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Showing 2 results for Javadian

F. Khaksar-Haghani, N. Javadian, R. Tavakkoli-Moghaddam , A. Baboli , R. Kia,
Volume 22, Issue 3 (IJIEPR 2011)
Abstract

 

  Dynamic cellular manufacturing systems,

  Mixed-integer non-linear programming,

  Production planning, Manufacturing attributes

 

This paper presents a novel mixed-integer non-linear programming model for the design of a dynamic cellular manufacturing system (DCMS) based on production planning (PP) decisions and several manufacturing attributes. Such an integrated DCMS model with an extensive coverage of important design features has not been proposed yet and incorporates several manufacturing attributes including alternative process routings, operation sequence, processing time, production volume of parts, purchasing machine, duplicate machines, machine depot, machine capacity, lot splitting, material flow conservation equations, inflation coefficient, cell workload balancing, budget constraints for cell construction and machine procurement, varying number of formed cells, worker capacity, holding inventories and backorders, outsourcing part-operations, warehouse capacity, and cell reconfiguration. The objective of the integrated model is to minimize the total costs of cell construction, cell unemployment, machine overhead and machine processing, part-operations setup and production, outsourcing, backorders, inventory holding, material handling between system and warehouse, intra-cell and inter-cell movements, purchasing new machines, and machine relocation/installation/uninstallation. A comprehensive numerical example taken from the literature is solved by the Lingo software to illustrate the performance of the proposed model in handling the PP decisions and to investigate the incorporated manufacturing attributes in an integrated DCMS .


Sina Nayeri, Mahla Zhain Vamarzani, Zeinab Asadi, Zeinab Sazvar, Nikbakhsh Javadian,
Volume 35, Issue 3 (IJIEPR 2024)
Abstract

This study focuses on evaluating potential raw material providers (RMPs) as one of the critical tasks of the logistics managers. In this regard, the literature showed that the simultaneous consideration of resilience, digitalization, and circular economy in the RMP selection problem (RMPSP) has been ignored by previous studies. Therefore, to cover the mentioned gap, this research attempts to study the RMPSP by considering other crucial concepts namely resilience and Circular Economy (CE). For this purpose, by considering a real-world case study in the steel industry, the current work first specifies the indicators of the research problem. Then, the indicators’ weights are measured using the stochastic Best-Worst Method (BWM). In the next step, the RMPs are prioritized by developing a novel approach called the stochastic Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In general, the main objective of this study is to evaluate the performance of the RMPs in the steel industry based on the CE, resilience, and digitalization aspects. According to the achieved results, “Reliability”, “Price”, “Quality”, “Reverse logistics and Waste management”, “Information systems usage”, and “Restorative Capacity”, are identified as the most desirable indicators. Moreover, the results confirm the effectiveness and validation of the developed method.


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