Solving Vehicle Assignment Problem Using Evolutionary Computation

Yusoff, Marina; Ariffin, Junaidah; Mohamed, Azlinah

doi:10.1007/978-3-642-13495-1_64

Cited by 12 publications

(6 citation statements)

References 13 publications

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“…DPSO-2 uses constriction factor. Generally, these results support the solution addressed in [9][10] and validate the feasible solution derived for the EVAP. The number of vehicles generated based on the results from Table 1 were used for the following the computational results for multiple inundated areas.…”

Section: Results and Findingssupporting

confidence: 85%

“…The objective function of the EVAP is to assign vehicles with the maximum number of people while the EVRP is to minimize the total travelling time for all the travelling vehicles to the inundated area. The problem formulation for EVAP can be referred in [10] meanwhile EVRP in [11]. Construction of solution representation defines and constructs solution representation for the EVAP and EVRP.…”

Section: Two-tier Solutionsmentioning

confidence: 99%

“…The route selection was sometimes based on the driver's experience, and this has led to late arrivals at relief center. Two main problems were identified and currently solved separately [10][11]. Particle swarm optimization was employed and provides good solutions to evacuation vehicle assignment problem (EVAP) [10] and evacuation vehicle routing problem (EVRP) [11].…”

Section: Current Evacuation Processmentioning

confidence: 99%

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A two-tier solution for disaster relief management

Yusoff

Ariffin

Mohamed

2013

2013 IEEE Symposium on Industrial Electronics &Amp; Applications

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Disaster relief management is essential for various types of disasters such as terrorist attacks, hazardous materials, hurricanes and flash floods. Planning for disaster relief is important in rescuing victims with the involvement of many agencies and the public in managing the event. This is due to poor coordination, late assistance and uneven distribution of vehicles, food, and medical assistants. Time and cost are very important in disaster relief. The need for a proper disaster relief plan to overcome these issues is crucial. This paper presents a two tier solutions to relieve flood victims focuses on the evacuation. The first tier is to complete the task of vehicles assignment at the inundated area. Then, the routing of vehicles from inundated areas to relief centres. A discrete particle swarm optimization were employed and tested reflecting the fitness functions identified from each tier. The findings show that the two-tier solution provides good results for the evacuation plan.

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Section: Results and Findingssupporting

confidence: 85%

Section: Two-tier Solutionsmentioning

confidence: 99%

Section: Current Evacuation Processmentioning

confidence: 99%

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A two-tier solution for disaster relief management

Yusoff

Ariffin

Mohamed

2013

2013 IEEE Symposium on Industrial Electronics &Amp; Applications

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“…For instance, Jean et al [ 3 ] used PSO to solve the problem of allocating a set of cabs to some customers with the goal of minimizing the distance traveled by the fleet with result that showed that PSO is capable of achieving optimal results. Similarly, a discrete PSO (DPSO) and genetic algorithm (GA) were compared for the problems of finding optimal solution to the allocation of the expected number of people in flooded areas to various types of available vehicle in an evacuation process [ 6 ] with DPSO having better performance than GA. DPSO has also shown great performance with different degrees of difficult knapsack problems [ 7 , 8 ]. In addition, experimental results in [ 9 ] showed that DPSO algorithm is highly efficient for solving the multiple knapsack problems (MKP) even with large problem instances.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Algorithm for Optimizing Assignment of Blood in Blood Banking System

Olusanya

Arasomwan

Adediran

2015

Computational and Mathematical Methods in Medicine

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This paper reports the performance of particle swarm optimization (PSO) for the assignment of blood to meet patients' blood transfusion requests for blood transfusion. While the drive for blood donation lingers, there is need for effective and efficient management of available blood in blood banking systems. Moreover, inherent danger of transfusing wrong blood types to patients, unnecessary importation of blood units from external sources, and wastage of blood products due to nonusage necessitate the development of mathematical models and techniques for effective handling of blood distribution among available blood types in order to minimize wastages and importation from external sources. This gives rise to the blood assignment problem (BAP) introduced recently in literature. We propose a queue and multiple knapsack models with PSO-based solution to address this challenge. Simulation is based on sets of randomly generated data that mimic real-world population distribution of blood types. Results obtained show the efficiency of the proposed algorithm for BAP with no blood units wasted and very low importation, where necessary, from outside the blood bank. The result therefore can serve as a benchmark and basis for decision support tools for real-life deployment.

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“…FastSLAM is a form of particle filter and can be considered as the second most used filter-based category in SLAM methods utilized to solve the SLAM problem. Apart from its implementation in SLAM, particle filters are also synonymous in path finding and searching methods for various other systems [23] [25]. FastSLAM was considered to be the first to model the non-linear processes and does not require Gaussian pose distribution in its method [3] [15].…”

Section: Fast Slammentioning

confidence: 99%

A Brief Survey on SLAM Methods in Autonomous Vehicle

Takleh¹,

Bakar²,

Abdul-Rahman³

et al. 2018

IJET

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The overall purpose of this paper is to provide an introductory survey in the area of Simultaneous Localization and Mapping (SLAM) particularly its utilization in autonomous vehicle or more specifically in self-driving cars, especially after the release of commercial semiautonomous car like the Tesla vehicles as well as the Google Waymo vehicle. Before we begin diving into the concept of SLAM, we need to understand the importance of SLAM and problems that expand to the various methods developed by numerous researchers to solve it. Thus, in this paper we will start by giving the general concept behind SLAM, followed by sharing details of its different categories and the various methods that form the SLAM function in today's autonomous vehicles; which can solve the SLAM problem. These methods are the current trends that are widely focused in the research community in producing solutions to the SLAM problem; not only in autonomous vehicle but in the robotics field as well. Next, we will compare each of these methods in terms of its pros and cons before concluding the paper by looking at future SLAM challenges.

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Solving Vehicle Assignment Problem Using Evolutionary Computation

Cited by 12 publications

References 13 publications

A two-tier solution for disaster relief management

A two-tier solution for disaster relief management

Particle Swarm Optimization Algorithm for Optimizing Assignment of Blood in Blood Banking System

A Brief Survey on SLAM Methods in Autonomous Vehicle

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