A multi-echelon multi-product stochastic model to supply chain of small-and-medium enterprises in industrial clusters

Kayvanfar, Vahid; Husseini, S. M. Moattar; Sajadieh, Mohsen S.; Karimi, Behrooz

doi:10.1016/j.cie.2017.11.003

Cited by 18 publications

(15 citation statements)

References 39 publications

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“…Constraint (25) indicates the time window for receiving products by distribution centers in each time period.…”

Section: Variablesmentioning

confidence: 99%

“…Haddad Sisakht and Rayan [23] developed a CLSC network by considering different modes of transportation under stochastic demand and uncertain carbon tax rates.Their goal was to minimize the total cost of the supply chain at three levels. Kavyanfar et al [25] developed a stochastic multi-product multi-level mathematical model to design the supply chain of small and medium industries in the clustering industry.Their suggested model aimed to minimize the total cost, which was solved by benders decomposition.They also presented a case study and sensitivity analysis to evaluate its efficiency. Dai et al [14] developed a nonlinear model with fuzzy constraints to solve the location-routing problem (LRP) using GA and harmonic search in a three-level supply chain network of perishable products.…”

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confidence: 99%

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A robust multi-objective model for managing the distribution of perishable products within a green closed-loop supply chain

Gerdrodbari

Harsej

Sadeghpour

et al. 2022

JIMO

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The required processes of supply chain management include optimal strategic, tactical, and operational decisions, all of which have important economic and environmental effects. In this regard, efficient supply chain planning for the production and distribution of perishable productsis of particular importance due to its leading role in the human food pyramid. One of the main challenges facing this chain is the time when products and goods are delivered to the customers and customer satisfaction will increase through this.In this research, a bi-objective mixed-integer linear programming (MILP)model is proposedto design a multi-level, multi-period, multi-product closed-loop supply chain (CLSC) for timely production and distribution of perishable products, taking into account the uncertainty of demand. To face the model uncertainty, the robust optimization (RO) method is utilized. Moreover, to solve and validate the bi-objective model in small-size problems, the -constraint method (EC) is presented. On the other hand, a Non-dominated Sorting Genetic Algorithm (NSGA-II) is developed for solving large-size problems. First, the deterministic and robust models are compared by applying the suggested solutions methods in a small-size problem, and then,the proposed solution methods are compared in large-size problems in terms of different well-known metrics. According to the comparison, the proposed model has an acceptable performance in providing the optimal solutions and the proposed algorithm obtains efficient solutions.Finally, managerial insights are proposed using sensitivity analysis of important parameters of the problem.

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“…Constraint (25) indicates the time window for receiving products by distribution centers in each time period.…”

Section: Variablesmentioning

confidence: 99%

mentioning

confidence: 99%

A robust multi-objective model for managing the distribution of perishable products within a green closed-loop supply chain

Gerdrodbari

Harsej

Sadeghpour

et al. 2022

JIMO

View full text Add to dashboard Cite

show abstract

“…Financial globalization relies on financial institutions (banking, insurance, securities and financial information management) and these financial conglomerates form the effective allocation of space-time resources, and through this way of distribution, it becomes a medium for transferring transnational investment or avoiding risks [8]. These financial activities are combined with the flow of international trade, and their root causes are accompanied by commerce, and services and Global trade turn into international trade activities [9].…”

Section: Introductionmentioning

confidence: 99%

Analysis of spatial distribution characteristics of financial services industry clusters based on big data

Liu

2021

E3S Web Conf.

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With the development of the times, the financial system is getting bigger and bigger, and the links between the various industries are getting closer, so we need to cluster the financial industry. But how to deal with is a problem, after thinking about comparison we found that we can make some treatment between them. So the purpose of this article is to analyze the spatial distribution characteristics of financial services industry clusters based on big data. Based on the experimental principle of data security, this paper processes some data that is known on the market and unknown within the enterprise, and simulates the experimental process by using the 4-model based V+ on big data evaluation, and then the experimental results are drawn. The experimental results show that our model can analyze the spatial distribution characteristics of industrial clusters by analyzing some characteristics of financial services enterprises.

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“…Moreover, they employed two other multi-objective meta-heuristic algorithms, named non-dominated ranking genetic algorithm (NRGA) and reference-point based non-dominated sorting genetic algorithm III (NSGA-III). Recently, Kayvanfar et al (2018) studied a multi-echelon multi-product stochastic model of supply chain of SMEs in an IC. They used a two-stage stochastic programming model powered by an acceleration technique for the Benders decomposition algorithm.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Analysis of a multi-echelon supply chain problem using revised multi-choice goal programming approach

Kayvanfar

Sajadieh

Husseini

et al. 2018

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Purpose In this paper, a multi-objective multi-echelon supply-distribution model is proposed to optimize interactions of entities located within an Industrial Cluster (IC) including small- and medium-sized enterprises (SMEs), using a third-party logistics provider (3PL)-managed supply-demand hub in industrial cluster (SDHIC) as a specific public provider of warehousing and logistics services. Design/methodology/approach The three considered objectives are minimizing the total logistics costs, maximizing the rate of demand satisfaction and maximizing the quality of delivery. Because some parameters such as “demand of customers” are naturally fuzzy because of incompleteness and/or inaccessibility of the needed information, the triangular fuzzy number is applied for all fuzzy parameters to handle this difficulty. The proposed model is primarily changed into a correspondent supplementary crisp model. To solve such a model, a revised multi-choice goal programming (RMCGP) approach is then used with the purpose of finding a compromise solution. Findings Experimental results demonstrate that all enterprises involved in such a supply chain benefit with several advantages using SDHIC by consolidating shipments and merging the storage space of inventories. The applicability of the presented model is shown by conducting these experiments over an applied industrial case study. Originality/value The main contributions of this research are proposing a practical mathematical approach to the supply chain of ICs using a specific public warehouse managed by a 3PL, called SDHIC, bridging the existing gaps with respect to the already published researches in this area by applying real-world assumptions such as uncertainty; optimizing the interactions of involved entities in the supply chain of an IC, comprising suppliers, SMEs as manufacturers and customers; minimizing the total incurred logistics costs to such a system through optimum usage of lands, facilities, labors, etc. and boosting the satisfaction of customers through maximizing the service level criteria, illustrating the positive consequences of cooperation of 3PL with the SMEs/manufacturers in an IC, showing the applicability of the adopted approach by implementing it on an applied industrial instance.

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A multi-echelon multi-product stochastic model to supply chain of small-and-medium enterprises in industrial clusters

Cited by 18 publications

References 39 publications

A robust multi-objective model for managing the distribution of perishable products within a green closed-loop supply chain

A robust multi-objective model for managing the distribution of perishable products within a green closed-loop supply chain

Analysis of spatial distribution characteristics of financial services industry clusters based on big data

Analysis of a multi-echelon supply chain problem using revised multi-choice goal programming approach

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