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;7.148698771011368;7.148698771011368;9.851247306791265;\

538.427300405584901;7.732987823144867;9.070787436894534;11.245150186528008;\

547.548472763230166;17.99718923960154;19.843363570212205;21.06416134257961

55trp.mean;32.97790594850951;24.596468674329277;30.563844531997784;\

5627.279429397719902;27.79703033632461;26.77907139640676;31.011169285683117;\

5722.68822054678187;43.68592626657671;44.53906054313191;50.332354253457126

58trp.max;81.85722054558573;61.64176267270341;81.27718406438362;\

5959.59867846381849;61.89396888322699;58.814665098144815;58.66937148871966;\

6046.20656698190578;81.15104324107142;78.27784582696768;81.85722054558573

61trp.sd;13.469211388152745;9.448650489856151;10.895076940486566;\

629.109918454636354;9.52161563781995;9.01547173838026;9.813054414557046;\

637.002160286247719;10.716869779123178;10.334832333293049;\

6411.999775884280249

65cwt.min;0.005395059989496076;0.9780139529375447;0.2348135728352645;\

660.03942528753759689;0.20585198680419126;0.5507500551393605;\

670.13329136607262626;0.2233135479809789;0.005395059989496076;\

680.363337511282225;0.39745336452870106

69cwt.mean;6.3170778026045005;7.056731365091693;5.2182919382446675;\

706.87124683902964;9.079584429859228;5.9399972995436725;3.4793130259088376;\

715.078417615955683;6.087077618707137;6.0301712727273555;0.39745336452870106

72cwt.max;30.3947134928394;18.927977914853727;14.788743864707612;\

7326.82679173953329;30.3947134928394;20.036018396819145;10.330677260388256;\

7414.073472667324495;24.646629221033436;21.919009444512994;\

750.39745336452870106

76cwt.sd;5.5974909506906085;4.221610650071981;4.562515459731836;\

776.170158967232;8.323952010113258;4.890025309334268;2.5970717405650108;\

783.9630879947212585;5.290686494450313;5.719967055634048;

79servicelevel;0.9444205701188941;0.9545454545454546;0.9840579710144928;\

800.8876080691642652;0.9431137724550899;0.9317851959361393;\

810.9789325842696629;0.9251412429378532;0.8884180790960452;0.9510086455331412;\

820.9985994397759104

83stocklevel.mean;28.41820760852814;2.560290636738579;3.9957232241217637;\

841.3649189126423142;2.3827363444008087;2.402699293752699;3.8512931890069;\

851.7423245168807433;1.641365095584735;2.6211003662097;5.8557560291898945

86fulfilled.rate;1;1;1;1;1;1;1;1;1;1;1

87utilization.mean;0.8028541903189094;0.7763654043603625;0.9032477434521591;\

880.7930399866318019;0.7489356574262395;0.7991766715886103;0.856983874648282;\

890.7192467665817516;0.7636631798947845;0.9405380117151168;0.717662602426817;\

900.7288606270267851;0.8136069049000355;0.8757770434930767

91-------- Instance 1: 'mfc_10_13_10000_0xc8' -------

92stat;total;product_0;product_1;product_2;product_3;product_4;product_5;\

93product_6;product_7;product_8;product_9

94trp.min;5.948427591903055;7.773461678094918;8.104543866997119;\

959.933972266073852;9.004947959652782;8.776700665666795;9.975332333055121;\

965.948427591903055;20.799002041634594;17.964307940585968;24.272536333651715

97trp.mean;35.167924925950125;27.18841637847806;33.27855240987676;\

9829.982146375833715;30.449231453771148;29.084101384288005;32.62138736043257;\

9922.83061361662951;46.50691768018183;45.7665440237148;53.59331009237047

100trp.max;95.9013844625315;67.73174100731194;75.85471491774206;\

10173.69881984770018;68.71451528269608;71.21493108382583;76.23405217026357;\

10248.44126891105043;95.9013844625315;80.51310862833998;93.74247469991224

103trp.sd;15.01651152311082;12.1357409577245;11.927832420121684;\

10412.110870983620881;12.231352766383868;11.422631005468508;10.870562799044663;\

1056.991683919460075;12.90335326836488;10.608823203464715;13.923445407906742

106cwt.min;0.029403377815469867;0.33968352876763674;0.8012226423225002;\

1070.4169237064834306;0.04608099701363244;0.13958597264536365;\

1080.1427841231916318;0.031861988642049255;0.04509163852708298;\

1090.029403377815469867;0.30706729430312407

110cwt.mean;9.125995226441729;7.635949273012871;8.099259381058715;\

11110.53104684412949;12.0613536196739;6.755248335968917;3.3923317916747693;\

1125.694018876707761;9.678250496815913;9.407512664676307;1.8529460623847172

113cwt.max;47.02757613742506;26.69298476563017;23.81131382935382;\

11433.68826448411983;47.02757613742506;21.765393608298837;8.465947593324927;\

11527.96794209550535;41.19798755026659;26.935112545980246;4.7028845058594015

116cwt.sd;8.312790326182888;5.699569122267945;7.46568137298234;\

1177.984381269231696;12.256771187209509;5.163135442961999;2.273816079756312;\

1186.655405008762987;8.35629247653758;7.251352236700332;1.9462759372992589

119servicelevel;0.9067748363222317;0.8929577464788733;0.9696969696969697;\

1200.7905982905982906;0.8695652173913043;0.8978723404255319;0.9854651162790697;\

1210.9276410998552822;0.8256484149855908;0.9142053445850914;0.9944367176634215

122stocklevel.mean;26.506300325777048;2.3225382137372628;3.7298268255006812;\

1231.2114694030566133;2.055027916866671;2.1359308723688653;3.803260303815258;\

1241.7840029880750334;1.5544812126506562;2.4214269649529525;5.488335624753054

125fulfilled.rate;1;1;1;1;1;1;1;1;1;1;1

126utilization.mean;0.807148475748266;0.7868543497811389;0.9044317782418774;\

1270.8033818248605281;0.7596125889483434;0.8088468279639388;0.8356338884488432;\

1280.7140483213425692;0.7588804801210093;0.9422462544368143;0.7228749764278876;\

1290.7537235522221261;0.8221827015767551;0.880212640355627

130

131>>> y2 = space.create()

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

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

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

135... print(s)

136-------- Instance 0: 'mfc_10_13_10000_0x64' -------

137stat;total;product_0;product_1;product_2;product_3;product_4;product_5;\

138product_6;product_7;product_8;product_9

139trp.min;7.148698771011368;7.148698771011368;9.851247306791265;\

1408.427300405584901;7.732987823144867;9.070787436894534;11.245150186528008;\

1417.548472763230166;17.99718923960154;19.843363570212205;21.06416134257961

142trp.mean;32.97790594850951;24.596468674329277;30.563844531997784;\

14327.279429397719902;27.79703033632461;26.77907139640676;31.011169285683117;\

14422.68822054678187;43.68592626657671;44.53906054313191;50.332354253457126

145trp.max;81.85722054558573;61.64176267270341;81.27718406438362;\

14659.59867846381849;61.89396888322699;58.814665098144815;58.66937148871966;\

14746.20656698190578;81.15104324107142;78.27784582696768;81.85722054558573

148trp.sd;13.469211388152745;9.448650489856151;10.895076940486566;\

1499.109918454636354;9.52161563781995;9.01547173838026;9.813054414557046;\

1507.002160286247719;10.716869779123178;10.334832333293049;\

15111.999775884280249

152cwt.min;0.005395059989496076;0.9780139529375447;0.2348135728352645;\

1530.03942528753759689;0.20585198680419126;0.5507500551393605;\

1540.13329136607262626;0.2233135479809789;0.005395059989496076;\

1550.363337511282225;0.39745336452870106

156cwt.mean;6.3170778026045005;7.056731365091693;5.2182919382446675;\

1576.87124683902964;9.079584429859228;5.9399972995436725;3.4793130259088376;\

1585.078417615955683;6.087077618707137;6.0301712727273555;0.39745336452870106

159cwt.max;30.3947134928394;18.927977914853727;14.788743864707612;\

16026.82679173953329;30.3947134928394;20.036018396819145;10.330677260388256;\

16114.073472667324495;24.646629221033436;21.919009444512994;\

1620.39745336452870106

163cwt.sd;5.5974909506906085;4.221610650071981;4.562515459731836;\

1646.170158967232;8.323952010113258;4.890025309334268;2.5970717405650108;\

1653.9630879947212585;5.290686494450313;5.719967055634048;

166servicelevel;0.9444205701188941;0.9545454545454546;0.9840579710144928;\

1670.8876080691642652;0.9431137724550899;0.9317851959361393;\

1680.9789325842696629;0.9251412429378532;0.8884180790960452;0.9510086455331412;\

1690.9985994397759104

170stocklevel.mean;28.41820760852814;2.560290636738579;3.9957232241217637;\

1711.3649189126423142;2.3827363444008087;2.402699293752699;3.8512931890069;\

1721.7423245168807433;1.641365095584735;2.6211003662097;5.8557560291898945

173fulfilled.rate;1;1;1;1;1;1;1;1;1;1;1

174utilization.mean;0.8028541903189094;0.7763654043603625;0.9032477434521591;\

1750.7930399866318019;0.7489356574262395;0.7991766715886103;0.856983874648282;\

1760.7192467665817516;0.7636631798947845;0.9405380117151168;0.717662602426817

177-------- Instance 1: 'mfc_10_13_10000_0xc8' -------

178stat;total;product_0;product_1;product_2;product_3;product_4;product_5;\

179product_6;product_7;product_8;product_9

180trp.min;5.948427591903055;7.773461678094918;8.104543866997119;\

1819.933972266073852;9.004947959652782;8.776700665666795;9.975332333055121;\

1825.948427591903055;20.799002041634594;17.964307940585968;24.272536333651715

183trp.mean;35.167924925950125;27.18841637847806;33.27855240987676;\

18429.982146375833715;30.449231453771148;29.084101384288005;32.62138736043257;\

18522.83061361662951;46.50691768018183;45.7665440237148;53.59331009237047

186trp.max;95.9013844625315;67.73174100731194;75.85471491774206;\

18773.69881984770018;68.71451528269608;71.21493108382583;76.23405217026357;\

18848.44126891105043;95.9013844625315;80.51310862833998;93.74247469991224

189trp.sd;15.01651152311082;12.1357409577245;11.927832420121684;\

19012.110870983620881;12.231352766383868;11.422631005468508;10.870562799044663;\

1916.991683919460075;12.90335326836488;10.608823203464715;13.923445407906742

192cwt.min;0.029403377815469867;0.33968352876763674;0.8012226423225002;\

1930.4169237064834306;0.04608099701363244;0.13958597264536365;\

1940.1427841231916318;0.031861988642049255;0.04509163852708298;\

1950.029403377815469867;0.30706729430312407

196cwt.mean;9.125995226441729;7.635949273012871;8.099259381058715;\

19710.53104684412949;12.0613536196739;6.755248335968917;3.3923317916747693;\

1985.694018876707761;9.678250496815913;9.407512664676307;1.8529460623847172

199cwt.max;47.02757613742506;26.69298476563017;23.81131382935382;\

20033.68826448411983;47.02757613742506;21.765393608298837;8.465947593324927;\

20127.96794209550535;41.19798755026659;26.935112545980246;4.7028845058594015

202cwt.sd;8.312790326182888;5.699569122267945;7.46568137298234;\

2037.984381269231696;12.256771187209509;5.163135442961999;2.273816079756312;\

2046.655405008762987;8.35629247653758;7.251352236700332;1.9462759372992589

205servicelevel;0.9067748363222317;0.8929577464788733;0.9696969696969697;\

2060.7905982905982906;0.8695652173913043;0.8978723404255319;0.9854651162790697;\

2070.9276410998552822;0.8256484149855908;0.9142053445850914;0.9944367176634215

208stocklevel.mean;26.506300325777048;2.3225382137372628;3.7298268255006812;\

2091.2114694030566133;2.055027916866671;2.1359308723688653;3.803260303815258;\

2101.7840029880750334;1.5544812126506562;2.4214269649529525;5.488335624753054

211fulfilled.rate;1;1;1;1;1;1;1;1;1;1;1

212utilization.mean;0.807148475748266;0.7868543497811389;0.9044317782418774;\

2130.8033818248605281;0.7596125889483434;0.8088468279639388;0.8356338884488432;\

2140.7140483213425692;0.7588804801210093;0.9422462544368143;0.7228749764278876

215

216

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

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

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

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

221... print(s)

222-------- Instance 0: 'mfc_10_13_10000_0x64' -------

223stat;total;product_0;product_1;product_2;product_3;product_4;product_5;\

224product_6;product_7;product_8;product_9

225trp.min;6.6579246980854805;6.6579246980854805;10.040428073632938;\

2269.144356382177648;8.296261623940154;7.667382695110064;7.49579069264837;\

2278.581919683902015;19.249891132463745;18.452787187654394;18.901212658200166

228trp.mean;30.74716064960712;22.55644591592345;28.15643187379386;\

22925.157306197204942;25.891453823516155;24.656654627623602;28.280298002722372;\

23021.884096260231306;41.33149767348687;42.01272258331701;47.0432009577156

231trp.max;79.69814356608413;50.3897190429866;60.417297663587306;\

23258.155910569312255;57.205821481757084;52.45021315253325;59.77053868767143;\

23343.246354315288954;71.73764560662357;68.38738538621237;79.69814356608413

234trp.sd;12.157985067348928;7.639186573173071;8.568931718969022;\

2357.973929260222244;8.038036986999414;7.925969997080636;8.599986516235049;\

2366.097255369156319;9.856734625477339;8.9409753618694;10.274842345336927

237cwt.min;0.007410165035253158;0.010823749293649598;0.05938309523889984;\

2380.07244315036041371;0.007410165035253158;0.007457627479197981;\

2390.040943728912679944;0.03445897573783441;0.03898586586547026;\

2400.08116594846524094;0.14281174169173028

241cwt.mean;16.08134651193112;9.407819301031829;13.599570996899798;\

2429.281088119494319;11.11399938015249;10.30717585037071;13.666658677426005;\

2438.030271931727988;21.81659380310111;22.632166486797452;28.69724449522347

244cwt.max;68.13273609246335;34.22959482141596;54.775201309764725;\

24540.73069593375112;41.04427051696348;38.24440503984806;42.85895780219471;\

24631.703705121431085;53.001747956494;51.120920634961294;68.13273609246335

247cwt.sd;11.41034407109861;6.914971921203133;8.553611465539651;\

2486.705681452336642;7.5489609746268345;7.570333206165438;8.32656793004751;\

2496.081437849778869;10.3176054520999;9.98985948404976;12.586922189036457

250servicelevel;0.23005300100272166;0.3877840909090909;0.26811594202898553;\

2510.30979827089337175;0.312874251497006;0.3439767779390421;\

2520.22752808988764045;0.384180790960452;0.009887005649717515;\

2530.03170028818443804;0.03361344537815126

254stocklevel.mean;1.673940782484679;0.31292966720234844;0.20715377564649276;\

2550.13834460042719282;0.25115561718382967;0.2398635057643522;\

2560.19840432550798928;0.2850204974085108;0.0023773765165652936;\

2570.011388858983845954;0.027302557843551636

258fulfilled.rate;0.9988540323735855;0.9985795454545454;1;0.9985590778097982;\

2591;1;0.9971910112359551;1;0.9971751412429378;0.9985590778097982;\

2600.9985994397759104

261utilization.mean;0.794909162274783;0.7763654043603625;0.9081892364495642;\

2620.7918425686625462;0.7331739301471062;0.7835902639278088;0.8346169766930579;\

2630.7124249384600392;0.7575113250154277;0.9306191609379052;0.7105158404571978;\

2640.7189580123724747;0.8151628323728414;0.8608486197158479

265-------- Instance 1: 'mfc_10_13_10000_0xc8' -------

266stat;total;product_0;product_1;product_2;product_3;product_4;product_5;\

267product_6;product_7;product_8;product_9

268trp.min;6.553059261284034;6.553059261284034;10.108606281321954;\

2699.703090589753629;11.174354779162059;8.321303147977233;11.439845494670408;\

2708.009630409889724;15.112291339387411;21.10066493332215;26.25138351485475

271trp.mean;35.457717990887495;27.190617501492195;33.285557575397284;\

27229.938089184363303;30.57060178357617;29.855403667616407;33.96324823266337;\

27322.77623364751058;47.106553173547205;45.91880621944926;53.65085012387992

274trp.max;88.07784608736165;56.968128655076725;67.76184441039186;\

27558.983867721681236;56.352778532964294;60.30516528044609;62.48506657539656;\

27647.527002503284166;76.8993130774079;72.71943839738833;88.07784608736165

277trp.sd;13.47506267495003;9.02066756428428;9.843605381506345;\

2789.041655339468601;8.972226014586374;9.64262838980214;9.682655730390962;\

2796.905743095003228;10.732554367883997;9.649376408388306;11.415428192248845

280cwt.min;0.006287345835517044;0.12899921433927375;0.12664124934963183;\

2810.018981504710609443;0.012018201038699772;0.006287345835517044;\

2820.07326658304918965;0.03329370768551598;0.687625592607219;\

2830.16628305106132757;0.8814572391811453

284cwt.mean;19.616157243489514;12.54124700723885;17.17954158092728;\

28512.797030006049777;14.78523355299519;14.531965580117795;17.509595977311385;\

2868.869499446666719;27.071380519490486;26.35203858982817;34.65830156782526

287cwt.max;74.59018342258969;41.182989166839434;49.26013039123245;\

28842.677472406387096;43.76841772841544;45.64997544083144;50.08378847398126;\

28934.685042479895856;58.25570602393418;58.60214532455484;74.59018342258969

290cwt.sd;12.79311806936005;8.220650581141385;10.036535907608853;\

2918.032836405284439;8.961327098641396;9.01894965593242;9.987307711032216;\

2926.809774156819015;11.394609551985406;11.22216829037131;13.382242351946935

293servicelevel;0.15328778821520067;0.2563380281690141;0.15728715728715728;\

2940.1581196581196581;0.17251051893408134;0.2170212765957447;0.1555232558139535;\

2950.40086830680173663;0.004322766570605188;0.004219409282700422;\

2960.012517385257301807

297stocklevel.mean;1.0688209713197157;0.1893746360753341;0.13978209374798592;\

2980.07751886834182369;0.1308059798092467;0.13779442473717082;\

2990.11712916644109925;0.2719222690871477;0.00018993221962948934;\

3000.0012621122002660092;0.003041488660012029

301fulfilled.rate;0.9981497295758611;0.9985915492957746;0.9985569985569985;1;1;\

3021;0.998546511627907;1;0.9971181556195965;0.9929676511954993;0.9958275382475661

303utilization.mean;0.8082338028980605;0.7868543497811389;0.9051603252768642;\

3040.8134939422379659;0.7566700358581937;0.8167982234100564;0.8509976451640071;\

3050.7133318620183866;0.7462458169886501;0.9583484128790276;0.7312103174404826;\

3060.7339761871684062;0.8127215565516549;0.8812307628999528

307"""

308from typing import Final

309

310import numpy as np

311from moptipy.api.encoding import Encoding

312from pycommons.types import type_error

313

314from moptipyapps.prodsched.multistatistics import (

315 MultiStatistics,

316 MultiStatisticsSpace,

317)

318from moptipyapps.prodsched.rop_simulation import ROPSimulation

319from moptipyapps.prodsched.statistics_collector import StatisticsCollector

320

321

322class ROPMultiSimulation(Encoding):

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

324

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

326 """

327 Instantiate the multi-statistics decoding.

328

329 :param instances: the packing instance

330 """

331 if not isinstance(space, MultiStatisticsSpace):

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

333 #: the statistics collectors

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

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

336 #: the simulations and collectors

337 self.__simulations: Final[tuple[tuple[

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

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

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

341 #: the internal space

342 self.__space: Final[MultiStatisticsSpace] = space

343

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

345 """

346 Map a ROP setting to a multi-statistics.

347

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

349 yield the same statistics.

350

351 :param x: the array

352 :param y: the Gantt chart

353 """

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

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

356

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

358 # So we simulate it.

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

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

361 sim.ctrl_reset()

362 sim.set_rop(x_tuple)

363 sim.ctrl_run()

364

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

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

367 self.__space.copy(cached, y)

368

369 def __str__(self) -> str:

370 """

371 Get the name of this decoding.

372

373 :return: `"rms"`

374 :rtype: str

375 """

376 return "rms"

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

25 statements