جستجوی مقالات مرتبط با کلیدواژه « نبود قطعیت » در نشریات گروه « صنایع »

In a competitive environment where business competition has become a fundamental challenge for managers, various ways to maintain, survive, or grow the organization are conceivable. Among these, marketing experts believe that customer loyalty is one of the most effective tools in facing this challenge. To achieve customer loyalty, various and diverse strategies have been mentioned in the literature by researchers and experts, which organizations can use, depending on the conditions, one or a combination of them. In such circumstances, managers usually have several strategies and must choose the most appropriate one(s). The present study aims to provide a combined approach for prioritizing customer loyalty strategies. This research uses a matrix-based approach to robustness analysis, which can deal with complexity and uncertainty. The proposed algorithm combines it with strategic planning tools (strategies derived from strategic objectives and SWOT analysis) for prioritizing and selecting strategies. The proposed approach was implemented in a case study on prioritizing customer loyalty strategies for a women's clothing boutique in Ramsar City. Available strategies, influential environmental variables, definitions of future scenarios, and the performance of strategies in different environmental conditions were determined based on the judgments of the problem owner. The results showed that considering influential environmental variables (national currency value, market access and raw materials, lifestyle changes, investment security, government-private sector relations, and the speed of technological change), supplier selection, contractor selection, and attracting a sponsor have the highest priority strategies. Afterwards, environmental advertising, collaborative production, and customer relationship management were placed in subsequent rankings. The outputs of the proposed approach indicate that considering the country's foreseeable future conditions, higher-priority strategies minimize environmental risks and their impact on the business.Originality/Value: The problem of choosing a customer loyalty strategy is a decision-making problem that can be addressed with the help of decision-making knowledge. However, the literature suggests that classical strategic planning approaches Quantitative Strategic Planning Matrix (QSPM) or multi-criteria Decision-Making Approaches (MCDM) are used in most cases of such decision-making. Despite their capabilities and features, these approaches face challenges in dealing with variable and evolving conditions (future uncertainty). An alternative approach, robustness analysis, can consider alternative futures but cannot define available strategies. Based on this, combining the matrix approach to robustness analysis with classical strategic planning approaches will be a response to the above problem.

One of the biggest challenges of the 21st century is meeting the needs of the growing world population. The supply chain of perishable goods, including food, dairy products, medicines, and blood products, has recently received attention due to their impact on human life. In this article, the design of a sustainable supply chain network for perishable items has been discussed. To optimize this supply chain, a multi-objective Mixed Integer Linear Programming (MILP) model has been developed to formulate the problem. Fixed deterioration rate (expiration date) is considered. To perform the research calculations, GAMS software and the combined method of Bander's analysis and Lagrange coefficient were used, and based on the data, results were obtained, and the relative weight of the stability of the solution (ω) was equal to 0.5 and the relative weight of the stability of the model was (ω) equal to 5000 has been developed to meet the proposed objectives. These comparisons show that the presented network was robust in all performance objectives. The results obtained from the combined method regarding the three objective functions defined for the main model show this fact. The results of the first iteration provide us with better answers compared to the other iterations.Originality/Value: This research can be considered as one of the first optimization papers that presented a multi-level and multi-product-multi-period supply chain with uncertainty in the parameters in the dairy and pharmaceutical industries and the environmental costs of production and transportation and sustainable social costs such as reported accidents and incidents, job satisfaction, safety, reduction of dispatch time and lost working days simultaneously with the economic dimension in management-related decisions. Allocation combines location and routing.

Lack of resources, ecosystems at risk, and climate change have a great impact on the living environment. One of the most important issues and challenges that countries, including our country, face is the waste of resources. In the past decade, due to the increasing importance of economic competitiveness, legal pressures, environmental concerns in the context of old products, and social impacts, the issue of a closed-loop supply chain has attracted many researchers. Increasing the eciency and eectiveness of supply chain activities is one of the sustainable competitive advantages for companies. Also, companies and organizations are not only aware of environmental factors but are also aware of the revenues of collecting and recycling their used products. The closedloop supply chain is a way of considering the recycling of products to control environmental barriers. This research aims to develop a robust optimization approach for designing a supply chain network in industrial bread. A multi-objective integrated integer linear programming model is presented as a single-product, single-cycle, and multi-capacity. In this model, two economic and environmental objectives are examined under certainty and uncertainty conditions. A balance between the goals of the equilibrium is established and the model is tested in dierent sizes and uncertainties by using the Torabi and Hassini (TH) method. The results demonstrate that in all cases, the robust approach overcomes the deterministic approach based on the mean value of the objective function. Regarding the standard deviation, the robust approach in the problem of 6*10*10*6 with uncertainties =0.2 and =1, the problem of 9*15*15*9 with uncertainties =0.2 and =0.5, and the problem of 12*20*20*12 with uncertainties =0.2, =0.5 and =1, extremely overcome but in other cases, the deterministic approach has better outcomes. A robust strategy model achieves higher quality and better performance than the deterministic strategy in large problems and higher uncertainty. Due to the size of the problem and the uncertainty, in most cases, the gap between these two approaches increases with respect to both coecients of performance.

The design of the integrated supply chain network plays a vital role in improving the operational efficiency of the company. In this regard, it is essential to integrate strategic issues such as location and transportation management with inventory management strategies in the design of the supply chain network. In the current research, the mathematical model of the three-level supply chain network, including supplier, warehouse, and retailer, has been developed by adopting two coordinated and non-coordinated inventory control strategies. The dynamic adaptive control approach is presented to deal with the uncertainty of the economic order quantity. The study's primary focus is examining the integrity or non-integrity of the inventory of supply chain echelons and its effect on the inventory allocation and the reliability levels of network storage points to guarantee a high level of service. A genetic meta-heuristic algorithm has been used to optimize the problem with an ample search space and provides reasonable solutions and reasonable improvement opportunities. The calculation results show that the amount of inventory and the costs of the entire supply chain network show a significant reduction using the coordinated inventory control strategy.

Nowadays, increasing the quality level in production systems and reducing costs are two of the signi cant goals of manufacturers. More manufacturers pay for more qualitative raw materials, more skilled labor, and more advanced and accurate machines the more waste is reduced. Increasing quality levels and decreasing costs become more complex when some parameters are under uncertainty. One of the methods to encounter uncertainties is robust optimization, where uncertainty probability distribution is unknown. As a consequence, the robust scenario-based approach, which is presented by Mulvey, is applied. In this paper, we present a biobjective scenario-based supply chain model. In this model, three echelons including suppliers, manufacturers, and customers are considered. Also, we consider uncertainty in backorder, demand, and cost values. The rst objective function aims to minimize supply chain costs including production, raw material purchasing, production inventory holding, raw material inventory holding, transportation, and backorder. The second objective function aims to minimize the total amount of raw material wastes in the production line and supplier batch. The proposed model has been de ned as a multi-product, multi-period, multiple suppliers, multiple customers, and multiple transportation modes mixed-integer linear programming model. Also, in this model, workforce e- ciency, storage and transportation capacities, and inventory planning are considered. The model parameters are considered randomly distributed. The Epsilon constraint method, NSGA-II, and SPEA2 algorithms are applied to solving the proposed model. Also, the Taguchi method is applied to tune the parameters of the algorithms. Then, a comparison between the quality of results and the CPU time of these methods is provided. This comparison indicates that the use of evolutionary algorithms provides close results with the exact method in a shorter CPU time. Afterward, the Mean Ideal Distance (MID) and Analytic Hierarchy Process (AHP) methods are respectively employed to evaluate Pareto fronts performance and make a decision about selecting the best cost and quality level policy.

In many real-world optimization problems, we are facing uncertainties in parameters describing the problem. In general, as a simplifying assumption, uncertainty is ignored. In the school bus routing problem, there are uncertain parameters that are assumed to have deterministic values. As a result of this simplifying assumption, the obtained solutions may be mismatched with the real world. This issue arose by violating some hard constraints.

In this research, a mixed linear integer programming for school bus routing with mixed loading by using a heterogeneous fleet is presented. The uncertainty of travel times is modeled as interval numbers. We propose a heuristic algorithm to generate extreme scenarios. Each scenario is generated in order to make the last found optimal solution into an infeasible one as much as possible.

Experimental results show that deploying this novel algorithm for generating extreme scenarios, efficiently produces diverse scenarios. After the scenario generation algorithm is converged, the intersection of the feasible optimal solutions under diverse scenarios is extracted as robust sub-tours or robust trips.

Originality/Value: 

It is the first time to apply the notions of robust optimization using the extreme scenarios generation scheme. At each iteration of the extreme scenario’s generation, the most conflicting scenario against a given optimum solution is generated. The main advantage of this method over other present robust optimization methods is its emphasis on maintaining the feasibility of the optimal solution when dealing with the most diverse set of uncertainty scenarios while keeping the computational effort needed as low as desired.

Today, the high importance of achieving food security for communities has made it necessary to provide a new perspective on the design of the grain supply chain distribution network to better adapt to real-world uncertainties and also to take into account disruptions. To this end, in this study, a mixed-integer linear programming model has been developed for the distribution network design problem in the grain supply chain, which has three objectives such as minimizing costs and maximizing job opportunities, both of which are examples of sustainability. In addition, considering the importance of grain in the food basket of households and the importance of food security, the third objective function has been developed focusing on the issue of resilience to deal with any disruption in the design of the distribution network. The robust-possibilistic programming approach is used to deal with uncertain demand and the multi-objective problem is solved using the improved epsilon constraint method. Finally, a compromised solution is selected using the TOPSIS method.</em>

Biomass supply chain network design decisions are the most important part of the strategic level of supply chain management decisions, which include determining the location of facilities, their number and capacity, their allocation to different resources and markets, and the integration flow among facilities. An appropriate design has a significant influence on flexibility, efficiency and consequently the performance of the biomass supply chain. In this paper, a mathematical model with a robust optimization approach is presented to design a resilient and sustainable biomass supply chain under uncertainty in bioenergy demand and disruption in bioenergy refinery. By determining resilience factors and sustainability indicators, the relationship between resilience factors was determined and then the resilience factors were prioritized using fuzzy TOPSIS. Resilience factors with high priority are considered in the mathematical model. The first objective function considers profit maximization by considering all sustainability costs and the penalty of shortage or surplus of bioenergy demand and minimizing resilience factors . In addition, Robust method is proposed to overcome the uncertainty in bioenergy demand and the results of model solving with GAMS software are presented to show the model capability and sensitivity analysis of basic parameters. One of the innovations of this paper is to provide a way to measure resiliency based on the residual capacity after the disruption compared to before the disruption, which is presented in the first constraint. Finally, the parameters in the mathematical model are tested using a case study in the Organization of Renewable Energy and Energy Efficiency through numerical, feasibility and applicability of the proposed research approach. The obtained results show the valuable efficiency of the proposed model in increasing the biomass supply chain performance.</em>

Since the short life cycle, uncertainty in demand, and speed in response are the most important characteristics of fast fashion, improving customer relationship management (CRM) is of particular importance in this industry. Recent developments in the world's clothing industry have created a competitive market, and reforming the structure of flexible supply chains in this field has become very important. This research aims to integrate CRM policies and reverse supply chain design in the field of fast fashion in order to reduce costs and increase customer satisfaction. The analysis of 659 thousand real records related to the information of the last purchase, the continuity of purchases and the value of purchases related to the customers of 21 clothing chain stores in Tehran during two years using the     K-means algorithm led to the clustering of customers into 5 groups and then the amount of discount for each group were determined. In order to determine the optimal location of the repair center and combined centers for the collection of returned goods, a mathematical model was designed and solved using the random optimization method in a scenario-oriented manner. The results showed that combined centers are not necessarily located in the area of stores with higher sales, but the return rate of goods and the amount of demand should be taken into account at the same time. According to the Pareto model diagram, the binary values of the objective function of cost and satisfaction vary between the numbers of 4.7 billion Rials for cost and 2185 for satisfaction, and 6.2 billion Rials for cost and 3156 for satisfaction. The range of minimum and maximum discounts for the purchase of new goods was calculated from 5% to 30% and for the repair of goods from 10% to 60%. The model presented in this research is generally used in fast-moving retail industries, including perishable goods.</em>

Reducing dependence on fossil fuels and environmental pollution is one of the most important incentives to produce fuels from renewable energy. Nowadays, renewable resources are being considered for reasons such as economic and environmental bene ts and easy access. They are used to generate electricity and clean fuels and heat. In recent years, biomass is considered as a renewable source, and its use is rapidly growing .Biofuels derived from biomass can play a key role nowadays as one of the main sources of renewable energies. Therefore, more and more researchers have been involved in modeling and optimizing biomass supply chains. Lignocellulosic biomass is a rich and renewable natural resource composed of cellulose, hemicellulose, and lignin. This source can replace fossil fuels to produce biofuels without compromising food security. Agricultural wastes are among the sustainable sources of lignocellulosic biomass, and a million tons of agricultural waste is produced, which is one of the major sources of biofuels. One of the obstacles to the use of these renewable sources is the cost associated with the supply chain such as transportation and production costs which are among the important costs in the supply chain. In this research, a singleobjective, multi-level and multi-period linear programing under uncertainty with chance constraints is presented to maximize the pro t, in which hub is used as an intermediate level. Hub facilitates the transmission of biomass between supply chain levels. In the proposed mathematical model, lignocellulosic biomass was used to generate bioethanol and lignin. Sales of lignin as byproducts and multimodal transportation represent other ways to reduce costs. After sensitivity analysis, the results showed that increasing the sales price more than reducing transportation costs and increasing demand had a positive eect on the pro tability of the entire supply chain and the proposed model was economically justi - able.

Currently, rapid economic change and increasing competitive market pressure are pushing organizations to focus on making supply chain operations more efficient and effective. Proper design and efficiency of logistics networks as part of supply chain planning, in addition to creating a sustainable competitive advantage, increases customer satisfaction and provides the opportunity to meet their needs, which is why the decisions related to the design of these networks are of great importance. Enjoy. Therefore, in this study, the design of a closed-loop logistics network to reduce pollution and environmental pollution using the Bertsimas and wire stabilization method was presented. The mathematical model to be presented in this research was presented by considering the objectives of minimizing transportation costs, minimizing the time of receiving raw materials from the supplier and minimizing the time of product return from the customer to the separation center. Due to the strategic nature of the closed-loop supply chain, which with the approximate solution space causes a lot of costs to be delivered to the system to increase the accuracy of the answers of the mathematical model and application of this goal in this study It is used to reduce the computational time of the model, the results obtained with high accuracy. On the other hand, because the operational logic of solving Lagrange release is based on a single-objective model, first multi-objective mathematical model with Augmented Epsilon-Constraint The target was converted and then the Lagrange release algorithm was implemented on it.

In the industrial world today, manufacturing units are trying to locate your requirements and the depot vehicle routing in order to transport the goods for reduce your cost. Needless to mention that the location of the warehouse is effective for vehicle routing. Therefore, in this paper, a mathematical programming model to optimize the storage location and vehicle routing are presented. The objective function of the model is minimizing the total cost associated with the transportation and storage of rental fee. Limitations of the model include vehicle capacity, the maximum distance traveled by vehicles and etc. In addition, labor costs, such as salaries, rent, warehouses, rental vehicles and etc. Approach to model the real world has been considered.Also, since each location and routing issues alone are a NP-hard problem, then location routing problem can be combined problem and It requires the use of meta- heuristic algorithms to solve.

Nowadays, companies are trying to meet customer demand due to the economic characteristics of modern global business and complex supply chain operations. Global demand uncertainty is a major challenge that is exacerbated by the occurrence of disruptions. In this study, a multi-product fuzzy stochastic nonlinear mathematical programming model is proposed to design a reliable supply chain network considering a product with modular manufacturing technology at the risk of disruption. Also, a decision support system is designed for modular production, which plays a major role in increasing the reusability of products and reducing waste. The case study, focuses on the production of cryogenic pumps used for the export of LPG, which is a critical equipment in the export of liquid propane and butane. The structure of this supply chain network includes levels of module suppliers, primary production centers, inspection centers, repair centers, reproduction centers, and customers. To deal with the disturbances, a scenario-based stochastic approach has been used and parametric uncertainty has been managed with the possibilistic-robust hybrid programming approach. The model evaluation has been performed with a precise approach in GAMS software with CPLEX solver and the sensitivity analysis has addressed the uncertain parameters. The results show that the current approach while controlling the uncertainty, ensures the optimal flow of facilities.

This paper deals with modeling a nurse scheduling problem considering service level in uncertain conditions. In accord with the urgent need of hospitals to provide better services to patients, it is necessary to consider nurses’ preferences in the work shift scheduling. Hence, a multi-objective model is presented by regarding the rules and regulations related to the assignment of nurses to work shifts, in which the service level to patients is also considered. Due to the uncertainty of the number of patients that refer to the hospital, this parameter is taken uncertain into account. In order to evaluate the output results, two numerical examples in small and large sizes with real data of Labbafinejad Hospital with 18-person and 90-person wards are designed and Epsilon constraint method is used to solve the small-sized problem. Computational results reveal that increasing the service level to patients is directly related to increasing the number of nurses per each shift. Moreover, because of NP-Hard nature of nurse scheduling problem, the large-sized problem (90-person ward) is solved by a Gray Wolf algorithm and on the basis of designing a new chromosome. The results obtained by this method include 35 different efficient solutions for nurse scheduling in this hospital.

Mining supply chain management is important because successful chain implementation reduces costs, increases profits and productivity, and reduces risk. The extraction of these mines and the processing of mineral products, despite the positive effects on the economy and job creation, have many negative effects on the environment. The need to pay attention to the positive economic and social effects and adverse environmental effects of supply chains has led researchers to model social responsibilities. In fact, by modeling the supply chain and optimizing it, in addition to increasing the economic benefits of the supply chain, we can have a more accurate view of the potential decisions and future effects of these decisions. The purpose of this study is to mathematically model the supply chain of the Zarshuran gold mine with the aim of maximizing the economic benefits and social responsibilities simultaneously in the condition of uncertainty. Gold prices and customer demand are considered uncertain in the proposed model. This two-objective supply chain optimization model is one of the NP hard problems that cannot be solved by using precise methods. Therefore, meta-heuristic algorithms, Strength Pareto Evolutionary Algorithm II (SPEA-II), and Nondominated Sorting Genetic Algorithm (NSGA-II) are used here to optimize the supply chain. The results showed that there was an inverse relationship between supply chain economic profit and corporate social responsibility. It was also shown that with factors such as increasing production efficiency, both economic profits and social responsibilities of the company could be increased simultaneously. Sensitivity analysis of gold sales capacity showed that upon increasing sales, economic profits and social responsibility would increase. In fact, with the increase of production, the amount of greenhouse gas production and the amount of waste soil production as well as the amount of water consumption increase, but the welfare benefits divided by the region and the employment created overcome these negative factors and promote social responsibility.

Designing an appropriate model/approach for decision-making (incredibly strategic decisions) in a complex and uncertain environment has always been one of the researchers' goals. This study proposes a method that can consider the above dimensions and provide a suitable answer to the challenge of choosing the best option in the Matrix Approach to Robustness Analysis.

In this research, the superior option is identified by converting the matrix elements of the robustness analysis into hesitant fuzzy elements and using the score function.

Implementation of the proposed approach in four different problems that in previous studies faced with the challenge of choosing the best option showed that a more appropriate answer could be achieved using hesitant fuzzy elements.

 Developing the matrix approach to robustness analysis to solve the problem of choosing a strategy regarding equal stability of options.

Today, the competitive business environment has led to increasing cooperation among companies as the members of supply chain networks. In this area, the supply chain logistics network design is an important subject due to its effect on the responsiveness and efficiency. Over the past few years, due to environmental issues, their legal requirements and economic benefits, great attention has been paid to inverse logistics. In this paper, a mixed integer stochastic location-inventory model has been proposed for the integrated design of the network of a multi-period multi-product closed loop supply chain considering multiple capacity levels for facilities. The market demand and correspondingly the return value are considered to be uncertain in order to make the model close to the real-life conditions. Since the capacitated facility location problem considered in this research is an NP-hard type problem, we have used two meta-heuristic algorithms including the genetic algorithm (GA) and the Bees algorithms (BA) for solving this problem. Some numerical problems are designed and solved to assess the performance of the model and solution heuristics. From the viewpoint of solution quality, the BA outperforms the GA (by an average of 11.6%) whilst from the viewpoint of solution time, the GA is five times faster than the BA on average.

Project portfolio selection problem has been investigated by many researchers over the decades. This paper presents a method for project selection problem that is able to integrate the evaluation of individual projects by considering their interactions on portfolio of company projects. Moreover, since experts and their knowledge is one of the primary and valuable resources of an organization which evolves over time, the proposed system utilizes an artificial neural network approach to discover the experts' knowledge. This system has been used in a pilot organization in an organization while the output is close to the portfolio of projects considered by the managers of the organization and has also contributed to the project portfolio risk balancing. In order to validate the proposed method, an optimization model similar to the problem has been developed. The proposed approach has obtained similar results to the optimization and also needs much less time to solve large-sized problems using the proposed approach. This study tried to consider the interactions of projects when they are selected simultaneously in the portfolio of projects as well as the use of expert opinions and technical knowledge and experience of the organization in project portfolio selection. The proposed approach has been implemented in a project-oriented organization with varying levels of the number and complexity of the projects that have achieved acceptable results. In order to validate the proposed approach, an optimization model is developed and implemented on three problems (two problems of the case study organization and one instance problem with a large number of projects). The results showed acceptable similarity of results (nearly 90% similarity), but the time taken to solve the proposed approach is far less than the optimization model. The proposed approach is able to solve small, medium, and large problems while it provides timely and reasonable solutions in a reliable manner.