Circular shareholding is an intriguing ownership structure that enables a major shareholder to gain control of subsidiaries with only small investments. The conflict between the public interest and corporate interest ...
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Circular shareholding is an intriguing ownership structure that enables a major shareholder to gain control of subsidiaries with only small investments. The conflict between the public interest and corporate interest regarding circular shareholding has presented policymakers with unresolved legal issues. To support systematic decision making by policymakers in this circumstance, we propose a multi-objective bilinear optimizationmodel to address the circular shareholding problem. The proposed model provides policymakers with various scenarios corporates could make, assuming the enforcement of regulatory law on circular shareholding along with consideration of the public interest with different weights. We also conduct computational analysis on the model with real data from conglomerates in Korea, where debates about circular shareholding are ongoing. The results show clear patterns between obtained solutions, suggesting the possibility of a quantitative basis for legislation regarding circular shareholding and corporate governance.
Purpose - Resource capacity and product changeovers must be both considered in the preparation of a realistic production plan. The purpose of this paper is to present a heuristic for enforcing resource availability an...
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Purpose - Resource capacity and product changeovers must be both considered in the preparation of a realistic production plan. The purpose of this paper is to present a heuristic for enforcing resource availability and the accompanying changeover realities into a continuous-variable linear programming formulation that would otherwise require a mixed-integermodel. The results from both approaches are compared in terms of objective function values and computational requirements;the effectiveness of the heuristic approach is demonstrated. Design/methodology/approach - A case study search was conducted to identify relevant data sets that could be used to exercise the optimization and heuristic models. The case studies found in the literature were too small and simple compared to the problem complexity desired. The authors developed a case study based on the development of a production plan for a typical flashlight. It includes two end-products that differ in their bills of materials and process requirements. Basic processes include plastic part preparation and final assembly;various raw materials with pre-defined lead times are purchased from external suppliers. The results of the LP-based heuristic and the mixed-integer programming (MIP) optimization algorithm are then compared through a statistical experiment. The experiment includes four factors: number of products, number of periods, number of machines, and percentage line capacity utilization. Findings - When a MIP algorithm is applied to obtain the results, most of the time the planner would have to wait days or even weeks for the algorithm to provide a solution. However, the authors' linear programming-based procedure provides the same quality of solution in minutes and for some problems in seconds. Originality/value - The originality of the heuristic approach resides on the avoidance of the lengthy MIP computer runs. At each iteration, the authors solve the LP production planning problem without changeover cons
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