Dynamic Traveling Salesman Problem: Value of Real-Time Traffic Information

Cheong, Taesu; White, Chelsea C.

doi:10.1109/tits.2011.2174050

Cited by 37 publications

(17 citation statements)

References 25 publications

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“…In the literature, the most similar models to ours are perhaps Cheong and White (2012), Secomandi (2003). The model in Cheong and White (2012) is also a TSP with random arc costs, but the entire network is visible to the salesman at all times and costs evolve according to an underlying Markov chain.…”

Section: Literature Reviewsupporting

confidence: 62%

“…For instance, in real-time routing it may be possible for the driver to observe outgoing traffic on different routes before selecting the next location to visit. The intelligent use of such real-time information within routing offers transportation companies the opportunity for differentiation and a competitive advantage (Cheong and White 2012, Larsen et al 2008, Psaraftis 1995. One specific example is urban pickup and delivery, where traffic congestion plays a major role in a route's duration and dynamic routing coupled with real-time traffic information can significantly reduce travel times; Cheong and White (2012) mention that such dynamic routing is informally implemented by urban pickup and delivery companies in Tokyo.…”

Section: Introductionmentioning

confidence: 99%

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A Dynamic Traveling Salesman Problem with Stochastic Arc Costs

Toriello

Haskell

Poremba

2014

Operations Research

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We propose a dynamic traveling salesman problem (TSP) with stochastic arc costs motivated by applications, such as dynamic vehicle routing, in which the cost of a decision is known only probabilistically beforehand but is revealed dynamically before the decision is executed. We formulate this as a dynamic program (DP) and compare it to static counterparts to demonstrate the advantage of the dynamic paradigm over an a priori approach. We then apply approximate linear programming (ALP) to overcome the DP's curse of dimensionality, obtain a semi-infinite linear programming lower bound, and discuss its tractability. We also analyze a rollout version of the price-directed policy implied by our ALP and derive worst-case guarantees for its performance. Our computational study demonstrates the quality of a heuristically modified rollout policy using a computationally effective a posteriori bound.Subject classifications: traveling salesman problem; stochastic vehicle routing; approximate dynamic program; semi-infinite linear program.

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Section: Literature Reviewsupporting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

A Dynamic Traveling Salesman Problem with Stochastic Arc Costs

Toriello

Haskell

Poremba

2014

Operations Research

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“…We remark that the optimality equations (1) and (2) affirm that the proposed model takes the non-stationary or timevariant aspects into account (refer to [7] for the stochastic TSP with stationary Markov chains). Then, the optimal policy under the current state at time t is determined as follows:…”

Section: Problem Description and Formulationmentioning

confidence: 67%

“…In most relevant studies, an arc between two nodes has been considered as a single unit (for example, [7]). However, in reality, an arc may consist of multiple road segments.…”

Section: Estimation Of Traffic Congestion and Travel Time Distribmentioning

confidence: 99%

Solving the Dynamic Vehicle Routing Problem Under Traffic Congestion

Kim

Ong

Cheong

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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This paper proposes a dynamic vehicle routing problem (DVRP) model with nonstationary stochastic travel times under traffic congestion. Depending on the traffic conditions, the travel time between two nodes, particularly in a city, may not be proportional to distance and changes both dynamically and stochastically over time. Considering this environment, we propose a Markov decision process model to solve this problem and adopt a rollout-based approach to the solution, using approximate dynamic programming to avoid the curse of dimensionality. We also investigate how to estimate the probability distribution of travel times of arcs which, reflecting reality, are considered to consist of multiple road segments. Experiments are conducted using a real-world problem faced by Singapore logistics/delivery company and authentic road traffic information.Index Terms-Dynamic vehicle routing problem, approximate dynamic programming, uncertain travel times, rollout algorithm.

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“…, C N }, and the distance between two randomly selected cities is denoted as d(c i , c j ), then the solution of the closed path passing all the cities within C just once will be C π ={C π(1) , C π(2) , C π(N ) } to minimize the total travel distance N −1 i=1 d (C π(i) , C π(i+1) ) + d (C π(N ) , C π(1) ). There are many types of TSPs, such as multiobjective TSP (Shim et al 2012), dynamic TSP (Cheong and White 2012), Dubins TSP (Le Ny et al 2012), sequence-dependent TSP (Alkaya and Duman 2013), double TSP (Carrabs et al 2013). For large-scale TSP problems, people tend to figure out an acceptable approximate solution within the time limit.…”

mentioning

confidence: 99%

Hybrid immune algorithm based on greedy algorithm and delete-cross operator for solving TSP

Guo

Ouyang

et al. 2014

Soft Comput

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This paper first introduces the fundamental principles of immune algorithm (IA), greedy algorithm (GA) and delete-cross operator (DO). Based on these basic algorithms, a hybrid immune algorithm (HIA) is constructed to solve the traveling salesman problem (TSP). HIA employs GA to initialize the routes of TSP and utilizes DO to delete routes of crossover. With dynamic mutation operator (DMO) adopted to improve searching precision, this proposed algorithm can increase the likelihood of global optimum after the hybridization. Experimental results demonstrate that the HIA algorithm is able to yield a better solution than that of other algorithms, which also takes less computation time.

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Dynamic Traveling Salesman Problem: Value of Real-Time Traffic Information

Cited by 37 publications

References 25 publications

A Dynamic Traveling Salesman Problem with Stochastic Arc Costs

A Dynamic Traveling Salesman Problem with Stochastic Arc Costs

Solving the Dynamic Vehicle Routing Problem Under Traffic Congestion

Hybrid immune algorithm based on greedy algorithm and delete-cross operator for solving TSP

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