Vehicle routing concepts in the closed-loop container network of ARN—a case study

Blanc, Ieke le; Krieken, Maaike van; Krikke, Harold; Fleuren, Hein

doi:10.1007/s00291-005-0003-6

Cited by 42 publications

(11 citation statements)

References 13 publications

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“…Note, we started with 10 replications for the smallest number of customers, but changed to n=5 once determining that statistical power was acceptable. This sample size provides sufficient statistical power and is similar to prior work in similar studies (Le Blanc, Krieken, Krikke, andFleuren 2006: Punakivi, Yrjola, andHolmstrom 2001;Sezen 2006;Smaros, Lehtonen, Appelqvist, and Holmstrom 2003). Customer orders were generated from a list of 18,000 customers in a six country metropolitan region-customer locations (without any names or identifying information) were obtained by accessing the alumni database of the business college for one of the authors' home institutions.…”

Section: Experimental Designmentioning

confidence: 98%

The Last Mile Challenge: Evaluating the Effects of Customer Density and Delivery Window Patterns

Boyer

Prud'homme

Chung

2009

J of Business Logistics

270

207

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Consumer direct delivery of packages ordered over the Internet has grown at well over 25 % per year over the past 10 years and now accounts for over $100 billion in sales in the U.S. alone. Retailers have rushed to capitalize on what has commonly been labeled multi‐channel retailing, while logisticians have faced a challenge in devising efficient methods of delivering billions of packages to customer homes. Inefficient deliveries in this “last mile” of the supply chain have led to numerous business collapses as well as a substantial increase in delivery costs. We present a study which examines the effect of two factors (customer density and delivery window length) on the overall efficiency of the delivery route. Data are collected based on empirically derived settings from interviews with several practicing managers. Results provide insight for logistics and marketing managers who must balance customer desires for convenience with business desires for efficiency. The data show that offering a 3 hour delivery window is 30–45% more expensive than offering unattended (9 hour delivery window) delivery. The results provide a tool for managers to address the tradeoffs between various settings for the independent variables (customer density and delivery window length) and the overall route efficiency.

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Section: Experimental Designmentioning

confidence: 98%

The Last Mile Challenge: Evaluating the Effects of Customer Density and Delivery Window Patterns

Boyer

Prud'homme

Chung

2009

J of Business Logistics

270

207

View full text Add to dashboard Cite

show abstract

“…Blanc et al (2004) use a VRP to obtain reliable estimates of transportation costs in a recycling network redesign problem. Blanc et al (2006) propose a multi-depot pickup and delivery model with capacitated vehicles and alternative delivery locations for the collection of containers from EOL vehicle dismantlers in the Netherlands. They solve the model using a heuristic developed by integrating route generation and set partitioning.…”

Section: Optimization Of Transportation Of Goodsmentioning

confidence: 99%

Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art

Ilgın

Gupta

2010

Journal of Environmental Management

808

377

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“…Le Blanc et al (2006) presented a paper dealing with the collection of containers from end-of-life vehicle dismantlers in the Netherlands. In the problem they considered vehicles can carry two containers at a time.…”

Section: Container/skip Problemsmentioning

confidence: 99%

Metaheuristics for the waste collection vehicle routing problem with time windows, driver rest period and multiple disposal facilities

Benjamin

Beasley

2010

Computers & Operations Research

149

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In this thesis there is a set of waste disposal facilities, a set of customers at which waste is collected and an unlimited number of homogeneous vehicles based at a single depot.Empty vehicles leave the depot and collect waste from customers, emptying themselves at the waste disposal facilities as and when necessary. Vehicles return to the depot empty. We take into consideration time windows associated with customers, disposal facilities and the depot. We also have a driver rest period. The problem is solved heuristically. A neighbour set is defined for each customer as the set of customers that are close, but with compatible time windows.This thesis uses six different procedures to obtain initial solutions for the problem.Then, the initial solutions from these procedures are improved in terms of the distance travelled using our phase 1 and phase 2 procedures, whereas we reduce the number of vehicles used using our vehicle reduction (VR) procedure.In a further attempt to improve the solutions three metaheuristic algorithms are presented, namely tabu search (TS), variable neighbourhood search (VNS) and variable neighbourhood tabu search (VNTS). Moreover, we present a modified disposal facility positioning (DFP), reverse order and change tracking procedures.Using all these procedures presented in the thesis, four solution procedures are reported for the two benchmark problem sets, namely waste collection vehicle routing problems with time windows (VRPTW) and multi-depot vehicle routing problem with inter-depot routes (MDVRPI).iii

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Vehicle routing concepts in the closed-loop container network of ARN—a case study

Cited by 42 publications

References 13 publications

The Last Mile Challenge: Evaluating the Effects of Customer Density and Delivery Window Patterns

The Last Mile Challenge: Evaluating the Effects of Customer Density and Delivery Window Patterns

Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art

Metaheuristics for the waste collection vehicle routing problem with time windows, driver rest period and multiple disposal facilities

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