Release Time Constraints Research Articles

Promoting Australian regional airports with subsidy schemes: Optimised downstream logistics using vehicle routing problem

The major metropolitan airports in Australia have become increasingly congested, resulting in substantial increases in operational costs and waiting time at these airports. This paper assesses the effectiveness of subsidy programs in shifting airfreight from metropolitan airports to regional airports assuming the vehicle routing problem approach is used to optimise the downstream (i.e., road) logistics. We analyse the freight distribution network structure and logistics decisions under two (government) subsidy scenarios. We develop a mixed integer linear programming model incorporating the time-window and release-time constraints. A case study in Australia is used to illustrate the application of the proposed framework. The results show that introducing subsidies can effectively reduce the total costs from the prospective of industries involved in the airfreight distribution. The subsidy program under a non-linear subsidy provides a better performance from the economic and delivery time perspectives. However, if the primary goal is to reduce the volume of cargo traffic at the metropolitan airport, a linear subsidy program is preferred.

Vehicle routing problems on a line-shaped network with release time constraints

This work considers the vehicle routing problem on a line with the constraint that each customer is visited after its release time. It is already known that the single-vehicle case is polynomially solvable. We present polynomial time algorithms for two variants of the multi-vehicle case.

A branch and bound to minimize the number of late jobs on a single machine with release time constraints

This paper presents a branch and bound algorithm for the single machine scheduling problem 1| r i |∑ U i where the objective function is to minimize the number of late jobs. Lower bounds based on a Lagrangian relaxation and no reductions to polynomially solvable cases are proposed. Efficient elimination rules together with strong dominance relations are also used to reduce the search space. A branch and bound exploiting these techniques solves to optimality instances with up to 200 jobs, improving drastically the size of problems that could be solved by exact methods up to now.

Minimizing the number of tardy job units under release time constraints

We study two single-machine scheduling problems: Minimizing the weighted and unweighted number of tardy units, when release times are present. Fast strongly polynomial algorithms are given for both problems: For problems with n jobs, we give algorithms which require O( n log n) and O( n 2) steps, for the unweighted and weighted problems respectively. Our results also imply an extension of the family of very efficiently solvable transportation problems, as well as these which are greedily solvable using the “Monge sequence” idea.

Routing and Scheduling on a Shoreline with Release Times

In this paper we examine computational complexity issues and develop algorithms for a class of “shoreline” single-vehicle routing and scheduling problems with release time constraints. Problems in this class are interesting for both practical and theoretical reasons. From a practical perspective, these problems arise in several transportation environments. For instance, in the routing and scheduling of cargo ships, the routing structure is “easy” because the ports to be visited are usually located along a shoreline. However, because release times of cargoes at ports generally complicate the routing structure, the combined routing and scheduling problem is nontrivial. For the straight-line case (a restriction of the shoreline case), our analysis shows that the problem of minimizing the maximum completion time can be solved exactly in quadratic time by dynamic programming. For the shoreline case we develop and analyze heuristic algorithms. We derive data-dependent worst-case performance ratios for these heuristics that are bounded by constant. We also discuss how these algorithms perform on practical data.