An EOQ Model for Non-Instantaneous Deteriorating Items under Pandemic Conditions using Triangular Intuitionistic Fuzzy Numbers

A portfolio selection model with return as triangular intuitionistic fuzzy number is developed in this study to assess and select portfolios on China Stock Exchange. Although the portfolio selection has been widely investigated, and most studies have regarded return and risk as the main decisive criteria, there are many uncertainties in the financial market, such as social, political and human psychological factors, which makes it difficult for us to describe the risks in line with empirical evidence. To fill this gap, first, a literature review was conducted to clarify the current situation and shortcomings of portfolio selection. Second, the triangular intuitionistic fuzzy number was used to fuzzify the coefficients of the objective function and the constraints in the portfolio model. Third, the triangular intuitionistic fuzzy number model was transformed into a linear programming model by using the selected exact ranking function, and the model was solved by MATLAB. Finally, the historical monthly returns of 10 stocks in China’s Stock Exchange from December 2017 to November 2020, lasting 36 months, were selected to demonstrate the model. The results indicate that the portfolio selection model with triangular intuitionistic fuzzy number can better meet the uncertainty of the real securities market, and more reasonable investment decision guidance can be provided for investors. Besides, the limitations of this study are pointed out, and implications and directions for future research are discussed.

In this paper, economic order quantity (EOQ) models without shortages for single item and multi-items are presented. Here, the holding cost of the item is a continuous function of the order quantity. The costs involved in this model are imprecise in nature. The main contributions of this research are as follows: The proposed EOQ model is discussed in two cases by describing the model in an uncertain environment. In case-1, EOQ models with fuzzy parameters (like ordering cost, holding cost, and unit product cost) are considered. Here all the fuzzy parameters are represented by trapezoidal fuzzy numbers. The said EOQ model is carried out by using the signed-distance method. In case-2, EOQ models with interval parameters (like ordering cost, holding cost, unit product cost, and the total money investment for the quantities) are considered. This proposed model is solved by using interval linear programming problem (ILPP) technique based on the best and the worst optimum values of the objective function. Numerical examples are given to exemplify the proposed model and also the results of different models are compared.KeywordsEOQTrapezoidal fuzzy numberInterval numberSigned-distance methodInterval linear programming problem (ILPP)

A TODIM-based multi-criteria group decision making with triangular intuitionistic fuzzy numbers

The Covid-19 pandemic has profoundly impacted businesses and consumers worldwide triggering significant changes in supply chains and marketing strategies. Particularly, the emergence of stockpiling at the onset of the pandemic has created both challenges and opportunities. This study aims to understand the effects of products stockpiling during the Covid-19 pandemic on businesses and consumers. Specifically, it investigates the reasons behind the increased stockpiling and examines its implications for business operations and consumer shopping behaviors. The research includes a literature review and various survey studies conducted to understand business and consumer behaviors during the Covid-19 period. The literature review synthesized findings from previous studies to analyze the effects of stockpiling on businesses and consumers. Survey studies aimed to gather real-world data by directly engaging with business Owners and consumers. Findings indicate that product stockpiling provides short term Advantages for does but leads to long term challenges in supply chain management and cost increases. For consumers stockpiling creates a sense of security during uncertain times but contributes to supply shortages and price fluctuations. These results contributed significantly to strategic management and consumer behavior studies during Crisis periods.

The urge to remanufacture and address environmental concerns in various industrial processes has drawn the attention of academics as well as practitioners towards Closed-loop Supply Chain Networks (CLSC). Although everchanging and complex external factors including social and economic ones, adversely impact the sustainable development of closed-loop supply chain networks. The basic aim of the research is to optimize the functioning of CLSC networks. For the above-said, two objective functions are made. The first objective is to minimize the cost of production and assembly expenses of the forward and reverse logistics. Secondly, an endeavour has been made to reduce the fixed costs associated with plants and retailers. For the sake of achieving two objective functions, two methods are employed: triangular fuzzy numbers and triangular intuitionistic fuzzy numbers. Among the two methods, triangular intuitionistic fuzzy numbers achieved the said objectives with greater optimization substantiated by statistics. This method can deal with uncertain external factors without undermining the optimization of the CLSC networks.

Purpose This study aims to synthesise existing research to provide a comprehensive understanding of the current state of food supply chains by evaluating technological advancements that can address the challenges in food supply chain management (SCM). Design/methodology/approach The scientometric analysis, a quantitative study using statistical methods, selected English publications from after 2014, excluding non-peer-reviewed articles. This study analysed 479 research papers, filtered from 621 articles indexed in Scopus. Findings The review highlights key challenges in food SCM: perishability, increased intermediaries, safety compliance and sustainability. Technological solutions such as blockchain, Internet of Things (IoT), artificial intelligence (AI) and big data (BD) analytics are promising tools to address these issues and attention to food wastage, security and catering services is also essential. Research limitations/implications The study relies on existing literature, possibly missing emerging technologies like multi-enterprise platforms for food certification. Practical implications This review offers valuable insights for food supply chain practitioners by highlighting current trends and contributions from different regions. It underscores the importance of technologies and considers adopting innovative technologies to enhance operations and efficiency. Social implications Accurate food SCM through technological solutions can reduce waste and lower the environmental impact of food production and distribution, emphasising sustainable SCM. Originality/value This study contributes to the existing body of knowledge by systematically reviewing the literature on food SCM and identifying the role of technology in overcoming its challenges. It also directs research on future trends of digitalisation of food SCM.

Purpose: The purpose of this paper is to assess if blockchain technology, which is hailed for its potential to improve supply chain transparency and efficiency, can deliver to this promise (Abreu et al., 2021; Mahyuni et al., 2020; Manzoor et al., 2022; Rennie, 2022; Yiannas, 2018). Supply chains are increasingly complex and full transparency is difficult to achieve and may sometimes be undesired, yet governments worldwide are demanding more transparency especially with respect to the social and environmental sustainability of supply chains (Francisco & Swanson, 2018; Franke, 2021; Najjar, 2021). If blockchain technology allows to close this gap, without compromising corporate confidentiality and efficiency requirements, it might change supply chain management drastically. Most other research in this field remains rather superficial with respect to the fundamentals of blockchain technology and supply chain management. Our study will add to the very limited theoretically founded research on blockchain potentials in supply chains.Study Design: Our paper is conceptual. Based on the extant literature, we compare blockchain technology and its advantages to the characteristics of supply chains and challenges of supply chain management to identify a theoretical fit. We investigate if published case studies of blockchain technology in supply chain management support this. Finally, we devise a conceptual framework for the application of blockchain technology in supply chain management.Findings: There is, currently, very limited use for blockchain technology in supply chain management. It does not provide an answer to several of the challenges that exist in supply chain management. Blockchain technology was primarily devised as encryption technology for virtual entities and transactions involving them and not for tangible objects. Published case studies describing the use of blockchain technology in supply chain management do not contradict these findings.They rely on private blockchain solutions lacking the key benefits of blockchain technology. Our conceptual framework constitutes a reference for a future public blockchain solution for supply chains. It offers solutions for some of the challenges but also cannot solve some fundamental challenges, like power imbalances in supply chains.Limitations: Blockchain technology undergoes rapid development. Future blockchain technology may be faster, less energy-intensive and more integrated with other technologies. This may be aided by developments in hardware, like quantum computing. The findings of this paper are limited to currently available technology.Implications: Companies and governments alike should be very cautious when considering investing in currently available blockchain solutions for supply chain management.

Diffusion pattern of new products have been well-documented in literature but research on integration of growth model and inventory policies are still scarce. Globalisation and technological breakthroughs' are creating significant risks of obsolescence at the product level. This calls for integration of diffusion dynamics in formulation of economic ordering policies for technology products. Unfortunately, most of the diffusion models available in literature do not recognise uncertainty in diffusion parameters, making it difficult to use in inventory-models. Here, we propose an economic order quantity model using fuzzy logic for new product where demand rate follows innovation diffusion process. Effect of deterioration is incorporated by considering the salvage value in the cost component. The comprehensive sensitivity analysis with respect to different parameters has also been performed to illustrate the effectiveness and behaviour of the model.

Modern companies face numerous challenges in supply chain management, particularly due to globalization, increasing competition, and the need for quick adaptation to market changes. Inefficient supply chain management can lead to higher costs, supply delays, and reduced customer satisfaction, which directly impacts financial performance and competitiveness. The study aims to identify methods for improving supply chain management efficiency, taking into account current economic and technological conditions. Given the volatility of global markets, it is crucial to develop adaptive and resilient supply chain strategies that align with both short-term objectives and long-term sustainability goals. The study uses methods of analysis, synthesis, comparative analysis, as well as a systems approach to assess modern supply chain management strategies. The impact of cutting-edge technologies such as Big Data, artificial intelligence, the Internet of Things, and blockchain on optimizing supply chain processes was analyzed. The effectiveness of ERP systems in the context of business process integration and risk management was evaluated. The study identified key issues in supply chain management: insufficient integration between systems, low supply chain transparency, inefficient inventory management, and high logistics costs. To address these issues, the following solutions were proposed: Implementing ERP systems and enhancing their functionality for business process integration; Using Big Data analytics for demand forecasting; Developing risk management strategies, particularly through supplier and transport route diversification; Optimizing inventory and reducing logistics costs by automating and applying flexible supply models. Improving supply chain management efficiency requires active use of modern technologies and innovative optimization methods. Integration of Big Data, artificial intelligence, and the Internet of Things helps reduce costs, shorten delivery times, increase process transparency, and ensure stability in order fulfillment. The proposed solutions will contribute to enhancing the competitiveness of enterprises in dynamic economic conditions.

Company A, engaged in the food and beverage industry, faced challenges in supply chain management due to the inefficient implementation of its Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) systems. The increasing market demand necessitated the integration of these technologies to improve operational efficiency, ensure timely availability of raw materials and product distribution while improving overall competitiveness. This study aims to analyze the effectiveness of ERP systems in optimizing operational efficiency through productive integration with SCM, focusing on workflow optimization and data accuracy across the supply chain. This study uses a descriptive qualitative approach drawn from secondary sources to understand the relationship between ERP, SCM, and operational performance. The results show that the implementation of ERP and SCM has not been optimized due to lack of training, weak managerial support, and technical constraints such as infrastructure and legacy system integration. This study recommends a comprehensive change management approach, continuous role-based training, and regular system monitoring to reduce downtime. By implementing these strategies, the company can sustainably improve supply chain efficiency and competitiveness.

In recent years, establishing partnerships with actors from the so-called base of the pyramid (BoP) has become a pivotal task for firms that operate or do business in developing countries. By engaging in BoP markets, firms are able to meet their own growth targets while simultaneously stimulating the economic development of poor countries. Due to the increasing relevance of sustainability, business decisions have to be made under consideration of economic, environmental, and social criteria. As a consequence, new challenges for supply chain management (SCM) arise. Even though these management theories are somehow connected, only little attention has been given to linking BoP research and sustainable supply chain management (SSCM) yet. This chapter sheds light on this research area. A systematic review and content analysis of 76 related papers is presented to examine the state of research. The examination of sustainability constructs reveals a clear dominance of social aspects and a deficit in environmental sustainability consideration in SCM, while the integration of all three dimensions of sustainability is still occasional. The study contributes to research by analyzing the coherence of sustainability, in particular its social and societal aspects, and SCM at the BoP. Practitioners gain insights on social and societal aspects of SCM in context to poverty alleviation and making business with the poorest members of the world.

The aim of this paper is to develop a new methodology for solving bimatrix games with payoffs of triangular intuitionistic fuzzy numbers (TIFNs), which are called TIFN bimatrix games for short. In this methodology, we define the concepts of the value-index and ambiguity-index and hereby develop a difference-index based ranking method, which is proven to be a total order. The parameterized bilinear programming models are derived from a pair of auxiliary TIFN mathematical programming models, which are used to determine solutions of TIFN bimatrix games. Validity and applicability of the models and method proposed in this paper are illustrated with a practical example.

Abstract. Effective and efficient inventory management is crucial for the sustainability of businesses in the culinary industry. Brebes Lembang Restaurant, as a culinary business providing food and beverages, requires proper inventory management to increase efficiency and reduce costs.This study aims to determine the Economic Order Quantity (EOQ) method in managing chicken broiler raw material inventory at Brebes Lembang Restaurant and to evaluate the effectiveness of the EOQ method in improving inventory management efficiency.This study employs a mixed-method approach, combining qualitative and quantitative methods. Data were collected through interviews, observations, and analysis of secondary data. Data analysis used the EOQ method to determine the optimal order quantity and minimum total cost.The resultsshow that Brebes Lembang Restaurant has effectively applied the EOQ method in managing chicken broiler raw material inventory. The analysis reveals that the EOQ method can improve inventory management efficiency and reduce total costs.This study concludes that the EOQ method can be used as a tool to support inventory management of chicken broiler raw materials at Brebes Lembang Restaurant. The findings of this study are expected to contribute to the development of inventory management theory and practice in the culinary industry. Abstrak. Pengelolaan persediaan bahan baku yang efektif dan efisien sangat penting bagi keberlangsungan usaha di bidang kuliner. Rumah Makan Brebes Lembang sebagai salah satu usaha kuliner yang bergerak di bidang penyediaan makanan dan minuman, memerlukan pengelolaan persediaan bahan baku yang tepat untuk meningkatkan efisiensi dan mengurangi biaya. Penelitian ini bertujuan untuk mengetahui metode Economic Order Quantity (EOQ) dalam pengelolaan persediaan bahan baku ayam gantung di Rumah Makan Brebes Lembang, serta untuk mengetahui efektivitas metode EOQ dalam meningkatkan efisiensi pengelolaan persediaan. Penelitian ini menggunakan metode campuran, yaitu kombinasi antara metode kualitatif dan kuantitatif. Data yang dikumpulkan melalui wawancara, observasi, dan analisis data sekunder. Analisis data menggunakan metode EOQ untuk mengetahui jumlah pesanan optimal dan biaya total yang minimal. Hasil penelitian menunjukkan bahwa Rumah Makan Brebes Lembang telah menggunakan metode EOQ dengan baik dalam pengelolaan persediaan bahan baku ayam gantung. Hasil analisis menunjukkan bahwa metode EOQ dapat meningkatkan efisiensi pengelolaan persediaan dan mengurangi biaya total. Penelitian ini menyimpulkan bahwa metode EOQ dapat digunakan sebagai alat bantu dalam pengelolaan persediaan bahan baku ayam gantung di Rumah Makan Brebes Lembang. Hasil penelitian ini diharapkan dapat memberikan kontribusi pada pengembangan teori dan praktik manajemen persediaan di bidang kuliner.

Study’s Excerpt/Novelty This study introduces a novel economic order quantity (EOQ) model for managing non-instantaneous deteriorating items under two-phase demand rates and linear holding costs, incorporating constant partial backlogging and two-level pricing strategies within a trade credit framework. The model uniquely addresses the complexities of time-dependent quadratic demand before deterioration and constant demand afterward, providing optimal solutions for maximizing total profit by determining the best timing for positive inventory, cycle length, and order quantity. The research not only establishes the theoretical existence and uniqueness of these optimal solutions but also demonstrates practical implications through numerical examples and sensitivity analyses, highlighting the critical impact of deterioration rates on inventory management and profitability. Full Abstract This research developed an economic order quantity model for non-instantaneous deteriorating items with two-phase demand rates, linear holding cost, constant partial backlogging rate and two-level pricing strategies under trade credit policy. It is assumed that the holding cost is linear time-dependent, the unit selling price before deterioration sets in is greater than that after deterioration sets and the demand rate before deterioration sets in is considered as continuous time-dependent quadratic, after which it is considered as constant up to when the inventory is completely exhausted. Shortages are allowed and partially backlogged. The purpose of the model is to determine the optimal time with positive inventory, cycle length and order quantity such that the total profit of the inventory system has a maximum value. The necessary and sufficient conditions for the existence and uniqueness of the optimal solutions have been established. Some numerical examples have been given to illustrate the theoretical result of the model. Sensitivity analysis of some model parameters on the decision variables has been carried out and suggestions towards maximising the total profit were also given. , it is seen that the higher the rate of deterioration the lower the optimal time with positive inventory ( ), cycle length order quantity and the total profit and vice versa. This implies that the retailer needs to take all the necessary measures to avoid or reduce deterioration to maximise higher profit. Based on the results application of the model led to an increase in revenue.

This paper presents EOQ (Economic Order Quantity) model with stock- level dependent demand and different types of holding cost function. We show that the total relevant inventory cost per unit time is convex with respect to cycle time. Mathematical models are established to determine optimal order quantity and total relevant inventory cost. Numerical examples are provided for two different models i.e. (i): Instantaneous replenishment with inventory dependent holding cost and (ii) Instantaneous replenishment with quadratic time dependent carrying cost. Numerical examples are provided to illustrate the proposed model. Sensitivity analysis of the optimal solution with respect to the parameters of the system is carried out. The second order approximation is used for finding closed form optimal solution. Mathematica 5.2 software is used to find numerical results.