جستجوی مقالات مرتبط با کلیدواژه « supply chain » در نشریات گروه « مواد و متالورژی »

In the modern world, effective and successful supply chain management is among the most significant strategies for all organizations and public/private companies. Nowadays, recommending new tools and attitudes to improve supply chain efficiency is a challenging topic in organizational management. Another important goal of organizational management is to allocate costs logically and conveniently. Even though there are numerous studies on fixed costs and many tools for addressing this issue, data envelopment analysis (DEA) is a powerful technique for fair allocation. Therefore, the present study aimed to investigate the allocation of fixed costs in four-level supply chains using DEA models. The proposed model was examined by implementing a common set of weights and cost allocations according to the size of the decision-making unit. Additionally, the goal programming technique was applied to minimize the deviation between efficient allocation and size-based allocation. The proposed model is linear and provides a unique fixed allocation for each stage of the supply chain. Given that this model is always feasible, it can always provide an optimal allocation for each stage of the supply chain in different industries. In this paper, we focused on the allocation of personnel costs in a cement supply chain in Iran. The results show that the allocation amount to the customer stage (fourth stage) of the supply chain is much higher than other stages and the lowest allocation is obtained in the supplier stage (first stage).

Considering the various pollutions caused by human activity, especially in the industry, which has led to the destruction of the environment and the reduction of the quality of human life, it has prompted human societies and governments to consider the issue of the environment as a concern and The important challenge in today's post-industrial age should be given more attention. Manufacturing companies and industries such as steel should also design and implement their production approach based on environmental standards to reduce this concern. One of the environmental factors in the steel industry is the choice of a green supplier, the present study is carried out using fuzzy Vicor multi-criteria decision-making processes in order to evaluate the environmental performance of suppliers. A supplier with the highest score is ranked number one. This means that the supplier has the highest environmental performance. Vicor technique selects and ranks the best supplier from among the options available in the supply chain. The total size of the statistical population is 30 people from the West Asia Steel Company, all of whom were given their opinion by completing the questionnaire. are According to the findings of the research, the ranking Finally, according to the collected data, the ranking of suppliers based on the fuzzy score is S3>S2>S1.

Competition among supply chains is an essential factor to be considered in determining supply chain strategies. Furthermore, due to an imbalance in market share, one of the supply chains may have a higher power to manipulate the market and usually play the role of the leader in the market. In this paper, two leader-follower supply chains consisting of one manufacturer and one retailer are considered. In-chain competition is addressed besides the chain-to-chain competition in which the retailer is the leader and the manufacturer is the follower. The competition elements are price and service, which are investigated in three different scenarios: decentralized leader-decentralized follower, integrated leader-decentralized follower, and decentralized leader-integrated follower. Using the backward induction, we start the solving process from the follower supply chain and derive the follower’s best response function. Then the leader strategies are examined after the substitution of the follower’s best response function in leader profit function. Finally, we analyze the effects of the price competition intensity, service investment coefficient, and the potential market size of both chains on the equilibrium values in all three scenarios. The results show that increasing the price competition intensity will decrease the profit of the leading supply chain. In contrast, small values of price competition intensity are beneficial for the follower supply chain. Further, the first scenario has the most retail and wholesale price while the integrated supply chains have the most service level.

One of the most important issues in area of health and hygiene is location-allocation of organ harvesting centers and transplant centers according to coordination between supply and demand. In this paper, a mathematical model is presented for location-allocation of organ harvesting centers and transplant centers. The proposed model does not only minimize the present value of the total system costs, but also minimizes the geographical inequalities. The presented model is a bi-objective nonlinear mathematical programming and some of the problem parameters, such as cost, transport time and the like are associated with uncertainty and considered as fuzzy sets in the mathematical formulation. In this paper, an Organ Transplant Supply Chain (OTSC) has been designed and the ε-constraint method has been used to solve the problem and Iran is considered as a case study. The results show that the patient's family satisfaction rate is more important than the viability rate in the number of transplant operations performed and for a transplant operation to be performed, the minimum satisfaction rate (Bh) should be 0.4 and organ viability rate (UD0) should be 0.2.

Developing an appropriate plan for distribution department is significant because of its influence on company's other costs and customers' satisfaction. In this study, a new bi-objective mix-integer linear programming model developed for scheduling two groups of experienced and inexperienced distribution staff based on capillary marketingin Pak Pasteurized Dairy Products Company of Guilan province in order to reduce costs and increase sales along with customer satisfaction. Several constraints are taken into account at the model.The model solving results using the epsilon constraint method, which provides a set of Pareto responses and solved by GAMS software, shows efficiency of the model to solve small-size problems. In order to evaluate the validity of model in large scale problem, with respect to NP-hardness of the problem, a multi objective water flow-like optimization (MOWFO) algorithm was expanded. For evaluation the suggested method, several problems were expanded and the efficiency of the method was compared with a multi-objective invasive weed optimization (MOIWO) algorithm based on the planned factors. For better algorithms performance, their input parameters were set using RSM technique; furthermore, in order to compare parameters statistically, the Tukey’s 95% confidence interval method was used. The results show the superiority of MOWFA compared to MOIWO algorithm in comparison indicators.

Taking into account competitive markets, manufacturers attend more customer’s personalization. Accordingly, build-to-order systems have been given more attention in recent years. In these systems, the customer is a very important asset for us and has been paid less attention in the previous studies. This paper introduces a new build-to-order problem in the supply chain. This study focuses on both manufacturer's profit and customer's utility simultaneously where demand is dependent on customer's utility. The customer's utility is a behavior based upon utility function that depends on quality and price and customer's preferences. The new bi-objective non-linear problem is a multi-period, multi-product and three-echelon supply chain in order to increase manufacturer's profit and customer's utility simultaneously. Solving the complicated problem, two multi-objective meta-heuristics, namely non-dominated ranked genetic algorithm (NRGA) and non-dominated sorting genetic algorithm (NSGA-II), were used to solve the given problem. Finally, the outcomes obtained by these meta-heuristics are analyzed.

Due to the rapid development of technology in recent years, market competition and customer expectations have increased more than ever. In this situation, it is vital for businesses survival to determine the appropriate policy for inventory control, pricing, and routing, and decisions regarding each of them are often made separately. If the products have a perishable nature, it will be more important to determ ine the above policies. For integration of the decision-making concerning the three key components of the supply chain ,i.e. pricing, routing, and inventory control, two mathematical models were developed for a two-echelon supply chain of perishable items with direct shipment, where fixed lifetime is assumed in one model and random lifetime in another, so that profit is maximized. The proposed mathematical model was solved using the CPLEX solver package of the GAMS software for specification of the optimal policy of the supply chain. The results demonstrated that the CPU time needed for solving the mathematical model for perishable items with random lifetime was less than that for fixed lifetime, while the value of the objective function for products with fixed lifetime was greater than that for products with random lifetime.

Nowadays, the design of a strategic supply chain network under disruption is one of the most important priorities of the governments. One of the strategic purposes of managers is to supply the sustainable agricultural products and food in stable conditions which require the production of soil nutrients. In this regard, some disruptions such as sanctions and natural disasters have a destructive effect on the supply of raw materials and the uncertainty of input parameters plays an undesirable impact on the decision-making levels including strategic, tactical, and operational levels. The present study introduced a new model of resilient supply chain network which was compatible with the realities of the structure of the supply chain for fertilizer in Iran. Notably, the effectiveness of the designed system was promoted by the dominant strategies of reliability. Further, a new robust possibilistic approach was proposed which guaranteed the optimality and feasibility robustness through the efficient solution to deal with the parametric uncertainty. Finally, the results showed that the proposed new robust possibilistic combination promoted the optimality robustness and its effectiveness using an optimal average cost and minimum standard deviation.

Social responsibility is a key factor that could result in success and achieving great benefits for supply chains. Responsiveness and reliability are important social responsibility measures for consumers and all stakeholders that strategists and company managers should be concerned about them in long-term planning horizon. Although, presence of uncertainties as an intrinsic part of supply chains could adversely affect the best set plans by field experts. Accordingly, uncertainty of parameters and uncertainties caused by disruptions should be regarded in planning process of networks to prevent unpredictable negative consequences of such uncertainties for all echelons of supply chain. Based on enumerated matters, the aim of this paper is to design a reliable multi-echelon closed loop supply chain network model that maximizes social responsibility while minimizing fixed establishing and variable processing costs of network design. To cope with uncertainty of parameters, stochastic programming is applied and an effective reliable modelling method is employed to appropriately control unpleasant economic impacts of disruptions. Notably, an efficient robust programming method is applied to give the decision makers the capability to control level of risk-averseness of decisions while modelling uncertain parameters. Finally, the proposed model is solved and its outputs are analyzed on the basis of generated test problems which shows correct performance and applicability of extended model in real world problems.

Key Performance Indicator (KPI) is a type of performance measurement that evaluates the success of an organization or a partial activity in which it engages. If during the running process instance the monitoring results show that the KPIs do not reach their target values, then the influential factors should be identified, and the appropriate adaptation strategies should be performed to prevent KPIs violations. In this paper, we propose an integrated monitoring, analysis, prediction and adaptation approach to prevent KPIs violations. We have considered more than one KPI for a specific process and have tried to reach their target values simultaneously by proactive runtime adaptation before the end of the running process. In order to identify the dependency between KPIs and lower-level influential factors, an analysis is done on the data collected from historical process executions. For this purpose, Data Mining techniques have been used. The result is used to predict the KPIs values of the running instance. If KPIs violations are detected, adaptation requirements and adaptation strategies are identified. Since it is possible to define several KPIs for one process, and each has its own importance, so in this paper we tried to satisfy several KPIs target values.

The facility location problem is a strategic decision-making for a supply chain, which determines the profitability and sustainability of its components. This paper deals with a scenario where two supply chains, consisting of a producer, a number of distribution centers and several retailers provided with similar products, compete to maintain their market shares by opening new distribution centers because of increasingin demand. The competition problem is formulated as a non-linear integer bilevel mathematical model, where the upper level represents thedecisions of the leader producer and the lower level administrates the decisions of the follower producer. It has been shown that even insmallscaleproblems, bilevel mathematical programming problems are strongly NP-hard, so an adapted bilevel ant colony algorithm with inter-level information sharing is developed to solve the problem.To evaluate the performance of the developed ant colony algorithm, the upper bound of the competitive facility location problem is determined by solving the upper-level problem as an integer linear programming model without considering the follower’s decision. Comparing the computational results of the developed ant colony algorithm with those of the determined upper bounds shows the satisfactory capability of the proposed approach of solving even medium- and large-scale problems.

In this paper, we study an outsourced supply chain consisting of one buyer and two suppliers in which the buyer outsources manufacturing of a physical product to two competing suppliers. The suppliers compete for the buyers'' demands share, and the buyer allocates the demands to the competing suppliers based on three-dimensional allocation functions. We consider two certain types of allocation function which depend on price, service level and product quality level. They include the exponential allocation function and the Cobb-Douglas allocation function. A three-stage game-theoretic framework is presented to derive the equilibrium values. Since the problem lacks a closed-form solution, numerical studies are conducted over a wide range of some key parameters.