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 .
Dr. A. Ghodratnama, Prof. R. Tavakkoli-Moghaddam, Dr. A. Ghodratnama Baboli Vahdani, Mr. B. Vahdani,
Volume 25, Issue 4 (IJIEPR 2014)
Abstract
Hub location-allocation problems are currently a subject of keen interest in the research community. However, when this issue is considered in practice, significant difficulties such as traffic, commodity transportation and telecommunication tend to be overlooked. In this paper, a novel robust mathematical model for a p-hub covering problem, which tackles the intrinsic uncertainty of some parameters, is investigated. The main aim of the mathematical model is to minimize costs involving: 1) the covering cost 2) the sum of the transportation costs 3) the sum of the opening cost of facilities in the hubs 4) the sum of the reopening cost of facilities in hubs 5) the sum of the activating cost facilities in hubs and 6) the sum of the transporters' purchasing cost. To solve this model, use has been made of the new extensions to the robust optimization theory. To evaluate the robustness of the solutions obtained by the novel robust optimization approach, they are compared to those generated by the deterministic mixed-integer linear programming (MILP) model for a number of different test problems. Finally, the conclusions are presented.
