Search

Fast-paced markets require complex interactions from all supply-chain agents to satisfy customer demands and needs. The manufacturing industries face some difficulties in terms of production amounts and smooth delivery rates. Technical experts found that a warm-up period before a production run helps address those challenges and improves the workability of machine tools in the manufacturing process. The use of a warm-up process causes a reduction of faulty products (an adverse production outcome) and improves operational efficiency. Also, a shortage in the supply of commodities creates difficult conditions for inventory management decisions, posing the same production problems as mentioned above. Consideration of the warm-up process has recently been included in the scope of operations research, but it is necessary to study its interaction with the presence of shortage. This study presents a system where a manufacturing environment utilizes the warm-up process in its initial phase and shortages are allowed during the production period, in addition, the study takes into account carbon emissions during manufacturing to integrate environmental concerns. We assume that the company has the capability to trade the surplus carbon capacity it hasn't produced. This study offers a comprehensive framework that incorporates former research that addresses warm-up process, carbon emissions, shortages, and defective items. To solve the proposed non-linear programming problem with inequality constraints, we employ the Karush-Kuhn-Tucker (KKT) conditions method to determine the optimal solutions. Managerial insights are derived, and sensitivity analysis highlights the effects of the system parameters on the decision variables. The sensitivity analysis results indicate that the carbon trading cost has a significant impact on the overall cost, and subsequently, the company's profit.

Growing items like chicken broilers play a critical role in consumption baskets; however, their real characteristics have been neglected in the research focusing on inventory models such as economic order quantity (EOQ). Mainstream research conducted in this field considers an approximation of the edible rate of chicken broilers in cost analysis. On the other hand, this subject was the objective of other research, and the function that represents the relationship between the weight and age of broilers was derived through extensive research. This study proposes an EOQ model that considers a realistic function for extracting the exact value of edible parts of broilers and the correct form of feeding function that is dependent on their age. In other words, this study not only addresses the challenge arising in the estimation of the edible part of broilers but also introduces another function to obtain the correct amount of feeding and nurturing cost of broilers. Solution methodology encompasses these mathematical functions and then provides a straightforward framework to find the optimal solution. An exact heuristic solution is obtained that considers the real factors, and then the approach is applied to case studies, and insights are derived to manage real situations for corporations.

Some sensitive industries with precise specifications in their manufacturing process, such as aircraft engine parts, impose 100% inspection processes in their inventory-manufacturing system. In some cases, a lot of small dimensions are measured and compared with strict acceptable tolerances during these inspection processes, causing manufacturing process times (machine time speed) to be less than inspection times. This study is relevant to manufacturing enterprises that implement comprehensive 100% inspection protocols within their production procedures, encompassing sectors such as pharmaceuticals, aerospace, electronics, medical devices, and textiles. In this paper, such a phenomenon is investigated, as an inventory system with a strict inspection model is deployed under three conditions: a) an inventory-manufacturing system with scrapped items without shortage, b) an inventory-manufacturing system with reworked items without shortage, and c) an inventory-manufacturing system with scrapped and reworkable items with backorder. The aim is to minimize total cost by considering strict inspection during production process. These proposed models are suitable for managing systems where quality control is a bottleneck due to its speed such as high-precision manufacturing systems. The optimal solutions for each model are obtained as a closed-form expression. In models 1 and 2, where the shortage is not allowed, the closed-form expression is focused on determining the economic production quantity. However, in model 3, there are two expressions of significance. First, it addresses the economic production quantity, like models 1 and 2. Additionally, it provides an optimal value for the shortage in each cycle, as it considers a scenario where shortages are permitted within the inventory-manufacturing system. This study has included a numerical example to illustrate the practical applicability of each model. Based on the optimal solutions obtained, it is evident that Model 3 stands out as the superior choice. This outcome aligns with expectations, as Model 3 encompasses and comprehensively addresses the scenarios presented in the two other models. Finally, a sensitivity analysis is performed to investigate the effects of key parameters on the optimal solutions of each model, and the results are summarized to present a comparison between them.

The classical economic growing quantity (EGQ) model is a key concept in the inventory control problems research literature. The EGQ model is commonly employed for the purpose of inventory control in the management of growing items, such as fish and farm animals, within industries such as livestock, seafood, and aviculture. The economic order quantity (EOQ) model assumes that customer demand is satisfied without interruption in each cycle; however, this assumption is not always true for some companies as they do not have continuous operations, except for item storage, during non-working times such as weekends, natural idle periods, or spare time. In this study, we extend the traditional EGQ model by incorporating the concept of working and non-working periods, resulting in the development of a new model called discontinuous economic growing quantity (DEGQ). Unlike the conventional EGQ model, the DEGQ model considers the presence of intermittent operational periods, in which the firm is actively engaged in its activities, and non-working periods, during which only storage-related operations occur. By incorporating this discontinuity, the DEGQ model provides a more accurate representation of real-world scenarios where businesses operate in a non-continuous manner, thus enhancing the effectiveness of inventory control and management strategies. The study aims to obtain the optimal number of periods in each cycle and the optimal slaughter age for the breeding items, and, subsequently, to find the optimal order size to minimize the total cost. Finally, we propose an optimal analytical procedure to determine the optimal solutions. This procedure entails finding the optimal number of periods using a closed-form equation and determining the optimal slaughter age by exhaustively searching the entire range of possible growth times.

Success in the industrial sector is compromised by diverse conditions such as imperfect product production, manufacturing line interruptions, and unscheduled maintenance. The precise use of common practices in production environments is an available solution to eliminate some of these issues. Applying a warm-up period in a manufacturing process is adequate and cost-effective for almost all companies. It improves the equipment’s productivity and helps the manufacturing line generate fewer defective products. Even though several inventory management studies have included a warm-up phase in their models, its use in economic production quantity (EPQ) models remains largely unexplored. Adding a warm-up phase to the production cycle minimizes maintenance expenses and defective products and increases the machine’s performance. In this study, the dependency between the machine downtime and the warm-up length is examined for the first time. The warm-up time depends on the machine’s off-state period: if the machine has a longer operation timeout, then a longer warm-up period is needed. The model includes a function to model the warm-up time relative to the machine downtime and two types of defective products: scrapping and reworking items. The study is concluded with some numerical examples, a sensitivity analysis, and some management insights related to the EPQ.

Determining the optimal slaughter age of fast-growing animals regarding the mortality rates and breeding costs plays an important and major role for companies that benefit from their meat. Additionally, the effects of carbon dioxide (CO2) emissions during the growth cycle of animals are a significant concern for governments. This study proposes an economic order quantity (EOQ) for growing items with a mortality function under a sustainable green breeding policy. It assumes that CO2 production is a practical polynomial function that depends on the age of the animals as well as the mortality function. The aim of the model is to determine the optimal slaughter age and the optimal number of newborn chicks, purchased from the supplier, to minimize the total costs. We propose an analytical approach, with five simple steps, to find the optimal solutions. Finally, we provide a numerical example and some model management insights to help practitioners in this area.