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We develop a price competition model for a new supply chain that competes in a market comprised of some rival supply chains. The new supply chain has one risk-neutral manufacturer and one risk-averse retailer in which the manufacturer is a leader and retailer is a follower. The manufacturer pays a fraction of the risk cost (caused by demand uncertainty) to the retailer. We apply this competitive model to a real-world case in a supply chain under uncertain environment and obtain the optimal wholesale and retail prices. We show that our obtained prices are better than the existing wholesale and retail prices and admit more profits for both manufacturer and retailer and generally for the entire supply chain. Also, using this case, the effects of risk sensitivity of retailer and fraction of risk cost shared by manufacturer in the total risk cost on the new supply chain’s optimal wholesale and retail prices and profits are illustrated.

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We focus on a three-stage supply chain problem for fast moving consumer goods including a supplier, a manufacturer and customers. There are different orders over identical cycles, to be processed in production site. The problem is to find a joint cyclic schedule of raw material procurement and job scheduling minimized the total cost comprised of raw material ordering cost and holding cost, production cost, holding cost of finished products, tardiness cost and rejection cost. An integrated mixed integer programing model is proposed and optimal solution of some instances are provided by solving the model.

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This paper proposes a robust model for optimizing collaborative reverse supply chains. The primary idea is to develop a collaborative framework that can achieve the best solutions in the uncertain environment. Firstly, we model the exact problem in the form of a mixed integer nonlinear programming. To regard uncertainty, the robust optimization is employed that searches for an optimum answer with nearly all possible deviations in mind. In order to allow the decision maker to vary the protection level, we used the "budget of uncertainty" approach. To solve the np-hard problem, we suggest a hybrid heuristic algorithm combining dynamic programming, ant colony optimization and tabu search. To confirm the performance of the algorithm, two validity tests are done firstly by comparing with the previously solved problems and next by solving a sample problem with more than 900 combinations of parameters and comparing the results with the nominal case. In conclusion, the results of different combinations and prices of robustness are compared and some directions for future researches are suggested finally.

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A multi-periodic, multi-echelon green supply chain network consisting of manufacturing plants, potential distribution centers, and customers is developed. The manufacturing plants can provide the products in three modes including production in regular time, production in over time, or by subcontracting. The problem has three objectives including minimization of the total costs of the green supply chain network, maximization of the average safe inventory levels of the manufacturing plants and the distribution centers and minimization of the environmental impacts of the manufacturing plants in producing, holding and dispatching the products and also the environmental impacts of the distribution centers in holding and dispatching the products. The problem is first formulated as a mixed-integer mathematical model. Then, in order to solve the model, the augmented weighted Tchebycheff method is employed and its performance in producing the Pareto optimal solutions is compared with the goal attainment method.

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This paper presents a structure that empower designing supervisory groups to survey the estimation of real options in projects of enormous scale, incompletely standardized frameworks actualized a couple of times over the medium term. Specific options writing is done using a methodology of planning the design and making prior decisions regarding the arrangements of specific options, with a recreation-based value measure designed to be near-current construction rehearsals and to resolve financial problems in particular cases. To study the case and demonstrate the actual application of this method, drug chain modeling at the tactical level was investigated. The physical and financial flow and their disturbance are simultaneously modulated. In order to complete the financial flow, financial ratios are also entered into the model. Problem uncertainty has been modeled using one of the most recent robust optimization approaches called Robust Possibilistic Programming (RPP) in combination with real options theory. The model was applied to a case study and its results were analyzed and validated by GAMS software. The results show that without violating the limitations of the problem, it shows appropriate decisions to deal with the problem.

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One important step to achieve a sustainable transportation system is to control the impact and evaluate the effect of various influencing factors toward three dimensions of sustainability. Within this context, diverse analytical approaches have been developed to assess the sustainability level of various transportation systems, however, sustainability evaluation based on fuzzy multiple criteria decision-making approaches are still limited. In current research activity, an integrated quantitative evaluation technique is proposed to narrow the identified gap. The developed decision-making approach is consisted of two main phases. Firstly, fuzzy analytic hierarchy process is utilized to weigh the sustainability dimensions resulting in the incorporation of the experts’ knowledge along with the evaluation process. Then, a proposed fuzzy inference mechanism is proposed to provide an indication on the performance of an evaluated road transportation system. The developed approach is applied on a real-world case study. Finally, future works are presented together with some concluding remarks.

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Nowadays, manufacturers need to satisfy consumer demands in order to compete in the real world. This requires the efficient operation of supply chain planning. On the other hand, increasing worldwide environmental, lack of food resources and social concerns are motivating manufacturers and consumers to implement recycling strategies such as product recovery, waste management, or usage of recycled materials. In this study, the closed-loop supply chain network has been proposed which consists of four echelons (suppliers, plants, distribution centers, and customers) in the forward chain and three echelons (collection centers, inspection centers, and disposal centers) in the backward chain. We present a multi-product and multi-period mixed-integer linear programming problem in this paper. The objective of this study is to maximize the profit in the closed-loop supply chain network. The proposed model is applied to an illustrative example based on inspiration from the dairy industry in Iran. The solution of the proposed model is achieved by using Gams software. The results give important insight for fostering the decision making process.

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The present study aims at designing a cold multi-cycle supply chain based on a multi cross-dock system taking into account uncertainty. In the first step, we identified the factors and variables of the model. In the second, by selecting the study period through designing data collection forms and using the documents reviewing methodologies, the raw data required to measure the final indicators were collected and processed in the project model. Then, they were analyzed considering the research topic and using the techniques of genetic algorithm and particle swarm optimization. The primary objective function is minimizing the cost of transportation and warehousing throughout the supply chain, the second minimizing the total operation time and the number of vehicles within the supply chain, and the third maximizing the product freshness time. Also meta-heuristic optimization methods (strongly adjustable) were adopted to deal with the travel time of suburban vehicles. We also provide an example of the performance of optimization models for a small-sized sample. The computational results showed that longer travel time and further distance do not necessarily increase costs. In fact, it is possible to distribute the products with the right number of trucks at an optimal cost at the right time.

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Developing realistic models for the evaluation of sustainable supply chains has turned into a major challenge facing managers. The decision-making approaches proposed here consist of two stages. At the first stage, a dynamic-network data envelopment analysis (DNDEA) model is established for the first time, wherein the current efficiency of a business can be influenced by its prior social and environmental activities, as two main dimensions of sustainability. The second stage correspondingly presents, for the first time, a model in which total efficiency is calculated based on the value of historical data. Sensitivity analysis is exploited to determine the more effective factors of sustainability in efficiency evaluations. To validate the model, it is used to assess the sustainability of the suppliers of an auto spare parts manufacturer. The study results reveal that the model is well-able to evaluate the performance of dynamic network structures, with a very high discriminating power. Following the implementation of this model, only the supplier(KARAN) is found to reach the efficiency limit, and  SIRIN S.N. is recognized as the most inefficient supplier with an efficiency score of 0.6409. The sensitivity analysis outcomes demonstrate that the least amount of efficiency change is related to the economic pillar; however, the rising trend in wage costs, compared with other economic factors, brings a better effect on augmenting the efficiency of some inefficient suppliers. The highest efficiency changes during sensitivity analysis are further observed in both social and environmental dimensions. Therefore, it is claimed that investing in these two pillars can have a significant impact on the efficiency of suppliers.
 

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Data envelopment analysis (DEA), as a well-established nonparametric method, is used to meet efficiency evaluation purposes in many businesses, organizations, and decision units. This paper aims to present a novel integrated approach to fuzzy interpretive structural modeling (FISM) and dynamic network data envelopment analysis (DNDEA) for the selection and ranking of sustainable suppliers. First, suppliers' efficiency values in a supply chain are determined, using the dynamic network data envelopment analysis (DNDEA) model developed for this purpose. Then, a heuristic method is presented based on the fuzzy interpretive structural modeling (FISM) to find a common set of weights (CSWs) for the variables involved. Depending on the data conditions, two approaches, viz. centralized and decentralized, are proposed for efficiency measurement. To illustrate the model's capability, the proposed methodology is further applied to the real data of a company, named Nirou Moharekeh Industries (NMI). The results of a study on 12 suppliers within the DNDEA model accordingly reveal that one unit (i.e. KARAN) obtains an efficient value, but an inefficient score is observed in 11 units, whose technical efficiency value is in the range of 0.6409 to 0.9983. After utilizing the weights gained from the heuristic method, the efficiency value of the most inefficient supplier (that is, SIRINS.N.) dwindles from 0.6409 to 0.6319.

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Nowadays, the capability of cloud management suppliers is one of the important advantages for suppliers that can improve the performance and flexibility and reduce costs in companies through easy access to resources. Also, the environmental impacts of suppliers are a significant issue in today’s industrialization and globalization world. This paper analyzes these subjects by fuzzy multi-objective scenario-based stochastic model. Its objective functions are minimizing the total cost, environmental impacts of suppliers, and maximizing the capability of cloud management of suppliers. Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) and Multi-objective Simulated Annealing meta-heuristic (MOSA) are developed to settle this problem. Five computational experiments analyze the performance of the solution algorithms. The results illustrate that the NSGA-II algorithm provides better solutions than the MOSA algorithm for the presented model.
 

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Abstract: This study developed a mathematical programming model in order to consider an SCND problem. In this model, the operational and financial decisions are simultaneously considered to design a supply chain network. It also paves the way for opening or closing facilities in order to adapt to fluctuations at market. Furthermore, an accounts payable policy is provided for the company managers because bank loans, liability repayment and new capital from shareholders are considered as decision variables in this model. The economic value added (EVA) index was also used besides the common operational objectives and constraints in order that the financial performance of supply chain and lower and / or upper limit value for financial rations to be measured. To demonstrate the efficiency of the proposed model, a test problem from the recent literature is used. And also, sensitivity analyses to evaluate the results are provided to obtain better insight and access to different aspects of the problem. The results illustrate that with appropriate financial decisions, creating more value for the company and its shareholders is achievable since the total created value by the proposed model with a new financial approach is able to improve the total created shareholder value as much as 21.05% and convince the decision-makers to apply it as an effective decision tool.
 

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Demand planning based on demand data in the supply chain includes the most significant steps in production planning. Therefore, the supply chain's correct demand forecasting may reduce this effect, known as the bullwhip effect or uncertainty concerning customer demand, thus reducing companies' and organizations' costs and surplus activities. Therefore, this article examined the statistical population characteristics to test the hypotheses through the path analysis drawn using descriptive statistics and FCM(fuzzy cognitive map)  method. Then, the model strength was investigated using structural equation modeling (SEM) in AMOS software, and structural equations were presented. This article selected the Aftab oil factory as a case study. The findings of this study emphasized that demand management performance is highly essential for industries. Companies can design the sector independently as a demand management sector for evaluating customer demands at different levels of the supply chain. According to the fit of the main model, CFI and NFI indices are equal to 0.99 and 0.97, respectively, which are close to the optimal fit threshold. RMSEA and SRMR indices are equal to 0.01 and 0.01, respectively, both showing a relatively good fit of the model.

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One of the most important and widely used problems in the logistics part ‎of ‎any supply chain is the location-routing problem (LRP) of vehicles. The ‎‎purpose is to select distribution centers to supply goods for ‎‎customers and create suitable travel routes for vehicles to serve ‎customers.‎ Studies conducted in the field of supply chain logistics systems have shown that if vehicle travel routing is neglected when locating supply centers, the costs of the logistics system may increase dramatically. Therefore, in the LRP problem, the location of supply centers and the routing of vehicles are considered simultaneously. In this paper, we will present a multi-objective model for vehicle location-routing problems with a flexible fuzzy ‎approach. Its' goals are to make strategic decisions to deploy ‎candidate supply centers at the beginning of the planning horizon, as well as ‎form the vehicle travel at the tactical level to serve the customers in ‎short-term periods of time. Therefore, in ‎order to adapt the mathematical model to the real conditions, the ‎constraints related to the capacity of the vehicles have been considered in a ‎flexible fuzzy state, and also the problem has been modeled in a multi-period state along with the presence of the distance limit and the ‎accessibility factor for each vehicle. The evaluation criterion is to minimize costs related to the establishment of candidate supply centers, the fixed cost of using vehicles and transportation costs, as well as maximizing customer satisfaction by reducing shortage costs and reducing harmful environmental effects. To solve the model, it is first converted into a single-objective model using the weight method and then solved using the proposed algorithm. Finally, using a numerical example in the field of waste management, the effectiveness of the proposed solution method is shown. It should be mentioned that the model was solved using GAMS software and the results are shown.
 

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As one of the main challenges of the 21st century supply chain, complexity has become a crisis, costly, and difficult for many supply chain managers. However, few studies have prioritized this strategy. This study aimed to investigate the effect of supply chain complexity on competitiveness performance in the Iran Khodro Company. To achieve this goal, the relationship between different sources of complexity (upstream complexity, domestic production, and downstream complexity) and competitiveness performance is examined using a conceptual model. The statistical population of this study included all Iran Khodro experts, whose number reached 218 in 2022. Owing to the limitations of the statistical community, all members were selected as a sample. The collection tools for this study included two standard questionnaires: the supply chain and Porter's competitiveness performance. The content validity of the questionnaires was confirmed by experts, its construct validity was confirmed by confirmatory factor analysis, and the reliability of the questionnaires was confirmed using Cronbach's alpha coefficient and the combined reliability index. The collected data were analyzed using the structural equations in the LISREL software. The results of hypothesis testing show that, in general, supply chain complexity has a significant inverse effect on competitiveness performance.
 

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Abstract
Objective: From economic, environmental and social perspectives, the sustainability of the supply chain can give a competitive advantage to organizations. By designing a hybrid discrete event agent-based simulation model based on the simulation-optimization approach and meta-heuristic algorithms, this study has sought to evaluate the sustainability of the supply chain and improve the economic, environmental and social objectives of the supply chain.
Method: First, by identifying supply chain agents, an agent-based simulation model is developed. After designing the hybrid simulation model, the verification and validation phases are performed. By combining the simulation model with meta-heuristic algorithms and using the simulation-optimization approach, the optimal/near-optimal values of the components affecting the sustainability of the supply chain are finally extracted.
Findings: In addition to being able to reflect all the complexities of supply chains, the hybrid simulation optimization approach can also improve the key components affecting the sustainability of the supply chain.
Results: Implementation of sustainable supply chain components without optimizing the key variables of the supply chain can lead to the deterioration of performance and sustainability of the supply chain. The components of the maximum levels of product and inventory maintenance and how to implement environmental and social aspects in all the elements of the supply chain have a direct effect on the chain performance and should have appropriate values in different scenarios.
 

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In this paper the pricing of reverse products in a two-level closed-loop supply chain is considered and a game theory approach is used to solve it. Pricing is a sensitive and vital issue for businesses. In the market of reverse products, this issue will be much more difficult and complex due to difficulties associated with collecting and re-manufacturing processes. On the other hand, the use of the internet and direct channels for collecting products from customers alongside traditional retailers is an important issue that requires management and coordination. The proposed price for buying second-hand and defective products from customers should be high enough to convince them that returning the products has more benefits than discarding or keeping them at home. At the same time, the price should be low enough to make it economically viable for producers to carry out the repair and re-manufacturing operations and resell them in the direct supply chain for the producer. The use of game theory, where the decisions of one player affect the decisions and outcomes of other players as well as their outcomes, is a suitable method for solving the problem of pricing reverse products in a two-level closed-loop supply chain

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In this paper, a closed-loop supply chain (CLSC) is modeled to obtain the best location of retailers and allocate them to other utilities. The structure of CLSC includes production centers, retailers’ centers, probabilistic customers, collection, and disposal centers. In this research, two strategies are considered to find the best location for retailers by focusing on 1) the type of expected movement and 2) expected coverage. To this end, a bi-objective nonlinear programming model is proposed. This model concurrently compares Strategies 1 and 2 to select the best competitor. Based on the chosen strategy, the best allocation is determined by employing two heuristic algorithms, and the locations of the best retailers are determined. As the proposed model is NP-hard, a meta-heuristics (non-dominated sorting genetic) algorithm is employed for the solution process. Afterward, the effectiveness of the proposed model is validated and confirmed, and the obtained results are analyzed. For this purpose, a numerical example is given and solved through the optimization software.
 

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Supply chains often have different technologies. Additionally, organizations with multiple stages can evaluate their operational efficiency by analyzing scale elasticity, which helps determine if they are functioning optimally or if there is room for improvement. This evaluation allows for the identification of potential inefficiencies and opportunities for enhancement. Consequently, this research introduces a two-stage DEA-based approach with undesirable outputs to examine the scale elasticity of supply chains within meta and group frontiers. The measurement of group and meta performance of general systems and stages is conducted for this purpose. Moreover, the study addresses the scale elasticity of supply chains with undesirable outputs by considering the heterogeneity of technologies. To achieve this, the study focuses on the right and left scale elasticity of efficient general systems and each stage. A real-world application from the soft drink industry is provided to illustrate the proposed model. The results show the applicability of the introduced methodology.
 

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The basic purpose of this study is to investigate and display causal relationships among collaboration components in supply chain 4.0 using a fuzzy framework. The power of collaboration increases with the effect of Industry 4.0 technologies for the improvement of supply chain performance, so supply chain 4.0 is the context of this study. To achieve the research purpose, after reviewing articles and extracting indicators, a collaboration model with trust, initiators, barriers, dimensions, and outcomes was designed. Then using the fuzzy DEMATEL method, the effect of each variable and its position were determined. To collect data targeted sampling and snowball methods were used. 20 questionnaires were distributed to supply chain and digital technologies experts. Findings show that Trust and Information and Communication Technology infrastructure are closely related and are considered the most fundamental factors of the collaboration conceptual model, and can lead to more serious and effective changes in SC 4.0 such as improved Economic and Social performance. SC 4.0 managers can facilitate the development of collaborative trust across the SC By investing in communication and technology infrastructure.