A Tabu Search Algorithm for a Routing and Container Loading Problem
Transportation Science
A cooperative strategy for a vehicle routing problem with pickup and delivery time windows
Computers and Industrial Engineering
The stack loading and unloading problem
Discrete Applied Mathematics
The double traveling salesman problem with multiple stacks: A variable neighborhood search approach
Computers and Operations Research
Vehicle Scheduling and Routing with Drivers' Working Hours
Transportation Science
A Column Generation Algorithm for a Rich Vehicle-Routing Problem
Transportation Science
Branch and Cut and Price for the Pickup and Delivery Problem with Time Windows
Transportation Science
Branch-and-cut for the pickup and delivery traveling salesman problem with FIFO loading
Computers and Operations Research
Transportation Science
Truck Driver Scheduling in the European Union
Transportation Science
European Driver Rules in Vehicle Routing with Time Windows
Transportation Science
A column generation heuristic for the general vehicle routing problem
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Truck driver scheduling in Australia
Computers and Operations Research
Dynamic pickup and delivery with transfers
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
The Canadian minimum duration truck driver scheduling problem
Computers and Operations Research
A GRASP with adaptive large neighborhood search for pickup and delivery problems with transshipment
Computers and Operations Research
Scheduling transportation events with grouping genetic algorithms and the heuristic DJD
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Truck Driver Scheduling in the United States
Transportation Science
Optimizing local pickup and delivery with uncertain loads
Proceedings of the Winter Simulation Conference
Long-Haul Vehicle Routing and Scheduling with Working Hour Rules
Transportation Science
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We consider a pickup and delivery vehicle routing problem commonly encountered in real-world logistics operations. The problem involves a set of practical complications that have received little attention in the vehicle routing literature. In this problem, there are multiple carriers and multiple vehicle types available to cover a set of pickup and delivery orders, each of which has multiple pickup time windows and multiple delivery time windows. Orders and carrier/vehicle types must satisfy a set of compatibility constraints that specify which orders cannot be covered by which carrier/vehicle types and which orders cannot be shipped together. Order loading and unloading sequence must satisfy the nested precedence constraint that requires that an order cannot be unloaded until all the orders loaded into the truck later than this order are unloaded. Each vehicle trip must satisfy the driver's work rules prescribed by the Department of Transportation which specify legal working hours of a driver. The cost of a trip is determined by several factors including a fixed charge, total mileage, total waiting time, and total layover time of the driver. We propose column generation based solution approaches to this complex problem. The problem is formulated as a set partitioning type formulation containing an exponential number of columns. We apply the standard column generation procedure to solve the linear relaxation of this set partitioning type formulation in which the resulting master problem is a linear program and solved very efficiently by an LP solver, while the resulting subproblems are computationally intractable and solved by fast heuristics. An integer solution is obtained by using an IP solver to solve a restricted version of the original set partitioning type formulation that only contains the columns generated in solving the linear relaxation. The approaches are evaluated based on lower bounds obtained by solving the linear relaxation to optimality by using an exact dynamic programming algorithm to solve the subproblems exactly. It is shown that the approaches are capable of generating near-optimal solutions quickly for randomly generated instances with up to 200 orders. For larger randomly generated instances with up to 500 orders, it is shown that computational times required by these approaches are acceptable.