• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.4
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• pp.1-10
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• 1998

We consider the precedence-constrained knapsack problem. which is a knapsack problem with precedence constraints imposed on the set of variables. Especially, we focus on the case where the precedence constraints cir be represented as a bipartite graph, which occurs most frequently in applications. Based on the previous studios for the general case, we specialize the polyhedral results on the related polytope and derive stronger results on the facet-defining properties of the inequalities.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.175-185
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• 2005

The scheduling methods of the distributed real-time systems have been proposed. However, they have some weak points. They did not schedule both sporadic and periodic tasks and messages at the same time or did not consider the end-to-end constraints such as precedence relations between sporadic tasks. This means that system scheduling must guarantee the constraints of practical systems and be applicable to them. This paper proposes a new scheduling method that can be applied to more practical model of distributed real-time systems. System model consists of sporadic and periodic tasks with precedence relations and sporadic and periodic messages and has end-to-end constraints. The proposed method is based on a binary search-based period assignment algorithm, an end-to-end laxity-based priority assignment algorithm, and three kinds of schedulability analysis, node, network, and end-to-end schedulability analysis. In addition, this paper describes the application model of sporadic tasks with precedence constraints in a distributed real-time system, shows that existing scheduling methods such as Rate Monotonic scheduling are not proper to be applied to the system having sporadic tasks with precedence constraints, and proposes an end-to-end laxity-based priority assignment algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.747-752
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• 2004

Researches about scheduling of the distributed real-time systems have been proposed. However, they have some weak points, not scheduling both sporadic and periodic tasks and messages or being unable to guaranteeing the end-to-end constraints due to omitting precedence relations between sporadic tasks. So this paper proposes a new scheduling method for distributed real-time systems consisting of sporadic and periodic tasks with precedence relations and sporadic and periodic messages, guaranteeing end-to-end constraints. The proposed method is based on a binary search-based period assignment algorithm, an end-to-end laxity-based priority assignment algorithm, and three kinds of schedulability analysis, node, network, and end-to-end schedulability analysis. In addition, this paper describes the application model of sporadic tasks with precedence constraints in a distributed real-time system, shows that existing scheduling methods such as Rate Monotonic (RM) scheduling are not proper to be applied to the system having sporadic tasks with precedence constraints, and proposes an end-to-end laxity-based priority assignment algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.563-570
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• 2004

Genetic algorithms have been successfully applied to various optimization problems belonging to NP-hard problems. The sequential ordering problems(SOP) and the job shop scheduling problems(JSP) are well-known NP-hard problems with strong influence on industrial applications. Both problems share some common properties in that they have some imposed precedence constraints. When genetic algorithms are applied to this kind of problems, it is desirable for genetic operators to be designed to produce chromosomes satisfying the imposed precedence constraints. Several genetic operators applicable to such problems have been proposed. We call such genetic operators precedence-preserving genetic operators. This paper presents three existing precedence-preserving genetic operators: Precedence -Preserving Crossover(PPX), Precedence-preserving Order-based Crossover (POX), and Maximum Partial Order! Arbitrary Insertion (MPO/AI). In addition, it proposes two new operators named Precedence-Preserving Edge Recombination (PPER) and Multiple Selection Precedence-preserving Order-based Crossover (MSPOX) applicable to such problems. It compares the performance of these genetic operators for SOP and JSP in the perspective of their solution quality and execution time.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.187-190
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• 2003

Many manufacturing companies consider the integrated and concurrent scheduling because they need the global optimization technology that could manufacture various products more responsive to customer needs. In this paper, we propose an advanced scheduling model to generate the schedules considering resource constraints and precedence constraints in make-to-order (MTO) manufacturing environments. Precedence of work- in-process(WIP) and resources constraints have recently emerged as one of the main constraints in advanced scheduling problems. The advanced scheduling problems is formulated as a multiobjective mathematical model for generating operation schedules which are obeyed resources constraints, alternative workstations of operations and the precedence constraints of WIP in MTO manufacturing. For effectively solving the advanced scheduling problem, the multi-objective hybrid genetic algorithm (m-hGA) is proposed in this paper. The m-hGA is to minimize the makespan, total flow time of order, and maximum tardiness for each order, simultaneously. The m-hGA approach with local search-based mutation through swap mutation is developed to solve the advanced scheduling problem. Numerical example is tested and presented for advanced scheduling problems with various orders to describe the performance of the proposed m-hGA.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.1
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• pp.1-17
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• 1993

It is well known that an assembly operation is usually constrained by the geometric interference between parts. These constraints are normally presented as AND/OR precedence relationships. To find a feasible assembly sequence which satisfies the geometric constraints is not an easy task because of the TSP(Traveling Salesman Problem) nature with precedence constraints. In this paper, we developed an automated system based on Neural Network for generating feasible assembly sequences. Modified Hopfield and Tank network is used to solve the problem of AND/OR precedence-constrained assembly sequences. An economic assembly sequence can be also obtained by applying the cost matrix that contains cost-reducing factors. To evaluate the performance and effectiveness of the developed system, a case of automobile generator is tested. The results show that the developed system can provide a "good" planning tool for an assembly planner within a reasonable computation time period.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.59-61
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• 2004

Researches about scheduling distributed real-time systems have some weak points, not scheduling both sporadic and periodic tasks and messages or being unable to guaranteeing the end-to-end constraints due to omitting precedence relations between sporadic tasks. This paper describes the application model of sporadic tasks with precedence constraints in a distributed real-time system. It is shown that existing scheduling methods such as Rate Monotonic scheduling are not proper to be applied to the system having sporadic tasks with precedence constraints. So this paper proposes an end-to-end laxity-based priority assignment algorithm which considers the practical laxity of a task and allocates a proper priority to a task.

• The Journal of Information Systems
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• v.25 no.1
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• pp.159-182
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• 2016

Purpose This paper aims to develop a novel tabu search algorithm for solving the sequencing problems with precedence constraints. Due to constraints, the traditional meta heuristic methods can generate infeasible solutions during search procedure, which must be carefully dealt with. On the contrary, the candidate order based tabu search (COTS) is based on a novel neighborhood structure that guarantees the feasibility of solutions, and can dealt with a wide range of sequencing problems in flexible manner. Design/methodology/approach Candidate order scheme is a strategy for constructing a feasible sequence by iteratively appending an item at a time, and it has been successfully applied to genetic algorithm. The primary benefit of the candidate order scheme is that it can effectively deal with the additional constraints of sequencing problems and always generates the feasible solutions. In this paper, the candidate order scheme is used to design the neighborhood structure, tabu list and diversification operation of tabu search. Findings The COTS has been applied to the single machine job sequencing problems, and we can see that COTS can find the good solutions whether additional constraints exist or not. Especially, the experiment results reveal that the COTS is a promising approach for solving the sequencing problems with precedence constraints. In addition, the operations of COTS are intuitive and easy to understand, and it is expected that this paper will provide useful insights into the sequencing problems to the practitioners.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.305-308
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• 2007

In this paper we suggest an efficient evolutionary approach based on topological sort techniques for precedence constrained TSPs. The determination of optimal sequence has much to offer to downstream project management and opens up new opportunities for supply chains and logistics. Experimental results show that the suggested approach is a good alternative to locate optimal solution for complicated precedence constrained sequencing as in optimization method for instance.

• Journal of Intelligence and Information Systems
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• v.17 no.2
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• pp.1-22
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• 2011

In this paper, we propose an adaptive genetic algorithm (aGA) approach for effectively solving the sequencing problem with precedence constraints (SPPC). For effective representation of the SPPC in the aGA approach, a new representation procedure, called the topological sort-based representation procedure, is used. The proposed aGA approach has an adaptive scheme using a fuzzy logic controller and adaptively regulates the rate of the crossover operator during the genetic search process. Experimental results using various types of the SPPC show that the proposed aGA approach outperforms conventional competing approaches. Finally the proposed aGA approach can be a good alternative for locating optimal solutions or sequences for various types of the SPPC.