Journal of Industrial and Systems Engineering
Volume:14 Issue: 3, Summer 2022

Contrary to the past that inventory decisions and pricing are taken into consideration separately, due to the influence of these decisions on each other and thus profit, researchers have investigated these two issues simultaneously. Sometimes, wholesalers offer incentive financial policies to their customers in order to increase their sale. In this paper, a different combined model of inventory control and the way of its pricing for a deteriorating item with different incentive schemes including totally advance payment and partially advance, partially delayed payment are developed. We adopt a demand function jointly time and price-dependent and a backordering rate waiting time-dependent. Also shortage of allowable inventory considered. In each case, optimum price, replenishment cycle, the time with no shortage are obtained. Sensitivity analysis is performed and represented in several figures and tables. The results show that with increasing deterioration and backordering rates, the total annual profit is reduced.

The competitive environment of the present age has focused the attention of organizations on meeting the requirements of quality and socially responsible, because organizations that adhere to the quality management framework achieve a higher level of customer satisfaction. In addition, the shorter product life due to the development of technology and changing customer needs reveals the need to pay attention to the concepts of sustainability and reliability in the design of the supply chain network. In this paper, the convergence of sustainability and reliability in supply chains is considered and a model of economic, responsible, and reliable supply chain is comprehensively and efficiently modeled. For this purpose, a nonlinear mixed-integer programming model for the supply chain network design problem is considered as three-objective, multi-product, multi-level, multi-source, multi-capacity, and multi-stage. In this study, the normalized normal constraint (NNC) method is used to solve the proposed multi-objective optimization problem and find Pareto optimal solutions. In addition, numerical examples with random data in different dimensions have been considered to measure the accuracy and overall performance of the proposed model and by changing the various parameters of the model, the sensitivity analysis of target functions has been performed to analyze the model behavior.

In this study, we introduce a new concept as loyalty factor of bank branches customers. Data envelopment analysis weight restrictions is used to develop a new loyalty model and define the loyalty factor. Assurance region weight restrictions are attached to basic data envelopment analysis models using some predefined loyalty codes based on services quality and in special, e-banking. This model enhances the discrimination power of decision making units. Using the proposed loyalty factor, we extend Malmquist productivity index to determine the contribution of loyalty factor changes in two time periods on the productivity changes. The presented method is implemented in a real world case study from 177 Iranian bank branches in 2018-2019 to approve its applicability. The results for both traditional and extended Malmquist index are analyzed.

Increasing demand for food, environmental degradation, postharvest losses, and lack of financial resources, especially in developing countries, encourage manufacturing supply chains to develop integrated decision models for jointly incorporating economic, environmental, and social aspects into the supply chain network design problems. This research aims to develop a novel multi-objective decision support model for designing a sustainable multi-product green supply chain network for perishable food products. The model aims to minimize the total costs and carbon dioxide emissions while maximizing the social impacts simultaneously. Numerical experiments on several test problems indicate that the total cost is mostly impacted by the fixed cost of constructing warehouses and maintenance costs, respectively. The total amount of carbon emissions is more influenced by the amount of carbon produced in warehouses than transportation activities. We also found that the number of jobs created plays a much more critical role on social satisfaction than the amount of traffic generated by the supply chain. Also, the number of jobs created and the amount of carbon gas produced in the warehouses have a direct relationship; therefore, these two factors should be considered together simultaneously in the supply chain network design problem.

Due to the expansion of the use of social networks, new areas of research in this field have been presented to researchers. One of these areas is using intelligent methods for friend recommender system. In this research, by using fuzzy methods and the gray wolf optimization algorithm, a solution for friend recommender system in social networks has been proposed. The use of fuzzy methods is considered to extend the extracted features in the network. The gray wolf algorithm has also been used to identify the appropriate subset of the feature set. Also, the process of learning the patterns in the extracted feature set has been done by the neural network method. The results of the implementation of this research and its comparison with other available methods showed that the artificial neural network was a good choice for choosing the learning model. The results showed that the feature selection mechanism using the gray wolf algorithm and the use of fuzzy information has a significant impact on improving system performance. In addition, the study of system performance on different data sets showed that the proposed method is highly accurate.

This article attempts to design the integrated supply chain of perishable products with considering agility and resilience. For this purpose, in the first stage, the evaluation and selection of suppliers are done with the network data envelopment analysis model based on resilience indicators, and the two groups of main and backup suppliers are selected through the evaluation. In the next step, the four-tier supply chain including suppliers, production centers, distribution centers, and customers is considered. In order to increase the agility of the integrated supply chain, there is a relationship between the distribution centers. In order to be close to the real environment, the demand for new products is considered as uncertainty, which is represented by a fuzzy number. To avoid wasting resources, a sales discount strategy has been considered for products that are approaching their expiration time. Due to the complexity of the model and the high solution time by MIP, a decomposition algorithm for column generation is considered, which significantly improves the solution time. The proposed model is used in the dairy industry.

Today, due to industrial development in the world, the variety of products has increased and products have special complexities. Lean supply chain is an approach aimed at producing and delivering products in the fastest possible time with the least production waste. The lean supply chain approach is one of the most important strategies to help managers in the organization due to the nature of its activities and the volume and variety of products, suppliers, and customers within the organization, with very diverse needs and very high geographical dispersion. This approach can, as an effective tool, play a very functional role in reducing waste from the supply chain and reducing organizational costs. Today, evolving technologies such as the Internet of Things and blockchain play a significant role in facilitating lean supply chain creation. The Internet of Things (IoT), along with blockchain technology, provides instant insight into every move of the goods made in the supply chain and more responsibility than ever before. In addition, IoT eliminates many of the paperwork requirements prone to supply chain management error and simplifies processes for less efficient product management from warehouse to final destination. In this study, a framework for a lean supply chain based on these technologies was first proposed. Then, the critical success factors in this lean supply chain were extracted using the literature and expert opinions. In order to evaluate these factors and study their internal relationships, a nonlinear fuzzy approach and fuzzy DEMATEL method were used. The results show that quick response to customer needs is one of the most important critical factors for the success of the lean IoT- blockchain based supply chain.

Developing an aggregate production planning, as one of the most important manufacture tasks, can provide an efficient planning to optimize the companies’ objectives such as minimizing costs and maximizing profits. Also, community’s competitive pressures cause the need for considering green principles in production planning in order to balance environmental and economic performances. Hence, a multi-period, multi-product, multi-supplier, and multi-site aggregate production planning model is proposed to formulate a mathematical model of maximizing profit in green supply chain. Integer quadratic programming is used to solve the problem. Carbon dioxide emission from production and transportation modes are considered as green principle.  The feasibility and validity of the formulated model was tested using data from iron and steel industry as well as a sensitivity analysis on profit function. The results demonstrate the optimal amount of productions in order to maximize profit as well as developing green supply chain. Also, sensitivity analysis shows that profit objective fell steadily due to increase in total CO2 emissions from transportation and production processes. Consequently, some useful managerial insights were suggested regarding the consideration of green practices in aggregate production planning.

The automotive industry is one of the most competitive industries globally. Hence, a proper pricing policy is vital to be able to compete. This paper develops an automotive supply chain consisting of one manufacturer and one retailer, with two types of one product (Basic and Premium). The Premium type of the product is equipped with extra features and technologies to convince customers to purchase it at a higher price, while the Basic one without these features is sold at a lower price. Furthermore, the manufacturer is intervened by the government regulations which results in receiving subsidies or paying taxes. This paper develops a mathematical model to maximize profit in both centralized and decentralized systems. The model examines the effects of feature and technology level, as well as advertisement on vehicle pricing. To examine the proposed approach and model, several numerical examples are applied. The results reveal that the model can increase the manufacturer's profit by considering the government benefits, as well as the retailer’s profit. Finally, some managerial implications are developed by a sensitivity analysis of the main parameters.

COVID-19 has infected more than 543 million people and killed more than 6 million people since it was first diagnosed in Wuhan, China, on December 1, 2019. Vaccines were needed to combat the epidemic from the start of the pandemic due to the high incidence of morbidity and mortality. After the development of vaccines, due to the need for extensive vaccination to stop the spread of the disease, the supply chain of COVID-19 vaccines and the need to develop mathematical optimization models became vital. Lack of admission capacity at vaccination centers is one of the main problems facing vaccination, which slows down the process and increases infection risk. For this purpose, this paper proposes a mathematical optimization model for the COVID-19 vaccine supply chain network design, considering two objectives maximizing the minimum demand coverage and minimizing the total time. With its equitable approach, the first objective function increases demand coverage. A second objective function accelerates vaccination by optimizing activities like allocating vaccines from storage centers to distribution centers and reducing the risk of spreading diseases by reducing transportation times to vaccination centers. According to this model, temporary vaccination centers can enhance or maintain vaccination rates by supplementing existing vaccination centers' admission capacity. Two numerical examples were used to validate the proposed mathematical model. The model's performance was then assessed using sensitivity analysis on its key parameters, demonstrating the effectiveness of temporary vaccination centers.

Expert systems are computer tools that, like an expert, advice on issues related to their area of expertise and support decision-making when required. These systems can be defined as counseling programs to solve complex problems that require experts to be solved. In this research, an expert system was designed to detect faults in the chemical process of polypropylene production. Using this system, all the information and experiences of experts can be accessed and used as a comprehensive resource. First, a diagnostic classification and fault detection is provided, which is prepared from a review of the literature related to the design of expert systems as well as the knowledge available in the polypropylene production process. Also, in this stage, the feasibility of the project was investigated, which was done by holding meetings with experts. In the second stage, the groups and constituent elements of the classification are explained. Likewise, more than 300 system faults were identified and coded to acquire the required knowledge for the system. In the next stage, the main elements of the fault detection system in the polypropylene production process are classified. Information related to Marun Petrochemical Company was used as a case study to further investigate the designed system. Also, the main reasons for defects and faults of the process were investigated and the frequency and percentage of each were calculated and reported. After classifying the reasons for the stoppages, the faults leading to each stoppage were extracted and classified. In the design stage, the prototype was coded for the system using JavaScript programming language and nodeje technology. In order to design the algorithm, each of the faults with one of the causes was considered as a scenario and related to a unique question to act as an intermediary between the expert system and the user in designing the user interface. Factors affecting the evaluation include the cost-consuming nature of the solution, the time-consuming nature of the solution, and the frequency of iteration of the fault. Finally, in the testing stage, the proper performance of the designed expert system was ensured.

In project management, the time–cost problem plays a key role in planning and development. However, this problem is coped with the uncertainties resulted from an integral component of project cost and duration estimates. Often techniques are disable to formulate such uncertainty. Therefore, it is necessary to deveop a model that can take into account the uncertainty imposed by projects. To achieving the aim, Monte Carlo simulation technique is employed to analize the uncertainties arisen from the project cost and time estimations. To offer a trade-off between project time and cost, an optimization technique based on the Gray Wolf Optimization algorithm is used. The next, a overdraft analysis is conducted to operationally investigate the contract for future finance. The proposed framework is capable of solving a time-cost problem while the uncertainty is associated with project cost and time. The results show that the developed model generates a more reliable and accurate result and diminishes the risks connected to projects.

Nowadays, the issue of the difference in core competencies has turned into the main factor of competition in the market in most organizations. In line with their operational area, the companies make decisions to further strengthen some of their capabilities, capacities, and specializations. Thus, when an organization concentrates on its strengths and makes efforts for its sustainable development, a competitive advantage evolves in the market. In this regard, the present study proposes a Multi- Objective, Multi-Level, Multi-Commodity, and Multi-Period Closed-Loop Mathematical Model for production, distribution, location, and allocation of the products. The presented model particularly aims to minimize the environmental effects and the total supply chain costs, and to control the social impacts of the supply chain. The present study is mainly innovative in the sense that it considers the quality of the manufactured and transported products, various scenarios in the closed-loop logistics as uncertainty, the capacity of the distribution and production centers, and along with the current multi-commodity discussions, considers the sustainability and resilience in the supply chain, the environmental effects in the model and minimizing the amount of the CO2 emissions. The introduced model was solved in small and medium scales using the Epsilon Constraint approach and in large scales for the case study of Sunny Plast Industries by the Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) approach. The results indicated that as the demand goes up, the costs rise. Costs increase is higher in the Boom Scenario than in the Bust Scenario. Also, with the rise in demands, the number of established centers increases. This increase is faster in the Boom case.

Humanitarian organizations are in the dire need of logistical resources for relief operations. Nevertheless, considering their limited resources, they have to seek to use the logistical capabilities of the business sector in order to improve the humanitarian operations. In this paper, we develop a bi-objective mathematical model for using the logistical capabilities of the business sector in the humanitarian logistics. The first objective function minimizes the logistics costs while the second one minimizes the shortage costs. We consider that suppliers are responsible for procurement of relief items and logistics service providers collaborate with a humanitarian organization by providing storage space for pre-positioning of relief items. The bi-objective model is converted into a single-objective one using the TH method as a well-known interactive fuzzy multiobjective programming approach. Finally, the presented model is validated by conducting several sensitivity analyses. The results emphasis on the effectiveness of collaborating with business sector in relief operations.

Economic order/production quantity (EOQ/EPQ) models for imperfect items have received great attention in the last decade. The main common feature of these models is the incorporation of the imperfect items into the problem. While in real-world problems, buyers usually work with multiple suppliers, the focus of the existing literature has been on a single-supplier variant of the problem. In this study, we formulate an EOQ model for items with imperfect quality when considering multiple suppliers, which is a particular type of supplier selection and EOQ models. The initial formulated problem is a mixed integer nonlinear programming that aims to maximize the total annual profit of the buyer such that customer demand is completely met and suppliers’ capacity constraints are satisfied. Since proposed model is nonlinear, first by using the unique property of model, it is converted to mixed integer linear programming then solved by GAMS/CPLEX software to obtain optimal solution. We then use a numerical example to illustrate the problem, and conduct a sensitivity analysis to study the sensitivity of the objective function and the decision variables to the imperfect rate of the items supplied by the selected suppliers.