2022-04-25 distace matrix concepts

seppeV2 · Apr 25, 2022 · d7265c3 · d7265c3
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diff --git a/README.md b/README.md
@@ -7,10 +7,164 @@ states are defined as followed:     1 = new contract
                                     2 = end contract 
                                     3 = exact same container
                                     4 = switch container 
-                                    5 = filling up
+                                    5 = filling `   up
                                     6 = dangerous waste
 
 The depot is always situated in Kampenhout and the closest suitable wpf is used. 
 
 For each element on the diagonal, the service time of client i is shown (matrix[i,i] = service time for i).
-Service time = time spend at the costumer. 
+Service time = time spend at the costumer. 
+
+
+
+
+after depot: do whatever you want.
+
+at wpf: we unload without taking the containr off, no need to calculate additional 12 min.
+
+if you can go direct, don't think about depot in the distance matrix!
+
+## possible courses of actions and their total service time
+### STARTING FROM END OF ACTIONTYPE 1,3
+- 1-1: 
+    - $$1-1 : idj = t_{id} + 6 + t_{dj} + 12 + EXTRA$$
+- 1-2
+    - $$1-2 : ij = t_{ij} + 12 + EXTRA$$
+- 1-3
+    - $$1-3: ijwj = t_{ij} + 12 + t_{jw} + 20 + t_{wj} + 12 + EXTRA$$
+- 1-4
+    - $$1-4 : idj = t_{id} + 6 + t_{dj} + 25 + EXTRA$$
+- 1-5
+    - $$1-5 : idj = t_{id} + 6 + t_{dj} + 30 + EXTRA$$
+- 1-d
+    - $$1-d : id = t_{id}$$
+- 3: same as 1 : both start unloaded (empty)
+
+### STARTING FROM END OF ACTIONTYPE 2,4,5
+- 2-1
+    - $$2-1 \& RS : iwj = t_{iw} + 20 + t_{wj} + 12 + EXTRA$$
+    - $$2-1 \& WS : iwdj = t_{iw} + 20 + t_{wd} + 2*6 + t_{dj} + 12 + EXTRA$$
+- 2-2
+    - $$2-2 : iwdj = t_{iw} + 20 + t_{wd} + 6 + t_{dj} + 12 + EXTRA$$
+- 2-3:
+    - $$2-3 : iwdjwj: t_{iw} + 20 + t_{wd} + 6 + t_{dj} + 12 + t_{jw} + 20 + t_{wj} + 12 + EXTRA$$
+- 2-4:
+    - $$2-4 \& RS : iwj = t_{iw} + 20 + t_{wj} + 25 + EXTRA$$
+    - $$2-4 \& WS : iwdj = t_{iw} + 20 + t_{wd} + 2*6 + t_{dj} + 25 + EXTRA$$
+- 2-5: very similar to 2-4
+    - $$2-5 \& RS : iwj = t_{iw} + 20 + t_{wj} + 30 + EXTRA$$
+    - $$2-5 \& WS : iwdj = t_{iw} + 20 + t_{wd} + 2*6 + t_{dj} + 30 + EXTRA$$
+- 2-d:
+    - $$2-d : iwd = t_{iw} + 20 + t_{wd} + 6$$
+- 4: same as 2 : both start loaded & full
+- 5: same as 2 : both start loaded & full
+
+### STARTING FROM DEPOT
+- $$d-1 : dj = 6 + t_{dj} + 12 + EXTRA$$
+- $$d-2 : dj = t_{dj} + 12 + EXTRA$$
+- $$d-3 : djwj = t_{dj} + 12 + t_{jw} + 20 + t_{wj} + 12 + EXTRA$$
+- $$d-4 : dj =  6 + t_{dj} + 25 + EXTRA$$
+- $$d-5 : dj = 6 + t_{dj} + 30 + EXTRA$$
+
+We have only 10 first-level pairs. As we care about the following pair:
+$[endState_i,actionType_{j}]$
+
+Then we consider size for 3 of them (when you start loaded & full): **13 in total**
+
+Lastly, dangerous waste or other conditions, can be considered for the wpf. They don't have a nest of their own.
+
+**ASSMPTION: trucks always start/end the day empty. all containers should be placed in depot not on trucks**
+
+## how to enumerate the matrix
+for each course of action, several routes might be available. we store them, and choose the "shortest path".
+
+These possiblites are "per available customer_j". So, 1-5 means if you finish at actionType 1 (state Empty), there is only 1 route per customer of actionType 5 (your next step).
+
+1-1: 1 
+1-2: 1
+1-3: #wpfs available
+1-4: 1
+1-5: 1
+
+2-1RS: #w available
+2-1WS: #w available
+2-2: #w
+2-3: #w@i * #w@j
+2-4RS: #w
+2-4WS: #w
+2-5RS: #w
+2-5WS: #w
+
+then for all clients, we consider the values above.
+
+
+
+### Now how to define number of wpfs available? (#w)?
+if i = dangerous : only antwerp (w=1)
+if i = construction : all (w=4)
+if i = rest : mechelen,brugge,gent (w=3)
+
+### some toy examples:
+we are done with first customer, and want to calculate service time for another one.
+bookstore20 to fish20: 
+3-4 = 1-4:
+only one value
+
+
+[construction 18, lab 15]
+
+
+[lab 10, end 12]
+
+
+## HOW TO DEAL with end of tour?
+if there are no feasible customers in the distance matrix, go back to depot and end your tour. feasiblity can be time cosntraint, fuel, work, etc.
+
+## HOW TO STRUCTURE THE CODE?
+```python
+getNodes(database) {
+    # here we save each node in JSON or sth, where we can access its attributes.
+    # these nodes (clients / depot) will be used in later functions.
+
+    node = {
+        'ID', ..., #0 is depot, minus values for wpfs;   the rest are clients. 
+        'coordinates', [x,y], # getX and getY: this can be taken from googlemaps API
+        'actionType',1 to 5, # 1: new cont. 2: end cont. 3: exact same 4: swap 5: fill  ; is 0 for non-clients
+        'containerSize',..., # whateever integer value ; is 0 for non-clients
+        'wasteType', ..., #1:general 2: paper 3:const 4:dangerous ; is 0 for non-clients
+        'wasteTypesHandled',... , # 0 for non-wpfs;   an array of wastetypes handled for wpfs. e.g. antwerp is dangerous only [4]
+        'opening',..., #starting time window
+        'closing ',..., #closing time window
+        'extraHandling',..., #0 if none; int if needed (e.g. 15)
+        ...
+    }
+}
+# I THINK THE CODE ABOVE IS ALREADY IMPLEMENTED VIA PANDAS, THIS IS JUST TO GIVE AN IDEA
+
+getShortestServiceTime(node_i,node_j) {
+    # for depot
+    # consider depot as customer with ID = 0
+    # first check ID=0, to see if it's depot
+    # here we consider services times starting from/end at depot:   d-x, or x-d
+
+
+    # for all nodes (clients)
+    # we get the following attributes from the client_i and client_j : ID
+    # first check ID!=0, to make sure it's not depot
+    # if not depot: get following attricuts containerSize, Location, actionType, wasteType
+    ...
+    # now we implement the conditions here, so that we can finally return a "serviceTime"
+    # the idea is to use these attributes to implement the formulas
+    # e.g. dangerous waste can be just an if statement inside one of the courses of action.
+
+
+    # at the end we select the shortest service time (path) from i to j, and return it;
+    # the output will be added to our pre-procssed distance matrix.
+}
+
+createDistanceMatrix {
+    # here we iterate over all i and j, and fill in the shortest service time value inside it.
+    # the values, are already taking into consideration all possible intermediate steps, and the shortest one is already selected.
+}
+
+```
diff --git a/run.ipynb b/run.ipynb
@@ -0,0 +1,94 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the script, to define all necessary functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run distanceMatrix.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TODO\n",
+    "- structure the logic in a different way:\n",
+    "    - currently there's a lot of overlap\n",
+    "    - "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[  0.  68.  86.  89.  68.  64. 119.]\n",
+      " [107.   0.  83. 133.  56. 112. 107.]\n",
+      " [117.  83.   0. 142.  66. 121. 117.]\n",
+      " [ 89.  93. 112.   0.  94.  24. 144.]\n",
+      " [ 19.  49.  68.  74.   0.  50. 100.]\n",
+      " [132.  98. 107. 157.  81.   0. 131.]\n",
+      " [157.  99. 133. 183. 104. 162.   0.]]\n"
+     ]
+    },
+    {
+     "ename": "PermissionError",
+     "evalue": "[Errno 13] Permission denied: 'distanceMatrix.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14856/1685011400.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mmatrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreateDistanceMatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'clientsTest.csv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'belgian-cities-geocoded.csv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'WPF.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'distanceMatrix.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\anaconda3\\envs\\ip1\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mto_csv\u001b[1;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options)\u001b[0m\n\u001b[0;32m   3464\u001b[0m         )\n\u001b[0;32m   3465\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3466\u001b[1;33m         return DataFrameRenderer(formatter).to_csv(\n\u001b[0m\u001b[0;32m   3467\u001b[0m             \u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3468\u001b[0m             \u001b[0mline_terminator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mline_terminator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\envs\\ip1\\lib\\site-packages\\pandas\\io\\formats\\format.py\u001b[0m in \u001b[0;36mto_csv\u001b[1;34m(self, path_or_buf, encoding, sep, columns, index_label, mode, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, errors, storage_options)\u001b[0m\n\u001b[0;32m   1103\u001b[0m             \u001b[0mformatter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfmt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1104\u001b[0m         )\n\u001b[1;32m-> 1105\u001b[1;33m         \u001b[0mcsv_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1106\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1107\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcreated_buffer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\envs\\ip1\\lib\\site-packages\\pandas\\io\\formats\\csvs.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    235\u001b[0m         \"\"\"\n\u001b[0;32m    236\u001b[0m         \u001b[1;31m# apply compression and byte/text conversion\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 237\u001b[1;33m         with get_handle(\n\u001b[0m\u001b[0;32m    238\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    239\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\envs\\ip1\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    700\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;34m\"b\"\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    701\u001b[0m             \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m             handle = open(\n\u001b[0m\u001b[0;32m    703\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    704\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'distanceMatrix.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "matrix = createDistanceMatrix('clientsTest.csv', 'belgian-cities-geocoded.csv', 'WPF.csv')\n",
+    "pd.DataFrame(matrix).to_csv('distanceMatrix.csv')"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "1ebd53531a265123f8d6b96a0394cf5c6958acea593669dc45d7411847354196"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.7 ('ip1')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}