• Journal of Sensor Science and Technology
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• v.6 no.2
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• pp.145-154
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• 1997

Based on a new search strategy using circuit simulation and simulated annealing with local search, a technique for design automation of high-performance operational amplifiers is proposed. For arbitrary circuit topology and performance specifications, through discrete optimization of a cost function with discrete design variables the design of operational amplifiers is performed. A special-purpose circuit simulator and some heuristics are used to reduce the design time. Through the design of a low-power high-speed fully differential CMOS operational amplifier usable in smart sensors and 10-b 25-MS/s pipelined A/D converters, it has been demonstrated that a design tool developed using the proposed technique can be used for designing high-performance operational amplifiers with less design knowledge and less design effort.

• Journal of The Korean Digital Architecture Interior Association
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• v.11 no.3
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• pp.53-59
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• 2011

In creative design, it is necessary to understand the characteristic of architectural design. In the world of design problem, a distinction can be made between those that are well-defined and those that are ill-defined. Well-defined problems are those for which the ends or goal, are already prescribed and apparent, their solution requires the provision of appropriate means. For ill-defined problems, on the other hand, both the ends and the means of solution are unknown at the outset of the problem solving exercise, at least in their entirety. Most of design problems is ill-defined, which is unknown at the beginning of the problem solving exercise. In order to solve the design problem, Designers take advantage of the search methods of problem space, such as global-search-methods(depth-first-methods, breath-first-methods), local-search-methods(generate and test, heuristics, hill-climbing, reasoning) and visual thinking, which is represented through sketching. Sketching is a real part of design reasoning and it does so through a special kind of visual imagery. Also in the design problem solving it have been an important means of problem exploration and solution generation. By sketching, they represent images held in the mind as well as makes graphic images which help generate mental images of entity that is being designed. The search methods of problem space and a visual thinking have been crucially considered in the architectural design. The purpose of this paper is to explore the property of design by means of the pre-existed-experiment data and literature research. The findings will help design the architectural design for more creative results.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.1
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• pp.37-48
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• 2022

In this study, a hybrid meta-heuristic approach is proposed for solving engineering design optimization problems. Various approaches in many literatures have been proposed to solve engineering optimization problems with various types of decision variables and complex constraints. Unfortunately, however, their efficiencies for locating optimal solution do not be highly improved. Therefore, we propose a hybrid meta-heuristic approach for improving their weaknesses. the proposed GA-VNS-HC approach is combining genetic algorithm (GA) for global search with variable neighborhood search (VNS) and hill climbing (HC) for local search. In case study, various types of engineering design optimization problems are used for proving the efficiency of the proposed GA-VNS-HC approach

• Industrial Engineering and Management Systems
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• v.7 no.2
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• pp.171-181
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• 2008

Home care, known also as domiciliary care, is part of the community care service that is a responsibility of the local government authorities in the UK as well as many other countries around the world. The aim is to provide the care and support needed to assist people, particularly older people, people with physical or learning disabilities and people who need assistance due to illness to live as independently as possible in their own homes. It is performed primarily by care workers visiting clients' homes where they provide help with daily activities. This paper is concerned with the dispatching of care workers to clients in an efficient manner. The optimized routine for each care worker determines a schedule to achieve the minimum total cost (in terms of distance traveled) without violating the capacity and time window constraints. A collaborative population-based meta-heuristic called Particle Swarm Optimization (PSO) is applied to solve the problem. A particle is defined as a multi-dimensional point in space which represents the corresponding schedule for care workers and their clients. Each dimension of a particle represents a care activity and the corresponding, allocated care worker. The continuous position value of each dimension determines the care worker to be assigned and also the assignment priority. A heuristic assignment scheme is specially designed to transform the continuous position value to the discrete job schedule. This job schedule represents the potential feasible solution to the problem. The Earliest Start Time Priority with Minimum Distance Assignment (ESTPMDA) technique is developed for generating an initial solution which guides the search direction of the particle. Local improvement procedures (LIP), insertion and swap, are embedded in the PSO algorithm in order to further improve the quality of the solution. The proposed methodology is implemented, tested, and compared with existing solutions for some 'real' problem instances.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.117-129
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• 2008

The cost-based scheduling work has been done in both the Operations Research (OR) and Artificial Intelligence (AI) literature. To deal with more realistic problems, AI-based heuristic scheduling approach with non-regular performance measures has been studied. However, there has been little research effort to develop a full inter-job cost propagation algorithm (CPA) for different jobs having multiple downstream and upstream activities. Without such a CPA, decision-making in scheduling heuristics relies upon local, incomplete cost information, resulting in poor schedule performance from the overall cost minimizing objective. For such a purpose, we need two types of CPAs : intra-job CPA and inter-job CPA. Whenever there is a change in cost information of an activity in a job in the process of scheduling, the intra-job CPA updates cost curves of other activities connected through temporal constraints within the same job. The inter-job CPA extends cost propagation into other jobs connected through precedence relationships. By utilizing the cost information provided by CPAs, we propose cost-based scheduling heuristics that attempt to minimize the total schedule cost. This paper develops inter-job CPAs that create and update cost curves of each activity in each search state, and propagate cost information throughout a whole network of temporal constraints. Also we propose various cost-based scheduling heuristics that attempt to minimize the total schedule cost by utilizing the cost propagation algorithm.

• Industrial Engineering and Management Systems
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• v.11 no.3
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• pp.288-298
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• 2012

Lot sizing and shipment scheduling are two interrelated decisions made by a manufacturing plant and a third-party logistics distribution center. This paper analyzes a dynamic inbound ordering problem and shipment problem with a freight container cost, in which the order size of multiple products and single container type are simultaneously considered. In the problem, each ordered product placed in a period is immediately shipped by some freight containers in the period, and the total freight cost is proportional to the number of containers employed. It is assumed that the load size of each product is equal and backlogging is not allowed. The objective of this study is to simultaneously determine the lot-sizes and the shipment schedule that minimize the total costs, which consist of production cost, inventory holding cost, and freight cost. Because the problem is NP-hard, we propose three meta-heuristic algorithms: a simulated annealing algorithm, a genetic algorithm, and a new population-based evolutionary meta-heuristic called self-evolution algorithm. The performance of the meta-heuristic algorithms is compared with a local search heuristic proposed by the previous paper in terms of the average deviation from the optimal solution in small size problems and the average deviation from the best one among the replications of the meta-heuristic algorithms in large size problems.