• 산업경영시스템학회지
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• 제17권31호
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• pp.115-123
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• 1994

Group scheduling, which is a kind of operations scheduling based on the GT concept is analyzed in a multi-stage manufacturing system. The purpose of this research is to develop and evaluate a heuristic algorithm for determining gro up sequence and job sequence within each group to minimize a complex cost function, i.e. the sum of the total pe-nalty cost for tardiness and the total holding cost for flow time, in a multi-stage manufacturing system with group setup time dependent upon group sequence. A heuristic algorithm for group sc heduling is developed, and a numerical example is illustrated. For the evaluation of the pro-posed heuristic algorithm, the heuristic solution of each of 63 problems is compared with that of random scheduling. The result shows that the proposed heuristic algorithm provides better solution in light of the proposed cost function.

• 산업경영시스템학회지
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• 제35권4호
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• pp.171-178
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• 2012

A possible heuristic to solve metropolitan area vehicle routing problems with variable vehicle speeds is suggested in this research. Delivery hours are classified into 4 different time zones to make variable vehicle speeds no change within the same time zone to make TDVRP simple to solve. The suggested heuristic consists of 2 stages such as initial solution development step and initial solution improvement step. A computer program using C++ is constructed to evaluate the suggested heuristic. Randomly generated vehicle routing problems are used for the experiments. This heuristic could be helpful to logistics companies by increasing delivery efficiencies since the 4 zone classification is taken from the observed traffic information offered by a local government.

• 한국경영과학회지
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• 제25권2호
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• pp.47-57
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• 2000

In this paper we consider an n-job non-preemptive and identical parallel machine scheduling problem of minimizing the sum of earliness and tardiness with different release times and due dates. In the real world this problem is more realistic than the problems that release times equal to zero or due dates are common. The problem is proved to be NP-complete. Thus a heuristic is developed to solve this problem To illustrate its suitability a proposed heuristic is compared with a genetic algorithm for a large number of randomly generated test problems. Computational results show the effectiveness and efficiency of proposed heuristic. In summary the proposed heuristic provides good solutions than genetic algorithm when the problem size is large.

• 산업경영시스템학회지
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• 제22권52호
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• pp.97-107
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• 1999

The asymmetric traveling salesman problem is a representative NP-Complete problem. Polynomial algorithm for this problem has not been yet found. So, many heuristic methods have been researched in this problem. We need heuristic methods that produce good answers for some larger problems in reasonable times. 3-opt is well known for the effective local-search heuristic method. It has been used in many applications of the asymmetric traveling salesman problem. This paper discusses 3-opt's properties and ineffective aspects and presents a highly effective heuristic method. 3-opt does not consider good arcs(shorter distance or little cost). This paper presents a fast heuritic algorithm compared with 3-opt by inserting good arcs and deleting related arcs later.

• Industrial Engineering and Management Systems
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• 제15권1호
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• pp.1-10
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• 2016

This paper presents a fast sequencing algorithm for a mixed model assembly line with multiple workstations which minimize the total utility work and idle time. We compare the proposed algorithms with another heuristic, the Tsai-based heuristic, for a sequencing problem that minimizes the total utility works. Numerical experiments are used to evaluate the performance and effectiveness of the proposed algorithm. The Tsai-based heuristic performs best in terms of utility work, but the fast sequencing algorithm performs well for both utility work and idle time. However, the computational complexity of the fast sequencing algorithm is O (KN) while the Tsai-based algorithm is O (KNlogN). Actual computational time of the fast sequencing heuristic is 2-6 times faster than that of the Tsai-based heuristic.

• 산업공학
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• 제22권2호
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• pp.116-125
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• 2009

We revisit the assembly block storage location assignment problem (ABSLAP) at a shipyard, in order to compensate for the deficiency in performance verification of the heuristic ABSLAP algorithm developed by the previous study. In this paper, we formulate a mathematical programming model of the ABSLAP, refine elaborately the heuristic ABSLAP algorithm, and show the performance of the developed mathematical programming model and the revised heuristic ABSLAP algorithm. In addition, we explain simulation experiments conducted using the revised heuristic ABSLAP algorithm to investigate the influences of block stockyard layouts and production schedule instability on the block stockyard operations.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2015년도 추계학술발표대회
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• pp.1305-1308
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• 2015

모바일 로봇의 경로 계획의 경우 주로 위치로 표현되는 2차원 공간 상에서 현재 위치에서 목표 위치까지 모바일 로봇이 도달하도록 경로를 계획한다. 그러나 nonholonomic 구조를 가지는 모바일 로봇의 경우 기구학적 제약에 의해 추종 불가능한 경로가 존재하게 된다. 또한 nonholonomic 모바일 로봇은 진행 방향을 포함한 3차원 공간 상에서의 경로 계획이 이루어져야 한다. 모바일 로봇의 경로 계획 알고리즘으로는 A* 경로 계획 알고리즘이 주로 사용되는데, A* 경로 계획 알고리즘은 경로 계획 시 현재 위치에서부터 노드를 확장시켜 가며 경로를 탐색한다. 이 때 각 노드로부터 목표 위치까지의 비용을 계산하기 위해 heuristic 함수가 사용된다. 기존의 A* 경로 계획 알고리즘의 경우 Euclidean 거리에 기반한 heuristic 함수가 사용되었으나, 이 경우 모바일 로봇의 진행 방향은 고려하지 않아, 로봇의 목표 위치에 도말만 할 뿐 목표 방향으로의 도달은 보장 할 수 없다. 본 논문에서는, A* 경로 계획 알고리즘을 통해 nonholonomic 모바일 로봇이 목표 위치에 목표 방향에 맞추어 도달할 수 있도록 경로 생성이 이루어지는 heuristic 함수를 제안하고, 시뮬레이션을 통해 그 성능을 검증한다.

• International journal of advanced smart convergence
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• 제12권3호
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• pp.13-18
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• 2023

This paper aims to investigate data features for neighbor path selection (NPS) in computer network with regional failures. It is necessary to find an available alternate communication path in advance when regional failures due to earthquakes or forest fires occur simultaneously. We describe previous general heuristics and simulation heuristic to solve the NPS problem in the regional fault network. The data features of general heuristics using proximity and sharing factor and the data features of simulation heuristic using machine learning are explained through examples. Simulation heuristic may be better than general heuristics in terms of communication success. However, additional data features are necessary in order to apply the simulation heuristic to the real environment. We propose novel data features for NPS in computer network with regional failures and Keras modeling for computing the communication success probability of candidate neighbor path.

• 경영과학
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• 제25권1호
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• pp.107-121
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• 2008

As to more efficiently manage the inventory in the retail supply chain and to meet the customer demand in a timely manner, vendor-managed inventory (VMI) has been widely accepted, which manages inventory in the retail supply chain via sharing information and collaborating with the retailers. Applying VMI generates vendor-managed inventory/distribution problem (VMIDP), which involves inventory management for both the vendor and the retailers, and the design of vehicle routes for delivery, to minimize the total operating cost in the supply chain. In this paper, we suggest a mixed integer programming (MIP) model to obtain the optimal solution for VMIDP in a two-echelon retail supply chain, and develop an efficient heuristic based on the operating principles of the MIP model. To evaluate the performance of the heuristic, its solution was compared with the one of the MIP model on a total of twenty seven test problems. As a result, the heuristic found optimal solutions on seven problems in a significantly reduced time, and generated a 4.3% error rate of total cost in average for all problems. The heuristic is applied to the case problem of the local famous franchise company together with GIS, showing that it is capable of providing a solution efficiently in a relatively short time even in the real world situation.

• Industrial Engineering and Management Systems
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• 제11권3호
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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.