International Journal of Research in Industrial Engineering
Volume:12 Issue: 4, Autumn 2023

The data envelopment analysis method is commonly used to measure efficiency. An estimate of the relative efficiency of this model is derived by calculating the ratio between inputs and outputs. Data envelopment analysis models can also be applied to network structures due to the extension of these models. Supply Chain Management (SCM) is a novel approach that governed production management in recent years. In complex and dynamic environments, the petrochemical industry requires an investigation system similar to those used by other organizations to inform about its activity's desirability, especially in complex and dynamic environments. This research focused on the petrochemical company supply chain. Laboratory studies, experts, and visits to petrochemical sites were used to identify production processes and determine indicators. After that, they were evaluated with an envelopment model and a coefficient corresponding to the identified petrochemical supply chain structure. The aggregate and componentwise efficiency of the studied units in petrochemical were also examined from 2016 to 2019.

The purpose of this research is to evaluate and rank the efficiency of pharmaceutical companies in creating operational cash flows in line with the objectives of financial reporting. The research method for collecting theoretical bases and research data is library studies. In this research, in order to evaluate the efficiency of pharmaceutical companies in creating operational cash flow, the Data Envelopment Analysis (DEA) model with weight limit is used. The results of this research show that Farabi pharmaceutical company has the highest efficiency score in creating Operating Cash Flows (OCFs) and Loqman pharmaceutical company has the lowest efficiency score. The findings of this research confirm that DEA is a suitable technique for evaluating the performance of companies in creating operational cash flow. Also, this technique, along with traditional financial analysis, can be considered a useful instrument for deciding and evaluating the performance and efficiency of companies. This article can make analysts more familiar; financial and accounting researchers with DEA applications in financial and accounting analysis. Also, this research can expand the use of scientific models in financial and accounting research.

Supply chain management is a process in which a number of organizations work together as a supply chain until the raw materials reach the manufacturer and finally, a valuable product is provided to the end consumer. With the increase in population and the increase in environmental sensitivities, the forward-reverse supply chain has attracted a lot of attention, which pursues goals such as optimization, customer satisfaction, responding to their needs in the shortest time with the lowest cost and high quality. In this paper, a forward- reverse multi-product and multi-period network is designed under the condition of uncertainty in the demand parameter. The purpose of the proposed model is to maximize profit by considering customer satisfaction simultaneously and reducing delay and the fuzzy approach has been used to solve the model under conditions of uncertainty. The proposed model is mixed-integer linear programming and for its validation and applicability, it has been solved by GAMS software, a numerical example using simulated data in deterministic and uncertain state. The results of the analysis of the numerical example show that the show that with increasing uncertainty in the demand parameter, the optimal value of the objective function decreases.

The COVID-19 pandemic has caused unprecedented disruption to the global economic structure, resulting in significant changes in spending patterns for households worldwide. Developed countries like the United States have been affected as well, struggling to return to pre-pandemic stable economic situations. This study focuses on the impact of the pandemic on household expenditure in the United States, using ANOVA to compare household expenses between the pre-COVID period in 2018 and the post-COVID period in 2021. The results of the study showed a significant increase in all types of household expenditure from pre-COVID to post-COVID periods, highlighting the correlation between the pandemic and changes in spending habits. This trend is further fueled by price increases in daily necessities, inflation of the dollar, and scarcity of goods. The analysis also revealed that the trend was increasing, emphasizing the need for immediate policy interventions to address the issue. Further research is needed to identify the specific types of expenditure driving this increase and the underlying reasons behind it. The implications of the study are significant for policymakers and economists as they underscore the need for effective interventions to stabilize household expenditure and promote economic recovery in the wake of the pandemic. The findings also highlight the importance of utilizing statistical methods such as ANOVA to evaluate complex economic systems and guide evidence-based policy interventions. As future research continues to explore the impact of the pandemic on economic structures worldwide, this study provides valuable insights into the specific changes in household expenditure in the United States, emphasizing the urgent need for targeted policy interventions to address these changes.

The inventory routing problem arises from the combination of the vehicle routing problem and the vendor-managed inventory problem. In this paper, we present a mathematical model and a novel genetic algorithm for solving the multi-period inventory routing problem. The objective is to supply products to scattered customers within a given time horizon while managing customer inventories to avoid shortages and minimize total inventory and transportation costs. To represent solutions for this problem, we introduce a new chromosomal structure. This structure offers simplicity in encoding and decoding solutions, maintains feasibility after crossover and mutation operations, addresses both routing and inventory management in a single step, and consolidates information about each solution method comprehensively. The algorithm parameters, including crossover and mutation rates, population size, number of iterations, and selection pressure, are fine-tuned using the Taguchi method. To assess algorithm efficiency, we utilize standard instances from the literature. Our results demonstrate that the proposed algorithm performs favorably compared to previous approaches.

Consignment stocks agreement had been very useful in inventory control. The benefit ranges from improved cash flow, reduced risk level, savings on investment, reduced ownership cost, low inventory carrying cost and regular restocking to mention few. Also, small batch delivery is an effective strategy for launching a product since it enables a business to assess the market and validate the product before committing to a large production run. In this paper, we combined small batch delivery and consignment stock policy by considering a supply chain setting where a vendor fulfilled the shipment requirement of each buyer sequentially in a single production set up. To achieve this, and as against the equal size shipments policy assumed in literature for different buyers, the vendor sends a smaller shipment first as early entry, followed by n equal shipments. These n shipments are proportionately increased according to the vendor rate of production to each buyer’s demand rate. A mathematical cost function is developed to reduce the overall cost of the integrated supply chain system through the optimal cycle time and the optimal numbers of shipments to be delivered to each buyer. Numerical example is given using data from an existing literature, results were compared, and the new distribution policy gives better financial savings in terms of cost over the equal shipment policy assumed in literature. Sensitivity analyses were performed on key parameters to evaluate the robustness of the model.

The Flexible Job shop Scheduling Problem (FJSP), as a Production Scheduling Problem (PSP), is generally an extension of the Job shop Scheduling Problem (JSP). In this paper, the FJSP with reverse flow consisting of two flows of jobs (direct and reverse) at each stage is studied; the first flow initiates in Stage 1 and goes to Stage C (the last stage), and the second flow starts with Stage c and ends up in Stage 1. The aim is to minimize the makespan of the jobs (the maximum completion time). A Mixed Integer Programming (MIP) is presented to model the problem and the Branch and Bound (B&B) method is used to solve the problem. A numerical small-size problem is presented to demonstrate the applicability, for which the Lingo16 software is employed for a solution. Due to the NP-hardness of the problem, a meta-heuristic, namely the Vibration Damping Optimization (VDO) algorithm with tuned parameters using the Taguchi method, is utilized to solve large-scale problems. To validate the results obtained using the proposed solution algorithm in terms of the solution quality and the required computational time, they are compared with those obtained by the Lingo 16 software for small-size problems. Finally, the performance of the proposed algorithm is compared with a Genetic Algorithm (GA) by solving some randomly generated larger-size test problems, based on which the results are analyzed statistically. Computational results confirm the efficiency and effectiveness of the proposed algorithm and show that the VDO algorithm performs well.