bi-objective mathematical model

The economic production quantity (EPQ) model considers the production rate, demand rate, setup costs, holding costs, and shortage costs to find the production quantity that minimizes the sum of these costs. The goal is to balance the costs associated with production, holding inventory, and potential shortages. In this paper, two objectives include the costs of production and ordering and others in a separate objective function. In the objectives of the other costs, The cost of storage space as a supply is defined to be minimized. This study considers scrap and reworks in the EPQ model. This inventory model accounts for many items on a single machine. The production capacity is reduced, and there are shortages when only one machine exists. By determining the quantities of the products produced by the manufacturing facility, the storage space for each product, cycle time, and product scarcity, we can reduce both the overall cost and the supply cost of warehouse space due to non-linearity and the inability to solve commercial software in large dimensions, a multi-objective meta-heuristic algorithm, namely the non-dominated sorting genetic algorithm (NSGA-II), is used. The findings are further validated using the non-dominated ranking genetic algorithm (NRGA). Also, the obtained Pareto front is studied with several indicators. To perform these two algorithms at the best condition, we employed the Taguchi approach and related orthogonal arrays and performed algorithms for each array considering several factors. Also, to validate the mathematical model, we used the augmented epsilon-constraint method executed in the GAMS environment. It is clear that GAMS commercial software yields better results; however, these two algorithms are justifiable when the problem becomes bigger. Finally, by performing a sensitivity analysis for these indicators and the objective functions, the behavior of the proposed algorithms is compared and examined in detail. Also, the superior algorithm is chosen using the TOPSIS as a multi-criteria decision-making method. Numerical examples show how the presented model and the proposed algorithms may be used efficiently. A surveying literature review clarifies that the related objective functions, constraints, and solution approaches have not been investigated until now.

Reviewing the efficiency of Research and Development (R&D) by giving an equal amount of importance to different R&D actions can make the measuring process too simple, which may cause an inaccurate interpretation of the R&D function and lead to an imprecise interpretation of R&D models. R&D comprises the creative work undertaken on a systematic basis to increase the stock of knowledge, including knowledge of man, culture, and society, and the use of this stock of knowledge to devise new applications. This research provides a two-phase approach to designing an R&D model in the auto battery manufacturing industry based on customer satisfaction. Due to the important role of R&D in customer satisfaction, no study has been conducted in this field and industry. In the first phase, the effective models for R&D management and the indices influencing customer satisfaction in R&D models are identified. In the second phase, the significance coefficients related to the customer satisfaction indices are obtained by using the fuzzy SWARA (Stepwise Weight Assessment Ratio Analysis) as a multi-criteria decision-making method. Furthermore, each model’s importance and final priority are calculated by the fuzzy COPRAS (Complex Proportional Assessment) method. Finally, to apply the proposed framework in the battery manufacturing industry, a bi-objective R&D model is presented. The coefficients obtained by the fuzzy COPRAS method are utilized as the input for the proposed model. Therefore, policy-makers and managers can perform their activities based on this method. The obtained results showed that the proposed framework is effective in the case under study.

This research builds a decision-based optimization model to evaluate and decide on the methods of technology transfer in the auto-battery industry under uncertainty. This research is conducted based on the needs of the country's battery industry and shows the impact of technology transfer on world-class manufacturing. At first, the effective indices in the assessment of a technology transfer method are singled out through reviewing the literature and the experts' judgment. The sample population in this research consists of experts from eight auto-battery manufacturing companies. Then, each of the approved indices is assessed via the best-worst method, and in continuation, the technology transfer methods are evaluated and prioritized using an MOORA method as multi-criteria decision-making under uncertainty. The gray theory is also used to deal with uncertainty. According to the results obtained from the best-worst method, the five significant indices (i.e., improving style management, business strategy, cost-effectiveness, how to communicate with the organization, and competitiveness) are considered to select the technology transfer methods in the auto-battery production industry. Finally, to implement the proposed framework in the state auto-battery manufacturing industries, a dual-purpose mathematical model is introduced for optimized world-class technology transfer methods. To solve the proposed model, the developed ε-constraint method is used. Finally, based on the results of the proposed method, the transfer method of joint investment is recognized as the most suitable technique for technology transfer in this industry.

In recent years, many industries in developed countries have integrated the important process of reverse logistics into their supply chain for different reasons, including growing environmental concerns. Given fish as perishable food, re-employing unused products and waste in each step of the chain constitute a major concern for the decision-makers. The present study is conducted to maximize responsiveness to customer demand and minimize the cost of the fish closed-loop supply chain (CLSC) by proposing a novel mathematical model. To solve this model, the epsilon-constraint method and Lp-metric were employed. Then, the solution methods were compared with each other based on the performance metrics and a statistical hypothesis. The superior method is ultimately determined using the TOPSIS method. The model application is tested on a case study of the trout CLSC in the north of Iran by performing a sensitivity analysis of demand. This analysis showed the promising results of using the proposed solution method and model.