Metadata-Version: 2.1
Name: msflta
Version: 1.0.1
Summary: This is the python package implementing several algorithms and strategy-proof mechanisms introduced in the paper Multi-stage Facility Location Problem with Transient Agents
Author-email: Xuezhen Wang <xuezhenwang2@gmail.com>
License: The MIT License (MIT)
        Copyright © 2022 <copyright holders>
        
        Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Project-URL: Homepage, https://github.com/William-WANG2/FYP-CityU
Keywords: algorithms,strategy-proof mechanisms,facility location problem
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
License-File: LICENSE

# Multi-stage Facility Location Problem with Transient Agent _(MSFL-TA)_

The **_msfl_ta_** is a package implementing several optimal algorithms and strategy-proof mechanisms introduced in the paper **_Multi-stage Facility Location Problem with Transient Agent_**. 

For more details about the algorithms, strategy-proof mechanisms, specifications on the concepts and terminologies in game theory community, you may later refer to the full paper to be published on [**AAAI 23**](https://aaai.org/Conferences/AAAI-23/) or the final year project report. 

## Installation  

You can install the **_msflta_** from [PyPI](https://pypi.org/project/realpython-reader/):  

```python
python -m pip install msflta
```

The package requires a Python 3.7 and above and a Numpy 1.21.5 and above.

## Introduction

The package implements 6 different optimal algorithms and 4 different strategy-proof mechanisms as introduced in the paper.  

### Optimal Algorithms

* **sc_nfcfs** module - **sc_nfcfs(T, r, X)** function: implements the optimal algorithm for the "_No First Come First Serve Without Moving Cost Model_" in terms of _social cost_ objective
* **sc_wfcfs** module - **sc_wfcfs(T, r, X)** function: implements the optimal algorithm for the "_With First Come First Serve Without Moving Cost Model_" in terms of _social cost_ objective  
* **mc_nfcfs** module - **mc_nfcfs(T, r, X)** function: implements the optimal algorithm for the "_No First Come First Serve Without Moving Cost Model_" in terms of _maximum cost_ objective
* **mc_wfcfs** module - **mc_wfcfs(T, r, X)** function: implements the optimal algorithm for the "_With First Come First Serve Without Moving Cost Model_" in terms of _maximum cost_ objective  
* **sc_nfcfs_mov** module - **sc_nfcfs_mov(T, r, X)** function: implements the optimal algorithm for the "_No First Come First Serve With Moving Cost Model_" in terms of _social cost_ objective
* **sc_wfcfs_mov** module - **sc_wfcfs_mov(T, r, X)** function: implements the optimal algorithm for the "_With First Come First Serve With Moving Cost Model_" in terms of _social cost_ objective  

### Strategy-proof Mechanisms

* **mechanism_fc_nfcfs** module - **fc_nfcfs(T, r, X)** function: implements the strategy-proof mechanism named **Full-Coverage** for the "_Without First Come First Serve Without Moving Cost Model_"

* **mechanism_fc_wfcfs** module - **fc_wfcfs(T, r, X)** function: implements the strategy-proof mechanism named **Full-Coverage** for the "_With First Come First Serve Without Moving Cost Model_"

* **mechanism_gs_nfcfs** module - **gs_nfcfs(T, r, X)** function: implements the strategy-proof mechanism named **Full-Coverage** for the "_Without First Come First Serve With Moving Cost Model_"

* **mechanism_gs_wfcfs** module - **gs_wfcfs(T, r, X)** function: implements the strategy-proof mechanism named **Full-Coverage** for the "_With First Come First Serve With Moving Cost Model_"

## How to Use

### Input

* $T \in \mathbb{N}$ (an integer in Python) is the total number of stages where agents can arrive

* $r \in \mathbb{N}$ (an integer in Python) is the _tolerance rate_, indicating the number of stage(s) agents are willing to stay

* $X$ is a nexted list in python, i.e., a list contains several sublists, where the $i^{th}$ sublist contains the location information for agents arriving at stage $i$ .       

  ( **Important Notice: All the sublist should be sorted in ascending order!** ) 

$$
\begin{align*}
	X = [X_1, \ldots, X_{T}], \text{ where } X_{t} \in \mathbb{R}^{N_{t}} \text{ and } N_{t} \text{ is the total number of agents arriving at stage } t
\end{align*}
$$

For example, 

$T = 2, r = 2, X = [[1, 3, 4, 5], [2, 4, 6]]$ is a valid input. In this case, there are in total $2$ stages agents can arrive, and the tolerace rate is $2$. The agents arrive at $1^{st}$ stage are located at $1, 3, 4, 5$ where those arrive at $2^{nd}$ stage are located at $2, 4, 6$

### Output

The output of all the algorithm is an Instance of Class _Sol_.

* $Sol.p$  is a $2d$ List, where $Sol.p[t][i] = k$ indicates the $i^{th}$ agent arriving at stage $t$ is served at stage $k$.
* Sol.y is a $1d$ List, where $Sol.y[t] = loc$ indicates that the facility at stage $t$ is located at position $loc$.
* $Sol.cost$ is the social cost which can be retrieved when the objective function is the social cost, i.e. the total connecting cost of agents (+ the moving cost of the facility).
* $Sol.maxcost$ is the maximum cost which can be retrieved when the objective function is the maximum cost, i.e., the largest distance between an agent and the facility that serves him or her.

