• Journal of Korean Society for Quality Management
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• v.19 no.1
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• pp.95-102
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• 1991

This paper is concerned with the production-distribution problem with multiple conflicting objectives. In reality business firms should take account not only of the profit maximization but of various environmental criteria, namely customer services, in order to improve the business logistics. A production-distribution model of goal porgramming type considering the lead time and distribution cost by products is constructed, the solution algorithm is developed, which is based on the Ignizio's method. A numerical example is given to demonstrate the applicability of goal programming for production-distribution problem.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.1
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• pp.95-103
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• 1998

This paper is concentraed on a study of operation for multiple objectives in a automated manufacturing system with multiple machining cells . Largest Sum Processing-time First(LSPF) was developed in order to minimize makespan, mean flowtime and maximize mean utilization and compare with Ho and Chang's algorithm(HC) and Hunsucker and Shah's algorithm(HS). The results show that LSPF provides better soutions than HC at 78.2% and than HS at 67.8% of total problems to frequency . LSPF reduces 5.8% of makespan by HC and 22.1% of the value by HS and curtails 15.8% , 7.5% of mean flowtime by receptive algorithms(HC, HS). And mean utilization is also higher about 5.5% than HC and HS.

• Management Science and Financial Engineering
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• v.11 no.2
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• pp.45-60
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• 2005

This paper investigates the application of the computer method COMSOAL (Computer Method of Sequencing Operations for Assembly Lines) to the process routing (PR) problem with multiple objectives. In any computer aided process planning (CAPP) system, one of the most critical activities for manufacturing a part could be to generate the sequence that optimizes production time, production cost, machine utilization or with multiple these criteria. The COMSOAL has been adopted to find the optimum sequence of operations that optimizes two major conflicting criteria : production cost and production quality. The COMSOAL is here slightly modified to simultaneously generate and evaluate a set of possible solutions (called as population) instead of processing a solution stepwise in each iteration. The significant features of the COMSOAL include : no parameters settings needed, and a guarantee of feasible solutions. Experimental results show that COMSOAL is a simple but powerful method to quickly generate multiple feasible solutions which are as good as the ones obtained from several other well-known process routing algorithms.

• Smart Structures and Systems
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• v.4 no.5
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• pp.655-666
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• 2008

In the past 20 years, seismic isolation has see a variety of applications in design of structures to mitigate seismic hazard. In particular, isolation has been seen as a means of achieving enhanced seismic performance objectives, such as those for hospitals, critical emergency response facilities, mass electronic data storage centers, and similar buildings whose functionality following a major seismic event is either critical to the public welfare or the financial solvency of an organization. While achieving these enhanced performance objectives is a natural (and oftentimes requisite) application of seismic isolation, little attention has been given to the extension of current design practice to isolated buildings which may have more conventional performance objectives. The development of a rational design methodology for isolated buildings requires thorough investigation of the behavior of isolated structures subjected to seismic input of various recurrence intervals, and which are designed to remain elastic only under frequent events. This paper summarizes these investigations, and proposed a consistent probabilistic framework within which any combination of performance objectives may be met. Analytical simulations are presented, the results are summarized. The intent of this work is to allow a building owner to make informed decisions regarding tradeoffs between superstructure performance (drifts, accelerations) and isolation system performance. Within this framework, it is possible to realize the benefits of designing isolated buildings for which the design criteria allows consideration of multiple performance goals.

• The Pure and Applied Mathematics
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• v.3 no.1
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• pp.73-81
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• 1996

Multiple objective programming has been a popular research area since 1970. The pervasiveness of multiple objective in decision problems have led to explosive growth during the 1980's. Several approaches (interactive methods, feasible direction methods, criterion weight space methods, Lagrange multiplies methods, etc) have been developed for solving decision problems having multiple objectives. However there are still many mathematically challengings including multiple objective integer, nonlinear optimization problems which require further mathematically oriented research. (omitted)

• Journal of Korean Institute of Industrial Engineers
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• v.14 no.1
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• pp.119-131
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• 1988

Vehicle Scheduling Problem (VSP) is a generic name given to a whole class of problems involving the visiting of "stations" by "vehicles," where a time is associated with each activity. The studies performed to date have the common feature of a single objective while satisfying a set of restrictions and known customer supplies or demands. However, VSPs may involve relevant multiple objectives and probabilistic supplies or demands at stations, creating multicriteria stochastic VSPs. This paper proposes a heuristic algorithm based on goal programming approach to schedule the most satisfactory vehicle routes of a bicriteria VSP with probabilistic supplies at stations. The two relevant objectives are the minimization of the expected travel distance of vehicles and the minimization of the due time violation for collection service at stations by vehicles. The algorithm developed consists of three major stages. In the first stage, an artificial capacity of vehicle is determined, on the basis of decision maker's subjective estimates. The second one clusters a set of stations into subsets by applying an efficient cluster method developed. In the third one, the stations in each subset are scheduled by applying an iterative goal programming heuristic procedure to each cluster.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.46
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• pp.207-219
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• 1998

Multiple Objective Programming(MOP) has been suggested for the solution of completed decision problems. Decision analysis in numerous areas, including energy and environmental planning, necessarily requires consideration of multiple conflicting objectives, MOP has been successfully applied to a number of these problems. The objective of this paper is to present a MOP process which are integrated model with the Input-Output(I-O) analysis for energy and environment planning in industrial sectors. In the model, three objectives are observed such as (1) value added (2) total energy consumption and (3) environmental impacts. Special emphasis is placed on the police implications of industrial structures.

• Journal of the military operations research society of Korea
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• v.27 no.2
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• pp.25-36
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• 2001

In this paper a new heuristic algorithm has been developed and presented for logistics for multiple objectives in an automated manufacturing systems. We proposed Simallest Processing and Average setup time Ratio First(SPARF) algorithm for multiple criteria under sequence setup time. The heuristic algorithm is implemented on the various problem cases by number of jobs and machines. The proposed algorithm provided smaller than the previously documented heuristics. The results obtained show a superior solution by the new heuristic over previous heuristics on all problem sizes. we perform analyses of variance to fortify the above results of comparison with the previous algorithms to the four cases using Statistical Analysis System(SAS) package. The results show that the larger is the number of groups or cells, the bigger is the amount of improvement by the proposed algorithms. It suggests that the algorithms proposed is strongly influenced by the number of cells, groups and interaction of these factors.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.54 no.5
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• pp.769-781
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• 2021

Given data about the annual fishery yield of the common squid Todarodes pacificus, and the catch-per-unit-effort (CPUE) data from multiple fisheries from 2000-2018, we applied a Bayesian state - space assessment model for the squid population. One of our objectives was to do a stock assessment, simultaneously incorporating CPUE data from the following three fisheries, (i) large trawl, (ii) jigger, and (iii) large purse seine, which comprised on average a year about 65% of all fisheries, allowing possible correlations to be reflected. Other objectives were to consider both observation and process errors and to apply objective priors of parameters. The estimated annual exploitable biomass was in the range of 3.50×10⁵ to 1.22×10⁶ MT, the estimated intrinsic growth rate was 1.02, and the estimated carrying capacity was 1,151,259 MT. Comparison with available results from stock assessment of independently analyzed single fisheries revealed a large difference from the estimated values, suggesting that stock assessment based on multiple fisheries should be performed.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1300-1304
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• 2003

The following paper deals with a control problem of a mobile robot in home network environment. The home network causes the mobile robot to communicate with sensors to get the sensor measurements and to be adapted to the environment changes. To get the improved performance of control of a mobile robot in spite of the change in home network environment, we use the fuzzy inference system with multiple reward reinforcement learning. The multiple reward reinforcement learning enables the mobile robot to consider the multiple control objectives and adapt itself to the change in home network environment. Multiple reward fuzzy Q-learning method is proposed for the multiple reward reinforcement learning. Multiple Q-values are considered and max-min optimization is applied to get the improved fuzzy rule. To show the effectiveness of the proposed method, some simulation results are given, which are performed in home network environment, i.e., LAN, wireless LAN, etc.