• 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.

• Journal of Korean Institute of Industrial Engineers
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• v.35 no.4
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• pp.257-265
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• 2009

This paper considers the hybrid flow shop scheduling problem for the objective of minimizing the number of tardy jobs. In hybrid flow shops, each job is processed through multiple production stages in series, each of which has multiple identical parallel machines. The problem is to determine the allocation of jobs to the parallel machines at each stage as well as the sequence of the jobs assigned to each machine. Due to the complexity of the problem, we suggest search heuristics, tabu search and simulated annealing algorithms with a new method to generate neighborhood solutions. In particular, to evaluate and select neighborhood solutions, three surrogate objectives are additionally suggested because not much difference in the number of tardy jobs can be found among the neighborhoods. To test the performances of the surrogate objective based search heuristics, computational experiments were performed on a number of test instances and the results show that the surrogate objective based search heuristics were better than the original ones. Also, they gave the optimal solutions for most small-size test instances.

• Industrial Engineering and Management Systems
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• v.8 no.1
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• pp.22-28
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• 2009

We consider facility layout problems, where mn facility units are assigned into mn cells. These cells are arranged into a rectangular pattern with m rows and n columns. In order to solve this cell type facility layout problem, many approximation algorithms with improved local search methods were studied because it was quite difficult to find exact optimum of such problem in case of large size problem. In this paper, new algorithms based on Simulated Annealing (SA) method with two neighborhood generation methods are proposed. The new neighborhood generation method adopts the exchanging operation of facility units in accordance with adjacent preference. For evaluating the performance of the neighborhood generation method, three algorithms, previous SA algorithm with random 2-opt neighborhood generation method, the SA-based algorithm with the new neighborhood generation method (SA1) and the SA-based algorithm with probabilistic selection of random 2-opt and the new neighborhood generation method (SA2), are developed and compared by experiment of solving same example problem. In case of numeric examples with problem type 1 (the optimum layout is given), SA1 algorithm could find excellent layout than other algorithms. However, in case of problem type 2 (random-prepared and optimum-unknown problem), SA2 was excellent more than other algorithms.

• Advances in aircraft and spacecraft science
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• v.10 no.5
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• pp.393-418
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• 2023

Drones are increasingly used in logistics delivery due to their low cost, high-speed and straight-line flight. Considering the small cargo capacity, limited endurance and other factors, this paper optimized the pickup and delivery vehicle routing problem with time windows in the mode of "truck+drone". A mixed integer programming model with the objective of minimizing transportation cost was proposed and an improved adaptive large neighborhood search algorithm is designed to solve the problem. In this algorithm, the performance of the algorithm is improved by designing various efficient destroy operators and repair operators based on the characteristics of the model and introducing a simulated annealing strategy to avoid falling into local optimum solutions. The effectiveness of the model and the algorithm is verified through the numerical experiments, and the impact of the "truck+drone" on the route cost is analyzed, the result of this study provides a decision basis for the route planning of "truck+drone" mode delivery.

• 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

• Korean Management Science Review
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• v.12 no.3
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• pp.177-185
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• 1995

This paper presents a Tabu Search method to minimize a number of tardy jobs in the nonidentical parallel machine scheduling. The Tabu Search method employs a restricted neighborhood for the reduction of computation time. In this paper, we use two different types of method for a single machine scheduling. One is Moore's algorithm and the other is insertion method. We discuss computational experiments on more than 1000 test problems.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.13-21
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• 2014

The set covering problem (SCP) is one of representative combinatorial optimization problems, which is defined as the problem of covering the m-rows by a subset of the n-columns at minimal cost. This paper proposes a method utilizing Integer Programming-based Local Search (IPbLS) to solve the set covering problem. IPbLS is a kind of local search technique in which the current solution is improved by searching neighborhood solutions. Integer programming is used to generate neighborhood solution in IPbLS. The effectiveness of the proposed algorithm has been tested on OR-Library test instances. The experimental results showed that IPbLS could search for the best known solutions in all the test instances. Especially, I confirmed that IPbLS could search for better solutions than the best known solutions in four test instances.

• Korean Journal of Construction Engineering and Management
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• v.20 no.5
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• pp.42-51
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• 2019

The train operation institution establishes a transportation plan based on the forecast of transport demand and the ability of train vehicles to transport, and establishes a train operation plan accordingly. The train operation plan adjusts the intervals between trains, creates a timetable (train diagram) for trains, and establishes a plan for the operation of train vehicles used for train operation. The train operation institution shall establish a crew schedule to determine and place the crew members of the trains arranged in the diagram in order to enhance the efficiency of the operation management of the trains. In this study, the authors apply the neighborhood search method that satisfies the constraints at the phase of generating the crew diagram. This suggests a methodology for efficient management of crew schedule plan. The crew diagram generated in this study compared with the existing crew diagram in accordance with the actual operating train timetable, and verified the effectiveness of the suggested method.

• Industrial Engineering and Management Systems
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• v.6 no.1
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• pp.64-71
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• 2007

J. Kennedy and R. Eberhart first introduced the concept called as Particle Swarm Optimization (PSO). They applied it to optimize continuous nonlinear functions and demonstrated the effectiveness of the algorithm. Since then a considerable number of researchers have attempted to apply this concept to a variety of optimization problems and obtained reasonable results. In PSO, individuals communicate and exchange simple information with each other. The information among individuals is communicated in the swarm and the information between individuals and their swarm is also shared. Finally, the swarm approaches the optimal behavior. It is reported that reasonable approximate solutions of various types of test functions are obtained by employing PSO. However, if more precise solutions are required, additional algorithms and/or hybrid algorithms would be necessary. For example, the heading vector of the swarm can be slightly adjusted under some conditions. In this paper, we propose a hybrid algorithm to obtain more precise solutions. In the algorithm, when a better solution in the swarm is found, the neighborhood of a certain distance from the solution is searched. Then, the algorithm returns to the original PSO search. By this hybrid method, we can obtain considerably better solutions in less iterations than by the standard PSO method.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.135-138
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• 1996

In solving the Travel Salesman Problem(TSP), we easily reach local optimal solutions with the existing methods such as TWO-OPT, THREE-OPT, and Lin-Kernighen. Tabu search, as a meta heuristic, is a good mechanism to get an optimal or a near optimal solution escaping from the local optimal. By utilizing AI concepts, tabu search continues to search for improved solutions. In this study, we focus on developing a new neighborhood structure that maintains the feasibility of the tours created by exchange operations in TSP. Intelligent methods are discussed, which keeps feasible tour routes even after exchanging several edges continuously. An extended tabu search model, performing cycle detection and diversification with memory structure, is applied to TSP. The model uses effectively the information gathered during the search process. Finally, the results of tabu search and simulated annealing are compared based on the TSP problems in the prior literatures.