Suk Ho Jin

In this study, deterministic and robust optimization models for single artillery unit fire scheduling are developed to minimize the total enemy threat to friendly forces by considering the enemy target threat level, enemy target destruction time, and target firing preparation time simultaneously. Many factors in war environments are uncertain. In particular, it is difficult to evaluate the threat levels of enemy targets definitively. We consider the threat level of an enemy target to be an uncertain parameter and propose a robust optimization model that minimizes the total enemy threat to friendly forces. The robust optimization model represents a semi-infinite problem that has infinitely many constraints. Therefore, we reformulate the robust optimization model into a tractable robust counterpart formulation with a finite number of constraints. In the robust counterpart formulation with cardinality-constrained uncertainty, the conservativeness and robustness of the solution can be adjusted with an uncertainty degree, Γ. Further, numerical experiments are conducted to verify that the robust counterpart formulation with cardinality-constrained uncertainty can be made equivalent to the deterministic optimization model and the robust counterpart formulation with box uncertainty by setting Γ accordingly.

Korea’s national carbon capture and storage (CCS) master plan aims to commercialize CCS projects by 2030. Furthermore, the Korean government is forced to reduce emissions from various sectors, including industries and power generation, by 219 million tons by 2030. This study analyzes a few scenarios of Korean CCS projects with a CO₂ pipeline transportation network optimization model for minimizing the total facility cost and pipeline cost. Our scenarios are based on the “2030 basic roadmap for reducing greenhouse gases” established by the government. The results for each scenario demonstrate that the effective design and implementation of CO₂ pipeline network enables the lowering of CO₂ units cost. These suggest that CO₂ transportation networks, which connect the capture and sequestration parts, will be more important in the future and can be used to substitute and supplement the emission reduction target in case the execution of other reduction options faces uncertainty. Our mathematical model and scenario designs will be helpful for various countries which plan to introduce CCS technology.

The nurse rostering problem is an important search problem that features many constraints. In a nurse rostering problem, these constraints are defined by processes such as maintaining work regulations, assigning nurse shifts, and considering nurse preferences. A number of approaches to address these constraints, such as penalty function methods, have been investigated in the literature. We propose two types of hybrid metaheuristic approaches for solving the nurse rostering problem, which are based on combining harmony search techniques and artificial immune systems to balance local and global searches and prevent slow convergence speeds and prematurity. The proposed algorithms are evaluated against a benchmarking dataset of nurse rostering problems; the results show that they identify better or best known solutions compared to those identified in other studies for most instances. The results also show that the combination of harmony search and artificial immune systems is better suited than using single metaheuristic or other hybridization methods for finding upper-bound solutions for nurse rostering problems and discrete optimization problems.

Although several studies have explored the relationship between the operation and performance of a supply chain (SC), a general SC model cannot deliver the expected financial results at a company-wide level. In this paper, we argue that this cannot guarantee the maximization of a firm’s overall value because short-term financial performance metrics do not reflect the risk to businesses and the invested capital. Owing to the varying natures of risk and the capital invested, firms with multiple divisions should assess each division separately, and the results can be compared for decisions concerning the allocation of the firm’s capital and resources to maximize the overall value of its businesses. We propose a linkage model to consider operational activities and financial performance simultaneously in a firm’s supply chain model. To exhibit the superiority of the proposed model that connects SC operation and financial indicators, we first compare the differences between models for maximizing profit and enterprise-wise economic value added (EVA) as objective functions. To examine uncertainty in the operational and financial parameters of the SC, the results of sensitivity analyses are then reported. Experimental results showed that our model, using the EVA approach, is more effective and superior in terms of maximizing the firm’s overall value from the long-term perspective while satisfying the target values for financial ratios set by the firm’s executives and shareholders for all periods, unlike the results of the general model.