Bicriterion Shortest Paths in Stochastic Time-Dependent Networks

Nielsen, Lars Relund; Pretolani, Daniele; Andersen, Kim Allan

doi:10.1007/978-3-540-85646-7_6

Cited by 9 publications

(10 citation statements)

References 11 publications

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“…As an illustrative example, five experts presented their points of view for four components of risk by linguistic variables for edge (14,15) which indicates the link (Polefasa, Sarvestan) in the network shown in Table 4. Node codes and their names are available in Appendix A. Triangular fuzzy numbers (TFN) of variables are also presented in the next rows of table according to Table 2.…”

Section: Case Study and Experimental Datamentioning

confidence: 99%

“…Population [5,10,11], environment [8,[11][12][13] and accident [1,5,7,9,14] are the main apprehensions to define risk associated with Hazmat incident impacts. Specified regional networks which have been assigned by local authorities for Hazmat transportation, particularly for better enforcement and improving road safety, lead researchers to solve Hazmat routing problem [15][16][17][18] under network constraints. In addition, the nature of hazardous materials [4,19] together with public security [17] have also been studied as important attributes in this area.…”

Section: Introductionmentioning

confidence: 99%

“…Within the first stage, a set of minimum and equitable risk routes are defined for each Hazmat transportation in order to spread the risk equitably over the population, while in the second stage, one among the aforementioned routes and a departure time not less than the preferred one, are assigned to each Hazmat transportation route in order to assure that at any given time, every two vehicles must be sufficiently far apart to minimize the sum of the Hazmat shipment delays [7]. K shortest path algorithm was developed for stochastic and dynamic networks and focused on identifying the exact solution to lead to computational inexplicit in large networks [15]. Defining bi-level objective function is another technique to solve the Hazmat routing problem [9,11,16].…”

Section: Introductionmentioning

confidence: 99%

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Developing a chaotic pattern of dynamic Hazmat routing problem

Mahmoudabadi¹,

Seyedhosseini²

2014

IATSS Research

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The present paper proposes an iterative procedure based on chaos theory on dynamic risk definition to determine the best route for transporting hazardous materials (Hazmat). In the case of possible natural disasters, the safety of roads may be seriously affected. So the main objective of this paper is to simultaneously improve the travel time and risk to satisfy the local and national authorities in the transportation network. Based on the proposed procedure, four important risk components including accident information, population, environment, and infrastructure aspects have been presented under linguistic variables. Furthermore, the extent analysis method was utilized to convert them to crisp values. To apply the proposed procedure, a road network that consists of fifty nine nodes and eighty two-way edges with a pre-specified affected area has been considered. The results indicate that applying the dynamic risk is more appropriate than having a constant risk. The application of the proposed model indicates that, while chaotic variables depend on the initial conditions, the most frequent path will remain independent. The points that would help authorities to come to the better decision when they are dealing with Hazmat transportation route selection.

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Section: Case Study and Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Developing a chaotic pattern of dynamic Hazmat routing problem

Mahmoudabadi¹,

Seyedhosseini²

2014

IATSS Research

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“…Stochastic shortest path problems are also widely studied [4,15]. However, research on stochastic problems that also considers multiple criteria is rare [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Multicriteria decentralized decision making in logistic chains: a dynamic programming approach for collaborative forwarding of air cargo freight

Berger

Schröder

2011

Logist. Res.

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Forwarding air freight cargo globally from the shipper’s door to the door of the consignee is a complex logistic process and involves freight handling by several collaborating logistics companies. The door to door process is currently standardized by the International Air Transport Association with the industry initiative Cargo 2000. Many individuals with their own perspectives along the logistic chain decide how freight is transported and handled but have only limited insights how their decisions influence the follow-up decisions and the final on time delivery. A central planning authority can not be realized due to the heterogeneity of decision makers, the individual interests of the logistics companies, and their global operations. We argue that it is possible to overcome the deficient situation of complex transportation chains like in the air cargo industry by using optimization methods for decentralized decision making. This paper proposes a dynamic programming approach, which enables the decision makers in the decentralized situation to align their decisions better with the decisions of the involved partners. The approach guarantees that sensitive information of the logistics companies is kept local and only the most necessary information is shared along the logistic chain for a better planning. The transportation is planned with regard to multiple criteria, like the expected transportation costs and the probability to deliver the freight on time. We further show that our decentralized and multicriteria approach leads to better results compared to a local strategy that only exploits each decision maker’s own perspective. Our approach is decentralized by nature and needs lean information exchange. Furthermore, it is as strong as a centralized approach that gathers all distributed information but that authorizes the logistic service providers to decide individually

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“…Furthermore, it is assumed that for each departure time, the travel time may not be fully known and hence a probability function is used to express possible travel times. By using the K minimum weight hyperpaths algorithm from Nielsen et al [21] it is possible to find the K best strategies/paths in stochastic time-dependent networks, Nielsen, Pretolani, and Andersen [23]. Furthermore, in Nielsen [20], the algorithm is used as a sub algorithm to solve bicriterion problems in stochastic time-dependent networks.…”

Section: Introductionmentioning

confidence: 99%

Finding the K best policies in a finite-horizon Markov decision process

Nielsen¹,

Kristensen²

2006

European Journal of Operational Research

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Directed hypergraphs represent a general modelling and algorithmic tool, which have been successfully used in many different research areas such as artificial intelligence, database systems, fuzzy systems, propositional logic and transportation networks. However, modelling Markov decision processes using directed hypergraphs has not yet been considered.In this paper we consider finite-horizon Markov decision processes (MDPs) with finite state and action space and present an algorithm for finding the K best deterministic Markov policies. That is, we are interested in ranking the first K deterministic Markov policies in non-decreasing order using an additive criterion of optimality. The algorithm uses a directed hypergraph to model the finite-horizon MDP. It is shown that the problem of finding the optimal policy can be formulated as a minimum weight hyperpath problem and be solved in linear time, with respect to the input data representing the MDP, using different additive optimality criteria.

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Bicriterion Shortest Paths in Stochastic Time-Dependent Networks

Cited by 9 publications

References 11 publications

Developing a chaotic pattern of dynamic Hazmat routing problem

Developing a chaotic pattern of dynamic Hazmat routing problem

Multicriteria decentralized decision making in logistic chains: a dynamic programming approach for collaborative forwarding of air cargo freight

Finding the K best policies in a finite-horizon Markov decision process

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