Only increasing either the truck speed or drone speed can reduce the total delivery time, but not to the greatest extent. Sensitivity analysis of different parameters shows that increasing the number of truck–drones can effectively save the total delivery time, but gradually reduce the marginal benefits. The results indicate that truck–drone delivery can effectively reduce the total delivery time by 20.1% compared with traditional pure-truck delivery. The 23 customer points are visited by three truck–drones. A delivery network with 1 warehouse and 23 customer points are employed as a case study to verify the effectiveness of the model and algorithm. Since the TSP-D is non-deterministic polynomial-time hard (NP-hard), the proposed model is solved by the variable neighborhood tabu search algorithm, where the neighborhood structure is changed by point exchange and link exchange to expand the tabu search range. To fill this gap, this paper builds a mixed integer nonlinear programming model subject to time constraints and route constraints, aiming to minimize the total delivery time. However, most of the existing studies ignore truck waiting time at rendezvous points. The optimal delivery route problem for truck–drone delivery is defined as a traveling salesman problem with drone (TSP-D), which has been studied in a wide range of previous literature. We integrate cycle mutation into the open-source Chips-n-Salsa library, and the new distance metrics into the open-source JavaPermutationTools library. We experimentally validate these findings showing cycle mutation's strengths on problems like QAP and LCS, and its limitations on problems like the TSP, while also showing that it is less prone to local optima than commonly used alternatives.
The fitness landscape analysis predicts that cycle mutation is better suited for assignment and mapping problems than it is for ordering problems. As a prerequisite, we develop new permutation distance measures: cycle distance, $k$-cycle distance, and cycle edit distance. We use fitness landscape analysis to explore the problem characteristics for which cycle mutation works best.
We propose cycle mutation, a new mutation operator whose inspiration is the well known cycle crossover operator, and the concept of a permutation cycle. We focus on evolving permutations for ordering problems like the traveling salesperson problem (TSP), as well as assignment problems like the quadratic assignment problem (QAP) and largest common subgraph (LCS). So, I'm including three classes, one is the non-threaded one, one is the one I tried to multithread and one is an extra class which does the permutation-job.Evolutionary algorithms solve problems by simulating the evolution of a population of candidate solutions.
#Java permute all numbes crack
So, since it's creating permutations and then needs to check every permutation against a ruleset, I thought, I could feed the process of checking into a threadpool, so everytime a permutation is created, the process of checking and if eligible, printing of the permutation is multithreaded, as of this example:īut apparently, now instead of running faster, it's going even slower, so I did something wrong, which is over my head, because this is the first thing I have ever done with Java, I'm actually not a programmer and just write some lines of Apple Script here and there, and this synchronizing and understanding the different class structures is really not something I can crack in a few days, alone. ).īut CPU and RAM is basically not used when running the program, so I thought, probably putting it in a bunch of threads will speed it up, because apparently the machine is waiting for new instructions a lot, along the way. But the release should be ready within the next month or two, so waiting 600 days is just not an option. Since it's literally billions of possibilities, narrowing it down like this would help a lot.
#Java permute all numbes code
So, upon running the code without any parallelisation it would take about 600 days to finish based on calculations how long it takes to compute permutations of 11 tracks ( I actually want to help my girlfriend with choosing the perfect track order for a music album she is releasing. The code works in general, but it gets awfully slow when having more tracks than 11 or 12. So, my approach was just to create all permutations and check them against the conditions. I know, doing it properly would involve already devalidating whole subtrees within the permutations-process, but I'm not able to do that, by which I mean, I literally am not able to do that. The code is for sorting an album track list and giving valid track listings under certain conditions which can be defined.
OK, I'm totally in over my head, but here we go.
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