2d:null 2e:["$","title",null,{"children":"Route Optimization Problem - Seating Preference | NB.AI Solutions Team | Observable"}] 33:T53a,### Initial Setup In this section we will set some initial parameters that are used to formulate the inputs to the MVRP solver. The `NB.ai API KEY` is necessary to interact with the MVRP services and it should be entered first to activate the tutorial tasks.

Next modify the other parameters according to the problem that you want to create: City Selection, Number of Jobs, Use Case and the Shift definitions. - City - creates the mock data in a defined geographic area based on city selection - Number of Jobs - the number of random pickup/delivery jobs to consider - Number of Vehicles - the number of vehicles to consider in the route optimization solutions - Front Seat Preference Upper - the starting point for front seat preference weight factor. A 1.0 makes front seat preference a hard constraint (passenger must sit in front seat). Lesser values will submit orders with fewer customers requiring front seating. - Front Seat Preference Lower - the lower bounds for the front seat preference weight factor. A lower bounds of 0.0 will eliminate use of the front seat in a solution. - Back Seat Capacity - how many back seats are available in the passenger vehicle for capacity - Shift Start - date/time of the vehicle shift commencement - Shift End - date/time of the vehicle shift completion

34:T41c,kpiTable = { let kpis = []; mvrpID.forEach((sol) => { kpis.push(sol.kpis); }); yield Inputs.table(kpis, { columns: [ "nbrRoutes", "unassigned", "frontSeatWeight", "totalTime", "totalDistance", "avgStops" ], header: { nbrRoutes: "Vehicles/Routes", unassigned: "Unassigned", frontSeatWeight: "Front Seat Factor", totalTime: "Total Time", totalDistance: "Total Distance", avgStops: "Avg. Stops per Veh" }, width: { nbrRoutes: 80, unassigned: 80, frontSeatWeight: 80, totalTime: 120, totalDistance: 120, avgStops: 120 }, format: { type: (x) => x.toUpperCase(), arrival: (x) => new Date(x * 1000).toLocaleTimeString([], { hour: "2-digit", minute: "2-digit", hour12: false }), load: (x) => `${x[0]} back ${x[1]} front`, duration: (x) => (x / 60).toFixed(1) + " mn" }, maxWidth: 1000, multiple: false, required: false }); }35:T152b,mvrpID = { if (inputData_a) { progress.value = 0; let location = []; //V2 API let locationV1 = ""; //V1 API let loc_index = 0; let shipments = []; let jobs = []; let vehicles = []; let locations = []; let bodyData = []; let nbrVehicles = parseInt(inputData_a.vehicles); // build passenger pickup-dropoffs for 3 scenarios // - front seat 'preference' is a hard constraint // - front seat 'preference' is removed for some // - all passengers in back seat for (let i = 0; i < 4; i++) { vehicles = []; shipments = []; locations = []; location = []; locationV1 = ""; loc_index = 0; let amount = [1, 0]; let frontSeatWeight = parseFloat(inputData_a.frontSeatFactorUpper); for (let j = 0; j < inputData_a.jobs; j++) { frontSeatWeight = inputData_a.frontSeatFactorUpper - ((parseFloat(inputData_a.frontSeatFactorUpper) - parseFloat(inputData_a.frontSeatFactorLower)) * i) / 3; if (i == 0) { amount = randomVRPInputs.pointsArray[j].frontSeat ? Math.random() < frontSeatWeight ? [0, 1] : [1, 0] : [1, 0]; } else if (i == 1) { amount = randomVRPInputs.pointsArray[j].frontSeat ? Math.random() < frontSeatWeight ? [0, 1] : [1, 0] : [1, 0]; } else if (i == 2) { amount = randomVRPInputs.pointsArray[j].frontSeat ? Math.random() < frontSeatWeight ? [0, 1] : [1, 0] : [1, 0]; } else { amount = randomVRPInputs.pointsArray[j].frontSeat ? Math.random() < frontSeatWeight ? [0, 1] : [1, 0] : [1, 0]; } let shipment = { pickup: { id: randomVRPInputs.pointsArray[j].id, description: randomVRPInputs.pointsArray[j].name, location_index: loc_index, service: parseInt(inputData_a.serviceTime) }, delivery: { id: randomVRPInputs.pointsArray[j].id, description: randomVRPInputs.pointsArray[j].name, location_index: loc_index + 1, service: parseInt(inputData_a.serviceTime) }, amount: amount }; shipments.push(shipment); // Location object different based on API version if (inputData_a.apiVersion.value === "V2") { location.push( `${randomVRPInputs.pointsArray[j].pickup_latitude},${randomVRPInputs.pointsArray[j].pickup_longitude}` ); location.push( `${randomVRPInputs.pointsArray[j].dropoff_latitude},${randomVRPInputs.pointsArray[j].dropoff_longitude}` ); } else { locationV1 += `${randomVRPInputs.pointsArray[j].pickup_latitude},${randomVRPInputs.pointsArray[j].pickup_longitude}|${randomVRPInputs.pointsArray[j].dropoff_latitude},${randomVRPInputs.pointsArray[j].dropoff_longitude}|`; } loc_index += 2; } // build vehicle location and inventory // increase number of vehicles at each pass for (let j = 0; j < nbrVehicles; j++) { if (inputData_a.apiVersion.value === "V2") { console.log(`Nbr veh ${nbrVehicles}`); console.log(`${randomVRPInputs.vehicleArray[j]}`); location.push( `${randomVRPInputs.vehicleArray[j].latitude},${randomVRPInputs.vehicleArray[j].longitude}` ); } else { locationV1 += `${randomVRPInputs.vehicleArray[j].latitude},${randomVRPInputs.vehicleArray[j].longitude}|`; } let vehicle = { id: randomVRPInputs.vehicleArray[j].id, description: randomVRPInputs.vehicleArray[j].vin, start_index: loc_index, end_index: loc_index, time_window: [ Date.parse(inputData_a.shiftStart) / 1000, Date.parse(inputData_a.shiftEnd) / 1000 ], capacity: [parseInt(inputData_a.vanCapacity), 1], skills: randomVRPInputs.vehicleArray[j].attr3 }; vehicles.push(vehicle); ++loc_index; } nbrVehicles += parseInt(inputData_a.vehicleIncrement); if (inputData_a.apiVersion.value === "V2") { locations = { id: 12, location: location }; } else { locationV1 = locationV1.substring(0, locationV1.length - 1); locations = { id: 12, location: locationV1 }; } bodyData.push({ description: `MVRP Tutorial ${i} ${new Date().toISOString()}|${frontSeatWeight.toFixed( 1 )}`, locations: locations, // set shipments information shipments: shipments, // set vehicles information vehicles: vehicles, jobs: [], options: { travel_cost: inputs.objectiveFunction.value.value, minimise_num_depots: false } }); } console.log(bodyData); const result = await Promise.all( bodyData.map(async (data) => { return mvrp(data, { objective: { minimise_num_depots: false, travel_cost: "duration" } }); }) ); progress.value = 0; return result; } }36:T955,inputs = { const offset = new Date().getTimezoneOffset(); let newBase = new Date(new Date().getTime() + 86400 * 1000); let tmrwStart = new Date( `${newBase.toLocaleDateString("en-US")} 10:00:00 GMT${ offset >= 0 ? "+" : "" }${Math.round(offset / 60)}${ Math.abs(offset % 60) ? Math.abs(offset % 60) : "" }` ); let tmrwEnd = new Date( `${newBase.toLocaleDateString("en-US")} 13:00:00 GMT${ offset >= 0 ? "+" : "" }${Math.round(offset / 60)}${ Math.abs(offset % 60) ? Math.abs(offset % 60) : "" }` ); console.log(tmrwEnd.toISOString().slice(0, -5)); const inputs = { city: Inputs.select(cities, { label: "Select city", format: (x) => x.name, value: cities.find((t) => t.name === "Los Angeles") }), jobs: Inputs.select(jobs, { label: "Number of Jobs", value: "25" }), vehicles: Inputs.text({ label: "Nbr of Vehicles", value: "10", max: 100, maxlength: 3 }), vehicleIncrement: Inputs.text({ label: "Vehicle Increment", value: "10" }), frontSeatFactorUpper: Inputs.text({ label: "Front Seat Preference Upper (0-1)", value: 1, max: 1, min: 0.1 }), frontSeatFactorLower: Inputs.text({ label: "Front Seat Preference Lower (0-1)", value: 0.1, max: 1, min: 0.0 }), vanCapacity: Inputs.select(backseatCapacity, { label: "Back Seat Capacity", value: "3" }), shiftStart: Inputs.datetime({ label: "Shift Start", value: tmrwStart.toISOString().slice(0, -1) }), shiftEnd: Inputs.datetime({ label: "Shift End", value: `${tmrwEnd.toISOString().slice(0, -1)}` }), useCase: Inputs.select(useCases, { label: "Use Case", format: (x) => x.name, value: useCases.find((t) => t.name === "service") }), objectiveFunction: Inputs.select(objectives, { label: "Objective Function", format: (x) => x.name, value: objectives.find((t) => t.name === "Minimize Time") }), serviceTime: Inputs.text({ label: "Service Time at Each Stop", value: "300" }), apiVersion: Inputs.select( [ { name: "V1", value: "V1" }, { name: "V2", value: "V2" } ], { label: "API Version", format: (x) => x.name } ) }; return inputs; }37:T5f3,routesGeoJson = { let routesGeoJson = { type: "FeatureCollection", features: [] }; // we create a geojson object to send to our mapview elements // It will be a feature collection of all the route polylines let bestRoute = -1; mvrpID.forEach((sol, idx) => { if (mvrpID[idx].result.summary.unassigned === 0) { if (bestRoute === -1) { bestRoute = idx; } } }); if (bestRoute === -1) { // no routes with 0 unassigned - just show the first route bestRoute = 0; } mvrpID[bestRoute].result.routes.forEach((rte, idx) => { //console.log(rte); let feature = { type: "Feature", properties: { id: rte.vehicle, vin: rte.description }, geometry: { coordinates: nextbillion.utils.polyline .decode(rte.geometry, 5) .map((c) => c.reverse()), type: "LineString" } }; routesGeoJson.features.push(feature); rte.steps.forEach((st, stIdx) => { let stop = { type: "Feature", properties: { id: rte.vehicle, vin: rte.description, description: st.description, stop_idx: stIdx, arrival: st.arrival, type: st.type, pointType: (st.type == "start") | (st.type == "end") ? 16 : 255 }, geometry: { coordinates: [st.location[1], st.location[0]], type: "Point" } }; routesGeoJson.features.push(stop); }); }); return routesGeoJson; }38:T6ee,async function mvrp( data, options = { objective: { minimise_num_depots: false, travel_cost: "distance" } } ) { let url = ""; if (inputData_a.apiVersion.value === "V2") { url = `https://api.nextbillion.io/optimization/v2`; } else { url = `https://api.nextbillion.io/optimise-mvrp`; } const optimizeRequest = await axios .post(`${url}` + `?key=${apiKey}`, { ...data, options }) .then((res) => res.data); const mvrpRequestId = optimizeRequest.id; let attempts = 0; while (attempts < 50) { const result = await axios({ url: `${url}/result?id=${mvrpRequestId}&key=${apiKey}`, method: "GET", headers: {} }); if ( result.data && result.data.status === "Ok" && result.data.message == "" ) { result.data.id = mvrpRequestId; // KPIs let deliveryCount = 0; result.data.result.routes.forEach((rte, rteidx) => { rte.steps.forEach((step, index) => { if (step.id && step.id != 99) { ++deliveryCount; } }); }); result.data.kpis = { totalDistance: `${(result.data.result.summary.distance / 1000).toFixed( 1 )} km`, totalTime: `${(result.data.result.summary.duration / 60).toFixed( 1 )} mn`, nbrRoutes: result.data.result.summary.routes, avgStops: (deliveryCount / result.data.result.summary.routes).toFixed( 1 ), unassigned: result.data.result.summary.unassigned, frontSeatWeight: result.data.description.split('|')[1] }; return result.data; } attempts++; progress.value += 1; await new Promise((resolve) => setTimeout(resolve, 5 * 1000)); } }39:[] 31:{"id":"80d4c8db45b20f74","slug":"route-optimization-problem-seating-preference","title":"Route Optimization Problem - Seating Preference","thumbnail":"880d200dc74c918981cb33daffdd27f88d882287a459cbf2afd21263c4b37a0b","access_level":"public","create_time":"$D2023-07-11T13:46:30.035Z","update_time":"$D2023-09-20T18:02:33.326Z","view_time":null,"first_public_time":"$D2023-07-11T18:08:34.969Z","has_unpublished_edits":null,"fork":{"id":"c8f9c8ec0954a1fe","slug":"route-optimization-problem-minimizing-vehicles","title":"Route Optimization Problem - Minimizing Vehicles","owner":{"id":"05baed136f9134c1","login":"nbai","name":"NB.AI Solutions Team"}},"stars":0,"starred":false,"owner":{"type":"team","id":"05baed136f9134c1","login":"nbai","name":"NB.AI Solutions Team","avatar_url":"https://avatars-next.observableusercontent.com/7a25d1b5deb9c5290b12e4e0914aacdbd4196bb5c3a94c63a0fda0fe706d28b2","tier":"starter_2024"},"version":2765,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"bcb6b2e256659128","login":"jim-welch","name":"Jim Welch","avatar_url":"https://avatars-next.observableusercontent.com/6334ed2ccd004bd8ab34e9f05c047bf4ef291dea914767f26079d7756d073607","tier":null}],"members":[{"type":"individual","id":"bcb6b2e256659128","login":"jim-welch","name":"Jim Welch","avatar_url":"https://avatars-next.observableusercontent.com/6334ed2ccd004bd8ab34e9f05c047bf4ef291dea914767f26079d7756d073607","tier":null,"role":"editor"},{"type":"team","id":"05baed136f9134c1","login":"nbai","name":"NB.AI Solutions Team","avatar_url":"https://avatars-next.observableusercontent.com/7a25d1b5deb9c5290b12e4e0914aacdbd4196bb5c3a94c63a0fda0fe706d28b2","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":"Route Optimization Problem - Seating Preference","cells":[{"id":0,"value":"![text](https://d12qcqjlhp2ahm.cloudfront.net/maps-logo.svg) \n# Route Optimization Problem - Seating Preference","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1935,"value":"toc(\"h2, h3\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":508,"value":"### Background\nNextBillion's Route Optimization API provides an interface for managing a list of orders/jobs/tasks with locations and creating the optimal, most efficient routes.\n

\nIn this tutorial, we'll guide you through a sequence of iterative steps to find an optimal solution that meets some customer requirements around expected seating. Customers can specify a preference to sit in the front seat when available but are also allowed to sit in the back seat of the passenger vehicle. \n\nThe main objective function for routing optimization is as follows:\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2575,"value":"\\text{Assume } {N}\\ldots \\text{ is a number of customers (1,2,\\ldots,{n}) that need to be serviced}\\newline {c_{ij}}\\ldots\\text{ the transportation cost for the customer from \\textit{i} to \\textit{j.}}\\text{ Cost is measured as either travel time } \\newline\\text{or travel distance}\\newline\\newline Z = \\sum_{i\\in{N}}\\sum_{j\\in{N}}{c_{ij}}\\rightarrow{min}","pinned":false,"hidden":false,"mode":"tex","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2658,"value":"The standard NB.ai route optimization algorithm introduces a capacity constraint for each vehicle. The constraint for overall capacity restriction adds this additional requirement to our problem statement:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2660,"value":"{V}\\ldots\\text{the set of all available vehicles } {k,k}\\in{V}\\newline{d}\\ldots\\text{the demand of the customer going from }{i} \\text{ to } {j}\\newline{q}\\ldots\\text{the vehicle's maximum capacity}\\newline \\sum_{i\\in{N}}\\sum_{j\\in{N}}{d_{ij}}\\leq{q,}\\forall{k\\in{V}}","pinned":false,"hidden":false,"mode":"tex","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2699,"value":"For the scenario we are creating in this notebook we want to manage a preference for sitting in the `front seat` of the passenger vehicle when possible while also being available for allocation to the back seat. Some passengers meet the requirements to travel in the front of the vehicle while others must be placed in a `back seat` location.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1189,"value":"viewof apiKey = secret2(\"apiKey\", {\n title: \"NextBillion.ai API Key\",\n submit: \"Set\",\n description: \"Contact support@nextbillion.ai to get your API key\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1934,"value":"{\n nextbillion.setApiKey(apiKey);\n return html`Set API key
nextbillion.setApiKey(${apiKey.substr(0,4)}....)`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":525,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1451,"value":"viewof panel = Inputs.form(inputs, {\n template: (v) => htl.html`\n

\n ${v.city}\n

\n ${v.jobs}\n ${v.vehicles}\n ${v.frontSeatFactorUpper}\n ${v.frontSeatFactorLower}\n ${v.vanCapacity}\n ${v.shiftStart}\n ${v.shiftEnd}\n ${v.serviceTime}\n\n ${v.apiVersion}\n

\n\n`\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1566,"value":"{\n const button = html` `;\n button.onclick = () => {\n mutable inputData_a = panel2;\n button.disabled = true;\n };\n return html`${button}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":594,"value":"---\n### Submit Problems to the Route Optimization API Endpoints\nWe can now submit a collection of route optimization requests with different capacity requirements based on our settings. Each route optimization request will have different front seat preference weight factors in an attempt to iterate on the optimal solution that balances seating preference with the overall objective function of minimizing travel 'cost'.\n

\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":348,"value":"viewof jobID = htl.html`

Route Optimization Request IDs: ${\n mvrpID ? `${mvrpID[0].id}/${mvrpID[1].id}/${mvrpID[2].id}` : mvrpID\n}

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2435,"value":"progress = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2496,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":221,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":605,"value":"---\n### Render Solution on a Map\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1261,"value":"viewof routeSelector = Inputs.select(routesGeoJson.features.filter(f => f.geometry.type === 'LineString'), {multiple: false, label: \"Select route to view\", format: x => x.properties.vin, value: t => t.properties.vin})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1676,"value":"green marker is the vehicle start location and red markers are stop locations.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":997,"value":"{\n return geoviewer(routesFiltered, {width:550,height:550,col:'#8058a0',fillOpacity:.9,lineWidth:3,style:'nbai',apiKey:apiKey});\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1862,"value":"Inputs.table(selectedRoute[0].steps, {\n columns: [\"type\", \"arrival\", \"load\", \"description\", \"duration\"],\n header: {\n type: \"Task\",\n arrival: \"Arrival Time\",\n load: \"Nbr Loaded\",\n description: \"Passenger\",\n duration: \"Duration\"\n },\n width: {\n type: 80,\n arrival: 120,\n load: 100,\n duration: 100\n },\n format: {\n type: (x) => x.toUpperCase(),\n arrival: (x) =>\n new Date(x * 1000).toLocaleTimeString([], {\n hour: \"2-digit\",\n minute: \"2-digit\",\n hour12: false\n }),\n load: (x) => `${x[0]} back ${x[1]} front`,\n duration: (x) => (x / 60).toFixed(1) + \" mn\"\n },\n rows: 10,\n maxWidth: 600,\n multiple: false,\n required: false\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1898,"value":"selectedRoute = {\n let bestRoute = -1;\n mvrpID.forEach((sol, idx) => {\n if (mvrpID[idx].result.summary.unassigned === 0) {\n if (bestRoute === -1) {\n bestRoute = idx;\n }\n }\n });\n\n if (bestRoute === -1) {\n // no routes with 0 unassigned - just show the first route\n bestRoute = 0;\n }\n const theRoute = mvrpID[bestRoute].result.routes.filter(\n (f) => f.description === routeSelector.properties.vin\n );\n return theRoute;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":341,"value":"### Other Dependencies","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1139,"value":"import { nextbillion, cities, secret2 } from \"@nbai/nextbillion-ai-dependencies\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1912,"value":"randomVRPInputs = {\n let retval = null;\n retval = await fetch(\n `https://m4aqpzp5ah6n7ilzunwyo5ma2u0ezqcc.lambda-url.us-east-2.on.aws/?region=${panel.city.value}&number=1500&vehicles=100&type=darp`\n ).then((response) => response.json());\n\n // randomize whether passenger can sit in front seat preference\n retval.pointsArray = retval.pointsArray.map((pt) => {\n let frontSeat = Math.random() < 0.5;\n return { ...pt, frontSeat: frontSeat };\n });\n return retval;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":352,"value":"styling = html`\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":849,"value":"geoviewer = require(\"https://d12qcqjlhp2ahm.cloudfront.net/nb_geoverview2.js\").then((f) => f.view)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1686,"value":"mutable inputData_a = null","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1098,"value":"jobs = [\"5\", \"10\", \"25\", \"50\", \"100\", \"200\", \"500\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2339,"value":"backseatCapacity = [\"2\", \"3\", \"4\", \"5\"];","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1111,"value":"vehicles = [\"1\", \"2\", \"5\", \"10\", \"15\", \"20\", \"50\", \"100\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1163,"value":"useCases = [\n {name: \"Delivery\", value: \"delivery\"},\n {name: \"Service Worker\", value: \"service\"}\n ]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1175,"value":"objectives = [\n {name: \"Minimize Time\", value: \"duration\"},\n {name: \"Minimize Distance\", value: \"distance\"}\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1523,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":924,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1279,"value":"routesFiltered = Object({\n type: 'FeatureCollection',\n features: routesGeoJson.features.filter(f => f.properties.vin === routeSelector.properties.vin)\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1931,"value":"import { toc } from \"@nebrius/indented-toc\" // This component generates the Table of Contents","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2191,"value":"axios = {\n let axios = await require(\"axios\");\n return axios;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2268,"value":"turf = require(\"https://unpkg.com/@turf/turf@6/turf.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2370,"value":"import { progressBar } from \"@kasivisu4/progress-bar\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2218,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W39","stdlib":"1","titling":"auto"}} 32:"$31:body"