move nix environment to fit lorri tooling

contributes to #20

Readme.md

@@ -2,7 +2,7 @@
 
 ## Tooling
 
-- Python 3.5
+- Python 3.6
 - [MyPy](http://www.mypy-lang.org/ ) für statische Typchecks
 - [Pandoc](https://pandoc.org/ ) für die Dokumentation
 - Python Module: siehe [requirements.txt](https://pip.pypa.io/en/latest/user_guide/#requirements-files )

Readme.txt

@@ -0,0 +1,23 @@
+README
+-----
+
+Für die Ausführung des Algorithmus wird Python 3 (empfohlene Version: 3.6.1) benötigt.
+Die Packages, die zusätzlich gebraucht werden, können der requirements.txt entnommen werden.
+(Installation kann hier einzeln oder über den Befehl: python -m pip install -r requirements.txt)
+
+Zur Ausführung bitte im Terminal in den Ordner src gehen und dort das Skript main.py starten.
+Parameter, die hierbei möglich sind:
+    -h zeigt alle Optionen an
+    -p aktiviert die Ausgabe über den Plotter als Diagramm
+    -l wird benötigt falls die Eingabe eine Liste von Problemen ist (d.h. für jobshop1.txt)
+    -i Index des Problems in der Liste (nur relevant bei -l)
+    -t setzt die Starttemperatur des Simulated Annealings
+    -s setzt die maximalen Umformungsschritte pro Generierung einer neuen Lösung
+    -a setzt die Wahrscheinlichkeit, pro Umformungsschritt auch eine Lösung zu akzeptieren, obwohl
+        noch nicht die maximalen Umformungsschritte erreicht sind
+
+-t -s und -a müssen nicht alle gesetzt sein, dann wird der jeweilige Defaultwert verwendet
+Defaultwerte: max_temp = 300, max_steps = 250, accept_prob = 0.01
+
+Beispielaufruf: 
+    python .\main.py -p -l -i 2 -t 50 ..\inputdata\jobshop1.txt

doc.md

@@ -0,0 +1,36 @@
+## scheduling problem defined by:
+  1. $m$ specialized machines
+  2. tasks $\tau$ of the form $(e, i)$ with $t \in \mathbb{N}$ the execution time and $i \in \{1,2,\dots,m\}$ the machine the task has to run on
+  3. $n$ jobs $T_k$ with $\forall T_k:$ linear order of tasks, with $k \in \{1,2,\dots,n\}$
+  4. Additionally, a multiset $\Omega$ of arbitrary but fixed size that contains wait states $\omega := (1, i)$ with $i \in \{1,2,\dots,m\}$ the blocked machine.
+
+The goal is to find the fastest feasible schedule $\sigma_{min}$.
+
+## evaluative function
+  - minimize the execution time of $\sigma$
+  - upper bound: largest processing time first
+  - lower bound: max sum of execution times on one machine
+
+## solutions
+  - list of tuples $(t, \tau)$ with $t \in \mathbb{N}$ the scheduled begin of $\tau$
+
+## operations
+  - $\operatorname{ins}(\omega, t)$: block a machine at time $t$ for $w$ time steps.
+  - $\operatorname{xchg}(\tau_1,\tau_2)$: exchange the position of two tasks.
+  
+Both operations require that the start times are recomputed.
+
+## neighbourhood of solution
+
+  - $\operatorname{neighbours}(\sigma) = \{x \in \Sigma | \delta(\sigma, x) \leq n\}$ with $\Sigma$ the set of all feasible schedules.
+  - $\delta$: $\delta ( \sigma )=0$, $\delta ( \operatorname{op}(x)) = \delta (x) + 1$ (ass. ins has the same penalty xchg has), $x$ either op($y$) or $\sigma$ 
+
+## constraints
+  - only schedule new $\tau$ if another $\tau$ is finished
+  - only schedule $\tau \in T_k$ that has no unscheduled predecessor in $T_k$
+  - only one task on a machine any given time
+
+## implementation in Python
+  - translate problem into list of jobs, jobs into lists of tasks, ie problem = [$T_0, T_1,\dots,T_{k-1}$], $T_i$ = [$\tau_1,\tau_2,\dots$]
+  - address tasks based on their indices, ie [0][1] is the second task of the first job.
+  - compute only one possible next solution, rate, drop/accept. $\delta$ is computed iteratively during generation

notes.md

@@ -26,7 +26,7 @@
     - $S = \left\{(o_j,t) | o_j \in O \cup \left\{w_n | n \in \mathbb{N} \wedge w_n \text{ v.d.F. } (1, m) \right\} \wedge o_j \text{ v.d.F. } (d, m, j) \wedge t \in T \forall o \in O : \exists (o,t) \in S\right\}$
     - indirekt lässt sich durch laufende Operation und Zeitpunkt auch Belegung einer Maschine zu einem Zeitpunkt ermitteln
     - Optimierung: sparse speichern
-1. Liste von (T, $o_j$) mit $T \in \mathbb{N}$ (Time), $o_j \in O$ (Tasks)
+1. Liste von (T, $o_j$) mit $T \in \mathbb{N}$ (Time), $o_j \in O$ (Tasks), j bezeichnet den Job
     - Operationen:
         - Vertauschen von 2 Jobs auf einer Maschine, selbstinvers
         - Verzögern von Operationen (keine expliziten Wartezustände nötig)

requirements.txt

@@ -1,2 +1,5 @@
 mypy
-Arpeggio
+arpeggio
+matplotlib
+numpy
+tkinter

shell.nix

@@ -0,0 +1,3 @@
+with import <nixpkgs> {};
+
+  (python3.withPackages (ps: [ps.numpy (ps.matplotlib.override {enableQt=true;}) ps.mypy ps.arpeggio])).env

src/Generator/generator.py

@@ -39,6 +39,7 @@ def accept(solution):
     random solution.
     """
     return tighten(solution)
+    #return solution
 
 
 def tighten(solution):

src/Output/output.py

@@ -0,0 +1,32 @@
+from matplotlib import pyplot as plt
+from matplotlib import colors
+from matplotlib import patches
+import numpy as np
+from SchedulingAlgorithms.simanneal import rate
+import random
+
+def create_plot(problem, solution):
+    
+    end = rate(solution)
+
+    with plt.xkcd():
+        fig,ax = plt.subplots()
+        col = colors.XKCD_COLORS
+        del col['xkcd:white']
+        colorlist = list(col.values())
+        random.shuffle(colorlist)
+        for m in range(0, problem.machines):
+            mach_ops = [ x for x in solution if problem.problem_data[x[1][0]][x[1][1]][1] == m ]
+            xranges = [ (x[0], problem.problem_data[x[1][0]][x[1][1]][0]) for x in mach_ops ]
+            ax.broken_barh(xranges, ((problem.machines - m)*10, 9), 
+                    facecolors=[colorlist[x[1][0]] for x in mach_ops])
+        ax.set_ylim(5, 5 + (problem.machines+1)*10)
+        ax.set_xlim(0, 1.25 * end)
+        ax.set_yticks([15 + m * 10 for m in range(0, problem.machines)])
+        ax.set_yticklabels([ problem.machines - 1 - m for m in range(0, problem.machines)] )
+        handlecolors = colorlist[0:problem.jobs]
+        handles = [ patches.Patch(color = handlecolors[j], label = "Job "+str(j)) for j in range(0,problem.jobs) ]
+        labels = ["Job "+str(j) for j in range(0,problem.jobs)]
+        ax.legend(handles, labels)
+
+    plt.show()

src/Parser/__init__.py

@@ -6,33 +6,33 @@ from collections.abc import Mapping
 __all__ = ["js1_style", "js2_style"]
 
 grammar = """
-        # starting point for jobshop1 input file
+        // starting point for jobshop1 input file
         job_shop1       = skip_preface
-        # eat away lines of preface, until first problem_instance is
-        # encountered; then the list of instances start
+        // eat away lines of preface, until first problem_instance is
+        // encountered; then the list of instances start
         skip_preface    = (!problem_instance r"[^\n]+" skip_preface) / (eol skip_preface) / instance_list
         instance_list   = problem_instance (sep_line trim_ws eol problem_instance eol?)* eof_sep
         problem_instance = trim_ws "instance" ' ' instance_name trim_ws eol trim_ws eol sep_line description eol problem_data
         description     = r"[^\n]*"
         instance_name   = r"\w+"
         sep_line        = trim_ws plus_line trim_ws eol
-        # lines out of multiple + signs
+        // lines out of multiple + signs
         plus_line       = r"\+\+\++"
-        # EOF is a builtin rule matching end of file
+        // EOF is a builtin rule matching end of file
         eof_sep         = trim_ws plus_line " EOF " plus_line trim_ws eol* EOF
-        # entry point for jobshop2 input files
+        // entry point for jobshop2 input files
         job_shop2       = problem_data EOF
         problem_data    = trim_ws num_jobs ' ' num_machines eol job_data+
-        # used for skipping arbitrary number of non-breaking whitespace
+        // used for skipping arbitrary number of non-breaking whitespace
         trim_ws         = r'[ \t]*'
-        # git may change line-endings on windows, so we have to match on both
+        // git may change line-endings on windows, so we have to match on both
         eol             = "\n" / "\r\n"
         nonneg_num      = r'\d+'
         num_jobs        = nonneg_num
         num_machines    = nonneg_num
         machine         = nonneg_num
         duration        = nonneg_num
-        # task data for 1 job
+        // task data for 1 job
         job_data        = ' '* machine ' '+ duration (' '+ machine ' '+ duration)* trim_ws eol
         """
 

src/example.py

@@ -1,7 +1,10 @@
-INSTANCES = [(5, 5)]
-TASKS = [[(1, 21), (0, 53), (4, 95), (3, 55), (2, 35)],
-         [(0, 21), (3, 52), (4, 16), (2, 26), (1, 71)],
-         [(3, 39), (4, 98), (1, 42), (2, 31), (0, 12)],
-         [(1, 77), (0, 55), (4, 79), (2, 66), (3, 77)],
-         [(0, 83), (3, 34), (2, 64), (1, 19), (4, 37)]]
+import Parser.js1_style as p
+#import Parser.js2_style as p
+from SchedulingAlgorithms import simanneal as sim
+from Output import output as o
 
+problem = p.parse_file("../inputdata/jobshop1.txt")[0]
+#problem = p.parse_file("../inputdata/sample")
+sim.init(problem)
+solution = sim.anneal()
+o.create_plot(problem, solution)

src/main.py

@@ -1,6 +1,97 @@
 #! /usr/bin/env python
-def main() -> None:
-    pass
 
-if "__name__" == "__main__":
+import sys
+import getopt
+from SchedulingAlgorithms import simanneal as sim
+from Output import output as o
+
+
+def usage():
+    s=  """
+Command line options:
+    -h      show this help
+    -p      activate pretty output (requires tkinter)
+    -l      assume that a file contains multiple problems, default is only 1
+    -i      index of the problem you want solved. has no effect without l
+    -t      set parameter max_temp of simulated annealing
+    -s      set parameter max_steps of simulated annealing
+    -a      set parameter accept_prob of simulated annealing
+
+Invocation:
+    python [-hlp] file
+"""
+    return s
+
+
+def main():
+    js1 = False
+    plot = False
+    try:
+        opts, args = getopt.getopt(sys.argv[1:], 'hpli:t:s:a:')
+    except getopt.GetoptError as err:
+        print(err)
+        sys.exit()
+    if ('-h', '') in opts:
+        print(usage())
+    if ('-p', '') in opts:
+        print("Plotting enabled.")
+        from Output import output as o
+        plot = True
+    if('-l', '') in opts:
+        js1 = True
+    idx = [int(x[1]) for x in opts if x[0]=='-i']   
+    idx = idx[0] if idx else -1
+    max_temp = [int(x[1]) for x in opts if x[0]=='-t']   
+    max_temp = max_temp[0] if max_temp else -1
+    max_steps = [int(x[1]) for x in opts if x[0]=='-s']   
+    max_steps = max_steps[0] if max_steps else -1
+    accept_prob = [int(x[1]) for x in opts if x[0]=='-a']   
+    accept_prob = accept_prob[0] if accept_prob else -1
+    if not args:
+        print("No file given.")
+        sys.exit()            
+    else:
+        infile = args[0]
+    if js1:
+        from Parser import js1_style as parser
+    else:
+        from Parser import js2_style as parser
+    print("Parsing file: " + infile)
+    problem = parser.parse_file(infile)
+    if js1:
+        print("File contains " + str(len(problem)) + " problems.")
+        if idx == -1:
+            idx = int(input("Which problem do you want so solve? [0-" + str(len(problem)-1) + "] "))
+        problem = problem[idx]
+    print(problem)
+    sim.init(problem)
+    if not max_temp == -1:
+        if not max_steps == -1:
+            if not accept_prob == -1:
+                solution = sim.anneal(max_temp, max_steps, accept_prob)
+            else:
+                solution = sim.anneal(max_temp = max_temp, max_steps = max_steps)
+        else:
+            if not accept_prob == -1:
+                solution = sim.anneal(max_temp = max_temp, accept_prob = accept_prob)
+            else:
+                solution = sim.anneal(max_temp = max_temp)
+    else:
+        if not max_steps == -1:
+            if not accept_prob == -1:
+                solution = sim.anneal(max_steps = max_steps, accept_prob = accept_prob)
+            else:
+                solution = sim.anneal(max_steps = max_steps)
+        else:
+            if not accept_prob == -1:
+                solution = sim.anneal(accept_prob = accept_prob)
+            else:
+                solution = sim.anneal()
+    print(solution)
+    print(sim.rate(solution))
+    if plot:
+        o.create_plot(problem, solution)
+
+if __name__ == "__main__":
     main()
+