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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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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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This paper develops an approximate cost function for a three-echelon supply chain that has two suppliers, a central warehouse and an arbitrary number of retailers. It takes an integrated approach to multi-echelon inventory control and order-splitting problems. It assumes that all facilities apply continuous review policy for replenishment, demand at the retailers follows a Poisson process, and lead times are stochastic with no predetermined probability distribution. Unsatisfied demand is considered as lost sales at the retailers and backlogged at the warehouse and suppliers. Due to information sharing between the existing echelons, order quantity at each higher level is assumed to be an integer multiple of the lower level. Order placed by the warehouse gets divided between the two suppliers and re-order point is not restricted at the warehouse or suppliers. The main contribution of this paper is its integrated approach and the practical assumption that it uses for the order arrival sequence and the unsatisfied demands. It adds two suppliers as the third echelon to the traditional two-echelon supply chain and considers dynamic sequence of orders arrival to the warehouse at each cycle. The fact that inventory control and sourcing decisions are interdependent and act as the main challenge of supply chain management, considering them in an integrated model can significantly influence operating costs and supply chain’s efficiency. Such approach can even have greater impact when blended with practical assumptions that consider lead-time as unpredictable and unsatisfied demand as lost sales. Total cost of the three-echelon inventory system is approximated based on the average unit cost and its accuracy is assessed through simulation. Numerical results with relatively low errors confirms the accuracy of the model. It also shows how to further enhance its accuracy by either increasing the holding cost at all echelons or the penalty cost at the retailers.

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Innovation is an essential tool for the supply chain to gain its competitive advantage and improve its performance. Many researchers have remarked that supply chain innovation is a vital tool for improving the performance of a supply chain and can be very productive. This research attempts to identify and analyze the effective factors on the innovation supply chain in the health sector. The effective factors on the innovative supply chain were extracted by reviewing the literature, similar studies, and experts’ surveys. In this regard, 49 criteria were determined in eight dimensions. Intuitive fuzzy DEMATEL (IFD) and AHP methods were used to determine the weight and the relationships between them. The results indicated organizational innovation as the most important dimension, government support innovation as the most effective dimension, and process innovation as the most affected dimension. Some researchers believe that this period guarantees the survival and success of service organizations in this competition. However, the logistics and communication network of a business is required as a new and innovative landscape to use the competitive advantage opportunities to perceive the global era.

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Productions of the industries around the world depend on using equipment and machines. Therefore, it is vital to support the supply of equipment and spare parts for maintenance operations, especially in strategic industries that separate optimization of inventory management, supplier selection, network design, and planning decisions may lead to sub-optimal solutions. The integration of forward and reverse spare part logistics network can help optimize total costs. In this paper, a  mathematical model is presented for designing and planning an integrated forward-reverse repairable spare parts supply chain to make optimal decisions. The model considers the uncertainty in demand during the lead-time and the optimal assignment of repairable equipment to inspection, disassembly, and repair centers. A METRIC (Multi-Echelon Technique for recoverable Item Control) model is integrated into the forward-reverse supply chain to handle inventory management. A case study of National Iranian Oil Company (NIOC) is presented to validate the model. The non-linear constraints are linearized by using a linearization technique; then the model is solved by an iterative procedure in GAMS. A prominent outcome of the analyses shows that the same policies for repair and purchase of all the equipment and spare parts do not result in optimal solutions. Also, considering supply, repair, and inventory management decisions of spare parts simultaneously helps decision-makers enhance the supply chain's performance by applying a well-balanced repairing and purchasing policy.

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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, 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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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.

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Supply chain management is one of the requirements for achieving economic growth in any supply chain. If managers' decisions are optimally allocated, it will be possible for companies and industries with a competitive and profitable advantage to grow and develop. The main desire of any company for survival is to minimize costs and maximize profitability. Due to the increasing complexity and dynamics of the situation, decision-making in this area requires more advanced analytical methods. Accordingly, the Real options theory has emerged, which introduces a new way of thinking about investing, especially in conditions of uncertainty. In this paper, a multi-period model is considered that examines the demand uncertainty in each period and also uses the Real options theory to seek the optimal strategy for investors in conditions of uncertainty and the effect of investors’ discretion on it. Using a decision tree to estimate the probable demand in each period and using Monte Carlo simulations to identify the lowest cost scenario in each period, the model has been solved in this research. In the case of the uncertainty parameter, sensitivity analysis is performed, and under different values ​​of this parameter, the obtained result is evaluated and validated. And the extension of outsourcing will increase the company’s profitability and meet higher demand and lower costs.

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A coalition loyalty program (CLP) is a business strategy employed by for-profit companies to increase or retain their customers. One of the operational challenges of these programs is how to choose the mechanism of coordination between business partners. This paper examines the role of revenue sharing contracts in the loyalty points supply chain of a CLP with stochastic advertising-dependent demand where the program operator (called the host) sells loyalty points to the partners of the program. The purpose of the study is to examine the effect of this coordination mechanism on the decisions and profits of the members of the chain using the Stackelberg game method and determine whether the presence of revenue sharing contracts benefits the chain members when the advertising is done by the host and when the advertising cost is shared between the host and its partners. The results show that when the host gives bonus points to end customers (advertising), revenue sharing contracts become a powerful incentive for the profitability of the host and its partners. The findings provide new insights into the management of CLPs, which can benefit business decision-makers.

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The introduction of blockchain technology into the food supply chain represents a digital revolution that has led to widespread advances in tracking food security. This article presents a comprehensive review of the literature on the use of blockchain in the food supply chain. This article is a review of the synthesis evidence Best group. We have focused on the supply chains of meat, fruits and vegetables. The Literature review has been conducted from seven different databases. For more insight, we categorized meat, fruit, and vegetable articles into four groups: descriptive, prescriptive, conceptual, and predictive. Due to the small number of case studies in research, the theoretical and conceptual frameworks proposed in most food supply chain articles, including the supply chain of meat, fruits and vegetables, have been less tested in reality. These surveys and small-scale case studies do not clearly and completely identify the impact of blockchain on the meat, fruit and vegetable supply chain and the challenges that blockchain implementation may pose to these supply chains. Findings indicate that little valid and quality research has been done in this field and more research is needed on the use of blockchain in the supply chain of fresh products.
 

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In this paper, a closed-loop supply chain is modeled based on hyperchaotic dynamics. Then, synchronization of the two hyperchaotic closed loop supply chains is performed with a proportional integral (PI) sliding mode controller design method. Using Lyapunov stability theory, it has been proved that the PI sliding mode controller can converge the synchronization error to zero in a limited time. The most important issue in the design of control strategies is the behavior of the control signal. In other words, it affects the cost of design and implementation. Numerical simulation results show that the control signal has low amplitude and fluctuations. so, the PI sliding mode control method can be implemented in the real world. Based on the numerical simulation results, the use of two controllers is proposed to reduce design costs.

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The main objective of this research is effective planning as well as greener production and distribution of mineral products in supply chain network. Through a case study in cement industry, it considers the design of the mining supply chain network including several factories with a number of production lines and multiple distribution centers. It leaves part of the transportation operation to contractor companies so as to enable the core company to better focus on its products’ quality and also create job opportunities to local people. It employs a multi-period and multi-product mixed integer linear programming model to both maximize the profit of the factory as well as minimize its carbon dioxide gas emissions which are released during cement production and transportation process. Due to the uncertainty of its cost parameters, fuzzy logic has been used for the modeling and solved via a novel fuzzy multi-choice goal programming approach. Sensitivity analysis has also been done on some key parameters. Comparing results of the model with those from the single-objective models, shows that the model has good efficiency and can be used by managers of mining industries such as cement. Although leaving part of the transportation operations to contractor companies increases the number of vehicles used by the contractor companies, its associated decrease in the number of required factory vehicles, improves both objectives of the model. This should be considered by the managers since on top of profit maximization, it can help them build an eco-friendly image. Mining industries generally generate significant amount of pollutions and companies that pay attention to different dimensions of their social responsibilities can remain stable in the competitive market.

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Palm oil is a commodity whose demand continues to increase, requiring proper risk management in the supply chain. This study aims to develop a hybrid method that integrates probability impact matrix, analytical network process, and house of risk to mitigate strategies in the palm oil supply chain. The Probability Impact Matrix (PIM) method is used to map the priority risk agents and determine the occurrence value of the risk agents, and Analytical Network Process (ANP) is used to determine the severity value of the risk event. Furthermore, the House of Risk (HOR) is proposed to determine the priority of the mitigation strategy. The proposed method was applied in a case study on the palm oil supply chain in Indonesia. The research results show that ten priority risk agents and 6 mitigation strategies were obtained based on the proposed method to overcome risk agents in palm oil supply chain

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Providing skills training is an essential need of different societies. Considering the significance of the role of skill training in empowerment and employment of individuals through the training of skilled labor required by the labor market and industry, in this study the supply chain of skills training has been designed. In the proposed supply chain, according to the skill training aspects, a network structure is conceptualized to include appropriate factors in different layers of the supply chain. Evaluating the performance of the supply chain is handled applying a network data envelopment analysis. Network Data Envelopment Analysis (NDEA) is an efficient method analyzing all the factors included in the evaluation network. Among the NDEA models, the output-oriented BCC model was selected due to the importance of the output of the supply chains of the skills training. In addition to efficiency, the concept of complementary loss is also introduced to validate the results. The research findings show the efficiency of various factors in the stages of the designed network. On the other hand, unlike the classic DEA method, which shows the maximum efficiency of a factor, in the proposed network model, the efficiency priority is calculated and the efficiency is determined at each stage of the supply chain.
 

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