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Today's digital world demands about automated sentiment analysis on visual and text content to significantly displaying people's feelings, opinions and emotions through text, images and videos across popular social networks. Earlier visual sentimental analysis faces many drawbacks like achieve low accuracy and more difficult to understand people opinions due to traditional techniques. Also, another major challenge is a huge number of images generated and uploaded every day across the world. This paper overcomes problems of visual sentiment analysis with the help of deep learning convolution neural network (CNN) and Affective Regions approach to achieve more meaningful sentiment reports with huge accuracy.

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In this paper, discrete time dynamic model for four-level supply chain system, including factory, wholesaler, retailer, and customer is designed with a recovery center as recycling hybrid channels. Due to the lack of coordination of the chain level and the unhealthy exchange of information in the system, almost all supply chains dynamic involving the stochastic noise. For the first time, in this paper, we proved that stochastic noise lead to the bullwhip effect and we mitigated this phenomenon with control theory. Also, we investigate the effects of the lead time, the various forecasting methods, and aggressive ordering on the bullwhip effect. In order to mitigate the bullwhip effect, we propose Kalman filter method. So, using linear quadratic Gaussian controller, not only effect of bullwhip was adjusted but, also the system become stable. Eventually, the simulation results in Meshkin match factory, indicate the efficiency of the proposed method.

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Supply chain management (SCM) is very well known efficient and effective managerial tool to check and analyze the performance of any enterprises. In the present work, efforts have taken to analyze and optimize the performance of small & medium enterprise (SME) in Pune region India. For this purpose a SCM based framework is prepared to get the realistic data from the industries through the questionnaire prepared on the basic of literature and the expert opinions. After finalizing the effective framework fitted to the various enterprises, a data in the pointer scale has been collected from the various stakeholders of the enterprises. The grey relational analysis (GRA), a multi-response optimization tool has been effectively used for getting the optimize result which will help the enterprises to plan the strategies for the betterment of the enterprises. Optimum results were implemented in the other enterprises. The responses were measured and compare with the optimum solution. From the responses, it has been observed that there is a significant enhancement in the response level of the other enterprises. Thus the SCM was effectively used for enhancing the performance of the SMEs in the region.      

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The Today’s digital world computations are tremendously difficult and always demands for essential requirements to significantly process and store enormous size of datasets for wide variety of applications. Since the volume of digital world data is enormous, this is mostly generated unstructured data with more velocity at beyond the limits and double day by day. In last decade, many organizations have been facing major problems to handling and process massive chunks of data, which could not be processed efficiently due to lack of enhancements on existing and conventional technologies. In this paper address, how to overcome these problems as efficiently by using the most recent and world primary powerful data processing tool, which is hadoop clean open source and one of the core component called Map Reduce, but which has few performance issues. This paper main goal is  address and overcome the limitations and weaknesses of Map Reduce with Apache Spark.

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The logistical problems that companies must face tend to have conflicting interests between customers and service providers, which makes them difficult to solve. In turn, when the activities involve the transport of hazardous materials, the problem becomes critical in security terms, and makes logistics operations even more difficult. In the hazardous materials transportation literature, problems related to the routing of vehicles and the geographic location of supply or service centres are often addressed. However, there are not many studies related to the study of the loading, unloading and weighing operations of trucks that handle hazardous materials within industrial plants. That is why this work presents a case study of the installation of a new truck balance in an industrial plant in Argentina. To do this, the internal logistics operation and the current state of the plant's infrastructure are analyzed. A detailed study of the alternatives for the location of the balance was carried out following the criteria set by the company's management and the problem was solved using an empirical weighting method coordinated with the heads of the Supply Chain Department. A satisfactory solution was obtained.

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The blood supply chain network is an especial case of the general supply chain network, which starts with the blood donating and ends with patients. Disasters such as earthquakes, floods, storms, and accidents usually event suddenly. Therefore, designing an efficient network for the blood supply chain network at emergencies is one of the most important challenging decisions for related managers. This paper aims to introduce a new blood supply chain network in disasters using the hub location approach. After introducing the last studies in blood supply chain and hub location separately, a new mixed-integer linear programming model based on hub location is presented for intercity transportation. Due to the complexity of this problem, two new methods are developed based on Particle Swarm Optimization and Differential Evolution algorithms to solve practical-sized problems. Real data related to a case study is used to test the developed mathematical model and to investigate the performance of the proposed algorithms. The result approves the accuracy of the new mathematical model and also the good performance of the proposed algorithms in solving the considered problem in real-sized dimensions. The proposed model is applicable considering new variables and operational constraints to more compatibility with reality. However, we considered the maximum possible demand for blood products in the proposed approach and so, lack of investigation of uncertainty conditions in key parameters is one of the most important limitations of this research.

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One of the most important fields of logistic network is transportation network design that has an important effect on strategic decisions in supply chain management. It has recently attracted the attention of many researchers. In this paper, a multi-stage and multi-product logistic network design is considered.
This paper presents a hybrid approach based on simulation and optimization (Simulation based optimization), the model is formulated and presented in three stages.  At first, the practical production capacity of each product is calculated using the Overall Equipment Effectiveness (OEE) index, in the second stage, the optimization of loading schedules is simulated. The layout of the loading equipment, the number of equipment per line, the time of each step of the loading process, the resources used by each equipment were simulated, and the output of the model determines the maximum number of loaded vehicles in each period. Finally, a multi-objective model is presented to optimize the transportation time and cost of products. A mixed integer nonlinear programming (MINLP) model is formulated in such a way as to minimize transportation costs and maximize the use of time on the planning horizon. We have used Arena simulation software to solve the second stage of the problem, the results of which will be explained. It is also used GAMS software to solve the final stage of the model and optimize the transporting cost and find the optimal solutions. Several test problems were generated and it showed that the proposed algorithm could find good solutions in reasonable time spans.

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The objective and purpose of this research paper are to provide a list of prospective research areas to revamp the supply chain of horticulture products as a relevant research topic and for the same we conducted an extensive review of the available literature in the domain. We performed a detailed review of academic articles, published in reputed peer-reviewed international journals, in the domains of horticulture products (fruits and vegetables, flowers, nuts and seeds, herbs, medicinal plants, sprouts, seaweeds, mushrooms, algae, and non-food crops like grass, ornamental trees, and plants) and its supply chain management. An extensive review has been developed to emphasize the need for alignment among the key aspects of horticulture products and its supply chain, the links between supply chain processes and strategy. We have taken a final sample of 70 articles published from 1994 to 2018 for the knowledge base of this research. A Literature survey in this respect indicates that most of the research has been conducted in the field of products having longer life cycles than the products having shorter life cycles like perishable (horticulture products) products. The scope of the research is to study the various levels and distinct forms of horticulture products’ supply chain. The results provide evidence about the journals, show the publication pattern over time, the research methodology adopted, and the content elements of horticulture products’ supply chain. The research findings apply to a large extent for managerial decisions. There is huge research scope available in the area of the horticulture supply chain as only limited research has been done in this field. This research work and future researches in this field would be helpful for managers, decision-makers, students as well as academicians. After extensive review and synthesis, important findings from the existing literature, critical review, and challenges have been derived to highlight how horticulture products and its supply chain should be best matched to its production and logistics processes.

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In this paper, a fuzzy multi-objective optimization model in the logistics of relief chain for response phase planning is addressed. The objectives of the model are: minimizing the costs, minimizing unresponsive demand, and maximizing the level of distribution and fair relief. A multi-objective integer programming model is developed to formulate the problem in fuzzy conditions and transformed to the deterministic model using Jime'nez approach. To solve the exact multi-objective model, the ε-constraint method is used. The resolved results for this method have shown that this method is only able to find the solution for problems with very small sizes. Therefore, in order to solve the problems with medium and large sizes, multi-objective cuckoo search optimization algorithm (MOCSOA) is implemented and its results are compared with the NSGA-II. The results showed that MOCSOA in all cases has the higher ability to produce higher quality and higher-dispersion solutions than NSGA-II.
 

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This paper aims to frame a two-player supply chain model with a production system's reliability influenced products’ defection rate.  Upon generating and inspecting the products, the producer reworks the defectives and sells the perfect and reworked items to a retailer providing him free products' delivery. The retailer stores both types of commodities in the respective showrooms of finite capacities and keeps the excess conforming products in a leased warehouse. Eventually, the formulation of these two partners' profit functions performed, and a numerical illustration demonstrates this model's applicability.   Results shows, hiring a storehouse is profitable for the retailer and the deterioration of the production system’s reliability impacts adversely on the manufacturer's profit.

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Sustainability is now increasingly recognized as an effective strategy to deal with the current challenges of global supply chains. Supply chains of the lead and zinc industries are most important. Because these two industries not only are among the high-risk in different countries, including Iran, but also can affect economic, social, and environmental sustainability. On the other hand, identifying and assessing the critical risks of supply chains have been less addressed in recent studies. This study aimed to identify and assess critical risks of sustainable supply chains (SSCs) in the Iranian lead and zinc industry. This study was a mixed-method (qualitative and quantitative) descriptive survey. Based on the literature, 24 risk factors that affect supply chain sustainability were identified, out of which 20 critical risk factors were confirmed in two steps by reviewing experts’ comments and the data obtained from in-depth interviews and questionnaires. The validity of questionnaires is verified based on the opinions of a group of 5 experts in the first step and another group of 17 experts and professionals of the lead and zinc industry in the second. The Cronbach’s alpha coefficient of the questionnaires was calculated to be 0.837, indicating the reliability of the questionnaires. The risk factors were analyzed using the Risk Priority Number (RPN), fuzzy DEMATEL, and risk matrices. Based on the results, “lack of technological/knowledge sustainability”, “price and cost fluctuations”, “inflation and exchange rates” and “environmental pollution” were the most important risk factors in the supply chain of the Iranian lead and zinc industry.

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In the field of logistics, there is a daily need for decision making, i.e. the need to solve business problems by selecting an appropriate solution. During the implementation of decision-making processes, it is necessary to find an optimal solution that will best meet the needs of companies. The selection of an optimal solution is crucial for the profitability, cost-effectiveness and long-term development of companies. The decision-making process in logistics is facilitated by applying various tools such as multi-criteria decision-making methods. In this paper, an integrated SWARA (Step-wise Weight Assessment Ratio Analysis) – MARCOS (Measurement Alternatives and Ranking according to Compromise Solution) model was developed and applied in order to classify products. Fifty alternatives, i.e. products were evaluated based on three criteria. The first criterion is the quantity of purchased products, the second criterion is the unit price of products and the third criterion is the annual value of purchase. The SWARA method was applied to determine the significance of the criteria, while the classification of products was performed using the MARCOS method. According to the results of the originally created MCDM model, the products were grouped into three categories A, B, and C. Then, a sensitivity analysis was performed using a model involving the integration of SWARA method and ABC analysis. Using this model, the classification of products into three groups was performed on the basis of the aforementioned criteria, and then a comparative analysis was conducted.

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The role of sustainability dimensions in the value creation process has received much attention. Adopting a proper set of key performance indicators sustainability leads to accurate calculation of chain value. This paper focuses on the dimensions of in the biofuel supply chain and seeks to evaluate the value in the chain. First, the importance of biofuels and its various types are discussed. Then, a new model is presented by designing the proposed energy chain and considering its sustainability dimensions and indicators in uncertain environment. Rough set theory is one of the best mathematical tools for dealing with uncertainty. The proposed biofuel energy supply chain is modeled to obtain the total value of the system considering sustainability indicators and layers of the supply chain. A multi-objective rough mathematical formulation is presented and solved. Best-worst method was integrated to determine the significance score of sustainability indicators. Finally, the model of the rough linear mathematical program is solved with optimization tools and the sustainable value of the chain is obtained.

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Nowadays, supply chain management (SCM) is an interesting problem that has attracted the attention of many researchers. Transportation network design is one of the most important fields of SCM. In this paper, a logistics network design is considered to optimize the total cost and increase the network stability and resiliency. First, a mixed integer nonlinear programming model (MINLP) is formulated to minimize the transportation time and transportation cost of products. The proposed model consists of two main stages.
One is a normal stage that minimizes the transportation and holding costs, all manufacturers are also assumed to be healthy and in service. In this stage, the quantity of customer demand met by each manufacturer is eventually determined.
The second is the resilience stage. A method is presented by creating an information network in this supply chain for achieving the resilient and sustainable production and distribution chain that, if some manufacturers break down or stop production, Using the Restarting and load sharing scenarios in the reactive approach to increase resilience with accepting the costs associated with it in the supply network and return to the original state in the shortest possible time, the consequences of accidental failure and shutdown of production units are managed.
Two capacities are also provided for each manufacturer

• Normal capacity to meet the producer's own demand
• Load sharing capacity, Determine the empty capacity and increase the capacity of alternative units to meet the out-of-service units demand

In order to solve the model, we used GAMS & Matlab software to find the optimal solutions. A hybrid priority-based Non-dominated Sorting Genetic Algorithms (NSGA-II) and Sub-population Genetic Algorithm (SPGA- II) is provided in two phases to find the optimal solutions. The solutions are represented with a priority matrix and an Allocated vector. To compare the efficiency of two algorithms several criteria are used such as NPS, CS and HV. Several Sample problems are generated and solved that show the Sub-population Genetic Algorithm (SPGA- II) can find good solutions in a reasonable time limit.

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Nowadays, major challenges in the cold chain of perishable products, such as dairy products, are that these products do not reach customers on time. Answering the question of how to make the cold supply chain of perishable products more agile, the possibility of more control over this issue can be increased. This study tries to investigate the factors affecting the agility of the cold supply chain and after identifying the effective factors, rank them using the GRAY-DEMATEL-AHP. To data gathering, the literature of the subject and the opinions of experts and stakeholders who have sufficient experience in the cold chain have been used and the identified factors have been confirmed after several revisions by the Delphi through snowball sampling. Also, in order to take advantage of both the GRAY and DEMATEL approaches, this paper uses a combination of these two methods to examine causal relationships among the factors affecting the agility of the cold supply chain. The results show that Among the sourcing sub-factor, government decision-making and policies with a weight of 0.212 has gained the first rank and in the sub-factor of distribution, loading time and speed of action in distribution, with a weight of 0.188, has gained the first rank. Also, among the sub-factor of production, accurate planning and speed of action in order production, with a weight of 0.342, has gained the first rank. This paper adds valuable knowledge to the study of the dairy industry cold supply chain agility.

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In large-scale emergency, the vehicle routing problem focuses on finding the best routes for vehicles. The equitable distribution has a vital role in this problem to decrease the number of death and save people's lives. In addition to this, air pollution is a threat to people’s life and it can be considered to omit other kinds of disasters happens because of it. So, a new MINLP model presented is going to face a real situation by considering real world assumptions such as fuzzy demands and travel time, multi depots and items, vehicle capacity and split delivery. The first objective function is to minimize the sum of unsatisfied demand which follows a piecewise function and the second one is to minimize the cost which depends on the fuel consumption. In order to solve the multi-objective problem with fuzzy parameters, nonlinear function has been linearized by convex combination and a new crisp model is presented by defusing fuzzy parameters. Finally, NSGA-П algorithm is applied to solve this problem and the numerical results gained by this procedure demonstrate its convergence and its efficiency in this problem.

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Forging Industry Supply Chain involves various actors and acts as Industry Intermediate providing various products for downstream industrial customers. This study aims to analyze supply chain performance and recommend improvement strategy at forging Industry. This study applied supply chain operation reference (SCOR) and Analytical Hierarchy Process (AHP) to analyze supply chain performance. A SWOT analysis assisted to improve supply chain performance. The data was validated at PT ABC and PT XYZ as two focal company in supply chain operations of forging industry. The results show that the supply chain performance at PT. ABC 99.42% and 99.05% in 2019 and 2020, respectively.  PT. XYZ showed supply chain performance score as 96.60% and 97.52% in 2019 and 2020, respectively. This study has succeeded in formulating efforts to improve the supply chain performance, namely: producing quality goods according to domestic market specifications, maintaining good relations with suppliers or outsourcing, improving services using high technology.
 

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Modeling supply chain planning problems is considered one of the most critical planning issues in Supply Chain Management (SCM). Nowadays, decisions making must be sufficiently sustainable to operate appropriately in a complex and uncertain environment of the market for many years to beyond the next decade. Therefore, making these decisions in the presence of uncertainty is a critical issue,as highlighted in a large number of relevant publications over the past two decades.The purpose of this investigation is to model a multilevel supply chain problem and determine the constraints that prevent the flow from performing properly, subject to various sources and types of uncertainty that characterize the flow. Therefore, it attempts to establish a generic model that relies on the stochastic approach.  Several studies have been conducted on uncertainty in order to propose an optimal solution to this type of problem. Thus, in this study, we will use the method of "Mixed integer optimization program" which is the basis of the algorithm that will be employed. This inaccuracy of the supply chain is handled by the fuzzy sets. In this paper, we intend to provide a new model for determining optimal planning of tactical and strategical decision-making levels, by building a conceptual model. Therefore, it enables us to model the mathematical programming problem. We investigate in this attempt, attention to solving the mathematical model. So in the resolution we are going through the algorithm in machine learning, therefore providing as in the end an optimal solution for the planning of production.