scholarly journals Freight Consolidation Problem with Time Windows, Pickup and Delivery Sequence

2019 ◽  
Vol 271 ◽  
pp. 06005
Author(s):  
Devaraj R. Krishnan ◽  
Tieming Liu
Keyword(s):  
Greenhouse Gas Emission ◽  
Time Windows ◽  
Branch And Cut ◽  
Mixed Integer ◽  
Pickup And Delivery ◽  
Shipping Industry ◽  
Delivery Problem ◽  
Mip Model ◽  
Freight Consolidation ◽  
Trucking Companies

The number of online market places for freight matching is on the rise. Online market places help small trucking companies find shipping customers. However, they do not provide cargo consolidation strategies. This lack of effective consolidation has adverse effects on the shipping industry and greenhouse gas emission. To that extent, this article addresses Multiple Vehicle Pickup and Delivery Problem with Time Windows (MVPDPTW). We propose a Mixed Integer Programming (MIP) model and a branch-and-cut algorithm geared towards identifying effective freight consolidation opportunities. For emission studies, we used a cost conversion technique from the literature to convert emission levels into monetary values. On real-world logistics company test instances, our model identified routes with lower cost and lower emission levels than the actual routes.

The pickup and delivery problem with time windows

1991 ◽  
Vol 54 (1) ◽  
pp. 7-22 ◽  
Author(s):  
Yvan Dumas ◽  
Jacques Desrosiers ◽  
François Soumis
Keyword(s):  
Time Windows ◽  
Pickup And Delivery ◽  
Delivery Problem ◽  
Pickup And Delivery Problem

The pickup and delivery problem with time windows and occasional drivers

2019 ◽  
Vol 109 ◽  
pp. 122-133 ◽  
Author(s):  
Lars Dahle ◽  
Henrik Andersson ◽  
Marielle Christiansen ◽  
M. Grazia Speranza
Keyword(s):  
Time Windows ◽  
Pickup And Delivery ◽  
Delivery Problem ◽  
Pickup And Delivery Problem

scholarly journals Platform-Based Collaborative Routing using Dynamic Prices as Incentives

2020 ◽  
Vol 2674 (10) ◽  
pp. 670-679
Author(s):  
Bilge Atasoy ◽  
Frederik Schulte ◽  
Alex Steenkamp
Keyword(s):  
Dynamic Pricing ◽  
Time Windows ◽  
Third Party ◽  
Individual Rationality ◽  
Mixed Integer ◽  
Delivery Problem ◽  
Price Range ◽  
Decision Support Models ◽  
Collaborative Routing ◽  
Transportation Research

Over the last decade, platforms have emerged in numerous industries and often transformed them, posing new challenges for transportation research. Platform providers such as Uber, Uber Freight, Blackbuck, or Lyft mostly do not have immediate control over the physical resources needed to move people or goods. They often operate in a multi-sided market setting, where it is crucial to design clear incentives to motivate a third party to engage in collaboration. As a consequence, collaboration incentives become an integral part of decision support models for platform providers and they need to be developed at the operational level and applied dynamically. Naturally, this involves a trade-off between the interests of platform providers, shippers, and carriers. In this work, we investigate the real-world case of a platform provider operating as an intermediary between shippers and carriers in a less-than-truckload (LTL) business. We propose a new mixed-integer programming (MIP) formulation for the underlying collaborative pickup and delivery problem with time windows (PDPTW) that minimizes the price the platform pays to the carriers and enforces collaboration incentives for carriers through individual rationality constraints. This is facilitated by a dynamic pricing approach which ensures that carriers are better off collaborating than working on their own. The pricing is bounded by the costs and market conditions to keep the price range reasonable. We explore possible policies to be implemented by the platform and find that their business remains profitable when individual rationality is enforced and the platform could even guarantee increased profit margins to the carriers as incentives.

scholarly journals Liner Shipping Schedule Design for Near-Sea Routes Considering Big Customers’ Preferences on Ship Arrival Time

2020 ◽  
Vol 12 (18) ◽  
pp. 7828 ◽  
Author(s):  
Xi Jiang ◽  
Haijun Mao ◽  
Yadong Wang ◽  
Hao Zhang
Keyword(s):  
Time Windows ◽  
Linear Optimization ◽  
Optimal Solution ◽  
Time Preferences ◽  
Mixed Integer ◽  
Shipping Industry ◽  
Fleet Size ◽  
Arrival Times ◽  
Soft Time Windows ◽  
Mixed Integer Linear Optimization

There usually exist a few big customers at ports of near-sea container shipping routes who have preferences on the weekly ship arrival times due to their own production and sale schedules. Therefore, in practice, when designing ship schedules, carriers must consider such customers’ time preferences, regarded as weekly soft-time windows, to improve customer retention, thereby achieving sustainable development during a depression in the shipping industry. In this regard, this study explores how to balance the tradeoff between the ship total operating costs and penalty costs from the violation of the weekly soft-time windows. A mixed-integer nonlinear nonconvex model is proposed and is further transformed into a mixed-integer linear optimization model that can be efficiently solved by extant solvers to provide a global optimal solution. The proposed model is applied to a near-sea service route from China to Southeast Asia. The results demonstrate that the time preferences of big customers affect the total cost, optimal sailing speeds, and optimal ship arrival times. Moreover, the voyage along a near-sea route is generally short, leaving carriers little room for adjusting the fleet size.

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