Multi-objective new product development by complete Pareto front and ripple-spreading algorithm

Evolutionary Computation

Leeson

et al. 2016

Self Cite

Inspirations from nature have contributed fundamentally to the development of evolutionary computation. Learning from the natural ripple-spreading phenomenon, this article proposes a novel ripple-spreading algorithm (RSA) for the path optimization problem (POP). In nature, a ripple spreads at a constant speed in all directions, and the node closest to the source is the first to be reached. This very simple principle forms the foundation of the proposed RSA. In contrast to most deterministic top-down centralized path optimization methods, such as Dijkstra’s algorithm, the RSA is a bottom-up decentralized agent-based simulation model. Moreover, it is distinguished from other agent-based algorithms, such as genetic algorithms and ant colony optimization, by being a deterministic method that can always guarantee the global optimal solution with very good scalability. Here, the RSA is specifically applied to four different POPs. The comparative simulation results illustrate the advantages of the RSA in terms of effectiveness and efficiency. Thanks to the agent-based and deterministic features, the RSA opens new opportunities to attack some problems, such as calculating the exact complete Pareto front in multiobjective optimization and determining the kth shortest project time in project management, which are very difficult, if not impossible, for existing methods to resolve. The ripple-spreading optimization principle and the new distinguishing features and capacities of the RSA enrich the theoretical foundations of evolutionary computation.

Section: The Relationships Of the Rsa With Other Methodsmentioning

confidence: 99%

Deterministic Agent-Based Path Optimization by Mimicking the Spreading of Ripples

Evolutionary Computation

Leeson

et al. 2016

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“…Some theoretical conditions and a general methodology were reported in Hu et al (2013), and they were successfully applied to a multiobjective route planning problem (Hu et al 2013) and a new products development problem (Hu et al 2014). In this article, by optimizing the investment scheme in agriculture risk governance (ARG), we will illustrate how to introduce the MOOP methodology of Hu et al (2013) into resource allocation optimization in DRRG.…”

Section: Introductionmentioning

confidence: 95%

“…In this article, by optimizing the investment scheme in agriculture risk governance (ARG), we will illustrate how to introduce the MOOP methodology of Hu et al (2013) into resource allocation optimization in DRRG. Since the resource allocation problem in ARG is different from those case studies in Hu et al (2013) and Hu et al (2014), we first make some necessary modifications in Sect. 2 to the methodology of Hu et al (2013).…”

Section: Introductionmentioning

confidence: 99%

A New Method for Resource Allocation Optimization in Disaster Reduction and Risk Governance

Int J Disaster Risk Sci

Tao

et al. 2016

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How to allocate and use resources play a crucial role in disaster reduction and risk governance (DRRG). The challenge comes largely from two aspects: the resources available for allocation are usually limited in quantity; and the multiple stakeholders involved in DRRG often have conflicting interests in the allocation of these limited resources. Therefore resource allocation in DRRG can be formulated as a constrained multiobjective optimization problem (MOOP). The Pareto front is a key concept in resolving a MOOP, and it is associated with the complete set of optimal solutions. However, most existing methods for solving a MOOPs only calculate a part or an approximation of the Pareto front, and thus can hardly provide the most effective or accurate support to decisionmakers in DRRG. This article introduces a new method whose goal is to find the complete Pareto front that resolves the resource allocation optimization problem in DRRG. The theoretical conditions needed to guarantee finding a complete Pareto front are given and a practicable, ripplespreading algorithm is developed to calculate the complete Pareto front. A resource allocation problem of risk governance in agriculture is then used as a case study to test the applicability and reliability of the proposed method. The results demonstrate the advantages of the proposed method in terms of both solution quality and computational efficiency when compared with traditional methods.

“…The response system for regional disasters is closely related to the developmental disaster risk governance paradigm (see below for more discussion). The efficiency and cost-effectiveness of a response system for regional disasters can be improved via optimization under the rules of effectiveness, efficiency, and equity (Shi 2011b ; Hu et al 2014 ).…”

Section: Frontiers In the Disaster Risk Science Researchmentioning

confidence: 99%

Disaster Risk Science: A Geographical Perspective and a Research Framework

Shi

Tao

Int J Disaster Risk Sci

et al. 2020

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In this article, we recall the United Nations’ 30-year journey in disaster risk reduction strategy and framework, review the latest progress and key scientific and technological questions related to the United Nations disaster risk reduction initiatives, and summarize the framework and contents of disaster risk science research. The object of disaster risk science research is the “disaster system” consisting of hazard, the geographical environment, and exposed units, with features of regionality, interconnectedness, coupling, and complexity. Environmental stability, hazard threat, and socioeconomic vulnerability together determine the way that disasters are formed, establish the spatial extent of disaster impact, and generate the scale of losses. In the formation of a disaster, a conducive environment is the prerequisite, a hazard is the necessary condition, and socioeconomic exposure is the sufficient condition. The geographical environment affects local hazard intensity and therefore can change the pattern of loss distribution. Regional multi-hazard, disaster chain, and disaster compound could induce complex impacts, amplifying or attenuating hazard intensity and changing the scope of affected areas. In the light of research progress, particularly in the context of China, we propose a three-layer disaster risk science disciplinary structure, which contains three pillars (disaster science, disaster technology, and disaster governance), nine core areas, and 27 research fields. Based on these elements, we discuss the frontiers in disaster risk science research.