scholarly journals Simulated Annealing with Mutation Strategy for the Share-a-Ride Problem with Flexible Compartments

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2320
Author(s):  
Vincent F. Yu ◽  
Putu A. Y. Indrakarna ◽  
Anak Agung Ngurah Perwira Redi ◽  
Shih-Wei Lin
Keyword(s):  
Simulated Annealing ◽  
Upper Bounds ◽  
Computational Studies ◽  
Lower And Upper Bounds ◽  
Fixed Size ◽  
New Variant ◽  
Mutation Strategy ◽  
Benchmark Instances ◽  
Total Profit ◽  
Total Capacity

The Share-a-Ride Problem with Flexible Compartments (SARPFC) is an extension of the Share-a-Ride Problem (SARP) where both passenger and freight transport are serviced by a single taxi network. The aim of SARPFC is to increase profit by introducing flexible compartments into the SARP model. SARPFC allows taxis to adjust their compartment size within the lower and upper bounds while maintaining the same total capacity permitting them to service more parcels while simultaneously serving at most one passenger. The main contribution of this study is that we formulated a new mathematical model for the problem and proposed a new variant of the Simulated Annealing (SA) algorithm called Simulated Annealing with Mutation Strategy (SAMS) to solve SARPFC. The mutation strategy is an intensification approach to improve the solution based on slack time, which is activated in the later stage of the algorithm. The proposed SAMS was tested on SARP benchmark instances, and the result shows that it outperforms existing algorithms. Several computational studies have also been conducted on the SARPFC instances. The analysis of the effects of compartment size and the portion of package requests to the total profit showed that, on average, utilizing flexible compartments as in SARPFC brings in more profit than using a fixed-size compartment as in SARP.

Optimal Electronic Pricing With Uncertain Parameters

2010 ◽  
Author(s):  
Seyed Jafar Sadjadi ◽  
Davood Shishebori
Keyword(s):  
Mathematical Model ◽  
Objective Function ◽  
Transaction Processing ◽  
Upper Bounds ◽  
Primary Objective ◽  
Optimal Pricing ◽  
Uncertain Parameters ◽  
Lower And Upper Bounds ◽  
Marketing Planning ◽  
Total Profit

Electronic commerce plays an important role in many business activities, operations, and transaction processing. The recent advances on e-businesses have created tremendous opportunities to increase profitability. We present an optimal pricing and marketing planning where the primary objective function is to maximize the total profit. The proposed method of this paper considers different input parameters in uncertain regions and we provide a lower and upper bounds for the objective function of mathematical model with uncertain state. The paper is supported with a numerical example to demonstrate the implementation of the proposed method.

Extension of the Spatial PDI Model to Three Dimensions

1997 ◽  
Vol 84 (1) ◽  
pp. 176-178
Author(s):  
Frank O'Brien
Keyword(s):  
Population Density ◽  
Dimensional Space ◽  
Finite Lattice ◽  
Three Dimensional ◽  
Upper Bounds ◽  
Three Dimensions ◽  
Lower And Upper Bounds ◽  
Density Index ◽  
Three Dimensional Space

The author's population density index ( PDI) model is extended to three-dimensional distributions. A derived formula is presented that allows for the calculation of the lower and upper bounds of density in three-dimensional space for any finite lattice.

Energy of spherical fuzzy graphs

2020 ◽  
pp. 321-332
Author(s):  
S. Yahya Mohamed ◽  
A. Mohamed Ali
Keyword(s):  
Adjacency Matrix ◽  
Upper Bounds ◽  
Fuzzy Graph ◽  
Lower And Upper Bounds ◽  
Fuzzy Graphs

In this paper, the notion of energy extended to spherical fuzzy graph. The adjacency matrix of a spherical fuzzy graph is defined and we compute the energy of a spherical fuzzy graph as the sum of absolute values of eigenvalues of the adjacency matrix of the spherical fuzzy graph. Also, the lower and upper bounds for the energy of spherical fuzzy graphs are obtained.

scholarly journals Relationship between Age and Value of Information for a Noisy Ornstein–Uhlenbeck Process

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 940
Author(s):  
Zijing Wang ◽  
Mihai-Alin Badiu ◽  
Justin P. Coon
Keyword(s):  
Value Of Information ◽  
Markov Models ◽  
Upper Bounds ◽  
Distribution Functions ◽  
Lower And Upper Bounds ◽  
Uhlenbeck Process ◽  
Source Data ◽  
Non Linear ◽  
Queue Model ◽  
Selection Of

The age of information (AoI) has been widely used to quantify the information freshness in real-time status update systems. As the AoI is independent of the inherent property of the source data and the context, we introduce a mutual information-based value of information (VoI) framework for hidden Markov models. In this paper, we investigate the VoI and its relationship to the AoI for a noisy Ornstein–Uhlenbeck (OU) process. We explore the effects of correlation and noise on their relationship, and find logarithmic, exponential and linear dependencies between the two in three different regimes. This gives the formal justification for the selection of non-linear AoI functions previously reported in other works. Moreover, we study the statistical properties of the VoI in the example of a queue model, deriving its distribution functions and moments. The lower and upper bounds of the average VoI are also analysed, which can be used for the design and optimisation of freshness-aware networks. Numerical results are presented and further show that, compared with the traditional linear age and some basic non-linear age functions, the proposed VoI framework is more general and suitable for various contexts.

The Lower and Upper Bounds of Turán Number for Odd Wheels

2021 ◽  
Vol 37 (3) ◽  
pp. 919-932
Author(s):  
Byeong Moon Kim ◽  
Byung Chul Song ◽  
Woonjae Hwang
Keyword(s):  
Upper Bounds ◽  
Lower And Upper Bounds ◽  
Turán Number

scholarly journals A two-agent one-machine multitasking scheduling problem solving by exact and metaheuristics

2021 ◽  
Author(s):  
Chin-Chia Wu ◽  
Ameni Azzouz ◽  
Jia-Yang Chen ◽  
Jianyou Xu ◽  
Wei-Lun Shen ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Completion Time ◽  
Simulated Annealing Algorithm ◽  
Optimal Schedule ◽  
Computational Studies ◽  
Scheduling Problem ◽  
Optimal Solutions ◽  
Large Size ◽  
Annealing Algorithm ◽  
One Machine

AbstractThis paper studies a single-machine multitasking scheduling problem together with two-agent consideration. The objective is to look for an optimal schedule to minimize the total tardiness of one agent subject to the total completion time of another agent has an upper bound. For this problem, a branch-and-bound method equipped with several dominant properties and a lower bound is exploited to search optimal solutions for small size jobs. Three metaheuristics, cloud simulated annealing algorithm, genetic algorithm, and simulated annealing algorithm, each with three improvement ways, are proposed to find the near-optimal solutions for large size jobs. The computational studies, experiments, are provided to evaluate the capabilities for the proposed algorithms. Finally, statistical analysis methods are applied to compare the performances of these algorithms.

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