flown instead of flying hours is analyzed by employing sorties in the United States Air Force (USAF)’s current reparable parts forecasting model. A comparative analysis on D200 forecasting error is conducted across F-16 and B-52 fleets.FindingsThis study finds that the USAF could improve its reparable parts forecast, and subsequently part availability, by employing a sortie-based demand rate for particular aircraft such as the F-16. Additionally, our findings indicate that forecasts for reparable parts on aircraft with low sortie count flying profiles, such as the B-52 fleet, perform better modeling demand as a function of flying hours. Thus, evidence is provided that the Air Force should employ multiple forecasting techniques across its possessed, organically supported aircraft fleets. The improvement of the forecast and subsequent decrease in forecast error will be presented in the Results and Discussion section.Research limitations/implicationsThis study is limited by the data-collection environment, which is only reported on an annual basis and is limited to 14 years of historical data. Furthermore, some observations were not included because significant data entry errors resulted in unusable observations.Originality/valueThere are few studies addressing the time measure of USAF reparable component failures. To the best of the authors’ knowledge, there are no studies that analyze spare component demand as a function of sortie numbers and compare the results of forecasts made on a sortie-based demand signal to the current flying hour-based approach to spare parts forecasting. The sortie-based forecast is a novel methodology and is shown to outperform the current flying hour-based method for some aircraft fleets.

Analysis of production capacity planning to meet the consumer request using the Rough Cut Capacity Planning (RCCP) method in PT. Maccon Generasi Mandiri Makassar, Indonesia

Maccon Generasi Mandiri Makassar company is one of a manufacturing company engaged in the production of light brick AAC (Autoclaved Aerated Concrete). PT. Maccon Generasi Mandiri Makassar has a production capacity of 15024 〖 m〗^3 in a month or 180288 〖 m〗^3 in a year. However, with this capacity, the company is often unable to meet high consumer demand of 181450 〖 m〗^3 in a year due to less than optimal engine performance, a number of hours of work and an unbalanced workforce in the producing light brick of ACC (Autoclaved Aerated Concrete). This requires the company to plan the optimal production of capacity in order to fulfill the consumer demand in a timely and appropriate amount so that the expected of company profits will be increased. The purpose of this research is to plan production capacity in the future based on the demand rate of the consumer using the Rough Cut Capacity Planning (RCCP) with the method is Bill of Labor Approach (BOLA) technique. Based on the data processing which has been done, the recommended made were a combination of engine additions and working time. This is realized to fulfill the lack of production capacity. For the January Period = 19872 hours/month, February = 19008 hours/month, March = 19872 hours/month, April = 19008 hours/month, May = 18144 hours/month, June = 18144 hours/month, July = 19872 hour/month, August = 18144 hours/month, September = 17280 hours/month, October = 18144 hours/month, November = 18144 hours/month, December = 17280 hours/month.

An Investigation of a Supply Chain Model for Co-Ordination of Finished Products and Raw Materials in a Production System under Different Situations

In the production system, the production of a perfect item is essential for existing competitive market situations. To produce a perfect finished product, the quality of a raw material is a crucial issue of a production system. This paper has examined this issue with an insightful production-inventory model of the manufacturer of a deteriorating item selling goods to multiple markets with different selling seasons. We have provided an answer strategy to track down the optimal production plan for raw materials and the ideal creation plan for completed items. Transportation cost was incorporated for transporting the raw material. Marketing of the finished product is another crucial factor for selling products and earning revenues. The main objective of the present study is to adopt a production model in inventory for inferring request capacities for multi-item, multi-outlet circumstances. As of late, much accentuation has been given to the consideration of the control and support of creation inventories of disintegrating things. The demand rate is persistent and holding cost is a direct function of time. This paper has followed an analytical approach to diminish the entire inventory cost. Finally, a sensitivity analysis was performed to study the effect of changes of different parameters of the model on the optimal policy. Moreover, in order to approve the determined models, we have clarified mathematical models and examined affectability.