> ## Content Index > Fetch the complete content index at: https://blog.afi.io/llms.txt > Use this file to discover other available public pages before exploring further. # GMPRO docs: Fixed vehicle costs - URL: https://blog.afi.io/blog/gmpro-docs-fixed-vehicle-costs/ - Published: 2024-08-31T05:21:04.000Z - Updated: 2026-08-22T04:55:02.000Z - Description: How to minimize the number of vehicles needed by adjusting fixed and variable costs in GMPRO. - Author: Afian Anwar - Tags: googlemaps In this blog post, I’ll show you how to optimize variable and fixed costs in [GMPRO](https://developers.google.com/maps/documentation/route-optimization?ref=blog.afi.io) to minimize the number of vehicles used. This approach ensures that all deliveries are made on time while keeping the vehicle count as low as possible. ![A route solution that uses fixed vehicle costs to minimize the number of drivers needed](https://storage.ghost.io/c/c6/4d/c64da7e8-63a6-4cff-acdc-2782a6ebc377/content/images/2024/09/Screenshot-2024-09-03-at-8.33.13-AM.png) A route solution that uses fixed vehicle costs to minimize the number of drivers needed Part 1: [GMPRO: Google Maps Platform route optimization API](https://afi.io/blog/gmpro-google-maps-platform-route-optimization-api/?ref=blog.afi.io) Part 2: [GMPRO TSP solver: Google Maps with more than 25 waypoints](https://afi.io/blog/gmpro-tsp-solver-google-maps-with-more-than-25-waypoints/?ref=blog.afi.io) Part 3: [Google Maps route optimization: multi vehicle](https://afi.io/blog/google-maps-route-optimization-multi-vehicle/?ref=blog.afi.io) Part 4: [Fleet routing app - free Google Maps route planner for multiple stops](https://www.afi.io/blog/fleet-routing-app-free-google-maps-route-planner-for-multiple-stops/?ref=blog.afi.io) **Part 5: GMPRO docs: Fixed vehicle costs (this article)** Part 6: [GMPRO docs: Territory optimization and route planning](https://www.afi.io/blog/gmpro-docs-territory-optimization-and-route-planning/?ref=blog.afi.io) Part 7: [GMPRO docs: Solving the VRP with route clustering and soft constraints](https://afi.io/blog/gmpro-docs-solving-the-vrp-with-route-clustering-and-soft-constraints/?ref=blog.afi.io) Part 8: [GMPRO docs: Driver load balancing with soft constraints](https://www.afi.io/blog/gmpro-docs-driver-load-balancing-with-soft-constraints/?ref=blog.afi.io) Part 9: [GMPRO docs: Driver breaks](https://www.afi.io/blog/gmpro-docs-driver-breaks/?ref=blog.afi.io) Part 10: [GMPRO docs: Complete deliveries before pickups in cargo bike logistics](https://www.afi.io/blog/gmpro-docs-complete-all-deliveries-before-pickups-in-cargo-bike-logistics/?ref=blog.afi.io) Part 11: [GMPRO docs: Force stop sequences using precedence rules](https://blog.afi.io/blog/gmpro-docs-force-stop-sequences-using-precedence-rules/) Part 12: [GMPRO docs: Cut cost / raise care with smart NEMT routing](https://blog.afi.io/blog/gmpro-docs-cut-cost-raise-care-with-smart-nemt-routing/) Part 13: [GMPRO docs: Routing for Demand Responsive Transport](https://blog.afi.io/blog/gmpro-docs-routing-for-demand-responsive-transport/) Part 14: [GMPRO docs: Squash durations and parking](https://blog.afi.io/blog/gmpro-docs-squash-durations-and-parking/) Part 15: [GMPRO docs: Google Large Vehicle Routing](https://www.afi.io/blog/gmpro-docs-google-large-vehicle-routing/?ref=blog.afi.io) ### How does a route optimization algorithm work? In a [previous blog post](https://afi.io/blog/google-maps-route-optimization-multi-vehicle?ref=blog.afi.io), I explained that route optimization algorithms, such as the one used by GMPRO, aim to assign vehicles to stops (`shipments` in GMPRO) in a way that minimizes costs while ensuring all stops are served. The simplest way to understand these costs is by considering them as the distance each vehicle travels, as distance directly correlates with fuel costs. 💡 If you want to view route solutions produced by GMPRO on a map like we did here, use our [GMPRO Viewer](https://gmpro-viewer.afi.dev/?ref=blog.afi.io). ### Worked GMPRO example with variable costs (`costPerKilometer`) Here’s a simple example with 4 `shipments` and 2 `vehicles` on a unit (1 km x 1 km) grid. ![4 shipment / 2 vehicle worked example without fixed costs](https://storage.ghost.io/c/c6/4d/c64da7e8-63a6-4cff-acdc-2782a6ebc377/content/images/2024/09/Screenshot-2024-09-03-at-9.44.43-AM.png) 4 shipment / 2 vehicle worked example without fixed costs A quick visual inspection shows that the most efficient way to solve this route plan would be for the `blue` driver to do stops A, B and C and the `orange` driver to do D for a total cost of 4 i.e. 1+1+1 (`blue`) and 1 (`orange`). ![4 shipment / 2 vehicle worked example without fixed costs](https://storage.ghost.io/c/c6/4d/c64da7e8-63a6-4cff-acdc-2782a6ebc377/content/images/2024/09/Screenshot-2024-09-03-at-9.50.59-AM.png) 4 shipment / 2 vehicle worked example without fixed costs In GMPRO, we use the `costPerKilometer` field in the `vehicle` object to indicate variable costs like so: ```JSON { "vehicles": [ { "label": "will-yvr", "costPerKilometer": 1 } ] } ``` Putting everything together, here’s the JSON for a 4 `shipment` / 2 `vehicle` worked example with a `"costPerKilometer": 1` variable cost: ![](https://storage.ghost.io/c/c6/4d/c64da7e8-63a6-4cff-acdc-2782a6ebc377/content/images/2024/09/Screenshot-2024-09-03-at-10.23.05-AM.png) **Input** ```JSON { "model": { "shipments": [ { "deliveries": [ { "arrivalLocation": { "latitude": 49.2545595, "longitude": -123.1096174 }, "timeWindows": [ { "startTime": "2024-07-08T16:00:00Z", "endTime": "2024-07-08T18:00:00Z" } ], "duration": "600s" } ], "loadDemands": { "weight": { "amount": 1 } }, "label": "yvr123" }, { "deliveries": [ { "arrivalLocation": { "latitude": 49.31374169999999, "longitude": -123.0514387 }, "timeWindows": [ { "startTime": "2024-07-08T16:00:00Z", "endTime": "2024-07-08T18:00:00Z" } ], "duration": "600s" } ], "loadDemands": { "weight": { "amount": 1 } }, "label": "yvr789" }, { "deliveries": [ { "arrivalLocation": { "latitude": 49.343481, "longitude": -123.0863414 }, "timeWindows": [ { "startTime": "2024-07-08T16:00:00Z", "endTime": "2024-07-08T18:00:00Z" } ], "duration": "600s" } ], "loadDemands": { "weight": { "amount": 1 } }, "label": "yvr987" }, { "deliveries": [ { "arrivalLocation": { "latitude": 49.3352081, "longitude": -123.1453979 }, "timeWindows": [ { "startTime": "2024-07-08T16:00:00Z", "endTime": "2024-07-08T18:00:00Z" } ], "duration": "600s" } ], "loadDemands": { "weight": { "amount": 1 } }, "label": "yvr654" } ], "vehicles": [ { "travelMode": "DRIVING", "startLocation": { "latitude": 49.2808422, "longitude": -123.0827831 }, "startTimeWindows": [ { "startTime": "2024-07-08T16:00:00Z" } ], "endTimeWindows": [ { "endTime": "2024-07-08T18:00:00Z" } ], "loadLimits": { "weight": { "maxLoad": 5 } }, "label": "mark-yvr", "costPerKilometer": 1 }, { "travelMode": "DRIVING", "startLocation": { "latitude": 49.2658769, "longitude": -123.0815619 }, "startTimeWindows": [ { "startTime": "2024-07-08T16:00:00Z" } ], "endTimeWindows": [ { "endTime": "2024-07-08T18:00:00Z" } ], "loadLimits": { "weight": { "maxLoad": 5 } }, "label": "will-yvr", "costPerKilometer": 1 } ], "globalStartTime": "2024-07-08T16:00:00Z", "globalEndTime": "2024-07-09T02:59:59Z" }, "populatePolylines": true } ``` **Output** ```JSON { "routes": [ { "vehicleLabel": "mark-yvr", "vehicleStartTime": "2024-07-08T16:00:00Z", "vehicleEndTime": "2024-07-08T17:03:40Z", "visits": [ { "shipmentIndex": 1, "startTime": "2024-07-08T16:14:35Z", "detour": "0s", "shipmentLabel": "yvr789", "loadDemands": { "weight": { "amount": "-1" } } }, { "shipmentIndex": 2, "startTime": "2024-07-08T16:34:03Z", "detour": "948s", "shipmentLabel": "yvr987", "loadDemands": { "weight": { "amount": "-1" } } }, { "shipmentIndex": 3, "startTime": "2024-07-08T16:53:40Z", "detour": "1934s", "shipmentLabel": "yvr654", "loadDemands": { "weight": { "amount": "-1" } } } ], "transitions": [ { "travelDuration": "875s", "travelDistanceMeters": 9486, "waitDuration": "0s", "totalDuration": "875s", "startTime": "2024-07-08T16:00:00Z", "vehicleLoads": { "weight": { "amount": "3" } } }, { "travelDuration": "568s", "travelDistanceMeters": 7110, "waitDuration": "0s", "totalDuration": "568s", "startTime": "2024-07-08T16:24:35Z", "vehicleLoads": { "weight": { "amount": "2" } } }, { "travelDuration": "577s", "travelDistanceMeters": 6406, "waitDuration": "0s", "totalDuration": "577s", "startTime": "2024-07-08T16:44:03Z", "vehicleLoads": { "weight": { "amount": "1" } } }, { "travelDuration": "0s", "waitDuration": "0s", "totalDuration": "0s", "startTime": "2024-07-08T17:03:40Z", "vehicleLoads": { "weight": {} } } ], "routePolyline": { "points": "_dxkHnrfnV?`@HAN??f@Ar@@^@TDPFRBP@F?@@P?^?^?X?BAp@eBC@sFBeB@}@@mB?C@oB?O?g@?O?k@@}@?Y?S?m@@O?]A[?SAICWCa@Gc@B_@EWQ}@CMAKAME[AS?IAQAc@?g@?kA?_@@_@U?}@AW?SAgACgAA}@?WCK_FEwAAa@EmBEaCCOCUI}@AKOwAKaAC[SsB_@}DUiCUaC[aDWiCE[CME[CUM{@CQESM_AM}@O_AOeACWCUCUASAS?MAg@?C?c@?gB@{@?s@?w@?cD@kC?uA@eK@qE?]?qC?wA?mBHk@Q?qA@yA?{A?aA?U?[A}@Aa@?y@Ag@?m@@W?OGOC[?EAK?ICECGCEGIUCI?GAEASAQAI?GACACAC@yA?e@?G?w@@u@?yD?uI?iH?{E?kO@aBDg@BqD?oE?u@?A?iD?cC?mB?AAQC_ACm@IkBC{@?W?UB]@UB_@DYHm@^wBH]F]PaAd@{C?AJo@Hq@@CHs@P}ALwAJcA?A`@iEHaAB[JmAr@uIDc@@SDg@De@@]?A@Y?[?O?QAUCQE_@GSK_@GQGMIOQUUUWS_Ai@A?{@e@i@YaAq@QKAAAASOo@m@c@c@m@o@SUY]SUGIA?QCQU_@e@CCOQUUWWe@[ECOGYKUEUEk@Ee@AS?C?u@@e@?a@@_@?E?m@@{@@o@?S@[?W?Q?e@@e@?_@?E?c@@]?C?e@@g@?Y@E?a@?s@@e@?c@@a@?O?_A@eA@k@@{@?{@@u@@_@?{@@m@?W@S?M?U?W@S?cA?cEDI?c@?[@i@?W@_@?[@Q?o@?a@@Q?e@@Y@M@M@YBQBa@FKBKBODMBKD[Hc@Re@Pc@XOJYPQJSLc@XWLOHQFOFIBYJSHMDMBQDSBUBWBk@@[?Y?O?C?K?Y?G?o@AUAS?_BCO?o@AY?k@AW?U?SAi@?]Ai@AW?k@AW?S?UAq@?M?M?K?G@O@WBYDa@E_@FIDYHQFm@Zi@^SL[X}@nASh@q@pBEPWdASx@Mr@ENU|@Ot@ELKf@CHOp@CLKb@Qn@Ql@On@ABETIVERAFMf@ABK^GXK^CNK^GZELMp@CFQn@IZGPKXGRSl@EJg@hAUf@e@dACFGLQ\\[h@ABEHA@EHSVR\\@Bj@hABFTb@Vd@r@fA^t@`@t@j@nALT`@p@JLRT\\VDBHDJDJD^Lz@PArBAvA?v@?t@?~@?p@?lECbA?lA?N@HBJ?v@?X?^Av@AfBAbCAjD?`AEhE?zAAhBAZ?pAAlB?v@A`AMWOWNVLV?hB?lAAdD?v@?~@AlB?vAClCAt@Av@?t@Af@M@c@AoCC]?a@AkBCG?s@C]?oC@c@?kBCc@AA?}@AoC@aA@{CGq@AoE?qBE}AEoCCc@A[?eCAO@K?O?OAMCoEEeCAkAA]C[E_@ESGOC]Im@U{@g@][GCe@i@IKOQAIAGMSS]q@oAe@iAa@}@Wo@M[GMSc@U?OLYTY^[`@MNm@x@i@|@EFKPWh@KTGJCDKTUb@Yj@A@C^O\\INWh@ABM\\M\\O`@ELITGTGNQl@K`@K`@K`@G^I`@EPCPO~@CTGb@ANCL?@Gj@C`@CPAPEh@A\\ATAZ?^?DA\\?`@@d@?b@@d@?^@L?X@~@BlA@bA@R@v@@jABhA?N?T@^@h@?`@@d@@`@?x@?d@?b@?r@?~@?^?n@AD?v@?l@?d@?zAAvA?F?z@@fA?j@?dA@bBAbA?lAAzA?r@?n@?R?H?h@?@A`A?l@?bA?j@A\\?V?n@?|@?l@AjA?j@?\\ArD?fA?p@?TAfA?hBCnBAlAAnAAz@AdAApAAp@?B?l@?^?fA?h@@X@tC@t@?P?P?H@t@@fA?x@@f@?p@OZA@?BExB?DCtACx@?@?n@A\\?B?X?nA?B@l@?p@?j@?XATA|@Ox@CLCDCDEHQTi@?cCDQPkA@wBCeAAA?iACO?w@?iB?_BCeACA?cBCS?A?wC@U?wAAq@Be@@]AMAIAICGCIEICGEGEIGiCcBuDcCcBkAkAu@gAo@qAy@On@It@Iz@INCHBIHOH{@Hu@No@pAx@fAn@jAt@bBjAtDbChCbBHFFDFDHBHDFBHBH@L@\\@d@Ap@CvA@T?vCA@?R?bBB@?dAB~ABhB?v@?N?hAB@?dA@vBBjAAPFjBB\\@lA@@l@?N?f@?F?F@d@?h@@T@\\@`@Bh@?BD|@@j@Bf@HzC?B?@HPAxA?vAAt@?x@A~A?TA`@?J?`@AR?p@ChBA^CpBAf@C~@An@A~@?XAd@?f@A^?p@A~@Al@Ab@?ZAv@?LAf@?@Ab@Ad@Aj@Al@?PChACnAAX?`@Aj@A@A~@Ab@Ab@Af@?h@CdAA`AAp@A`@Ad@?^Af@C`BAh@AxAAvB?`A?r@?f@A|A?rA?\\?R?|E?f@?L?d@?z@?b@?j@?h@?|@?l@?Z?lA?~@?`AAlC?N?dC?rB?fB?nA?`@?hA?v@?hA?z@?~A?pA?|@?\\?h@?|@?j@?Z?^?d@?N?T?b@A`@?h@AZAFA`@Eb@?FEXG`@GZADI^K\\M\\EHIPM\\MTOZa@t@k@hACBOVQ\\]n@S`@U`@GL]n@QZOXQZOXOXQ\\O\\MVOXIPEJOZEFIP]r@MVMVKTEHWh@EFMZOVUNA@EHINa@r@i@`AQZKROZ]v@Qb@CFEJUp@Od@[dAM`@Mj@Mv@Gj@SxACZA^Cj@Ch@ClAChA?JCz@A|@AZ?`@AN?`AChBAr@CzCA`A^?r@@T@f@@pAEfAEb@Ah@?LA@?XIvB@H?|@@l@@z@@@x@?z@Az@?NAf@?J?h@A`@?FAD?DCDADE@C@C@Q?S@O?_@@I?[@S?O@G@S@G@UFSHURA?EHEDOPEHIJGLENG^I\\CTCRCTAXA^Af@?@?P?hE?j@@H?J?H@HB^Fh@?F?F?`A?dD?lA?lB?lB?lBAdD?t@A|ExA?zA??u@?o@" }, "metrics": { "performedShipmentCount": 3, "travelDuration": "2020s", "waitDuration": "0s", "delayDuration": "0s", "breakDuration": "0s", "visitDuration": "1800s", "totalDuration": "3820s", "travelDistanceMeters": 23002, "maxLoads": { "weight": { "amount": "3" } } }, "routeCosts": { "model.vehicles.cost_per_kilometer": 23.002 }, "routeTotalCost": 23.002 }, { "vehicleIndex": 1, "vehicleLabel": "will-yvr", "vehicleStartTime": "2024-07-08T16:00:00Z", "vehicleEndTime": "2024-07-08T16:19:02Z", "visits": [ { "startTime": "2024-07-08T16:09:02Z", "detour": "0s", "shipmentLabel": "yvr123", "loadDemands": { "weight": { "amount": "-1" } } } ], "transitions": [ { "travelDuration": "542s", "travelDistanceMeters": 3321, "waitDuration": "0s", "totalDuration": "542s", "startTime": "2024-07-08T16:00:00Z", "vehicleLoads": { "weight": { "amount": "1" } } }, { "travelDuration": "0s", "waitDuration": "0s", "totalDuration": "0s", "startTime": "2024-07-08T16:19:02Z", "vehicleLoads": { "weight": {} } } ], "routePolyline": { "points": "qfukHjifnVxA@`@@nDI?@?@?@?@@??@?@?@@??@?@@??@@??@@?@??@@?@??A@?@??A@??A@A?A@??A?A?A?A?Ab@?h@@l@?n@@xA@z@@@?`@?AbK?t@ClI?t@A~FKhA?vA?tB?jB?jAP@zA@JADA@?bA@zAAP@x@?H@zA@|A?xA@@?vA@zA@rA@VDR@Z@@@DBFBBBNHDBNHTJb@`@f@b@VPVBXD?j@CzA?bAAv@?JA`BAnAAlC?n@AtBC`D?x@CxAAjA?dAAxA?xB?bB?dDClB?zAAbC?lB?h@@hAApA?fA?LCfFAzDAvCAl@CxCEtE?V?RGvFGrFdBFxADD?|ABF@bBBh@@" }, "metrics": { "performedShipmentCount": 1, "travelDuration": "542s", "waitDuration": "0s", "delayDuration": "0s", "breakDuration": "0s", "visitDuration": "600s", "totalDuration": "1142s", "travelDistanceMeters": 3321, "maxLoads": { "weight": { "amount": "1" } } }, "routeCosts": { "model.vehicles.cost_per_kilometer": 3.321 }, "routeTotalCost": 3.321 } ], "metrics": { "aggregatedRouteMetrics": { "performedShipmentCount": 4, "travelDuration": "2562s", "waitDuration": "0s", "delayDuration": "0s", "breakDuration": "0s", "visitDuration": "2400s", "totalDuration": "4962s", "travelDistanceMeters": 26323, "maxLoads": { "weight": { "amount": "3" } } }, "usedVehicleCount": 2, "earliestVehicleStartTime": "2024-07-08T16:00:00Z", "latestVehicleEndTime": "2024-07-08T17:03:40Z", "totalCost": 26.323, "costs": { "model.vehicles.cost_per_kilometer": 26.323 } } } ``` ### Worked GMPRO example with fixed costs (`fixedCost`) In a logistics company, costs go beyond just distance or fuel expenses. You must also consider fixed costs, such as the investment in purchasing a new delivery van or the minimum daily salary required for a driver, even if they only complete a single delivery. So when you use a route optimization algorithm, it might be helpful to use fewer vehicles to achieve the same result. We can do this by assigning fixed costs to vehicles so that the route optimization algorithm is "discouraged" from adding a new vehicle to the route solution. ![4 shipment / 2 vehicle worked example with fixed costs](https://storage.ghost.io/c/c6/4d/c64da7e8-63a6-4cff-acdc-2782a6ebc377/content/images/2024/09/Screenshot-2024-09-03-at-11.02.43-AM.png) 4 shipment / 2 vehicle worked example with fixed costs Let’s define fixed costs as 5 if a vehicle is included in the route solution and 0 otherwise. In the same example from earlier, the new optimal route solution is for `blue` to do A, B, C and D for a total cost of 11 i.e. 5 (fixed cost) + 1+1+1+3 (variable costs). The alternative would be for `blue` to do A, B and C and `orange`, D, for a total cost of 14 i.e. 5 + 5 (fixed cost) + 1+1+1+1 (variable costs). ![4 shipment / 2 vehicle worked example with fixed costs](https://storage.ghost.io/c/c6/4d/c64da7e8-63a6-4cff-acdc-2782a6ebc377/content/images/2024/09/Screenshot-2024-09-03-at-11.08.18-AM.png) 4 shipment / 2 vehicle worked example with fixed costs We can combine fixed costs with variable costs to the `vehicle` object like this: ```JSON { "vehicles": [ { "label": "will-yvr", "costPerKilometer": 1 "fixedCost": 5 } ] } ``` The quality of the route solution depends largely on the balance between fixed and variable costs. If fixed costs are relatively low compared to variable costs, such as in long-distance trucking, the resulting route solution may be the same as one that doesn't account for fixed costs at all. 👨‍💻 Screenshots in this blog post were taken with [GMPRO-viewer](https://gmpro-viewer.afi.dev/?ref=blog.afi.io) , using data imported via [GMPRO-json-converter](https://gmpro-json-converter.afi.dev/?ref=blog.afi.io). Both tools are free to use. And here’s the full JSON for the same 4 `shipment` / 2 `vehicle` worked example, but now with fixed costs `"fixedCosts": 50` added to the mix: ![Real world 4 shipment / 2 vehicle worked example with fixed costs](https://storage.ghost.io/c/c6/4d/c64da7e8-63a6-4cff-acdc-2782a6ebc377/content/images/2024/09/Screenshot-2024-09-03-at-4.24.15-PM.png) Real world 4 shipment / 2 vehicle worked example with fixed costs **Input** ```JSON { "model": { "shipments": [ { "deliveries": [ { "arrivalLocation": { "latitude": 49.2545595, "longitude": -123.1096174 }, "timeWindows": [ { "startTime": "2024-07-08T16:00:00Z", "endTime": "2024-07-08T18:00:00Z" } ], "duration": "600s" } ], "loadDemands": { "weight": { "amount": 1 } }, "label": "yvr123" }, { "deliveries": [ { "arrivalLocation": { "latitude": 49.31374169999999, "longitude": -123.0514387 }, "timeWindows": [ { "startTime": "2024-07-08T16:00:00Z", "endTime": "2024-07-08T18:00:00Z" } ], "duration": "600s" } ], "loadDemands": { "weight": { "amount": 1 } }, "label": "yvr789" }, { "deliveries": [ { "arrivalLocation": { "latitude": 49.343481, "longitude": -123.0863414 }, "timeWindows": [ { "startTime": "2024-07-08T16:00:00Z", "endTime": "2024-07-08T18:00:00Z" } ], "duration": "600s" } ], "loadDemands": { "weight": { "amount": 1 } }, "label": "yvr987" }, { "deliveries": [ { "arrivalLocation": { "latitude": 49.3352081, "longitude": -123.1453979 }, "timeWindows": [ { "startTime": "2024-07-08T16:00:00Z", "endTime": "2024-07-08T18:00:00Z" } ], "duration": "600s" } ], "loadDemands": { "weight": { "amount": 1 } }, "label": "yvr654" } ], "vehicles": [ { "travelMode": "DRIVING", "startLocation": { "latitude": 49.2808422, "longitude": -123.0827831 }, "startTimeWindows": [ { "startTime": "2024-07-08T16:00:00Z" } ], "endTimeWindows": [ { "endTime": "2024-07-08T18:00:00Z" } ], "loadLimits": { "weight": { "maxLoad": 5 } }, "label": "mark-yvr", "costPerKilometer": 1, "fixedCost": 50 }, { "travelMode": "DRIVING", "startLocation": { "latitude": 49.2658769, "longitude": -123.0815619 }, "startTimeWindows": [ { "startTime": "2024-07-08T16:00:00Z" } ], "endTimeWindows": [ { "endTime": "2024-07-08T18:00:00Z" } ], "loadLimits": { "weight": { "maxLoad": 5 } }, "label": "will-yvr", "costPerKilometer": 1, "fixedCost": 50 } ], "globalStartTime": "2024-07-08T16:00:00Z", "globalEndTime": "2024-07-09T02:59:59Z" }, "populatePolylines": true } ``` **Output** ``` { "routes": [ { "vehicleLabel": "mark-yvr", "vehicleStartTime": "2024-07-08T16:00:00Z", "vehicleEndTime": "2024-07-08T17:38:48Z", "visits": [ { "startTime": "2024-07-08T16:14:49Z", "detour": "0s", "shipmentLabel": "yvr123", "loadDemands": { "weight": { "amount": "-1" } } }, { "shipmentIndex": 3, "startTime": "2024-07-08T16:49:55Z", "detour": "1702s", "shipmentLabel": "yvr654", "loadDemands": { "weight": { "amount": "-1" } } }, { "shipmentIndex": 2, "startTime": "2024-07-08T17:09:30Z", "detour": "3070s", "shipmentLabel": "yvr987", "loadDemands": { "weight": { "amount": "-1" } } }, { "shipmentIndex": 1, "startTime": "2024-07-08T17:28:48Z", "detour": "4444s", "shipmentLabel": "yvr789", "loadDemands": { "weight": { "amount": "-1" } } } ], "transitions": [ { "travelDuration": "889s", "travelDistanceMeters": 6170, "waitDuration": "0s", "totalDuration": "889s", "startTime": "2024-07-08T16:00:00Z", "vehicleLoads": { "weight": { "amount": "4" } } }, { "travelDuration": "1506s", "travelDistanceMeters": 12372, "waitDuration": "0s", "totalDuration": "1506s", "startTime": "2024-07-08T16:24:49Z", "vehicleLoads": { "weight": { "amount": "3" } } }, { "travelDuration": "575s", "travelDistanceMeters": 6353, "waitDuration": "0s", "totalDuration": "575s", "startTime": "2024-07-08T16:59:55Z", "vehicleLoads": { "weight": { "amount": "2" } } }, { "travelDuration": "558s", "travelDistanceMeters": 7092, "waitDuration": "0s", "totalDuration": "558s", "startTime": "2024-07-08T17:19:30Z", "vehicleLoads": { "weight": { "amount": "1" } } }, { "travelDuration": "0s", "waitDuration": "0s", "totalDuration": "0s", "startTime": "2024-07-08T17:38:48Z", "vehicleLoads": { "weight": {} } } ], "routePolyline": { "points": "_dxkHnrfnV?`@HAN??f@Ar@@^@TDPFRBP@F?@@P?^?^?X?BAp@eBC@sFBeB@}@@mB?C@oB?O?g@?O?k@@}@?Y?S?m@@O?]A[?SAICWCa@Gc@B_@EWQ}@CMAKAME[AS?IAQAc@?g@?kA?_@RALOn@@H?|ABvA@|@?ZBvA@vAB|A@|A?vA?vAD|A@rA@xDBd@?t@@tA?jB?lA@tEBjB@R@fA@f@BRFNBV@h@Bh@B|@@^@vA@vA?r@ERALCPI`@?j@?xD@`@@r@@f@@Z?^@R?T?Z?J@N?V?n@@v@BpABr@?\\?H?PHn@BfA@zADBArA@RBtA@B?`@@R?\\?B@DAF?hAAX?B?p@AL?NAhACpA@?|@?z@?r@@l@A|BDV?^CfJA`K?~A?JAh@?v@?fB?jAAhI?x@ClC?^AdC?h@?^?v@?LAd@?X?~C?F?F?F?H?J?P?b@AV?D?F?F?D?nAAvF?v@CnI?~A?d@Ax@AVCPCr@Ar@_@|BIz@OdACXADGTER@x@AtC?h@?l@?`C?j@?`A?b@L?jDF\\AP?R?H?N@d@?B?V@T?NKxABr@@D?L?rCDb@@ApA?fA?LCfFAzDAvCAl@CxCEtE?V?RGvFGrFdBFxADD?|ABF@bBBh@@i@AcBCGA}ACE?yAEeBGC`EAfBAbCAbCCfCEpEA^APAr@?t@{A?}AAs@@c@@Q?g@Ac@?qCGQCA?g@CkB@KAQ@uAAgAAaACQ?M?y@GQA[Ac@Au@AIAc@Am@CUAo@?eB?aBAsAEkAAq@DU@c@?S?m@@e@@G?I?K?cA?}@B_AB]B]@o@@o@B_BBG@q@@kA@G?e@GsDGeAA_@?C?E?E?QAQ?C?yAEqACYAW?i@Ag@E]?]AW@Y?o@Co@Co@COCSCQ?m@CG?C?u@Ae@CO?G@SBKBI@G@C@IDMDKDIHUJSJKHSRKLQNKPIPGJCBEJCHMTg@|@[l@k@`AMRQXa@p@Wf@[n@w@pAMX_AbBOUKQe@y@mCwEGGYo@oC{EEEk@cA_A~AOVa@p@QXOXi@`A_A~A_AbB}@bBo@dAw@vA[h@S`@i@~@GHe@z@SZ{@~AeAlBaGpKoDlGYd@kAtBi@`AaDxFYf@qA|B{@|AKNYf@a@t@g@|@QXGJYf@MXq@fAKRMRe@r@ELINIPmAzBe@x@e@x@q@lAILaCfEU`@g@`AcBvCU`@ADYd@Wf@EHS\\KPc@x@IDEBS\\a@n@MRQXEFABQVQNGDIFQFKBK@I@I?A?GAI?EAIAKESKSKOGMG]QUMUIa@MQAQ?Q@QBMBQDKBKBKDKDKDGDIDKF]VA@GDGDWX]`@CBABABMZCDCFGHGLCFCDEJA?INCFCFKVCFELK\\a@dBEJADADGVCLMd@Oh@GVABOd@IZGROd@M\\GPITELQb@EJA@ELCFMZS`@KTKPS`@CDINEJGLW`@Q\\e@t@QXORORQTa@f@c@f@cAjAo@l@C@_@ZGFURIFSPQJOJo@`@q@^a@RGB_@P[JC@]J_@JC@c@HIBIBG@C?[FI@I@WBG@A?YBE?[@A?I@W?A?qA?W?i@A_@AGA[AQA[C[CEAYCE?a@Gg@Iq@K_@GQEKC_@IYGCA]I]K[IA?IESGA?MEME[M]M[MAAWMIEQIYQc@S[S]QOIIGg@YoAcAe@a@UUq@o@QOQQCCIG}@y@y@q@ECWWk@i@MKAAOMgA_AGGIGgAaASQGEOM][_@[_@[SQII_@[}@w@SQII_@[QOAAII_@[][SQKIOOMKSQKISQII][_@[KKQO_@]][[WCC}@w@SQII]]gA_AMKMMOM[Y_@]][][_@[UUg@c@oAiAi@g@_@[IIIIWUGEc@c@yFgFEEGEIIUUCCEC][e@c@k@g@gAcA][y@u@eAaAEEGEIGCEGECCCCCEMKCCCEIGAA][MKWUEEGEGGECAAQKc@[UQEM?AAAKKq@k@_Aw@IGUQEE]W]U]SOGIGKGKE[QIEMIIGKGCACCGGAACEEGEGEIEKCKAICQAI?I?A?G?K?M@G@MBKBMDKBIFGDIFEFEFEFCFAHAJ@J@JDHFHHHLBBDHBJBHBNBR@RAR?RARK|AGVSbAGZOv@Or@CXEZKr@WbBCTGb@YlBm@pEQrACVANCX?B?f@?h@?vAKd@CLAFCFCDCDEBCBE@KDKJ]?e@@A?C@CBEJ]A[AqAGeA@U?S@U?c@@]?K@[@a@?aA?aA@KAKA?dA?V?@AD?LAL?@?J?T?^?J?f@?L@PAB?@A??@?@?@?@?@?@?@?@?@?@?@@@?@?@@@?@?B?D?D?D@HAzHAt@?v@@PDn@@NDp@@D@HLlA@JHt@Fj@F`@BZN|@BX@D?@BRBL?BDX@D@`@?n@?r@?B?r@?@?v@?t@?B?v@?r@CjIAv@?t@iB?_B?kBAyAAwBA}A?mB?cB?{AAyA??u@?o@?n@?t@{A?yA?@}E?u@@eD?mB?mB?mB?kA?mB?y@?}@?E?GAEC_@Ei@AI?I?KAI?k@?iE?Q?A@g@@_@@YBUBSBUH]F_@DOFMHKDINQDEDI@?TSRITGFARAFANAR?ZAH?^AN?RAP?BABADA@EBE?E@E?G@a@?i@?K@g@?O@{@?{@Ay@{@Am@A}@AI?wBA[Kc@AuAGq@?QYIEIGEECGCCACCGCIIWCuACw@Am@Ao@AU?CCq@Ag@As@Aa@AS?SAU?K?e@A{@@qA@_@@C@YBi@Ba@Bc@DYDc@Fc@Hg@Jk@Hc@Jc@H]DM?E@C@CA[@AHWJ[JWDIFQL[LY@A\\u@JUJUFMLUL[JQBINYLYP]Zo@\\s@P]LYZq@P]Te@DIBEJUP]LW@ALWLUJQDIHODI`@u@LWNYLUP[HQNWNYP]LUFKFMR_@HODKNWN[NY?AJWLYBEFODMH[FS?ADMBQ@GF]@GDW@IB]B]@O@U@_@?e@@a@?_@?g@?iB?mB?i@?_@?cA?cA?_A?y@?}@?c@?_@?y@?aA?_@?[?e@?g@?]@e@Ag@?I@}@?a@?s@?Y?g@?w@?a@?g@?W?e@?s@?c@?y@?W?c@?Y?m@?w@?K?]?c@?a@?c@?yA?[?g@?cA?a@?q@?c@?C?a@?c@?g@@{@?g@?[?iB@eB@uADeDBkBDiCBiA@gA@m@B_A@u@@u@@c@BiA?S@c@@a@?SBw@@_A@m@@S@m@?[@a@?c@@a@?g@@{@BiA@aA?M@_@@_@BeADuABw@BiB?M@S?a@@]?E?k@@Q?G@m@@oA?c@?a@?a@@c@?]?{@H[@EByADkABq@F}A@OF}ABw@?EBw@?i@?c@?wBBc@I?c@AM?UAc@Ao@?i@?cCDQPkA@wBCeAAA?iACO?w@?iB?_BCeACA?cBCS?A?wC@U?wAAq@Be@@]AMAIAICGCIEICGEGEIGiCcBuDcCcBkAkAu@gAo@qAy@On@It@Iz@INCHBIHOH{@Hu@No@pAx@fAn@jAt@bBjAtDbChCbBHFFDFDHBHDFBHBH@L@\\@d@Ap@CvA@T?vCA@?R?bBB@?dAB~ABhB?v@?N?hAB@?dA@vBBjAAPFjBB\\@lA@R?N@H?H?N?D?H?R?J?Bc@?iAC{AEoAAo@?ECc@?K?ECw@Cg@?MCs@ACAa@Co@?EQi@?eB?Y?q@A}@?u@AkEAI?O?SA]CkDAgA?e@?c@?w@?oA@gA?M@w@?O@q@@gA@sA@}@@yA@_B?m@?e@@{C?eB@kB?iA@eA?oC?}@@M?W?i@?]?m@?O?a@?W@cA?S?sA@gA?oB@}@?iA?Y?I?_@?c@@g@?gA?[@a@?y@@m@?g@@gA?a@@y@@mA?g@@}@?_@?M?c@?Y?g@@i@?c@?a@?}@?Q?{@?a@?g@?a@Aa@Ac@Ae@Aa@C_@CiACa@Ak@Aa@?Y?e@?c@@i@?[Ba@@WBk@Ba@BU@OBU@IDc@Fa@D_@Fc@@IDSFa@Hc@Ha@H[?AH]J_@H_@Ng@BENi@Na@Na@Vo@P_@N[HODGN[NWPYNWPUPWPSBERUPSPSRSHGHITSPORQXWRQJKXWVUJKFGXY\\c@PULQHKHMTYJQJQHMNYP[N[JWN_@Rc@Tq@Na@J_@La@HYJ_@Ha@H]Jc@F_@Ha@H]Hc@Ny@FWF_@@Ed@_CBMHc@H]F_@FYLi@H[La@J]DMFOL_@L[@CJWNYN[NYb@q@NUBCNSNQRUTUTSPOPOVQXUv@i@j@a@h@_@ROTQPMVO?AROJGHGRQRMTOb@]HGt@i@TOROTOPMj@a@ROTOd@]RMRO@AtAaAb@]RM`@YPB@?NKl@]DCl@]r@e@f@YVOrAy@VQ^UhBmAv@i@LIv@i@FGBAz@o@BAFG|AkAHEFCD?B?BAD@ND@@r@fA^t@`@t@j@nALT`@p@JLRT\\VDBHDJDJD^Lz@PArBAvA?v@?t@?~@?p@?lECbA?lA?N@HBJ?v@?X?^Av@AfBAbCAjDA`ACdF?TArC?ZApAAnB?v@A~@MWKSAA" }, "metrics": { "performedShipmentCount": 4, "travelDuration": "3528s", "waitDuration": "0s", "delayDuration": "0s", "breakDuration": "0s", "visitDuration": "2400s", "totalDuration": "5928s", "travelDistanceMeters": 31987, "maxLoads": { "weight": { "amount": "4" } } }, "routeCosts": { "model.vehicles.cost_per_kilometer": 31.987 }, "routeTotalCost": 31.987 }, { "vehicleIndex": 1, "vehicleLabel": "will-yvr" } ], "metrics": { "aggregatedRouteMetrics": { "performedShipmentCount": 4, "travelDuration": "3528s", "waitDuration": "0s", "delayDuration": "0s", "breakDuration": "0s", "visitDuration": "2400s", "totalDuration": "5928s", "travelDistanceMeters": 31987, "maxLoads": { "weight": { "amount": "4" } } }, "usedVehicleCount": 1, "earliestVehicleStartTime": "2024-07-08T16:00:00Z", "latestVehicleEndTime": "2024-07-08T17:38:48Z", "totalCost": 31.987, "costs": { "model.vehicles.cost_per_kilometer": 31.987 } } } ``` With these fixed costs added to the `vehicle` object, it now is optimal to only send out driver `mark-yvr` (blue) or `will-yvr` (orange), but not both. ### What next? TLDR: If you want to use fewer vehicles, add a `fixedCost` to your `vehicle` object and make it large enough to be meaningful. This brief (but hopefully helpful) blog post explained how to incorporate fixed costs into a GMPRO routing problem. By understanding both fixed and variable costs, you can model complex real-world scenarios, such as optimizing the balance between employees (who have high fixed costs but low variable costs) and contractors (who have low fixed costs but high variable costs). This approach can also be applied to managing mixed fleets with vehicles of varying fuel efficiencies or selecting drivers with different hourly wage rates. **This article was written by** [**Afi Labs**](https://afi.io/?ref=blog.afi.io)**, a** [**Google Maps Premier Partner**](https://www.afi.io/?ref=blog.afi.io) **and reseller. We build route optimization, navigation, and fleet tracking software on Google Maps, and offer volume pricing on GMP licensing.** [**Talk to an engineer**](https://www.afi.io/contact%5Fus?ref=blog.afi.io) **or** [**follow Afian on LinkedIn**](https://www.linkedin.com/in/afian-anwar/?ref=blog.afi.io)**.** Next: [Part 6: GMPRO docs: Territory optimization and route planning](https://www.afi.io/blog/gmpro-docs-territory-optimization-and-route-planning/?ref=blog.afi.io)