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.

APPLICATIONS OF ITEM RESPONSE THEORY MODELS TO ASSESS ITEM PROPERTIES AND STUDENTS’ ABILITIES IN DICHOTOMOUS RESPONSES ITEMS

A test is a tool meant to measure the ability level of the students, and how well they can recall the subject matter, but items making up a test may be defectives, and thereby unable to measure students’ ability or traits satisfactorily as intended if proper attention is not paid to item properties such as difficulty, discrimination, and pseudo guessing indices (power) of each item. This could be remedied by item analysis and moderation. It is a known fact that the absence or improper use of item analysis could undermine the integrity of assessment, certification and placement in our educational institutions. Both appropriateness and spread of items properties in accessing students’ abilities distribution, and the adequacy of information provided by dichotomous response items in a compulsory university undergraduate statistics course which was scored dichotomously, and analyzed with stata 16 SE on window 7 were focused here. In view of this, three dichotomous Item Response Theory (IRT) measurement models were used in the context of their potential usefulness in an education setting such as in determining these items properties. Ability, item discrimination, difficulty, and guessing parameters as unobservable characteristics were quantified with a binary response test, then discrete item response becomes an observable outcome variable which is associated with student’s ability level is thereby linked by Item Characteristic Curves that is defined by a set of item parameters that models the probability of observing a given item response by conditioning on a specific ability level. These models were used to assess each of the three items properties together with students’ abilities; then identified defective items that were needed to be discarded, moderated, and non-defectives items as the case may be while some of these chosen items were discussed based on underlining models. Finally, the information provided by these items was also discussed.

Ordering policy estimation for high utility item-sets considering negative item values in large databases

Utility mining with negative item values has recently received interest in the data mining field due to its practical considerations. Previously, the values of utility item-sets have been taken into consideration as positive. However, in real-world applications an item-set may be related to negative item values. This paper presents a method for redesigning the ordering policy by including high utility item-sets with negative items. Initially, utility mining algorithm is used to find high utility item-sets. Then, ordering policy is estimated for high utility items considering defective and non-defective items. A numerical example is illustrated to validate the results

Design of single and double acceptance sampling plans based on neutrosophic sets

Although traditional acceptance sampling plans (ASPs) need certain mass quality characteristics, it is not easy to define them as crisp value in some real case problems. The fuzzy set theory (FST) is one of the popular techniques to model uncertainties of the process and therefore fuzzy ASPs have been offered in the literature. Fuzzy set extensions have been proposed recently for better modeling of the uncertainties having different sources and characteristics. One of these extensions named neutrosophic sets (NSs) can be used to increase the sensitiveness and flexibility of ASPs. The ASPs based on NSs can give ability to classify the items as defective, non-defective and indeterminate. Since the operator can become indecisive for slightly defective items, these plans can provide a good representation of human evaluations under uncertainty. In this study, single and double ASPs are designed based on NSs by using binomial and poisson distributions that are also re-analyzed based on NSs. For this aim, some characteristics functions of ASPs such as probability of accepting a lot (Pa), average outgoing quality (AOQ), average total inspection (ATI) and average sample number (ASN) have also been analyzed based on NSs. Numerical examples are presented to analyze the proposed plans.

Nonlinear pentagonal intuitionistic fuzzy number and its application in EPQ model under learning and forgetting

In this current era, the concept of nonlinearity plays an important and essential role in intuitionistic fuzzy arena. This article portrays an impression of different representation of nonlinear pentagonal intuitionistic fuzzy number (PIFN) and its classification under different scenarios. A new de-intuitification technique of non-linear PIFN is addressed in this article along with its various graphical representations. Additionally, in this paper, we have observed this by applying it in an economic production quantity model where the production is not perfect and defective items are produced which are reworked. The model is considered under learning and forgetting, where learning is considered as linear, nonlinear PIFN and crisps arena. It is observed from the numerical study that high learning effects in rework lead to decrease in production of defective item, which, besides an economic advantage, may have a positive effect on the environment. Even though forgetting has an adverse effect, the average total cost is much less than that of the basic model which ignores worker learning and forgetting. Finally, comparative and sensitivity analysis result shows the utility of this noble work.

A single-manufacturer multi-retailer integrated inventory model for items with imperfect quality, price sensitive demand and planned back orders

In this paper, we develop an integrated two-echelon supply chain inventory model with a single-manufacturer and multi-retailers in which each retailer’s demand is dependent on selling price of the product. The manufacturer produces a single product and dispatched the order quantities of the retailers in some equal batches. The production process is imperfect and produces imperfect quality of products with a defective percentage which is random in nature and follows binomial distribution. Inspection process is performed by the retailers to classify the defective items in each lot delivered from the manufacturer. The defective items that were found by the retailer will be returned to the manufacturer at the next delivery. Lead time is random and it follows an exponential distribution. We also assume that shortages are allowed and are completely backlogged at each retailer’s end. A closed form solution to maximize the expected average profit for both the centralized and the decentralized scenarios are obtained. The developed models are illustrated with the help of some numerical examples using stochastic search genetic algorithm (GA). It is found that integration of the supply chain players results an impressive increment in the profit of the whole supply chain. Sensitivity analysis is also performed to explore the impacts of key-model parameters on the expected average profit of the supply chain.

Risk Management in Oil and Gas (EPC) Industry

The five most important risk factors identified in the design phase are 1] scope uncertainty, 2] failed management and planning, 3] changes in errors and omissions, 4] inadequate projectS team structure, 5] inadequate quality requirements. The top five risk factors determined by the procurement category are 1] Inadequate online resources and equipment, 2] Distribution of suppliers, 3] Uncertainty in design and style, codes, requirements and standards, 4] Defective items, and 5] Compromise. The 5 most important risk factors identified in the construction phase are 1] weak project capability, 2] excessive construction costs, 3] major construction delays, 4] strong project plan, and 5] poor safety management .This was initiated by the link between risk factors and the effects on price, quality and timing, as well as the potential for expensive, common, and high-quality outcomes. Keywords: Disaster risk management, risk management strategies, project risk management, oil and gas industry.

Setting Standards With Multiple-Choice Tests: A Preliminary Intended-User Evaluation of SmartStandardSet

Software that easily helps higher education instructors to remove poor quality items and set appropriate grade boundaries is generally lacking. To address these challenges, the SmartStandardSet system provides a graphical-user interface for removing defective items, weighting student scores using a two-parameter model IRT score analysis, and a mechanism for standard-setting. We evaluated the system through a series of six interviews with teachers and six focus groups involving 19 students to understand how key stakeholders would view the use of the tool in practice. Generally, both groups of participants reported high levels of feasibility, accuracy, and utility in SmartStandardSet’s statistical scoring of items and score calculation for test-takers. Teachers indicated the data displays would help them improve future test items; students indicated the system would be fairer and would motivate greater effort on more difficult test items. However, both groups had concerns about implementing the system without institutional policy endorsement. Students specifically were concerned that academics may set grade boundaries on arbitrary and invalid grounds. Our results provide useful insights into the perceived benefits of using the tool for standard setting, and suggest concrete next steps for gaining wider acceptance that will be the focus of future work.

A multi-item manufacturing system with an outsourcer and random defective items

This study examines a multi-item manufacturing problem with a single machine, an outsourcer, and random defective items. To cope with the increasing multi-product demands from global markets, modern manufacturing firms must make an efficient production plan to satisfy customer’s needs with quality goods and smooth the in-house fabrication schedule and utilization. Outsourcing is an effective option to avoid machine overloads and smooth fabrication schedules. Further, the fabrication of random defective products is inevitable because of unanticipated factors in real manufacturing environments. These products must be identified, separated and discarded to retain the desired quality of the finished lot. To address the above-mentioned concerns, this study develops a mathematical model to represent a hybrid stock refilling system, employs mathematical derivations to find long-run average system expenses, and uses an optimization technique to derive a closed-form common rotation time for this hybrid system. The results of this study show the individual and combined impacts of variations in outsourcing percentages and scrap rates on optimal rotation time and diverse core system parameters (such as machine utilization, specific cost component, etc.) to facilitate planning, controlling, and decision making in such a particular hybrid fabrication system.