البحوث المنشورة
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البحث (1):
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عنوان البحث:
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Extended Decomposition for Mixed Integer
Programming to Solve a Workforce Scheduling and Routing Problem
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رابط إلى البحث:
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Click
here
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تاريخ النشر:
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15 December
2015
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موجز عن البحث:
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We
propose an approach based on mixed integer programming (MIP) with
decomposition to solve a workforce scheduling and routing problem, in which a
set of workers should be assigned to tasks that are distributed across
different geographical locations. We present a mixed integer programming
model that incorporates important real-world features of the problem such as
defined geographical regions and flexibility in the workers’ availability. We
decompose the problem based on geographical areas. The quality of the overall
solution is affected by the ordering in which the sub-problems are tackled.
Hence, we investigate different ordering strategies to solve the
sub-problems. We also use a procedure to have additional workforce from
neighbouring regions and this helps to improve results in some instances. We
also developed a genetic algorithm to compare the results produced by the
decomposition methods. Our experimental results show that although the
decomposition method does not always outperform the genetic algorithm, it
finds high quality solutions in practical computational times using an exact
optimization method.
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المؤتمرات العلمية:
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المؤتمر (1):
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عنوان المؤتمر:
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8th Saudi
Students Conference in the UK
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تاريخ الإنعقاد:
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31/01/2015
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مكان
الإنعقاد:
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London, UK
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طبيعة المشاركة:
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Oral presentation
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عنوان المشاركة:
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Genetic Algorithm For Workforce Scheduling
And Routing Problem
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ملخص المشاركة:
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The
purpose of this research is to produce solutions to the Workforce Scheduling
and Routing problem (WSRP) with particular interest in the Home Health Care
problem (HHC) for which we have access to data from real-world instances
provided by an industrial collaborator. In this work, we describe the ongoing
development of a Genetic Algorithm (GA) to tackle the WSRP. Genetic
Algorithms (GAs) have shown success in tackling complex optimisation problems
such as scheduling and routing. However, penalty schemes and repair functions
are often used within GAs to deal with constraints in combinatorial
optimisation problems. Also, problem specific information usually needs to be
incorporated to improve the search performance and ensure the satisfaction of
constraints when tackling combinatorial problems with GAs. The present work
focuses on investigating a range of genetic operators that have been used in
problems that are somehow related to the WSRP. The aim is to conduct an
in-depth analysis to identify the effects that the different operators have
when tackling the WSRP. Our implementation consists of a GA equipped with a
range of genetic operators from the literature, both generic and specialises
operators are considered. The objective is to identify the most effective
genetic operators that can help to tackle the WSRP. So far, the combination
of mutation and k-point crossover has produced good results, providing us
with evidence that tackling WSRP with GAs seems a promising research
direction.
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المؤتمر (2):
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عنوان المؤتمر:
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MIC’2015
Metaheuristics International Conference
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تاريخ الإنعقاد:
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7-10 June
2015
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مكان
الإنعقاد:
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Agadir, Morocco
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طبيعة المشاركة:
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Oral presentation
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عنوان المشاركة:
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A Study of Genetic Operators for the
Workforce Scheduling and Routing Problem
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ملخص المشاركة:
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The
Workforce Scheduling and Routing Problem (WSRP) is concerned with planning
visits of qualified workers to different locations to perform a set of tasks,
while satisfying each task time-window plus additional requirements such as
customer/workers preferences. This type of mobile workforce scheduling
problem arises in many real-world operational scenarios. We investigate a set
of genetic operators including problem-specific and well-known generic
operators used in related problems. The aim is to conduct an in-depth
analysis on their performance on this very constrained scheduling problem. In
particular, we want to identify genetic operators that could help to minimise
the violation of customer/workers preferences. We also develop two cost-based
genetic operators tailored to the WSRP. A Steady State Genetic Algorithm
(SSGA) is used in the study and experiments are conducted on a set of problem
instances from a real-world Home Health Care scenario (HHC). The experimental
analysis allows us to better understand how we can more effectively employ
genetic operators to tackle WSRPs.
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المؤتمر (3):
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عنوان المؤتمر:
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9th Saudi
Students Conference in the UK
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تاريخ الإنعقاد:
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14/02/2016
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مكان
الإنعقاد:
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Birmingham, UK
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طبيعة المشاركة:
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Oral presentation
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عنوان المشاركة:
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An Indirect Genetic Algorithm with Greedy
and Flat-Costs operators for Workforce Scheduling and Routing Problems
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ملخص المشاركة:
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A
Workforce Scheduling and Routing Problem tackle personnel assignment to
different tasks at different locations within a time-window. The problem
arises in real-world scenarios such as Home Health Care (HCSP), Home Care,
Security Guards Routing and Rostering (SPRR), Manpower Allocation and The
Maintenance Personnel Scheduling Problem (MPSP) (Castillo-Salazar,
Landa-Silva and Qu, 2012). WSRP is a combination of two combinatorial
sub-problems found in real-world scenarios (Lassaigne and Rougemont, 2012). Thus,
a robust solution is a needed to tackle the conflicted constraints while
minimizing the total travel cost. Genetic Algorithms (GAs) have been
effective in finding good solutions relatively quickly to real-world
scheduling problems. Thus, GAs are chosen as a meta-heuristic method that
tackles different segments of WSRP simultaneity. So far, not many research
contributed to problems where scheduling and routing are combined. We present
tailored chromosome representation integrated in Genetic Algorithm while
using estimated-costs and greedy crossover operators. This study highlights
the effects of employing problem-specific information on solutions
feasibility. Well-known operators are implemented in this paper along with
the newly proposed crossovers. The performance analysis of the Cost-Based
Genetic Algorithm is measures by the solutions quality obtained by parameters
comparison known in standard criteria used in literature. The comparative
study shows that a suitable vector representation plus cost-based operators
performs considerably better than the general operators considered in this
study.
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المؤتمر (4):
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عنوان المؤتمر:
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IEEE
World Congress on Computational Intelligence
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تاريخ الإنعقاد:
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24-29 July 2016
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مكان
الإنعقاد:
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Vancouver,
Canada
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طبيعة المشاركة:
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Oral
presentation
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عنوان المشاركة:
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A
Genetic Algorithm for a Workforce Scheduling and Routing Problem
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ملخص المشاركة:
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The
Workforce Scheduling and Routing Problem refers to the assignment of
personnel to visits across various geographical locations. Solving this
problem demands tackling scheduling and routing constraints while aiming to
minimise the total operational cost. This paper presents a Genetic Algorithm
(GA) tailored to tackle a set of real-world instances of this problem. The
proposed GA uses a customised chromosome representation to maintain the
feasibility of solutions. The performance of several genetic operators is
investigated in relation to the tailored chromosome representation. This
paper also presents a study of parameter settings for the proposed GA in
relation to the various problem instances considered. Results show that the
proposed GA, which incorporates tailored components, performs very well and is
an effective baseline evolutionary algorithm for this difficult problem.
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المؤتمر (5):
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عنوان المؤتمر:
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6th
International Conference on Operations Research and Enterprise Systems
(ICORES 2017)
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تاريخ الإنعقاد:
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23-25 February 2017
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مكان
الإنعقاد:
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Porto,
Portugal
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طبيعة المشاركة:
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Oral
presentation
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عنوان المشاركة:
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Selecting
Genetic Operators to Maximise Preference Satisfaction in a Workforce
Scheduling and Routing Problem
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ملخص المشاركة:
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The
Workforce Scheduling and Routing Problem (WSRP) is a combinatorial
optimisation problem that involves scheduling and routing of workforce.
Tackling this type of problem often requires handling a considerable number
of requirements, including customers and workers preferences while minimising
both operational costs and travelling distance. This study seeks to determine
effective combinations of genetic operators combined with heuristics that
help to find good solutions for this constrained combinatorial optimisation
problem. In particular, it aims to identify the best set of operators that
help to maximise customers and workers preferences satisfaction. This paper
advances the understanding of how to effectively employ different operators
within two variants of genetic algorithms to tackle WSRPs. To tackle infeasibility,
an initialisation heuristic is used to generate a conflict-free initial plan
and a repair heuristic is used to ensure the satisfaction of constraints.
Experiments are conducted using three sets of real-world Home Health Care
(HHC) planning problem instances.
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