3,341 research outputs found
Cloud engineering is search based software engineering too
Many of the problems posed by the migration of computation to cloud platforms can be formulated and solved using techniques associated with Search Based Software Engineering (SBSE). Much of cloud software engineering involves problems of optimisation: performance, allocation, assignment and the dynamic balancing of resources to achieve pragmatic trade-offs between many competing technical and business objectives. SBSE is concerned with the application of computational search and optimisation to solve precisely these kinds of software engineering challenges. Interest in both cloud computing and SBSE has grown rapidly in the past five years, yet there has been little work on SBSE as a means of addressing cloud computing challenges. Like many computationally demanding activities, SBSE has the potential to benefit from the cloud; ‘SBSE in the cloud’. However, this paper focuses, instead, of the ways in which SBSE can benefit cloud computing. It thus develops the theme of ‘SBSE for the cloud’, formulating cloud computing challenges in ways that can be addressed using SBSE
Arbitrarily Scalable Environment Generators via Neural Cellular Automata
We study the problem of generating arbitrarily large environments to improve
the throughput of multi-robot systems. Prior work proposes Quality Diversity
(QD) algorithms as an effective method for optimizing the environments of
automated warehouses. However, these approaches optimize only relatively small
environments, falling short when it comes to replicating real-world warehouse
sizes. The challenge arises from the exponential increase in the search space
as the environment size increases. Additionally, the previous methods have only
been tested with up to 350 robots in simulations, while practical warehouses
could host thousands of robots. In this paper, instead of optimizing
environments, we propose to optimize Neural Cellular Automata (NCA) environment
generators via QD algorithms. We train a collection of NCA generators with QD
algorithms in small environments and then generate arbitrarily large
environments from the generators at test time. We show that NCA environment
generators maintain consistent, regularized patterns regardless of environment
size, significantly enhancing the scalability of multi-robot systems in two
different domains with up to 2,350 robots. Additionally, we demonstrate that
our method scales a single-agent reinforcement learning policy to arbitrarily
large environments with similar patterns. We include the source code at
\url{https://github.com/lunjohnzhang/warehouse_env_gen_nca_public}.Comment: Accepted to Advances in Neural Information Processing Systems
(NeurIPS), 202
High-Level Object Oriented Genetic Programming in Logistic Warehouse Optimization
Disertační práce je zaměřena na optimalizaci průběhu pracovních operací v logistických skladech a distribučních centrech. Hlavním cílem je optimalizovat procesy plánování, rozvrhování a odbavování. Jelikož jde o problém patřící do třídy složitosti NP-težký, je výpočetně velmi náročné nalézt optimální řešení. Motivací pro řešení této práce je vyplnění pomyslné mezery mezi metodami zkoumanými na vědecké a akademické půdě a metodami používanými v produkčních komerčních prostředích. Jádro optimalizačního algoritmu je založeno na základě genetického programování řízeného bezkontextovou gramatikou. Hlavním přínosem této práce je a) navrhnout nový optimalizační algoritmus, který respektuje následující optimalizační podmínky: celkový čas zpracování, využití zdrojů, a zahlcení skladových uliček, které může nastat během zpracování úkolů, b) analyzovat historická data z provozu skladu a vyvinout sadu testovacích příkladů, které mohou sloužit jako referenční výsledky pro další výzkum, a dále c) pokusit se předčit stanovené referenční výsledky dosažené kvalifikovaným a trénovaným operačním manažerem jednoho z největších skladů ve střední Evropě.This work is focused on the work-flow optimization in logistic warehouses and distribution centers. The main aim is to optimize process planning, scheduling, and dispatching. The problem is quite accented in recent years. The problem is of NP hard class of problems and where is very computationally demanding to find an optimal solution. The main motivation for solving this problem is to fill the gap between the new optimization methods developed by researchers in academic world and the methods used in business world. The core of the optimization algorithm is built on the genetic programming driven by the context-free grammar. The main contribution of the thesis is a) to propose a new optimization algorithm which respects the makespan, the utilization, and the congestions of aisles which may occur, b) to analyze historical operational data from warehouse and to develop the set of benchmarks which could serve as the reference baseline results for further research, and c) to try outperform the baseline results set by the skilled and trained operational manager of the one of the biggest warehouses in the middle Europe.
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