Coverage for moptipyapps/prodsched/rop

1"""

2A simulator for multiple runs of the ROP scenario.

4Re-Order-Point (ROP) scenarios are such that for each product, a value `X` is

5provided. Once there are no more than `X` elements of that product in the

6warehouse, one new unit is ordered to be produced.

7Therefore, we have `n_products` such `X` values.

8A simulation in this scenario is implemented in

9:mod:`~moptipyapps.prodsched.rop_simulation` as class

10:class:`~moptipyapps.prodsched.rop_simulation.ROPSimulation`.

12This module here provides the functionality to simulate this ROP-approach

13over *multiple* instances (:class:`~moptipyapps.prodsched.instance.Instance`).

14It acts as an :class:`~moptipy.api.encoding.Encoding` that converts a re-order

15point into an instance of

16:class:`~moptipyapps.prodsched.multistatistics.MultiStatistics` which can then

17be used as basis to compute the values of (potentially multiple)

18objective functions, such as those given in package

19:mod:`~moptipyapps.prodsched.objectives`.

21Now, doing a multi-simulation is costly.

22It takes from half a second to several seconds.

23It is unlikely that we can do more than a million in any run of an

24optimization algorithm.

25Therefore, we use an internal caching mechanism to store all the input

26vectors and output statistics.

27This may consume quite some memory, but it might be faster.

29>>> from moptipyapps.prodsched.mfc_generator import sample_mfc_instance

30>>> from moptipyapps.prodsched.mfc_generator import Product

31>>> from moptipyapps.prodsched.mfc_generator import Station

32>>> from moptipyapps.utils.sampling import Gamma

33>>> from moptipyapps.prodsched.multistatistics import to_stream

35>>> inst1 = sample_mfc_instance(seed=100)

36>>> inst2 = sample_mfc_instance(seed=200)

38>>> instances = (inst1, inst2)

39>>> space = MultiStatisticsSpace(instances)

40>>> ms = ROPMultiSimulation(space)

42>>> x1 = np.array([4, 6, 3, 4, 4, 6, 3, 5, 6, 10])

43>>> y = space.create()

44>>> ms.decode(x1, y)

45>>> data = list(to_stream(y))

46>>> for s in data:

47... if "time" not in s:

48... print(s)

49-------- Instance 0: 'mfc_10_13_10000_0x64' -------

50stat;total;product_0;product_1;product_2;product_3;product_4;product_5;\

51product_6;product_7;product_8;product_9

52trp.min;5.2184145757182705;5.2184145757182705;8.901258396714184;\

539.297175556856018;7.298761505635412;8.93985302431065;12.606621860129053;\

547.656178022299173;20.904266810249283;19.5732602761791;20.522703426695443

55trp.mean;32.405411220867336;22.45718865883824;29.222031978321247;\

5625.136275651698107;25.884250065648388;27.37383814423524;32.10269885414455;\

5721.905835255853667;45.11627023942567;44.78387873021794;49.465651426661886

58trp.max;101.42283298962138;56.30942379913131;74.17811849023519;\

5958.955465373336665;60.209164983262326;60.66621167329777;74.4357130671624;\

6047.944841355481;83.29062031809917;96.5279603056706;101.42283298962138

61trp.sd;14.352456423754838;8.966715472534952;10.260311480773648;\

628.824583884361449;9.375043161957333;9.776116378207652;10.962890512009123;\

636.691465594834237;12.613587720623373;12.30244975508726;13.52381481631245

64cwt.min;0.026454757136889384;0.026454757136889384;2.30483638259102;\

650.054286282554130594;0.16648942892061314;0.1385205442725237;\

662.2444075501571206;0.16694429707604286;0.2360807359827959;0.2894682031374032;\

670.7020815078112719

68cwt.mean;8.296470547393602;4.437533958350662;12.615320110639914;\

694.7164029806433545;6.611310521050654;8.350460109042185;7.55452574755307;\

705.82304931358363;9.068938196877168;17.21719619156937;6.710247622764048

71cwt.max;37.76460269749805;11.383554877750612;25.02207987420161;\

7216.22657205109499;24.355088852856625;23.499199642416897;24.507644884798083;\

7327.834403782263507;29.068687291493006;37.76460269749805;12.188275706294007

74cwt.sd;8.072214752396842;3.8478370296451603;9.569679438559595;\

754.197433045217076;7.324722986150041;6.142260031783497;6.213481379280693;\

767.142777751769366;7.629569041217826;11.995928244823393;5.761419012759379

77fill.rate;0.9475719810915342;0.9673295454545454;0.9869565217391304;\

780.9121037463976945;0.9595808383233533;0.93033381712627;0.9775280898876404;\

790.9477401129943502;0.8531073446327684;0.9452449567723343;0.9957983193277311

80stocklevel.mean;29.106301317812118;2.760696025360742;4.137393715988243;\

811.5507610335648132;2.5500355305055966;2.3659654563794095;3.7559706390001586;\

821.811228677305335;1.5668982789103876;2.657874734996926;5.949477225800507

83fulfilled.rate;1;1;1;1;1;1;1;1;1;1;1

84-------- Instance 1: 'mfc_10_13_10000_0xc8' -------

85stat;total;product_0;product_1;product_2;product_3;product_4;product_5;\

86product_6;product_7;product_8;product_9

87trp.min;6.530138861169689;6.530138861169689;9.628902017961991;\

888.559079500931148;9.008831732480758;8.17345928487157;10.857776143797764;\

897.1989408754006945;20.221688833546978;20.764475417566246;18.683929092542712

90trp.mean;31.011119751546097;20.636089799689874;27.67447485260846;\

9123.897005119242046;24.208477302980572;25.25418917347286;29.761302044694876;\

9223.04493364417017;43.39775607053671;45.37848659776272;46.77244686997264

93trp.max;82.04580382289168;45.2064686844069;55.755010710267015;\

9447.241916696964836;46.46518160018422;56.86270345999128;58.305458902948885;\

9552.54891087334727;80.98768370429207;80.62246465566932;82.04580382289168

96trp.sd;12.96227404463328;6.684031626500774;7.995431160774385;\

977.132422793760531;7.239745166217043;8.109774006790918;8.249688118912145;\

987.915621083543569;10.500873112173776;10.750658149592526;10.934157433002026

99cwt.min;0.011249992165176081;0.5337058348654864;0.8014497560352538;\

1000.2827261678148716;1.4241222500077129;0.011249992165176081;7.901938969392177;\

1010.041148101390717784;0.08459428467904218;0.10726475210685749;

102cwt.mean;7.183224986008803;1.9535417603494485;0.8014497560352538;\

1034.619753737796203;9.370441335605987;7.744917661655692;7.901938969392177;\

1048.052111019031262;8.505184343659167;5.922113521223626;

105cwt.max;31.446190263142853;5.290086057329063;0.8014497560352538;\

10610.883639956720799;25.608040164921476;24.178558590047942;7.901938969392177;\

10731.446190263142853;21.35684648685401;16.312679904455308;

108cwt.sd;6.15364337829274;1.7473126971982065;;3.440991075922831;\

1096.485559248771155;6.715154696592823;;7.703968965710594;6.633960138504885;\

1103.8259446158453017;

111fill.rate;0.9628522630230573;0.9901408450704225;0.9985569985569985;\

1120.9544159544159544;0.9635343618513323;0.9702127659574468;0.998546511627907;\

1130.9175108538350217;0.9149855907780979;0.919831223628692;1

114stocklevel.mean;30.090461520030797;2.907712049060156;4.261495763524114;\

1151.6231331746350672;2.565318363579915;2.472060544282644;4.079218485848546;\

1161.786007080448915;1.7664180248890007;2.4348085700118425;6.194289463750593

117fulfilled.rate;1;1;1;1;1;1;1;1;1;1;1

118

119>>> y2 = space.create()

120>>> y2 = y2.from_stream(data)

121>>> for s in to_stream(y2):

122... if "time" not in s:

123... print(s)

124-------- Instance 0: 'mfc_10_13_10000_0x64' -------

125stat;total;product_0;product_1;product_2;product_3;product_4;product_5;\

126product_6;product_7;product_8;product_9

127trp.min;5.2184145757182705;5.2184145757182705;8.901258396714184;\

1289.297175556856018;7.298761505635412;8.93985302431065;12.606621860129053;\

1297.656178022299173;20.904266810249283;19.5732602761791;20.522703426695443

130trp.mean;32.405411220867336;22.45718865883824;29.222031978321247;\

13125.136275651698107;25.884250065648388;27.37383814423524;32.10269885414455;\

13221.905835255853667;45.11627023942567;44.78387873021794;49.465651426661886

133trp.max;101.42283298962138;56.30942379913131;74.17811849023519;\

13458.955465373336665;60.209164983262326;60.66621167329777;74.4357130671624;\

13547.944841355481;83.29062031809917;96.5279603056706;101.42283298962138

136trp.sd;14.352456423754838;8.966715472534952;10.260311480773648;\

1378.824583884361449;9.375043161957333;9.776116378207652;10.962890512009123;\

1386.691465594834237;12.613587720623373;12.30244975508726;13.52381481631245

139cwt.min;0.026454757136889384;0.026454757136889384;2.30483638259102;\

1400.054286282554130594;0.16648942892061314;0.1385205442725237;\

1412.2444075501571206;0.16694429707604286;0.2360807359827959;0.2894682031374032;\

1420.7020815078112719

143cwt.mean;8.296470547393602;4.437533958350662;12.615320110639914;\

1444.7164029806433545;6.611310521050654;8.350460109042185;7.55452574755307;\

1455.82304931358363;9.068938196877168;17.21719619156937;6.710247622764048

146cwt.max;37.76460269749805;11.383554877750612;25.02207987420161;\

14716.22657205109499;24.355088852856625;23.499199642416897;24.507644884798083;\

14827.834403782263507;29.068687291493006;37.76460269749805;12.188275706294007

149cwt.sd;8.072214752396842;3.8478370296451603;9.569679438559595;\

1504.197433045217076;7.324722986150041;6.142260031783497;6.213481379280693;\

1517.142777751769366;7.629569041217826;11.995928244823393;5.761419012759379

152fill.rate;0.9475719810915342;0.9673295454545454;0.9869565217391304;\

1530.9121037463976945;0.9595808383233533;0.93033381712627;0.9775280898876404;\

1540.9477401129943502;0.8531073446327684;0.9452449567723343;0.9957983193277311

155stocklevel.mean;29.106301317812118;2.760696025360742;4.137393715988243;\

1561.5507610335648132;2.5500355305055966;2.3659654563794095;3.7559706390001586;\

1571.811228677305335;1.5668982789103876;2.657874734996926;5.949477225800507

158fulfilled.rate;1;1;1;1;1;1;1;1;1;1;1

159-------- Instance 1: 'mfc_10_13_10000_0xc8' -------

160stat;total;product_0;product_1;product_2;product_3;product_4;product_5;\

161product_6;product_7;product_8;product_9

162trp.min;6.530138861169689;6.530138861169689;9.628902017961991;\

1638.559079500931148;9.008831732480758;8.17345928487157;10.857776143797764;\

1647.1989408754006945;20.221688833546978;20.764475417566246;18.683929092542712

165trp.mean;31.011119751546097;20.636089799689874;27.67447485260846;\

16623.897005119242046;24.208477302980572;25.25418917347286;29.761302044694876;\

16723.04493364417017;43.39775607053671;45.37848659776272;46.77244686997264

168trp.max;82.04580382289168;45.2064686844069;55.755010710267015;\

16947.241916696964836;46.46518160018422;56.86270345999128;58.305458902948885;\

17052.54891087334727;80.98768370429207;80.62246465566932;82.04580382289168

171trp.sd;12.96227404463328;6.684031626500774;7.995431160774385;\

1727.132422793760531;7.239745166217043;8.109774006790918;8.249688118912145;\

1737.915621083543569;10.500873112173776;10.750658149592526;10.934157433002026

174cwt.min;0.011249992165176081;0.5337058348654864;0.8014497560352538;\

1750.2827261678148716;1.4241222500077129;0.011249992165176081;7.901938969392177;\

1760.041148101390717784;0.08459428467904218;0.10726475210685749;

177cwt.mean;7.183224986008803;1.9535417603494485;0.8014497560352538;\

1784.619753737796203;9.370441335605987;7.744917661655692;7.901938969392177;\

1798.052111019031262;8.505184343659167;5.922113521223626;

180cwt.max;31.446190263142853;5.290086057329063;0.8014497560352538;\

18110.883639956720799;25.608040164921476;24.178558590047942;7.901938969392177;\

18231.446190263142853;21.35684648685401;16.312679904455308;

183cwt.sd;6.15364337829274;1.7473126971982065;;3.440991075922831;\

1846.485559248771155;6.715154696592823;;7.703968965710594;6.633960138504885;\

1853.8259446158453017;

186fill.rate;0.9628522630230573;0.9901408450704225;0.9985569985569985;\

1870.9544159544159544;0.9635343618513323;0.9702127659574468;0.998546511627907;\

1880.9175108538350217;0.9149855907780979;0.919831223628692;1

189stocklevel.mean;30.090461520030797;2.907712049060156;4.261495763524114;\

1901.6231331746350672;2.565318363579915;2.472060544282644;4.079218485848546;\

1911.786007080448915;1.7664180248890007;2.4348085700118425;6.194289463750593

192fulfilled.rate;1;1;1;1;1;1;1;1;1;1;1

193

194

195>>> x2 = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

196>>> ms.decode(x2, y)

197>>> for s in to_stream(y):

198... if "time" not in s:

199... print(s)

200-------- Instance 0: 'mfc_10_13_10000_0x64' -------

201stat;total;product_0;product_1;product_2;product_3;product_4;product_5;\

202product_6;product_7;product_8;product_9

203trp.min;7.663287715103252;7.663287715103252;9.095457148523565;\

2047.766240497990111;8.562181846958993;9.316040128631357;10.985466232731596;\

2057.918294809653162;21.60147394131036;17.547777378904357;21.955240879517987

206trp.mean;32.182938074849496;22.140701244808632;28.47300655868274;\

20725.03367526681401;25.540081292675946;27.45355145426555;31.790509174952756;\

20822.848381852082664;44.52135481443732;44.628532680799054;48.780078255964575

209trp.max;90.86353698620042;59.31124925844961;67.52985535834341;\

21053.98557871595767;59.61194121202789;59.45241944832833;67.31898313910006;\

21156.0614850141219;81.02729637887933;80.10102521519639;90.86353698620042

212trp.sd;13.55080270389196;8.168678848316368;9.338600023998156;\

2138.450479951599732;8.617636871353389;9.348284373344056;10.208637939870723;\

2147.518466597403155;10.930234897788171;10.544143459268716;12.157006455407252

215cwt.min;0.011910352755876374;0.052322628985166375;0.011910352755876374;\

2160.10146378360695962;0.0524298188602188;0.025453817918787536;\

2170.05183870049131656;0.019666362641146407;0.08505119605797518;\

2180.041104098391770094;0.35634199392734445

219cwt.mean;17.582567285448004;9.404399139690337;13.569942297813908;\

2209.604699962010896;10.98311819636398;13.02578680799254;16.36199335629276;\

2219.05433728777719;24.801083270074987;24.958902762745797;30.451958550810023

222cwt.max;77.9385378023253;38.626600188006705;53.98496252245468;\

22338.07134509071511;42.09266935074265;47.99381408201043;48.82502650356764;\

22444.910304177168655;61.19671763574479;61.32131436097461;77.9385378023253

225cwt.sd;12.680595891084934;6.984547071390763;9.43383821564051;\

2267.012918522038577;7.891903498485814;9.033444211571616;9.757435017677162;\

2277.598286359665955;11.356538993918976;11.731098098924933;14.099099363785335

228fill.rate;0.2175906030654634;0.40767045454545453;0.2492753623188406;\

2290.3314121037463977;0.3158682634730539;0.28156748911465895;\

2300.17134831460674158;0.3714689265536723;0.002824858757062147;\

2310.018731988472622477;0.0350140056022409

232stocklevel.mean;1.6302006108805502;0.3367466779925779;0.19929696858529114;\

2330.15581052878690257;0.27567206398796995;0.2210582880452966;\

2340.14995708383534714;0.261766524058465;0.00048013843604374936;\

2350.006324013813991379;0.02308832333866472

236fulfilled.rate;0.9982810485603781;0.9985795454545454;1;1;0.9985029940119761;\

2370.9985486211901307;0.9929775280898876;1;0.9971751412429378;\

2380.9985590778097982;0.9985994397759104

239-------- Instance 1: 'mfc_10_13_10000_0xc8' -------

240stat;total;product_0;product_1;product_2;product_3;product_4;product_5;\

241product_6;product_7;product_8;product_9

242trp.min;6.456526072982342;6.456526072982342;10.280447092151007;\

2436.8487732247867825;7.0104482929637015;6.928763679217354;10.310002410918969;\

2446.5546482603685945;18.98639893585596;20.305760878712135;19.684816736973517

245trp.mean;30.62233294764437;20.55618285379736;26.886775958390697;2\

2463.309369645366303;23.990410121167177;25.071330781848836;29.668457645437183;\

24722.39107965050359;43.25662277895463;44.56350867653903;46.348319919246514

248trp.max;78.42550095496154;50.8966871369812;54.1070436589971;\

24948.669180978890836;52.42258290758127;55.73600798325697;57.290713516881624;\

25053.713961902238225;78.26718994789553;78.42550095496154;76.74843649876311

251trp.sd;13.071194037915653;7.013910120253621;8.186009510157717;\

2527.519377794772064;7.702542096043028;8.526420667544688;8.8970364463244;\

2537.682875953156183;10.548987944489301;10.828886793958864;10.898005970008226

254cwt.min;0.0004850386339967372;0.00456283385574352;0.056539264354796614;\

2550.005326168571627932;0.008960725403085235;0.08762150885104347;\

2560.01698932920680818;0.0004850386339967372;0.2800049970701366;\

2570.31679782128776424;0.12185953505104408

258cwt.mean;16.459929680370593;8.156568809736969;12.374646871831668;\

2598.300740034967504;10.371317340036168;10.931842774336376;14.222659911890325;\

2609.16345905328603;23.137826511457558;25.434148525686762;27.66379761065284

261cwt.max;60.919637988946306;29.480300784184692;35.16766467176876;\

26236.653507062645986;39.00025180235298;45.453680537177206;43.580941370786604;\

26345.28946949821329;56.024945551713245;59.28160733189452;60.919637988946306

264cwt.sd;12.118506999914882;6.044408962707157;7.728932055062374;\

2656.268950876605313;7.854789647395754;7.732399117687471;8.863583544578608;\

2667.7525558824150504;11.241315012712777;12.118136175485233;12.596354952532373

267fill.rate;0.24110446911471678;0.44084507042253523;0.2712842712842713;\

2680.36182336182336183;0.3394109396914446;0.32340425531914896;\

2690.21075581395348839;0.4196816208393632;0.002881844380403458;\

2700.01969057665260197;0.025034770514603615

271stocklevel.mean;1.8113476672429796;0.3802859636659473;0.23439669327549575;\

2720.19451906423091544;0.2559188646462229;0.2206691076289149;0.1935442874513864;\

2730.3124656055438941;0.00017487014444265826;0.008074900603352099;\

2740.011298310052408007

275fulfilled.rate;0.9987190435525192;1;0.9985569985569985;1;1;1;1;1;\

2760.9985590778097982;0.9929676511954993;0.9972183588317107

277

278Now the caching kicks in:

279

280>>> x2 = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

281>>> ms.decode(x2, y)

282>>> for s in to_stream(y):

283... if "time" not in s:

284... print(s)

285-------- Instance 0: 'mfc_10_13_10000_0x64' -------

286stat;total;product_0;product_1;product_2;product_3;product_4;product_5;\

287product_6;product_7;product_8;product_9

288trp.min;7.663287715103252;7.663287715103252;9.095457148523565;\

2897.766240497990111;8.562181846958993;9.316040128631357;10.985466232731596;\

2907.918294809653162;21.60147394131036;17.547777378904357;21.955240879517987

291trp.mean;32.182938074849496;22.140701244808632;28.47300655868274;\

29225.03367526681401;25.540081292675946;27.45355145426555;31.790509174952756;\

29322.848381852082664;44.52135481443732;44.628532680799054;48.780078255964575

294trp.max;90.86353698620042;59.31124925844961;67.52985535834341;\

29553.98557871595767;59.61194121202789;59.45241944832833;67.31898313910006;\

29656.0614850141219;81.02729637887933;80.10102521519639;90.86353698620042

297trp.sd;13.55080270389196;8.168678848316368;9.338600023998156;\

2988.450479951599732;8.617636871353389;9.348284373344056;10.208637939870723;\

2997.518466597403155;10.930234897788171;10.544143459268716;12.157006455407252

300cwt.min;0.011910352755876374;0.052322628985166375;0.011910352755876374;\

3010.10146378360695962;0.0524298188602188;0.025453817918787536;\

3020.05183870049131656;0.019666362641146407;0.08505119605797518;\

3030.041104098391770094;0.35634199392734445

304cwt.mean;17.582567285448004;9.404399139690337;13.569942297813908;\

3059.604699962010896;10.98311819636398;13.02578680799254;16.36199335629276;\

3069.05433728777719;24.801083270074987;24.958902762745797;30.451958550810023

307cwt.max;77.9385378023253;38.626600188006705;53.98496252245468;\

30838.07134509071511;42.09266935074265;47.99381408201043;48.82502650356764;\

30944.910304177168655;61.19671763574479;61.32131436097461;77.9385378023253

310cwt.sd;12.680595891084934;6.984547071390763;9.43383821564051;\

3117.012918522038577;7.891903498485814;9.033444211571616;9.757435017677162;\

3127.598286359665955;11.356538993918976;11.731098098924933;14.099099363785335

313fill.rate;0.2175906030654634;0.40767045454545453;0.2492753623188406;\

3140.3314121037463977;0.3158682634730539;0.28156748911465895;\

3150.17134831460674158;0.3714689265536723;0.002824858757062147;\

3160.018731988472622477;0.0350140056022409

317stocklevel.mean;1.6302006108805502;0.3367466779925779;0.19929696858529114;\

3180.15581052878690257;0.27567206398796995;0.2210582880452966;\

3190.14995708383534714;0.261766524058465;0.00048013843604374936;\

3200.006324013813991379;0.02308832333866472

321fulfilled.rate;0.9982810485603781;0.9985795454545454;1;1;0.9985029940119761;\

3220.9985486211901307;0.9929775280898876;1;0.9971751412429378;\

3230.9985590778097982;0.9985994397759104

324-------- Instance 1: 'mfc_10_13_10000_0xc8' -------

325stat;total;product_0;product_1;product_2;product_3;product_4;product_5;\

326product_6;product_7;product_8;product_9

327trp.min;6.456526072982342;6.456526072982342;10.280447092151007;\

3286.8487732247867825;7.0104482929637015;6.928763679217354;10.310002410918969;\

3296.5546482603685945;18.98639893585596;20.305760878712135;19.684816736973517

330trp.mean;30.62233294764437;20.55618285379736;26.886775958390697;2\

3313.309369645366303;23.990410121167177;25.071330781848836;29.668457645437183;\

33222.39107965050359;43.25662277895463;44.56350867653903;46.348319919246514

333trp.max;78.42550095496154;50.8966871369812;54.1070436589971;\

33448.669180978890836;52.42258290758127;55.73600798325697;57.290713516881624;\

33553.713961902238225;78.26718994789553;78.42550095496154;76.74843649876311

336trp.sd;13.071194037915653;7.013910120253621;8.186009510157717;\

3377.519377794772064;7.702542096043028;8.526420667544688;8.8970364463244;\

3387.682875953156183;10.548987944489301;10.828886793958864;10.898005970008226

339cwt.min;0.0004850386339967372;0.00456283385574352;0.056539264354796614;\

3400.005326168571627932;0.008960725403085235;0.08762150885104347;\

3410.01698932920680818;0.0004850386339967372;0.2800049970701366;\

3420.31679782128776424;0.12185953505104408

343cwt.mean;16.459929680370593;8.156568809736969;12.374646871831668;\

3448.300740034967504;10.371317340036168;10.931842774336376;14.222659911890325;\

3459.16345905328603;23.137826511457558;25.434148525686762;27.66379761065284

346cwt.max;60.919637988946306;29.480300784184692;35.16766467176876;\

34736.653507062645986;39.00025180235298;45.453680537177206;43.580941370786604;\

34845.28946949821329;56.024945551713245;59.28160733189452;60.919637988946306

349cwt.sd;12.118506999914882;6.044408962707157;7.728932055062374;\

3506.268950876605313;7.854789647395754;7.732399117687471;8.863583544578608;\

3517.7525558824150504;11.241315012712777;12.118136175485233;12.596354952532373

352fill.rate;0.24110446911471678;0.44084507042253523;0.2712842712842713;\

3530.36182336182336183;0.3394109396914446;0.32340425531914896;\

3540.21075581395348839;0.4196816208393632;0.002881844380403458;\

3550.01969057665260197;0.025034770514603615

356stocklevel.mean;1.8113476672429796;0.3802859636659473;0.23439669327549575;\

3570.19451906423091544;0.2559188646462229;0.2206691076289149;0.1935442874513864;\

3580.3124656055438941;0.00017487014444265826;0.008074900603352099;\

3590.011298310052408007

360fulfilled.rate;0.9987190435525192;1;0.9985569985569985;1;1;1;1;1;\

3610.9985590778097982;0.9929676511954993;0.9972183588317107

362

363>>> x1 = np.array([4, 6, 3, 4, 4, 6, 3, 5, 6, 10])

364>>> y = space.create()

365>>> ms.decode(x1, y)

366>>> for s in to_stream(y):

367... if "time" not in s:

368... print(s)

369-------- Instance 0: 'mfc_10_13_10000_0x64' -------

370stat;total;product_0;product_1;product_2;product_3;product_4;product_5;\

371product_6;product_7;product_8;product_9

372trp.min;5.2184145757182705;5.2184145757182705;8.901258396714184;\

3739.297175556856018;7.298761505635412;8.93985302431065;12.606621860129053;\

3747.656178022299173;20.904266810249283;19.5732602761791;20.522703426695443

375trp.mean;32.405411220867336;22.45718865883824;29.222031978321247;\

37625.136275651698107;25.884250065648388;27.37383814423524;32.10269885414455;\

37721.905835255853667;45.11627023942567;44.78387873021794;49.465651426661886

378trp.max;101.42283298962138;56.30942379913131;74.17811849023519;\

37958.955465373336665;60.209164983262326;60.66621167329777;74.4357130671624;\

38047.944841355481;83.29062031809917;96.5279603056706;101.42283298962138

381trp.sd;14.352456423754838;8.966715472534952;10.260311480773648;\

3828.824583884361449;9.375043161957333;9.776116378207652;10.962890512009123;\

3836.691465594834237;12.613587720623373;12.30244975508726;13.52381481631245

384cwt.min;0.026454757136889384;0.026454757136889384;2.30483638259102;\

3850.054286282554130594;0.16648942892061314;0.1385205442725237;\

3862.2444075501571206;0.16694429707604286;0.2360807359827959;0.2894682031374032;\

3870.7020815078112719

388cwt.mean;8.296470547393602;4.437533958350662;12.615320110639914;\

3894.7164029806433545;6.611310521050654;8.350460109042185;7.55452574755307;\

3905.82304931358363;9.068938196877168;17.21719619156937;6.710247622764048

391cwt.max;37.76460269749805;11.383554877750612;25.02207987420161;\

39216.22657205109499;24.355088852856625;23.499199642416897;24.507644884798083;\

39327.834403782263507;29.068687291493006;37.76460269749805;12.188275706294007

394cwt.sd;8.072214752396842;3.8478370296451603;9.569679438559595;\

3954.197433045217076;7.324722986150041;6.142260031783497;6.213481379280693;\

3967.142777751769366;7.629569041217826;11.995928244823393;5.761419012759379

397fill.rate;0.9475719810915342;0.9673295454545454;0.9869565217391304;\

3980.9121037463976945;0.9595808383233533;0.93033381712627;0.9775280898876404;\

3990.9477401129943502;0.8531073446327684;0.9452449567723343;0.9957983193277311

400stocklevel.mean;29.106301317812118;2.760696025360742;4.137393715988243;\

4011.5507610335648132;2.5500355305055966;2.3659654563794095;3.7559706390001586;\

4021.811228677305335;1.5668982789103876;2.657874734996926;5.949477225800507

403fulfilled.rate;1;1;1;1;1;1;1;1;1;1;1

404-------- Instance 1: 'mfc_10_13_10000_0xc8' -------

405stat;total;product_0;product_1;product_2;product_3;product_4;product_5;\

406product_6;product_7;product_8;product_9

407trp.min;6.530138861169689;6.530138861169689;9.628902017961991;\

4088.559079500931148;9.008831732480758;8.17345928487157;10.857776143797764;\

4097.1989408754006945;20.221688833546978;20.764475417566246;18.683929092542712

410trp.mean;31.011119751546097;20.636089799689874;27.67447485260846;\

41123.897005119242046;24.208477302980572;25.25418917347286;29.761302044694876;\

41223.04493364417017;43.39775607053671;45.37848659776272;46.77244686997264

413trp.max;82.04580382289168;45.2064686844069;55.755010710267015;\

41447.241916696964836;46.46518160018422;56.86270345999128;58.305458902948885;\

41552.54891087334727;80.98768370429207;80.62246465566932;82.04580382289168

416trp.sd;12.96227404463328;6.684031626500774;7.995431160774385;\

4177.132422793760531;7.239745166217043;8.109774006790918;8.249688118912145;\

4187.915621083543569;10.500873112173776;10.750658149592526;10.934157433002026

419cwt.min;0.011249992165176081;0.5337058348654864;0.8014497560352538;\

4200.2827261678148716;1.4241222500077129;0.011249992165176081;7.901938969392177;\

4210.041148101390717784;0.08459428467904218;0.10726475210685749;

422cwt.mean;7.183224986008803;1.9535417603494485;0.8014497560352538;\

4234.619753737796203;9.370441335605987;7.744917661655692;7.901938969392177;\

4248.052111019031262;8.505184343659167;5.922113521223626;

425cwt.max;31.446190263142853;5.290086057329063;0.8014497560352538;\

42610.883639956720799;25.608040164921476;24.178558590047942;7.901938969392177;\

42731.446190263142853;21.35684648685401;16.312679904455308;

428cwt.sd;6.15364337829274;1.7473126971982065;;3.440991075922831;\

4296.485559248771155;6.715154696592823;;7.703968965710594;6.633960138504885;\

4303.8259446158453017;

431fill.rate;0.9628522630230573;0.9901408450704225;0.9985569985569985;\

4320.9544159544159544;0.9635343618513323;0.9702127659574468;0.998546511627907;\

4330.9175108538350217;0.9149855907780979;0.919831223628692;1

434stocklevel.mean;30.090461520030797;2.907712049060156;4.261495763524114;\

4351.6231331746350672;2.565318363579915;2.472060544282644;4.079218485848546;\

4361.786007080448915;1.7664180248890007;2.4348085700118425;6.194289463750593

437fulfilled.rate;1;1;1;1;1;1;1;1;1;1;1

438"""

439from typing import Final

440

441import numpy as np

442from moptipy.api.encoding import Encoding

443from pycommons.types import type_error

444

445from moptipyapps.prodsched.multistatistics import (

446 MultiStatistics,

447 MultiStatisticsSpace,

448)

449from moptipyapps.prodsched.rop_simulation import ROPSimulation

450from moptipyapps.prodsched.statistics_collector import StatisticsCollector

451

452

453class ROPMultiSimulation(Encoding):

454 """A multi-simulation that caches the results for reuse."""

455

456 def __init__(self, space: MultiStatisticsSpace) -> None:

457 """

458 Instantiate the multi-statistics decoding.

459

460 :param instances: the packing instance

461 """

462 if not isinstance(space, MultiStatisticsSpace):

463 raise type_error(space, "space", MultiStatisticsSpace)

464 #: the statistics collectors

465 col: Final[tuple[StatisticsCollector, ...]] = tuple(

466 StatisticsCollector(inst) for inst in space.instances)

467 #: the simulations and collectors

468 self.__simulations: Final[tuple[tuple[

469 ROPSimulation, StatisticsCollector], ...]] = tuple(

470 (ROPSimulation(inst, col[i]), col[i])

471 for i, inst in enumerate(space.instances))

472 #: the internal space

473 self.__space: Final[MultiStatisticsSpace] = space

474

475 def decode(self, x: np.ndarray, y: MultiStatistics) -> None:

476 """

477 Map a ROP setting to a multi-statistics.

478

479 This method uses an internal cache: The same re-order points will

480 yield the same statistics.

481

482 :param x: the array

483 :param y: the Gantt chart

484 """

485 # First we map the vector to a tuple of integers.

486 x_tuple: Final[tuple[int, ...]] = tuple(map(int, x))

487

488 # If we get here, the ROP is new.

489 # So we simulate it.

490 for i, (sim, col) in enumerate(self.__simulations):

491 col.set_dest(y.per_instance[i])

492 sim.ctrl_reset()

493 sim.set_rop(x_tuple)

494 sim.ctrl_run()

495

496 # The simulation is completed. We can now cache the result.

497 cached: Final[MultiStatistics] = self.__space.create()

498 self.__space.copy(cached, y)

499

500 def __str__(self) -> str:

501 """

502 Get the name of this decoding.

503

504 :return: `"rms"`

505 :rtype: str

506 """

507 return "rms"

Coverage for moptipyapps / prodsched / rop_multisimulation.py: 92%

25 statements