Institute of Computing and Informatics (ICI)Contains PDF journal articles for this institutehttp://ir.tum.ac.ke/handle/123456789/1752024-03-28T15:38:15Z2024-03-28T15:38:15ZAdaptive Large Neighborhood Search for Circle Bin Packing ProblemHe, KunTole, KevinNi, FeiYuan, YongLiao, Linyunhttp://ir.tum.ac.ke/handle/123456789/175722024-03-28T00:00:45Z2020-01-01T00:00:00ZAdaptive Large Neighborhood Search for Circle Bin Packing Problem
He, Kun; Tole, Kevin; Ni, Fei; Yuan, Yong; Liao, Linyun
We address a new variant of packing problem called the circle bin packing problem (CBPP), which
is to find a dense packing of circle items to multiple square bins so as to minimize the number of
used bins. To this end, we propose an adaptive large neighborhood search (ALNS) algorithm, which
uses our Greedy Algorithm with Corner Occupying Action (GACOA) to construct an initial layout.
The greedy solution is usually in a local optimum trap, and ALNS enables multiple neighborhood
search that depends on the stochastic annealing schedule to avoid getting stuck in local minimum
traps. Specifically, ALNS perturbs the current layout to jump out of a local optimum by iteratively
reassigns some circles and accepts the new layout with some probability during the search. The acceptance probability is adjusted adaptively using simulated annealing that fine-tunes the search direction
in order to reach the global optimum. We benchmark computational results against GACOA in heterogeneous instances. ALNS always outperforms GACOA in improving the objective function, and
in several cases, there is a significant reduction on the number of bins used in the packing.
https://doi.org/10.48550/arXiv.2001.07709
2020-01-01T00:00:00ZAdaptive simulated annealing with greedy search for the circle bin packing problemYuan, YongTole, KevinNi, FeiHe, KunXiong, ZhengdaLiu, Jinfahttp://ir.tum.ac.ke/handle/123456789/175712024-03-28T00:00:44Z2022-01-01T00:00:00ZAdaptive simulated annealing with greedy search for the circle bin packing problem
Yuan, Yong; Tole, Kevin; Ni, Fei; He, Kun; Xiong, Zhengda; Liu, Jinfa
We introduce a new bin packing problem, termed the circle bin packing problem with circular items
(CBPP-CI). The problem involves packing all the circular items into multiple identical circle bins as
compact as possible with the objective of minimizing the number of used bins. We first define the
tangent occupying action (TOA) and propose a constructive greedy algorithm that sequentially packs
the items into places tangent to the packed items or the bin boundaries. Moreover, to avoid falling
into a local minimum trap and efficiently judge whether an optimal solution has been established, we
continue to present the adaptive simulated annealing with greedy search (ASA-GS) algorithm that
explores and exploits the search space efficiently. Specifically, we offer two novel local perturbation
strategies to jump out of the local optimum and incorporate the greedy search to achieve faster
convergence. The parameters of ASA-GS are adaptive according to the number of items so that they
can be size-agnostic across the problem scale. We design two sets of new benchmark instances, and
the empirical results show that ASA-GS completely outperforms the constructive greedy algorithm.
Moreover, the packing density of ASA-GS on the top few dense bins is much higher than that of
the state-of-the-art algorithm for the single circle packing problem, inferring the high quality of the
packing solutions for CBPP-CI.
https://doi.org/10.1016/j.cor.2022.105826
2022-01-01T00:00:00ZAdaptive large neighborhood search for solving the circle bin packing problemHe, KunTole, KevinNi, FeiYuan, YongLiao, Linyunhttp://ir.tum.ac.ke/handle/123456789/175702024-03-28T00:00:42Z2021-01-01T00:00:00ZAdaptive large neighborhood search for solving the circle bin packing problem
He, Kun; Tole, Kevin; Ni, Fei; Yuan, Yong; Liao, Linyun
We address a new variant of packing problem called the circle bin packing problem (CBPP), which
is to find a dense packing of circle items to multiple square bins so as to minimize the number of
used bins. To this end, we propose an adaptive large neighborhood search (ALNS) algorithm, which
uses our Greedy Algorithm with Corner Occupying Action (GACOA) to construct an initial layout.
The greedy solution is usually in a local optimum trap, and ALNS enables multiple neighborhood
search that depends on the stochastic annealing schedule to avoid getting stuck in local minimum
traps. Specifically, ALNS perturbs the current layout to jump out of a local optimum by iteratively
reassigns some circles and accepts the new layout with some probability during the search. The acceptance probability is adjusted adaptively using simulated annealing that fine-tunes the search direction
in order to reach the global optimum. We benchmark computational results against GACOA in heterogeneous instances. ALNS always outperforms GACOA in improving the objective function, and
in several cases, there is a significant reduction on the number of bins used in the packing.
https://doi.org/10.1016/j.cor.2020.105140
2021-01-01T00:00:00ZTackling Data Related Challenges in Healthcare Process Mining using Visual AnalyticsOndimu, Kennedy O.Omieno, Kelvin K.Muchiri, Geoffrey M.Lukandu, Ismael A.http://ir.tum.ac.ke/handle/123456789/175512024-03-20T00:00:28Z2018-01-01T00:00:00ZTackling Data Related Challenges in Healthcare Process Mining using Visual Analytics
Ondimu, Kennedy O.; Omieno, Kelvin K.; Muchiri, Geoffrey M.; Lukandu, Ismael A.
Data-science approaches such as Visual analytics tend to be process blind whereas process-science approaches such as
process mining tend to be model-driven without considering the "evidence" hidden in the data. Use of either approach separately faces
limitations in analysis of healthcare data. Visual analytics allows humans to exploit their perceptual and cognitive capabilities in
processing data, while process mining represents the data in terms of activities and resources thereby giving a complete process picture.
We use a literature survey on both Visual analytics and process mining in the healthcare environments, to discover strengths that can help
solve open problems in healthcare data when using process mining. We present a visual analytics approach in solving data challenges in
healthcare process mining. Historical data (event logs) obtained from organizational archives are used to generate accurate and evidence
based activity sequences that are manipulated and analyzed to answer questions that could not be tackled by process mining. The
approach can help hospital management and clinicians among others, audit their business processes in addition to providing important
operational information. Other beneficiaries include those organizations interested in forensic information regarding individuals and
groups of patients.
2018-01-01T00:00:00Z