• Industrial Engineering and Management Systems
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• v.14 no.1
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• pp.11-21
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• 2015

The purpose of this research is to develop algorithms using the Differential Evolution Algorithm (DE) to solve a multi-objective, sources and stages location-allocation problem. The development process starts from the design of a standard DE, then modifies the recombination process of the DE in order improve the efficiency of the standard DE. The modified algorithm is called modified DE. The proposed algorithms have been tested with one real case study (large size problem) and 2 randomly selected data sets (small and medium size problems). The computational results show that the modified DE gives better solutions and uses less computational time than the standard DE. The proposed heuristics can find solutions 0 to 3.56% different from the optimal solution in small test instances, while differences are 1.4-3.5% higher than that of the lower bound generated by optimization software in medium and large test instances, while using more than 99% less computational time than the optimization software.

• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.7-14
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• 2011

In solving the Job Shop Scheduling Problem, the best solution rarely is completely random; it follows one or more rules (heuristics). The Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing, and Tabu search, which belong to the Evolutionary Computations Algorithms (ECs), are not efficient enough in solving this problem as they neglect all conventional heuristics and hence they need to be hybridized with different heuristics. In this paper a new algorithm titled "Shaking Optimization Algorithm" is proposed that follows the common methodology of the Evolutionary Computations while utilizing different heuristics during the evolution process of the solution. The results show that the proposed algorithm outperforms the GA, PSO, SA, and TS algorithms, while being a good competitor to some other hybridized techniques in solving a selected number of benchmark Job Shop Scheduling problems.

• Structural Engineering and Mechanics
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• v.27 no.5
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• pp.555-574
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• 2007

Static response of an elastic beam on a two-parameter tensionless foundation is investigated by assuming that the beam is symmetrically subjected to a uniformly distributed load and concentrated edge loads. Governing equations of the problem are obtained and solved by pointing out that a concentrated edge foundation reaction in addition to a continuous foundation reaction along the beam axis in the case of complete contact and a discontinuity in the foundation reactions in the case of partial contact come into being as a direct result of the two-parameter foundation model. The numerical solution of the complete contact problem is straightforward. However, it is shown that the problem displays a highly non-linear character when the beam lifts off from the foundation. Numerical treatment of the governing equations is accomplished by adopting an iterative process to establish the contact length. Results are presented in figures to demonstrate the linear and non-linear behavior of the beam-foundation system for various values of the parameters of the problem comparatively.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.530-533
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• 1995

This paper deals with the problem of motion planning for a unicycle-like robot. We present a simple local planner for unicycle model, based on an approximation of the desired configuration generated by local holonomic planner that ignores motion constraints. To guarantee a collision avoidance, we propose an inequality constraint, based on the motion analysis with the constant control input and time interval. Consequently, we formulate our problem as the constrained optimization problem and a feedback scheme based on local sensor information is established by simply solving this problem. Through simulations, we confirm the validity and effectiveness of our algorithm.

• Industrial Engineering and Management Systems
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• v.10 no.3
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• pp.203-208
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• 2011

Job shop scheduling is well-known as one of the hardest combinatorial optimization problems and has been demonstrated to be NP-hard problem. In the past decades, several researchers have devoted their effort to develop evolutionary algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for job shop scheduling problem. Differential Evolution (DE) algorithm is a more recent evolutionary algorithm which has been widely applied and shown its strength in many application areas. However, the applications of DE on scheduling problems are still limited. This paper proposes a one-stage differential evolution algorithm (1ST-DE) for job shop scheduling problem. The proposed algorithm employs random key representation and permutation of m-job repetition to generate active schedules. The performance of proposed method is evaluated on a set of benchmark problems and compared with results from an existing PSO algorithm. The numerical results demonstrated that the proposed algorithm is able to provide good solutions especially for the large size problems with relatively fast computing time.

• ETRI Journal
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• v.34 no.2
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• pp.272-275
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• 2012

This letter studies the problem of exploiting multichannel diversity in a spectrum sharing system, where the secondary user (SU) sequentially explores channel state information on the licensed channels with time consumption. To maximize the expected achievable throughput for the SU, we formulate this problem as an optimal stopping problem, whose objective is to choose the right channel to stop exploration based on the observed signal-to-noise ratio sequence. Moreover, we propose a myopic but optimal rule, called one-stage look-ahead rule, to solve the stopping problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.53
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• pp.37-44
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• 1999

The problem to be studied here is the minimization of the total travel distance and the number of vehicles used for delivering goods to customers. Vehicle routes must also satisfy a variety of constraints such as fixed vehicle capacity, allowed operating time. Genetic algorithm to solve the VRPTW with heterogeneous fleet is presented. The chromosome of the proposed GA in this study has the 3-dimension. We propose GA that has the cubic-chromosome for VRPTW with heterogeneous fleet. The newly suggested ‘Cubic-GA (or 3-D GA)’ in this paper means the 2-D GA with GLS(Genetic Local Search) algorithms and is quite flexible. To evaluate the performance of the algorithm, we apply it to the Solomon's VRPTW instances. It produces a set of good routes and the reasonable number of vehicles.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.686-693
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• 2013

Generation Expansion Planning (GEP) is one of the most important decision-making activities in electric utilities. Least-cost GEP is to determine the minimum-cost capacity addition plan (i.e., the type and number of candidate plants) that meets forecasted demand within a pre specified reliability criterion over a planning horizon. In this paper, Differential Evolution (DE), and Opposition-based Differential Evolution (ODE) algorithms have been applied to the GEP problem. The original GEP problem has been modified by incorporating Virtual Mapping Procedure (VMP). The GEP problem of a synthetic test systems for 6-year, 14-year and 24-year planning horizons having five types of candidate units have been considered. The results have been compared with Dynamic Programming (DP) method. The ODE performs well and converges faster than DE.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.198-206
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• 2013

The team orienteering problem (TOP) or the multiple tour maximum collection problem can be considered as a generic model that can be applied to a number of challenging applications in logistics, tourism, and other fields. This problem is generally defined as the problem of determining P paths, in which the traveling time of each path is limited by $T_{max}$ that maximizes the total collected score. In the TOP, a set of N vertices i is given, each with a score $S_i$. The starting point (vertex 1) and the end point (vertex N) of all paths are fixed. The time $t_{ij}$ needed to travel from vertex i to j is known for all vertices. Some exact and heuristics approaches had been proposed in the past for solving the TOP. This paper proposes a new solution methodology for solving the TOP using the particle swarm optimization, especially by proposing a solution representation and its decoding method. The performance of the proposed algorithm is then evaluated using several benchmark datasets for the TOP. The computational results show that the proposed algorithm using specific settings is capable of finding good solution for the corresponding TOP instance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.36-42
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• 2021

This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.