Tieju Ma

Agent-based modeling on technological innovation as an evolutionary process

Abstract This paper describes a multi-agent model built to simulate the process of technological innovation, based on the widely accepted theory that technological innovation can be seen as an evolutionary process. The actors in the simulation include producers and a large number of consumers. Every producer will produce several types of products at each step. Each product is composed of several design parameters and several performance parameters (fitness components). Kauffman’s famous NK model is used to deal with the mapping from a design parameter space (DPS) to a performance parameter space (PPS). In addition to the constructional selection, which can be illustrated by the NK model, we added environmental selection into the simulation and explored technological innovation as the result of the interaction between these two kinds of selection.

A group decision-making approach to uncertain quality function deployment based on fuzzy preference relation and fuzzy majority

Quality function deployment (QFD) is one of the very effective customer-driven quality system tools typically applied to fulfill customer needs or requirements (CRs). It is a crucial step in QFD to derive the prioritization of design requirements (DRs) from CRs for a product. However, effective prioritization of DRs is seriously challenged due to two types of uncertainties: human subjective perception and customer heterogeneity. This paper tries to propose a novel two-stage group decision-making approach to simultaneously address the two types of uncertainties underlying QFD. The first stage is to determine the fuzzy preference relations of different DRs with respect to each customer based on the order-based semantics of linguistic information. The second stage is to determine the prioritization of DRs by synthesizing all customers’ fuzzy preference relations into an overall one by fuzzy majority. Two examples, a Chinese restaurant and a flexible manufacturing system, are used to illustrate the proposed approach. The restaurant example is also used to compare with three existing approaches. Implementation results show that the proposed approach can eliminate the burden of quantifying qualitative concepts and model customer heterogeneity and design team’s preference. Due to its easiness, our approach can reduce the cognitive burden of QFD planning team and give a practical convenience in QFD planning. Extensions to the proposed approach are also given to address application contexts involving a wider set of HOQ elements.

Technology adoption with limited foresight and uncertain technological learning

Most previous optimization models on technology adoption assume perfect foresight over the long term. In reality, decision-makers do not have perfect foresight, and the endogenous driving force of technology adoption is uncertain. With a stylized optimization model, this paper explores the adoption of a new technology, its associated cost dynamics, and technological bifurcations with limited foresight and uncertain technological learning. The study shows that when modeling with limited foresight and technological learning, (1) the longer the length of the decision period, the earlier the adoption of a new technology, and the value of a foresight can be amplified with a high learning rate. However, when the decision period is beyond a certain length, further extending its length has little influence on adopting the new technology; (2) with limited foresight, decisions aiming at minimizing the total cost of each decision period will commonly result in a non-optimal solution from the perspective of the entire decision horizon; and (3) the range of technological bifurcation is much larger than that with perfect foresight, but uncertainty in technological learning tends to reduce the range by removing the early adoption paths of a new technology.

Optimizing layouts of initial AFV refueling stations targeting different drivers, and experiments with agent-based simulations

The number of refuelling stations for AFVs (alternative fuel vehicles) is limited during the early stages of the diffusion of AFVs. Different layouts of these initial stations will result in different degrees of driver concern regarding refueling and will therefore influence individuals’ decisions to adopt AFVs. The question becomes “what is an optimal layout for these initial stations? Should it target all drivers or just a portion of them, and if so, which portion?” Further, how does the number of initial AFV refueling stations influence the adoption of AFVs? This paper explores these questions with agent-based simulations. Using Shanghai as the basis of computational experiments, this paper first generates different optimal layouts using a genetic algorithm to minimize the total concern of different targeted drivers and then conducts agent-based simulations on the diffusion of AFVs with these layouts. The main findings of this study are that (1) targeting drivers in the city center can induce the fastest diffusion of AFVs if AFV technologies are mature and (2) it is possible that a larger number of initial AFV refueling stations may result in slower diffusion of AFVs because these initial stations may not have sufficient customers to survive. The simulations can provide some insights for cities that are trying to promote the diffusion of AFVs.

Coping with Group Behaviors in Uncertain Quality Function Deployment

Quality function deployment (QFD) is a planning and problem-solving tool gaining wide acceptance for translating customer needs (CNs) into technical attributes (TAs) of a product. It is a crucial step to derive the prioritization of TAs from CNs in QFD. However, it is not so straightforward to prioritize TAs due to two types of uncertainties: human subjective perception and user variability. The main focus of this article is to propose a group decision-making approach to uncertain QFD with an application to a flexible manufacturing system design. The proposed approach performs computations solely based on the order-based semantics of linguistic labels to eliminate the burden of quantifying qualitative concepts in QFD. Moreover, it incorporates the importance weights of users and the concept of fuzzy majority into aggregations of individual fuzzy preference relations of different TAs in order to model the group behaviors in QFD. Finally, based on a quantifier-guided net flow score procedure, the proposed approach derives a priority ranking with a classification of TAs into important and unimportant ones so as to provide a better decision-support to the decision-maker. Due to the easiness in articulating preferential information, our approach can reduce the cognitive burden of QFD planning team and give a practical convenience in the process of QFD planning.

Adoption of an emerging infrastructure with uncertain technological learning and spatial reconfiguration

This paper develops a stylized (or conceptual) system optimization model to analyze the adoption of an emerging infrastructure associated with uncertain technological learning and spatial reconfigurations. The model first assumes that the emerging infrastructure will be implemented for the entire system when it is adopted. With the model, this paper explores (1) how the emerging infrastructures initial investment cost, technological learning and its uncertainty, market size, and efficiency influence the adoption of the emerging infrastructure and (2) how the efficiency and investment cost of the associated technology (which will be located in a different place with the adoption of the emerging infrastructure) influence the adoption of the emerging infrastructure. Then, this paper extends the model and explores whether it is a better solution to implement the emerging infrastructure for part of the distance from resource site to demand site if its efficiency is a function of the implemented distance. With optimizations under three types of efficiency dynamics, this paper finds that whether the emerging infrastructure should be implemented partly or entirely is not determined by the value of its efficiency but by the dynamics of its efficiency.

A simulation method to generate commute trips-for agent-based modeling on co-diffusion of alternative fuel vehicles and their filling stations

Existing agent-based models of co-diffusion of alternative fuel vehicles and their corresponding filling stations are very stylized. In addition, there is a lack of methodologies for linking such models with a real social and economic background. Aiming at solving this problem, this paper puts forward a method that uses widely available social, economic, and spatial data to generate driver agents’ commute trips, which play an important role in such models. We tested the method with the data of Shanghai and Beijing, two of the largest cities in China, and found the commute times resulting from the method were in accordance with survey results, which validates the potential usefulness of the method.

A computing with words based approach to multicriteria energy planning

Exploitation of new and innovative energy alternatives is a key means towards a sustainable energy system. This paper proposes a linguistic energy planning model with computation solely on words as well as considering the policy-makers preference information. To do so, a probabilistic approach is first proposed to derive the underlying semantic overlapping of linguistic labels from their associated fuzzy membership functions. Second, a satisfactory-oriented choice function is proposed to incorporate the policy-makers preference information. Third, our model is extended to multicriteria case with linguistic importance weights. One example, borrowed from the literature, is used to show the effectiveness and advantages of our model.

Environmental Kuznets curve for some countries - regression and agent-based approach

In the past few years there has been a lots of research on the topic of environmental quality and economic growth. This paper describes the theory of Environmental Kuznets Curve and illustrates the concept with special reference to some developed countries. One of the objectives of this study is to test the shape of the relationship between income and environmental degradation. We use regression and agent based approach to identi@ the models of income and environmental abuse. This study uses data to try and capture some of the effects that have happened in recent years in some countries. Our goal is to attempt if model given by the agents can fit the data better than regression models. The paper emphasises ecological consequences of economical development.

Optimizing systematic technology adoption with heterogeneous agents

The traditional operational optimization models of systematic technology adoption commonly assume the existence of a global social planner and ignore the existence of heterogeneous decision makers who interact with each other. This paper develops a stylized (or conceptual) optimization model of systematic technology adoption with heterogeneous agents (i.e., decision makers) and uncertain technological learning. Each agent attempts to identify optimal solutions to adopting technologies for a portion of the entire system. The agents in the model have different foresight and different risk attitudes and interact with one another in terms of technological spillover.