• Journal of Korean Society of Industrial and Systems Engineering
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• v.6 no.8
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• pp.49-53
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• 1983

The purpose of this study is to consider models for the production planning of multiproduct. Because these multiproducts use common facilities, labor, and materials, they are able to be considered jointly instead of planned independently. Initially linear programming models will be considered, followed by some examples of modeling and analysis when the cost structure is nonlinear. Basic model components are the following ; (1) inventory balance equations for each product to link successive time periods, and (2) capacity constraints for each period to represent resource limitations.

• The Journal of Fisheries Business Administration
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• v.45 no.1
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• pp.109-119
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• 2014

This study aimed to examine the applicability of a portfolio approach to the ecosystem-based fisheries management targeting the large purse seine fishery. Most fisheries are targeting multispecies and species are biologically and technically interacted each other. It enables a portfolio approach to be applied to find optimal production of each species through expected returns and risk analyses. Under specific assumptions on the harvest quota by species, efficient risk-return frontiers were generated and they showed a combination of optimal production level. Comparisons between portfolio and actual production provided a useful information for targeting strategy and management. Results also showed the possibility of effective multispecies fisheries management by imposing constraints on each species such as total allowable catch quotas.

• Nuclear Engineering and Technology
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• v.31 no.4
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• pp.365-374
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• 1999

A nuclear design of fission moly production targets for a research reactor, HANARO was peformed. It was found that the use of MCNP-4A, ORIGEN-2 code was reliable for the analysis of production characteristics of $^{99}$ Mo in a target fuel at an irradiation holes. A parametric study was done for the optimization of target location, target dimension, target shape and fuel materials. It was shown that a fuel thickness was the most sensitive parameters and electro-deposited target gave the highest 99Mo yield ratio. A pellet target with vibro-compaction powder, however, showed the largest production capacity and better engineering feasibility even with less yield ratio. Ten kinds of optimized target design for both LEU and HEU satisfied all the given design constraints. The most favorable design was the HEU ring-shaped electro-deposited target, considered the safety limit, production yield, chemical process easiness, yield ratio, and amount of radioactive waste.

• The Journal of Fisheries Business Administration
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• v.46 no.3
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• pp.129-141
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• 2015

Because land based aquaculture is restricted by high investment per rearing volume and control cost, good management planning is important in Land-based aquaculture system case. In this paper master production planning was made to decide the number of rearing, production schedule and efficient allocation of water resources considering biological and economic condition. The purpose of this article is to build the mathematical decision making model that finds the value of decision variable to maximize profit under the constraints. Stocking and harvesting decisions that are made by master production planning are affected by the price system, feed cost, labour cost, power cost and investment cost. To solve the proposed mathematical model, heuristic search algorithm is proposed. The model Input variables are (1) the fish price (2) the fish growth rate (3) critical standing corp (4) labour cost (5) power cost (6) feed coefficient (7) fixed cost. The model outputs are (1) number of rearing fish (2) sales price (3) efficient allocation of water pool.

• Proceedings of the Korea Society for Simulation Conference
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• 2000.11a
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• pp.165-173
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• 2000

Analytic models have been developed to solve integrated production-distribution problems in supply chain management (SCM). As one of major constraints in analytic models, capacity, which is the total operation time in this paper has mostly been known or disregarded assuming infinite capacity. Also, as major factors, machine processing time to fabricate or assemble a part or product at a certain machine center in production system and vehicle processing time to deliver a product to a customer by a certain vehicle in distribution system have been fixed and regarded as a static factor, But in the real systems significant differences exit between capacity and the required time to achieve the production-distribution plan and between processing time and consumed time to process a part or product. In this paper, capacity and processing times in the analytic model are considered as dynamic factors and adjusted by the results from independently developed simulation model, which includes general production-distribution characteristics. Through experiments, we obtain the more realistic solutions reflecting stochastic natures by performing the iterative analytic-simulation procedure.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.121-135
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• 2007

Constraint directed scheduling techniques, representing problem constraints explicitly and constructing schedules by constrained heuristic search, have been successfully applied to real world scheduling problems that require satisfying a wide variety of constraints. However, there has been little basic research on the representation and optimization of the objective value of a schedule in the constraint directed scheduling literature. In particular, the cost objective is very crucial for enterprise decision making to analyze the effects of alternative business plans not only from operational shop floor scheduling but also through strategic resource planning. This paper aims to explicitly represent and optimize the total cost of a schedule including the tardiness and inventory costs while satisfying non-relaxable constraints such as resource capacity and temporal constraints. Within the cost based scheduling framework, a cost propagation algorithm is presented to update cost information throughout temporal constraints within the same job.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.363-370
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• 2002

This paper presents a production planning algorithm for minimizing the costs of production and subcontracting in SCM (supply chain management) environment. In our SCM environment, the several local plants that aye dispersed geographically produce parts and products. In this environment, we have to decide the production volumes of both parts and products considering the BOM (bill-of-material) structure to meet the fixed order quantity or forecasted demand quantity. Each plant produces the specified parts of product with finite production capacity. There exist subcontracting decisions relevant to the production capacity of each plant except the core process plant, and when we use the subcontractor's capacities we should be charged for the fixed subcontracting fees. The objective of this study is to solve the production planning problem, which minimizes the total costs of production, inventory, setup, and subcontracting under constraints of production and subcontracting capacity. For this problem, an integrated production planning model based on the multi-level capacitated lot sizing problem was formulated, and efficient decomposition algorithm was proposed. The experimental investigation shows that the proposed heuristic generates quite good solutions at very low computational costs.

• Journal of the Society of Naval Architects of Korea
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• v.48 no.4
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• pp.325-329
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• 2011

Floating, production, storage and offloading (FPSO) is a production facility that refines and saves the drilled crude oil from a drilling facility in the ocean. The flare system in the FPSO is a major part of the pressure relieving system for hydrocarbon processing plants. The flare system consists of a number of pipes and complicated connection systems. Decision of pipe support types is important since the load on the support and the stress in the pipe are influenced by the pipe support type. In this study, we optimally determined the pipe support types that minimized the support cost while satisfying the design constraints on maximum support load, maximum nozzle load and maximum pipe stress ratio. Performance indices included in the design constraints for a specified design were evaluated by pipe structural analysis using CAESAR II. Since pipe support types were all discrete design variables, an evolutionary algorithm (EA) was used as an optimizer. We successfully obtained the optimal solution that reduced the support cost by 27.2% compared to the initial support cost while all the design requirements were satisfied.

• IE interfaces
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• v.12 no.1
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• pp.10-18
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• 1999

Job shop manufacturing environments are using the concept of cellular manufacturing systems(CMS) which has several advantages in reducing production lead times, setup times, work-in-process, etc. Utilizing the similarities between cell-machine, part-machine, and the shape/size of parts, CMS can group machines and parts resulting in improved efficiency of this system. However, when grouping machines and parts in machine cells, there inevitably occurs exceptional elements(EEs), which can not operate in the same machine cell. Minimizing these EEs in CMS is a critical point that improving production efficiency. Constraints in machine duplication cost, machining process technology, machining capability, and factory space limitations are main problems that prevent achiving the goal of maintaining an ideal CMS environment. This paper presents an algorithm that minimizes EEs under the constraints of machine duplication cost and factory space limitation. Developing exceptional operation similarity(EOS) by cell-machine incidence matrix and part-machine incidence matrix, it brings the machine cells that operate the parts or not. A mathematical model to minimize machine duplication is developed by EOS, followed by a heuristic algorithm in order to reflect dynamic situation resulting from minimizing exceptional elements process and the mathematical model. A numerical example is provided to illustrate the algorithm.

• Industrial Engineering and Management Systems
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• v.9 no.3
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• pp.227-241
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• 2010

A memory module industry's supply chain usually consists of multiple manufacturing sites and multiple distribution centers. In order to fulfill the variety of demands from downstream customers, production planners need not only to decide the order allocation among multiple manufacturing sites but also to consider memory module industrial characteristics and supply chain constraints, such as multiple material substitution relationships, capacity, and transportation lead time, fluctuation of component purchasing prices and available supply quantities of critical materials (e.g., DRAM, chip), based on human experience. In this research, a directed graph-based supply network planning (DGSNP) model is developed for memory module industry. In addition to multi-site order allocation, the DGSNP model explicitly considers production planning for each manufacturing site, and purchasing planning from each supplier. First, the research formulates the supply network's structure and constraints in a directed-graph form. Then, a proposed genetic algorithm (GA) solves the matrix form which is transformed from the directed-graph model. Finally, the final matrix, with a calculated maximum profit, can be transformed back to a directed-graph based supply network plan as a reference for planners. The results of the illustrative experiments show that the DGSNP model, compared to current memory module industry practices, determines a convincing supply network planning solution, as measured by total profit.