A copula-based joint multinomial discrete–continuous model of vehicle type choice and miles of travel

A major issue in freight modeling is the interrelationship between logistics choices that can be seen as a learning process that shippers or carriers undertake to optimize their logistics process, with the aim of minimizing their cost, maximizing their level of service, or both. This study looked at the interrelated decisions of vehicle type choice and shipment size in urban freight transportation by formulating a model that considered ( a) the nature of these two dependent variables via a joint discrete–continuous model, ( b) the correlation between the two decisions via a copula-based approach, ( c) the differences in decision making between carriers and shippers via the estimation of two models, and ( d) the relaxation of the assumption of pure utility maximization via a hybrid utility–regret specification. Results show differences between shippers’ and carriers’ preferences. These differences were logical because many urban shippers owned an efficient fleet of commercial vehicles, whereas carriers evaluated alternatives to maximize their aggregated utility and minimize their direct costs. Results also show the importance of considering jointly the two decisions as well as the relevance of using a hybrid utility–regret formulation for the cost. Practical findings emerge from the model: ( a) when faced with night delivery and intercity trips, carriers were more likely to use heavier vehicles and more voluminous shipments, whereas smaller shipments were preferred during the afternoon peak hour; ( b) urban shippers tended to deliver larger shipments during night by light trucks but preferred trailers for longer distances; and ( c) commodity types played a role in these joint decisions because some commodities were more likely to be transported by for-hire carriers and others were more likely to be transported by shippers.

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A Two-Phase Heuristic Algorithm for the Common Frequency Routing Problem with Vehicle Type Choice in the Milk Run

Mathematical Problems in Engineering ◽

10.1155/2015/404868 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Yu Lin ◽

Tianyi Xu ◽

Zheyong Bian

Keyword(s):

Heuristic Algorithm ◽

Operating Cost ◽

Greedy Heuristic ◽

Lot Size ◽

Satisfactory Solution ◽

Two Phase ◽

Vehicle Type ◽

The Common ◽

Milk Run ◽

Vehicle Type Choice

High frequency and small lot size are characteristics of milk runs and are often used to implement the just-in-time (JIT) strategy in logistical systems. The common frequency problem, which simultaneously involves planning of the route and frequency, has been extensively researched in milk run systems. In addition, vehicle type choice in the milk run system also has a significant influence on the operating cost. Therefore, in this paper, we simultaneously consider vehicle routing planning, frequency planning, and vehicle type choice in order to optimize the sum of the cost of transportation, inventory, and dispatch. To this end, we develop a mathematical model to describe the common frequency problem with vehicle type choice. Since the problem is NP hard, we develop a two-phase heuristic algorithm to solve the model. More specifically, an initial satisfactory solution is first generated through a greedy heuristic algorithm to maximize the ratio of the superior arc frequency to the inferior arc frequency. Following this, a tabu search (TS) with limited search scope is used to improve the initial satisfactory solution. Numerical examples with different sizes establish the efficacy of our model and our proposed algorithm.

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