New Insertion and Postoptimization Procedures for the Traveling Salesman Problem

Gendreau, Michel; Hertz, Alain; Laporte, Gilbert

doi:10.1287/opre.40.6.1086

Cited by 445 publications

(217 citation statements)

References 16 publications

Supporting

Mentioning

192

Contrasting

Unclassified

Order By: Relevance

“…Gendreau et al [67] proposed a TS method based on the generalized insertion heuristic GENIUS by Gendreau et al [66] and the AMP developed by Rochat and Taillard [123]. The method was compared to the results presented in Taillard [137] and shown to perform better on instances with variable unit running costs.…”

Section: Standard Fleet Size and MIX Vehicle Routing Problemmentioning

confidence: 99%

Industrial aspects and literature survey: Fleet composition and routing

Hoff

Andersson

Christiansen

et al. 2010

Computers & Operations Research

293

150

View full text Add to dashboard Cite

The purpose of this paper is to describe industrial aspects of combined fleet composition and routing in maritime and road-based transportation, and to present the current status of research in the form of a comprehensive literature review. With a backdrop of industrial aspects, a categorized survey of relevant literature since the first published papers in the 1950's is given. First, the literature review discusses some early seminal and application-oriented papers, presents a classification of problems, and then focuses on a basic definition of combined fleet composition and routing: the fleet size and mix vehicle routing problem. Three basic mathematical formulations from the literature are presented and compared. Further, the literature of extended and related problems is described and categorized. Surveys of application oriented research in road-based and maritime transportation conclude the review. Finally, we contrast the literature with aspects of industrial applications from a critical, but constructive stance. Major issues for future work are suggested.

show abstract

Section: Standard Fleet Size and MIX Vehicle Routing Problemmentioning

confidence: 99%

Industrial aspects and literature survey: Fleet composition and routing

Hoff

Andersson

Christiansen

et al. 2010

Computers & Operations Research

293

150

View full text Add to dashboard Cite

show abstract

“…The authors develop a tabu search algorithm (called SPLITABU) for solving the k-SDVRP. Their algorithm has three phases: (1) initial feasible solution phase-make as many direct trips to customers as possible and then solve a giant traveling salesman tour using the GENIUS algorithm (Gendreau et al [12]); (2) tabu search phaseremove a customer from a current set of routes and insert it on a new route or an existing route with available capacity in the cheapest way, and consider inserting a customer on a route without removing it from its current route; and (3) final improvement phase-improve the solution from the second phase (apply the GENIUS algorithm to individual routes). SPLITABU has only two parameters that need to be set: the length of the tabu list and the maximum number of iterations.…”

Section: Heuristicsmentioning

confidence: 99%

Recent Developments in Modeling and Solving the Split Delivery Vehicle Routing Problem

Gulczynski¹,

Golden²,

Wasil³

2008

State-of-the-Art Decision-Making Tools in the Information-Intensive Age

View full text Add to dashboard Cite

In the split delivery vehicle routing problem, a customer's demand can be split among several vehicles. In the last five years or so, researchers have proposed exact and approximate solution methods, modeled variants with time windows and pickups, and developed large-scale benchmark problems. In this tutorial, we summarize the recent literature on the split delivery vehicle routing problem, describe solution procedures and results of computational experiments, and suggest directions for future research.

show abstract

“…It uses the solution procedures described earlier for all six oneunit cycles, as well as heuristic solutions using GENIUS (Gendreau et al 1992) to the TSPs formulated in Theorems 1 and 2. The smallest cycle time is chosen.…”

Section: Proof Note That E = ( ; ; ; M −mentioning

confidence: 99%

Scheduling in Robotic Cells: Heuristics and Cell Design

Kamoun¹,

Hall

Sriskandarajah

1999

Operations Research

View full text Add to dashboard Cite

This paper considers scheduling problems arising in robot-served manufacturing cells in which the machines are configured in a flowshop that repetitively produces a family of similar parts. We study the problem of determining the robot move cycle and the part sequence that jointly minimize the average steady-state cycle time required for the repetitive production of a minimal part set, or equivalently maximize the long-run throughput rate. Three earlier related papers provide algorithms, or proofs of intractability, for a variety of cell configurations. We use the intuition developed there to design and test simple heuristic procedures for the part sequencing problem under different robot move cycles in three-machine cells where that problem is intractable. This enables us to develop a heuristic procedure for a general three-machine cell. A methodology for extending this heuristic to four-machine cells is described and tested. Ideas for extension to even larger cells are also discussed. We also describe and test two heuristics for a cell design problem that involves partitioning machines into cells as well as determining the sequence of robot moves and parts. Finally, we provide a list of open research problems.

show abstract

New Insertion and Postoptimization Procedures for the Traveling Salesman Problem

Cited by 445 publications

References 16 publications

Industrial aspects and literature survey: Fleet composition and routing

Industrial aspects and literature survey: Fleet composition and routing

Recent Developments in Modeling and Solving the Split Delivery Vehicle Routing Problem

Scheduling in Robotic Cells: Heuristics and Cell Design

Contact Info

Product

Resources

About