Solving large batches of traveling salesman problems with parallel and distributed computing

•Gives a practical and effective method for computationally solving numerous TSP instances.•Can use different TSP solvers we test an exact method and a heuristic.•Can handle TSPs of differing sizes efficiently with a simple processing rule.•This problem arises in design of transportation networks, d...

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Veröffentlicht in:	Computers & operations research Jg. 85; S. 87 - 96
Hauptverfasser:	Ozden, S.G., Smith, A.E., Gue, K.R.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Ltd 01.09.2017 Pergamon Press Inc
Schlagworte:	Computer networks Distributed computing Distributed processing Facilities design Heuristic Operations research Order picking Parallel computing Parallel processing Routing Traveling salesman problem TSP Warehouses TSP Facilities design Routing Parallel computing Distributed computing
ISSN:	0305-0548, 1873-765X, 0305-0548
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Abstract	•Gives a practical and effective method for computationally solving numerous TSP instances.•Can use different TSP solvers we test an exact method and a heuristic.•Can handle TSPs of differing sizes efficiently with a simple processing rule.•This problem arises in design of transportation networks, distribution networks and warehouse facilities. In this paper, we describe and compare serial, parallel, and distributed solver implementations for large batches of Traveling Salesman Problems using the Lin–Kernighan Heuristic (LKH) and the Concorde exact TSP Solver. Parallel and distributed solver implementations are useful when many medium to large size TSP instances must be solved simultaneously. These implementations are found to be straightforward and highly efficient compared to serial implementations. Our results indicate that parallel computing using hyper-threading for solving 150- and 200-city TSPs can increase the overall utilization of computer resources up to 25% compared to single thread computing. The resulting speed-up/physical core ratios are as much as ten times better than a parallel and concurrent version of the LKH heuristic using SPC3 in the literature. For variable TSP sizes, a longest processing time first heuristic performs better than an equal distribution rule. We illustrate our approach with an application in the design of order picking warehouses.
AbstractList	In this paper, we describe and compare serial, parallel, and distributed solver implementations for large batches of Traveling Salesman Problems using the Lin-Kernighan Heuristic (LKH) and the Concorde exact TSP Solver. Parallel and distributed solver implementations are useful when many medium to large size TSP instances must be solved simultaneously. These implementations are found to be straightforward and highly efficient compared to serial implementations. Our results indicate that parallel computing using hyper-threading for solving 150- and 200-city TSPs can increase the overall utilization of computer resources up to 25% compared to single thread computing. The resulting speed-tip/physical core ratios are as much as ten times better than a parallel and concurrent version of the LKH heuristic using SPC3 in the literature. For variable TSP sizes, a longest processing time first heuristic performs better than an equal distribution rule. We illustrate our approach with an application in the design of order picking warehouses. •Gives a practical and effective method for computationally solving numerous TSP instances.•Can use different TSP solvers we test an exact method and a heuristic.•Can handle TSPs of differing sizes efficiently with a simple processing rule.•This problem arises in design of transportation networks, distribution networks and warehouse facilities. In this paper, we describe and compare serial, parallel, and distributed solver implementations for large batches of Traveling Salesman Problems using the Lin–Kernighan Heuristic (LKH) and the Concorde exact TSP Solver. Parallel and distributed solver implementations are useful when many medium to large size TSP instances must be solved simultaneously. These implementations are found to be straightforward and highly efficient compared to serial implementations. Our results indicate that parallel computing using hyper-threading for solving 150- and 200-city TSPs can increase the overall utilization of computer resources up to 25% compared to single thread computing. The resulting speed-up/physical core ratios are as much as ten times better than a parallel and concurrent version of the LKH heuristic using SPC3 in the literature. For variable TSP sizes, a longest processing time first heuristic performs better than an equal distribution rule. We illustrate our approach with an application in the design of order picking warehouses.
Author	Ozden, S.G. Smith, A.E. Gue, K.R.
Author_xml	– sequence: 1 givenname: S.G. surname: Ozden fullname: Ozden, S.G. email: gokhan@auburn.edu organization: Department of Industrial & Systems Engineering, Auburn University, Auburn, AL 36849, USA – sequence: 2 givenname: A.E. surname: Smith fullname: Smith, A.E. email: smithae@auburn.edu organization: Department of Industrial & Systems Engineering, Auburn University, Auburn, AL 36849, USA – sequence: 3 givenname: K.R. surname: Gue fullname: Gue, K.R. email: kevin.gue@louisville.edu organization: Department of Industrial Engineering, University of Louisville, Louisville, KY 40292, USA
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Snippet	•Gives a practical and effective method for computationally solving numerous TSP instances.•Can use different TSP solvers we test an exact method and a... In this paper, we describe and compare serial, parallel, and distributed solver implementations for large batches of Traveling Salesman Problems using the...
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SubjectTerms	Computer networks Distributed computing Distributed processing Facilities design Heuristic Operations research Order picking Parallel computing Parallel processing Routing Traveling salesman problem TSP Warehouses
Title	Solving large batches of traveling salesman problems with parallel and distributed computing
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