Stochastic multi-objective optimization: a survey on non-scalarizing methods

Gutjahr, Walter J.; Pichler, Alois

doi:10.1007/s10479-013-1369-5

Cited by 118 publications

(66 citation statements)

References 53 publications

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“…Until now, many methods have been utilized to solve the MODM problems [31][32][33][34]. For example, Zhang et al [31] study the city sustainability evaluation problem by using objective weights approach; Govindan and Sivakumar [32] propose a fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to solve the multi-objective linear programming method in green and low-carbon development; Gutjahr and Pichler [33] use non-scalarizing methods to optimize stochastic multi-objective decision making; Delgoda et al [34] design a novel generic optimization method for irrigation scheduling under multiple objectives; Deng et al [35] propose an improved APSO algorithm to solve the multi-objective optimization model. In particular, MODM [36] and multi-criteria [37] have been used to assess people's life and environment for sustainable urbanization.…”

Section: Modm Methodsmentioning

confidence: 99%

Gathered Village Location Optimization for Chinese Sustainable Urbanization Using an Integrated MODM Approach under Bi-Uncertain Environment

Lu¹,

Wang²,

Hu³

2017

Sustainability

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Urbanization has become a main challenge all over developing countries in the 21st Century. However, decision making should take into account the different national situations with their complex factors to achieve sustainable development. As standards of living have risen in urban areas, local/neighbor urbanization has become a coming trend in China. With this in mind, the paper focuses on the optimization of nearby gathered village locations in Population Migration (PM) with consideration of both qualitative and quantitative criteria. Therefore, an integrated multiple objective decision making approach (MODM) under a bi-uncertain environment is proposed to solve this problem, which is based on the comprehensive Economy-Society-Ecology-Resource-Religion (ESERR) urbanization concept. The first step is to establish a bi-uncertain multiple objective programming model orienting the problem. Secondly, the model process is composed of fuzzy random variable transformation and the expected value model based on a new fuzzy measure, which is given accordingly to obtain the equivalent model. Thirdly, in order to describe the model efficiently, the Multi-Objective Adaptive Global Local Neighbor Particle Swarm Optimization (MOAGLNPSO) with three-dimensional Pareto optimal judgment criteria is designed. Finally, a case study is tested to validate the effectiveness and to illustrate the advantages of the whole approach. This novel approach can help optimize sustainable urbanization strategies and ensure their realistic application.

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Section: Modm Methodsmentioning

confidence: 99%

Gathered Village Location Optimization for Chinese Sustainable Urbanization Using an Integrated MODM Approach under Bi-Uncertain Environment

Lu¹,

Wang²,

Hu³

2017

Sustainability

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“…HRC coordinators need to 5 understand and interpret the trade-offs between different measures quickly in changing and dynamic situation of real world problems [12]. Therefore, developing multi-objective optimization models for HRCs raises enormous challenges [25]. Among various approaches, DSS can accommodate the changing and unpredictable nature of such multi-objective problems [12].…”

Section: Humanitarian Relief Chain Designmentioning

confidence: 99%

A hybrid decision support system for managing humanitarian relief chains

Sahebjamnia

Torabi

Mansouri

2017

Decision Support Systems

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Decisions regarding location, allocation and distribution of relief items are among the main concerns of the Humanitarian Relief Chain (HRC) managers in response to no-notice large-scale disasters such as earthquakes.In this paper, a Hybrid Decision Support System (HDSS) consisting of a simulator, a rule-based inference engine, and a knowledge-based system (KBS) is developed to configure a three level HRC. Three main performance measures including the coverage, total cost, and response time are considered to make an explicit trade-off analysis between cost efficiency and responsiveness of the designed HRC. In the first step, the simulator calculates the performance measures of the different configurations of the HRC under generated number of disaster scenarios. Then, the rule-based inference engine attempts to build the best configuration of the HRC including facilities' locations, relief items' allocation and distribution plan of the scenario under investigation based on calculated performance measures. Finally, the best configuration for each scenario is stored in the KBS as the extracted knowledge from the above analyses. In this way, the HRC managers can retrieve the most appropriate HRC configuration in accordance with the realized post-disaster scenario in an effective and timely manner. The results of a real case study in Tehran demonstrate that the developed HDSS is an effective tool for fast configuration of HRCs using stochastic data.

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“…Dentcheva and Ruszczyński, 2009;Gutjahr and Pichler, 2013;Müller and Stoyan, 2002). For random variables X = (X 1 , … , X q ) and Y = (Y 1 , … , Y q ), this order is defined by…”

Section: Risk-averse Extensionmentioning

confidence: 99%