Gyunghyun Choi

An efficient genetic algorithm for the traveling salesman problem with precedence constraints

Abstract The traveling salesman problem with precedence constraints (TSPPC) is one of the most difficult combinatorial optimization problems. In this paper, an efficient genetic algorithm (GA) to solve the TSPPC is presented. The key concept of the proposed GA is a topological sort (TS), which is defined as an ordering of vertices in a directed graph. Also, a new crossover operation is developed for the proposed GA. The results of numerical experiments show that the proposed GA produces an optimal solution and shows superior performance compared to the traditional algorithms.

A goal programming mixed-model line balancing for processing time and physical workload

In this paper, we present a new mathematical model of line balancing for processing time and physical workload at the same time. Line balancing is the problem to assign tasks to stations while satisfying some managerial viewpoints. Most researches about the line balancing problems are focused on the conventional industrial measures that minimizing total processing time and/or number of workstations. Also, independently, some research reports insist some industrial ergonomic issues to be considered for balancing. So, we propose a zero-one integer program model that combines the overload of processing time and physical workload with various risk elements. For the solution techniques, we adopt the goal programming approach and design an appropriate algorithm process. Various computational test runs are performed on the processing time only model, the physical workload only model, and the integrated model. Comparing the pay-offs between the two overloads, test results show us that well balanced job allocation is able to be obtained through the proposed model. And we conclude that the model may be very useful for the operation managers to make decisions on their job scheduling efforts.

Limited Memory Space Dilation and Reduction Algorithms

In this paper, we present variants of Shor and Zhurbenkos r-algorithm, motivated by the memoryless and limited memory updates for differentiable quasi-Newton methods. This well known r-algorithm, which employs a space dilation strategy in the direction of the difference between two successive subgradients, is recognized as being one of the most effective procedures for solving nondifferentiable optimization problems. However, the method needs to store the space dilation matrix and update it at every iteration, resulting in a substantial computational burden for large-sized problems. To circumvent this difficulty, we first propose a memoryless update scheme, which under a suitable choice of parameters, yields a direction of motion that turns out to be a convex combination of two successive anti-subgradients. Moreover, in the space transformation sense, the new update scheme can be viewed as a combination of space dilation and reduction operations. We prove convergence of this new method, and demonstrate how it can be used in conjunction with a variable target value method that allows a practical, convergent implementation of the method. We also examine a memoryless variant that uses a fixed dilation parameter instead of varying degrees of dilation and/or reduction as in the former algorithm, as well as another variant that examines a two-step limited memory update. These variants are tested along with Shors r-algorithm and also a modified version of a related algorithm due to Polyak that employs a projection onto a pair of Kelleys cutting planes. We use a set of standard test problems from the literature as well as randomly generated dual transportation and assignment problems in our computational experiments. The results exhibit that the proposed space dilation and reduction method and the modification of Polyaks method are competitive, and offer a substantial advantage over the r-algorithm and over the other tested limited memory variants with respect to accuracy as well as effort.

Multi-step ART1 algorithm for recognition of defect patterns on semiconductor wafers

The integrated circuits (ICs) on wafers are highly vulnerable to defects generated during the semiconductor manufacturing process. The spatial patterns of locally clustered defects are likely to contain information related to the defect generating mechanism. For the purpose of yield management, we propose a multi-step adaptive resonance theory (ART1) algorithm in order to accurately recognise the defect patterns scattered over a wafer. The proposed algorithm consists of a new similarity measure, based on the p-norm ratio and run-length encoding technique and pre-processing procedure: the variable resolution array and zooming strategy. The performance of the algorithm is evaluated based on the statistical models for four types of simulated defect patterns, each of which typically occurs during fabrication of ICs: random patterns by a spatial homogeneous Poisson process, ellipsoid patterns by a multivariate normal, curvilinear patterns by a principal curve, and ring patterns by a spherical shell. Computational testing results show that the proposed algorithm provides high accuracy and robustness in detecting IC defects, regardless of the types of defect patterns residing on the wafer.

Enhancing Competence and Autonomy in Computer-Based Instruction Using a Skill-Challenge Balancing Strategy.

The present study aims to show if a skill-challenge balancing (SCB) instruction strategy can assist learners to motivationally engage in computer-based learning. Csikszentmihalyis flow theory (self-control, curiosity, focus of attention, and intrinsic interest) was applied to an account of the optimal learning experience in SCB-based learning activities. Two empirical studies were carried out, where a group of learners were taught “Computer Networks” as part of a statutory curriculum at a tertiary institution. The empirical results suggested that a degree of self-control to compensate for the fully automatic SCB instruction strategy (i.e., competence and autonomy) would be of a greater value for learning motivation enhancement in adaptive computer-based learning systems.

Real Option Decision Tree Models for R&D Project Investment

RD they are based on traditional methods such as Discounted Cash Flow (DCF), Decision Tree Analysis (DTA) and Real Option Analysis (ROA) or some fusion forms of the traditional methods. However, almost of the models have constraints in practical use owing to limits on application, procedural complexity and incomplete reflection of the uncertainties. In this study, to make the constraints minimized, we propose a new model named Real Option Decision Tree Model which is a conceptual combination form of ROA and DTA. With this model, it is possible for the decision-makers to simulate the project value applying the uncertainties onto the decision making nodes.

An exterior-point polytope sliding and deformation algorithm for linear programming problems

In this research, we develop an algorithm for linear programming problems based on a new interpretation of Karmarkars representation for this problem. Accordingly, we examine a suitable polytope for which the origin is an exterior point, and in order to determine an optimal solution, we need to ascertain the minimum extent by which this polytope needs to be slid along a one-dimensional axis so that the origin belongs to it. To accomplish this, we employ strongly separating hyperplanes between the origin and the polytope using a closest point routine. The algorithm is further enhanced by the generation of dual solutions which enable us to deform the polytope so that it is favorably positioned with respect to the origin and the axis of sliding motion. The overall scheme is easy to implement, requires a minimal amount of storage, and produces quick good quality lower bounds for the problem in its infinite convergence process. A switchover to the simplex method or an interior point method is also possible, using the current available solution as an advanced start. Preliminary computational results are provided along with implementation guidelines.

Consumer Preferences Toward Product Attributes of Dietary Supplements Under Mandatory Food Traceability Systems in Korea

ABSTRACT Korea, mandatory Food Traceability System (FTS) regulation on dietary supplements has been enforced since the end of 2014, which has allowed government to collect electronic data to trace food. The aim of this study was to determine which additional attributes of dietary food supplements are preferred by consumers and to assess the value of consumers’ willingness to pay (WTP) for these attributes. This investigation revealed that gender, age, income, and awareness were the most frequently reported drivers of purchase behavior. Gender was the most influential socioeconomic characteristic. The results of the conditional and random parameter logit model suggested that Korean consumers have the highest WTP for origin of all ingredients, followed by side-effect warnings, quality-related certifications, and date of import. It will be worthwhile to identify the value of relevant attributes once other nations establish FTS policies and improve food traceability services or regulations.

The Influence of Open Data Policies on Public Innovation

Nowadays, the policy of open data disclosure has become one of the globally used tools for public innovation. For this reason, this study investigates whether the policy has eventually created the achievement of public innovation in Korea. To this end, this study evaluates qualities of the fourteen thousand open data in Korea that is disclosed to the public and compares it with indexes such as the usage of data, transparency index, and Government 3.0 Excellency Index, which are regarded as the outcome of the disclosure. Based on the result, this study aims to suggest future orientation for the policy.

A Case Study in an Automotive Assembly Line: Exploring the Design Framework for Manufacturing Gamification

Industrial revolution which is represented by specialization, standardization, and simplification significantly improves productivity, however it makes tasks in production line more simple and repetitive. This monotonous work environment affects most factory workers to be suffered from lack of motivation and boredom, so consequently makes workers to perceive their job unsatisfied and meaningless. We believe that gamification approach can make this tedious workplace more playful and motivating. In this context, a case study was conducted for a bolt-tightening task in the automotive assembly line. Especially, we explored our five-step design framework which can be useful as a basic procedure for the manufacturing gamification: (1) target system analysis; (2) goal and constraints identification; (3) concept generation; (4) concept evaluation; and (5) scenario development. Based on this design framework, a gamified interface for a bolt-tightening task was developed. The effectiveness of gamified interface was evaluated by lab-based experiment with semi-structured interview, and lessons learnt and related design suggestions are also dealt with.