Expressing and processing complex preferences in route planning queries: Towards a fuzzy-set-based approach

Hadjali, Allel; Mokhtari, Amine; Pivert, Olivier

doi:10.1016/j.fss.2012.01.006

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…To model uncertainty in preferences, small perturbations can be introduced into goals, weights, or reference vector-based methods. Thus, fuzzy logic can be used as a natural means for handling uncertainty in preferences [78,79], such as reference points [35], weights [80], preference relation [81,82], and outranking [63]. Preference relation, utility function, and outranking are not strictly based on objective importance in values, which allow uncertainty to a certain degree.…”

Section: Discussionmentioning

confidence: 99%

A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges

Wang

Olhofer

Jin

2017

Complex Intell. Syst.

122

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Evolutionary multi-objective optimization aims to provide a representative subset of the Pareto front to decision makers. In practice, however, decision makers are usually interested in only a particular part of the Pareto front of the multi-objective optimization problem. This is particularly true when the number of objectives becomes large. Over the past decade, preference-based multi-objective optimization has attracted increasing attention from both academia and industry due to its significance in both theory and practice. Significant progress has been made in evolutionary multiobjective optimization and multi-criteria decision communities, although many open issues still remain to be addressed. This paper provides a concise review on preference-based multi-objective optimization, including various preference modeling methods and existing preference-based optimization methods, as well as a brief discussion of the main future challenges.

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Section: Discussionmentioning

confidence: 99%

A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges

Wang

Olhofer

Jin

2017

Complex Intell. Syst.

122

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“…Similarly, the parameters in the dominance relations represent the DM's preferences of solutions or ROIs. • Decision rules: Decision rules aim to model the DM's pairwise solution comparisons so that the solutions can be differentiated according to the defined preference rules or relations [78], such as the rough set based approach [79], [80], [81], fuzzy rules [82], [83], and preference rules [84], [85]. In summary, preference models bridge the gap between the DM and the optimization algorithm in various ways, in order to meet different situations and preference articulations.…”

Section: A Multi-objective Optimizationmentioning

confidence: 99%

Towards Fairness-Aware Multi-Objective Optimization

Yu¹,

Ma²,

Du³

et al. 2022

Preprint

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Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in real life, such as fair resource allocation problems and datadriven multi-objective optimization problems. This paper aims to illuminate and broaden our understanding of multi-objective optimization from the perspective of fairness. To this end, we start with a discussion of user preferences in multi-objective optimization and then explore its relationship to fairness in machine learning and multi-objective optimization. Following the above discussions, representative cases of fairness-aware multiobjective optimization are presented, further elaborating the importance of fairness in traditional multi-objective optimization, data-driven optimization and federated optimization. Finally, challenges and opportunities in fairness-aware multi-objective optimization are addressed. We hope that this article makes a small step forward towards understanding fairness in the context of optimization and promote research interest in fairness-aware multi-objective optimization.

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“…where δ ep is equal to 1, if edge e is contained in path p, and 0, otherwise. Expression (7) states that the flow on an edge e is equal to the sum of all the path flows on paths p that contain (traverse) edge e. Let d rs denote the demand associated with O/D pair rs, which should be the sum of the flows on different paths:…”

Section: Optimization Problemmentioning

confidence: 99%

“…Lot of works deal with route choice (mostly for cars or vans, but for aircraft [3] as well), and navigation [4] [5] in transportation area [2], and some of them take uncertainty into account as well, e.g. dealing with stochastic shortest path [6], using fuzzy [7] or two limit values [8] or neural networks [9]. Route planning problem and uncertainty can occur in many different networks, like electric power systems, telecommunication networks, water distribution, networks, and transportation systems, but this paper (especially from session 4.2) focuses on transportation.…”

Section: Introductionmentioning

confidence: 99%

Decision Support for Route Search and Optimum Finding in Transport Networks under Uncertainty

Szücs¹

2015

Journal of Applied Research and Technology

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The aim of this paper is to find solution for route planning in road network for a user, and to find the equilibrium in the path optimization problem, where the roads have uncertain attributes. The concept is based on the Dempster-Shafer theory and Dijkstra's algorithm, which help to model the uncertainty and to find the best route, respectively. Based on uncertain influencing factors an interval of travel time (so called cost interval) of each road can be calculated. An algorithm has been outlined for determining the best route comparing the intervals and using decision rules depending on the user's attitude. Priorities can be defined among the rules, and the constructed rule based mechanism for users' demands is great contribution of this paper. The first task is discussed in more general in this paper, i.e. instead of travel time a general cost is investigated for any kind of network. At the solution of the second task, where the goal is to find equilibrium in transport network at case of uncertain situation, the result of the first task is used. Simulation tool has been used to find the equilibrium, which gives only approximate solution, but this is sufficient and appropriate solution for large networks. Furthermore this is built in a decision support system, which is another contribution of this work. At the end of the paper the implementation of the theoretical concept is presented with a test bed of a town presenting effects of different uncertain influencing factors for the roads.

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Expressing and processing complex preferences in route planning queries: Towards a fuzzy-set-based approach

Cited by 7 publications

References 19 publications

A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges

A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges

Towards Fairness-Aware Multi-Objective Optimization

Decision Support for Route Search and Optimum Finding in Transport Networks under Uncertainty

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