مطالب

Objective : In the patient care chain, medicines are essential items and play a critical role in patient recovery. Inefficient inventory management leads to drug shortages, lack of continuity of drug inventory, reduced patient safety, poor performance, distribution defects, and technological errors, which lead to drug shortages in hospital pharmacies. Providing an efficient approach can minimize costs in the supply chain. Methods : This study presented a simulation-optimization model for pharmacy inventory management. A training-learning-based optimization algorithm was used to solve the model. The model was programmed and solved in MATLAB software.  Results : Given that the initial inventory is assumed to be zero, the drug price is lower at the beginning of the year, and the number of patients is lower than in the summer. Therefore, the volume of orders is high at the start of the year. The model adjusts the level of orders so that the costs are minimal. As the disease re-emerges and the number of patients increases, demand increases in the ninth and tenth months, and the volume of orders increases again. As demand decreases at the end of the year, the volume of orders also decreases. Conclusion : By implementing the model during the planning period, while minimizing system costs, the inventory level for all drug categories will be at the desired level, and no inventory shortages will occur.

Objective : This study aims to design and optimize a sustainable biofuel supply chain focusing on water resource management, uncertainty reduction, and enhancing economic, environmental, and social performance. Sustainable biomass, such as Paulownia trees, and recycled water are considered key inputs, providing an integrated solution to the challenges posed by fossil fuels and the urgent need for renewable energy development. Methods : A multi-objective mathematical model is proposed to minimize costs, satisfy demand, and mitigate environmental impacts. The model incorporates uncertainties in supply and demand using the LP-metric method and applies the Fuzzy Analytic Hierarchy Process (FAHP) to weight objectives, ensuring balance among conflicting goals. Sensitivity analysis examines variations in biomass supply, prices, and demand, while Pareto frontier analysis evaluates trade-offs across objectives.  Results : Results show that scenario-based modeling enables a comprehensive assessment of supply and demand impacts on supply chain performance. Incorporating wastewater and sewage sludge reduces pressure on natural resources and improves economic and environmental efficiency. The ε-constraint method generates Pareto-optimal solutions, offering decision-makers alternatives consistent with their priorities. Sensitivity analysis highlights that using Paulownia biomass and recycled water enhances flexibility, reduces risks, and promotes balance among economic, environmental, and social objectives, while lowering costs and unmet demand. Conclusion : This study provides a practical framework for designing and managing a sustainable biofuel supply chain by presenting a comprehensive and practical model. The findings can serve as a roadmap for developing renewable energy and resource efficiency in the energy sector. Additionally, the proposed model offers a robust decision-making tool under conditions of uncertainty and environmental and economic fluctuations. Its application can significantly support sustainable development policies and reduce dependence on fossil fuel resources.

Objective : The Supply chain plays a key role in adapting the organization to variable conditions and an uncertain future. The selection of appropriate suppliers can significantly increase the competitiveness and ability of a business in the market. One of the essential factors in supply chain optimization is controlling and managing inventory cost. This paper aims to simultaneously optimize supplier selection and order allocation while considering inventory control using a fractional programming approach.  Methodology: The methodology integrates quantitative analytical techniques in a multi-phase approach. First, the most frequent supplier selection criteria are identified with a literature review. The Delphi method was used to select the supplier selection criteria. In the next step, fuzzy Shannon entropy determines criterion weights. Then, fuzzy EDAS calculates supplier performance scores. Finally, fractional programming facilitates supplier selection and order allocation. Results : The most frequent supplier selection criteria were extracted from the literature review. In the Delphi technique, experts ultimately agreed on six key criteria: price, quality, delivery, flexibility, responsiveness, and financial stability. The results of the Shannon entropy analysis indicate that flexibility, with a weight of 0.20, holds the highest relative importance among the criteria. The suppliers score obtained from the fuzzy EDAS method is used as one of the parameters of the mathematical model. Conclusion : The proposed hybrid MADM approach and mathematical model have been validated using empirical data obtained from Sirjan Steel Company. The result shows that the hybrid MADM approach and fractional programming have high accuracy in selecting the best supplier.

Objective : Service quality is vital in securing competitive advantage, fostering sustainable growth, and improving organizational performance—especially within digital platforms. Despite its recognized importance, the concept of digital service quality remains underdefined in existing literature. This study aims to address this gap by (i) identifying the key dimensions of service quality in online sales platforms and (ii) uncovering the specific components within each dimension.  Methodology: As an exploratory research study, the systematic literature review employed the Kitchenham model to identify relevant studies and establish a theoretical foundation for assessing service quality in online sales. The model consists of three main phases and seven stages: planning, conducting the review, and reporting. The seven procedural steps in Kitchenham’s approach are as follows: (i) formulating research questions, (ii) developing a search strategy, (iii) determining selection criteria, (iv) searching, (v) selecting studies, (vi) extracting and analyzing data, and (vii) ensuring quality control. Results : A systematic literature review since 1980, filtered by defined criteria, yielded 100 relevant sources using the Caldwell method. Through open coding, key components were extracted and grouped into categories, which were then clustered into three core themes for evaluating online store service quality: infrastructure, design, and trust. These encompass content relevance, responsiveness, credibility, and security. Conclusion : The conceptual model developed from the identified themes and categories offers a foundation for evaluating online store service quality and may be validated through future qualitative or quantitative studies. Recommended directions for further research include investigating the underlying causes of consumer distrust and assessing the influence of emerging technologies—such as blockchain, crowdsourcing, and artificial intelligence—on the dynamics of service quality.