Combining vehicle routing and packing for optimal delivery schedules of water tanks

Stolk, Jacob; Mann, Isaac; Mohais, Arvind; Michalewicz, Zbigniew

doi:10.1057/ori.2013.1

Cited by 26 publications

(11 citation statements)

References 17 publications

(14 reference statements)

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“…The first example relates to optimization of the transportation of water tanks (Stolk et al, 2013). An Australian company produces water tanks with different sizes based on some orders coming from its customers.…”

Section: Two Examplesmentioning

confidence: 99%

Evolutionary Computation for Multicomponent Problems: Opportunities and Future Directions

Bonyadi

Michalewicz

Wagner

et al. 2018

Management and Industrial Engineering

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Over the past 30 years many researchers in the field of evolutionary computation have put a lot of effort to introduce various approaches for solving hard problems. Most of these problems have been inspired by major industries so that solving them, by providing either optimal or near optimal solution, was of major significance. Indeed, this was a very promising trajectory as advances in these problem-solving approaches could result in adding values to major industries. In this paper we revisit this trajectory to find out whether the attempts that started three decades ago are still aligned with the same goal, as complexities of real-world problems increased significantly. We present some examples of modern real-world problems, discuss why they might be difficult to solve, and whether there is any mismatch between these examples and the problems that are investigated in the evolutionary computation area.1 See (Michalewicz, 2012b) for details on different interpretations of the term "real-world problems".

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Section: Two Examplesmentioning

confidence: 99%

Evolutionary Computation for Multicomponent Problems: Opportunities and Future Directions

Bonyadi

Michalewicz

Wagner

et al. 2018

Management and Industrial Engineering

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“…The first example relates to optimization of the transportation of water tanks [21]. An Australian company produces water tanks with different sizes based on some orders coming from its customers.…”

Section: Transportation Of Water Tankmentioning

confidence: 99%

Evolutionary Computation for Real-World Problems

Bonyadi

Michalewicz

2015

Studies in Computational Intelligence

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In this paper we discuss three topics that are present in the area of realworld optimization, but are often neglected in academic research in evolutionary computation community. First, problems that are a combination of several interacting sub-problems (so-called multi-component problems) are common in many real-world applications and they deserve better attention of research community. Second, research on optimisation algorithms that focus the search on the edges of feasible regions of the search space is important as high quality solutions usually are the boundary points between feasible and infeasible parts of the search space in many real-world problems. Third, finding bottlenecks and best possible investment in real-world processes are important topics that are also of interest in real-world optimization. In this chapter we discuss application opportunities for evolutionary computation methods in these three areas.

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“…It is an artificial benchmark problem modelling features of complex real-world applications emerging in the areas of planning, scheduling and routing. For example, Stolk, Mann, Mohais, and Michalewicz [24] exemplify a delivery problem that consist of a routing part for the vehicle(s) and a packing part of the goods onto the vehicle(s).…”

Section: Introductionmentioning

confidence: 99%

Evolutionary computation plus dynamic programming for the bi-objective travelling thief problem

Polyakovskiy

Wagner

et al. 2018

Proceedings of the Genetic and Evolutionary Computation Conference

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This research proposes a novel indicator-based hybrid evolutionary approach that combines approximate and exact algorithms. We apply it to a new bi-criteria formulation of the travelling thief problem, which is known to the Evolutionary Computation community as a benchmark multi-component optimisation problem that interconnects two classical N P-hard problems: the travelling salesman problem and the 0-1 knapsack problem. Our approach employs the exact dynamic programming algorithm for the underlying Packing-While-Travelling problem as a subroutine within a bi-objective evolutionary algorithm. This design takes advantage of the data extracted from Pareto fronts generated by the dynamic program to achieve better solutions. Furthermore, we develop a number of novel indicators and selection mechanisms to strengthen synergy of the two algorithmic components of our approach. The results of computational experiments show that the approach is capable to outperform the state-of-the-art results for the single-objective case of the problem.

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Combining vehicle routing and packing for optimal delivery schedules of water tanks

Cited by 26 publications

References 17 publications

Evolutionary Computation for Multicomponent Problems: Opportunities and Future Directions

Evolutionary Computation for Multicomponent Problems: Opportunities and Future Directions

Evolutionary Computation for Real-World Problems

Evolutionary computation plus dynamic programming for the bi-objective travelling thief problem

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