2d:null 2e:["$","title",null,{"children":"Employee Transportation Solution | NB.AI Solutions Team | Observable"}] 33:Tc86,### Background In this notebook, we will apply a combination of NextBillion's components, namely the [Optimization, Distance Matrix and Web Map SDK](https://docs.nextbillion.ai), to provide one possible solution to the Employee Transportation Use-Case. In this Use-Case the employees satisfaction is the main criteria when evaluating the quality of the transportation service. As such three main requirements will need to be satisfied (as expressed by employees themselves): * _"I want to spend minimal time in the minivan to go to work."_ * _"I do not want to wait too long for the minivan to arrive at my place."_ * _"I do not want to reach the office too much in advance before my actual work starts."_
### Problem Statement The employer, with one central office (factory) proposed to his 200 employees to pick them up every morning to take them to the office. The employees are spread across the city or suburb. Up to 50 minivans, starting from the office, are available to complete the employees transportation. The drivers shift are from 06:00 to 09:00am. Each minivan has a capacity of 5, driver excluded. The key criteria of a successful service is employee satisfaction, as such the following needs to be taken into account when planning: * An accurate pickup time should be provide to each employee to minimize the wait time of the minivan. * Each employee should be picked-up onward (starting with the furthest employee) to the office to minimize time spend in transport. * The minivans should reach ideally the office between ${panel_mvrp.workDropoffStart.toLocaleTimeString("en-uk")} and ${panel_mvrp.workDropoffEnd.toLocaleTimeString("en-uk")}, just before the employees shift starts, to minimize wait time at the factory.
### Approach The approach we will follow to propose one solution to this problem is described below: * Taking into account all employees locations, available minivans, and constraints (capacity, driver shift), compute the optimal route for each driver to pickup up to 5 employees. This will lead to groups of employees assigned for pickup to different minivans. * Compute for each group of employees a Distance Matrix from office location to each employee location to find the furthest employee location. * For each minivan, set the driver start location to the furthest employee location and end location to the office. * Add an artificial job at the destination (office) with a window time constraint between ${panel_mvrp.workDropoffStart.toLocaleTimeString("en-uk")} and ${panel_mvrp.workDropoffEnd.toLocaleTimeString("en-uk")} * Run Optimization within each group with these new constraints. * Render results.
In this tutorial, we'll guide you through the steps that you can take to build, solve, and visualize complex multi-vehicle routing problem applied to this particular Use-Case.

Refer to the API documentation at this page for more supporting details: [API doc](https://docs.nextbillion.ai/docs/navigation/api/optimization-mvrp)

Note: this demo has the possibility of generating a large number of Route Optimization transactions against the key being used and should only be used with evaluation/demo API keys. 34:Tc1f,myMap = { apiKey; let employeeLocations = []; await visibility(); const container = html`

`; yield container; let pos = { lat: panel_mvrp.city.centroid.split(",")[0], lng: panel_mvrp.city.centroid.split(",")[1] }; const nbmap = (container.value = new nextbillion.maps.Map({ container: document.getElementById("map"), center: pos, zoom: 8, maxZoom: 15, minZoom: 5, style: "https://api.nextbillion.io/maps/streets/style.json", scrollZoom: true })); nbmap.map.on("load", function () { let mapPoints = []; let vehPoints = []; if (nbmap.map.getLayer("employees")) { nbmap.map.removeLayer("employees"); } randomVRPInputs.pointsArray.forEach((pt) => { mapPoints.push(pt); }); // for simplicity we'll just show one vehicle point // since they are all at the employer location vehPoints.push(randomVRPInputs.vehicleArray[0]); let firstSymbolId; const layers = nbmap.map.getStyle().layers; for (const layer of layers) { if (layer.type === "symbol") { firstSymbolId = layer.id; break; } } let employeesjson = { type: "FeatureCollection", features: [] }; mapPoints.forEach((pt, i) => { let feature = { type: "Feature", properties: { business: pt.business, name: pt.name, phone: pt.phone }, geometry: { type: "Point", coordinates: [pt.pickup_longitude, pt.pickup_latitude] } }; employeesjson.features.push(feature); }); if (!nbmap.map.getSource("employees")) { nbmap.map.addSource("employees", { type: "geojson", data: employeesjson, cluster: false }); } nbmap.map.addLayer( { type: "circle", source: "employees", id: "employees", paint: { "circle-color": "#e26666", "circle-radius": 5, "circle-stroke-opacity": 0.4, "circle-stroke-width": 1.5 } }, firstSymbolId ); let vehiclejson = { type: "FeatureCollection", features: [] }; vehPoints.forEach((pt, i) => { let feature = { type: "Feature", properties: { license: pt.license, make: pt.make, vin: pt.vin }, geometry: { type: "Point", coordinates: [pt.longitude, pt.latitude] } }; vehiclejson.features.push(feature); }); if (!nbmap.map.getSource("vehicles")) { nbmap.map.addSource("vehicles", { type: "geojson", data: vehiclejson, cluster: false }); } nbmap.map.addLayer( { type: "circle", source: "vehicles", id: "vehicles", paint: { "circle-color": "#93cea3", "circle-radius": 8, "circle-stroke-opacity": 0.4, "circle-stroke-width": 1.5 } }, firstSymbolId ); }); }35:T7c5,mvrpID = { if (run_optimization && inputData_a) { let location = ""; let loc_index = 0; let shipments = []; let jobs = []; let vehicles = []; let jobs_index = 0; randomVRPInputs.pointsArray.forEach((p) => { let job = { id: p.id, description: p.business, pickup: [1], location_index: loc_index++, service: parseInt(inputData_a.serviceTime) }; jobs.push(job); if ( jobs_index != 0 && jobs.length <= randomVRPInputs.pointsArray.length ) { location += "|"; } location += `${p.pickup_latitude},${p.pickup_longitude}`; jobs_index++; }); let vehicles_index = 0; randomVRPInputs.vehicleArray.forEach((f) => { if (vehicles_index < randomVRPInputs.vehicleArray.length) { location += "|"; } location += `${f.latitude},${f.longitude}`; let vehicle = { id: f.id, description: f.vin, start_index: loc_index, end_index: loc_index++, time_window: [ Date.parse(inputData_a.workDropoffStart) / 1000 - 7200, Date.parse(inputData_a.workDropoffStart) / 1000 ], capacity: [parseInt(inputData_a.capacity)] }; vehicles.push(vehicle); vehicles_index++; }); let locations = { id: 1, location: location }; const mvrpRequest = { description: `MVRP Tutorial ${new Date().toISOString()}`, locations: locations, vehicles: vehicles, jobs: jobs, options: { objective: { travel_cost: inputs.objectiveFunction.value.value, minimise_num_depots: false } } }; console.log(JSON.stringify(mvrpRequest)); let resp = axios .post( `https://api.nextbillion.io/optimise-mvrp?key=${apiKey}`, mvrpRequest ) .then((response) => response.data) .catch((response) => response.data); return resp; } }36:T6e4,selectedMVRPProblem = { if (run_optimization && inputData_a) { let location = ""; let loc_index = 0; let shipments = []; let jobs = []; let vehicles = []; let jobs_index = 0; selectedRoute[0].steps.forEach((step) => { let job = {}; if (step.type == "job") { job = { id: step.id, description: step.business, pickup: [1], location_index: loc_index++ //service: parseInt(inputData_a.serviceTime) }; jobs.push(job); if ( jobs_index != 0 && jobs.length <= selectedRoute[0].steps.length - 2 ) { location += "|"; } location += step.location[0] + "," + step.location[1]; jobs_index++; } }); var startEpoch = panel_mvrp.workDropoffStart.getTime() / 1000.0; var endEpoch = panel_mvrp.workDropoffEnd.getTime() / 1000.0; let fakeJob = { id: 9999, description: "Job at office with time window", pickup: [0], location_index: loc_index++, time_windows: [[startEpoch, endEpoch]] }; jobs.push(fakeJob); location += "|" + officeLocation; jobs_index++; // vehicle start & end location location += "|" + furthestEmployee.location[0] + "," + furthestEmployee.location[1]; location += "|" + officeLocation; let vehicle = { id: 1, description: routeSelector.properties.vin, start_index: loc_index++, end_index: loc_index++, capacity: [parseInt(inputData_a.capacity)] }; vehicles.push(vehicle); //console.log(location); let locations = { id: 1, location: location }; return { locations: locations, jobs: jobs, vehicles: vehicles }; } }37:T674,inputs = { let tomorrowStart = new Date(); tomorrowStart.setDate(new Date().getDate() + 1); tomorrowStart.setHours(5); tomorrowStart.setMinutes(45); tomorrowStart.setSeconds(0); tomorrowStart.setMilliseconds(0); tomorrowStart.toLocaleDateString(); let tomorrowEnd = new Date(); tomorrowEnd.setDate(new Date().getDate() + 1); tomorrowEnd.setHours(6); tomorrowEnd.setMinutes(0); tomorrowEnd.setSeconds(0); tomorrowEnd.setMilliseconds(0); tomorrowStart.toLocaleDateString(); const inputs = { city: Inputs.select(cities, { label: "Select city", format: (x) => x.name, value: cities.find((t) => t.name === "Columbus") }), jobs: Inputs.select(jobs, { label: "Nbr of Employees", value: "25" }), vehicles: Inputs.select(vehicles, { label: "Nbr of vehicles", value: "10" }), workDropoffStart: Inputs.datetime({ label: "Earliest work arrival", value: tomorrowStart.toISOString().slice(0, -1) }), workDropoffEnd: Inputs.datetime({ label: "Latest work arrival", value: tomorrowEnd.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: "30" }), capacity: Inputs.text({ label: "Van Capacity", value: "5" }) }; return inputs; }38:T4c6,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 checkStatus.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); }) }); console.log(routesGeoJson); return routesGeoJson; }39:T4cd,routesGeoJson2 = { let routesGeoJson2 = { 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 checkStatus2.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' } } routesGeoJson2.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' } } routesGeoJson2.features.push(stop); }) }); console.log(routesGeoJson2); return routesGeoJson2; }3a:[] 31:{"id":"9f1edf32f999e49a","slug":"employee-transportation-solution","title":"Employee Transportation Solution","thumbnail":"c4187248633e62540274bec3990d00e64ebcdd998741d6b6bf4fb9b0ca60c735","access_level":"public","create_time":"$D2023-02-16T18:46:51.581Z","update_time":"$D2025-04-15T13:56:09.112Z","view_time":null,"first_public_time":"$D2023-02-17T20:37:53.947Z","has_unpublished_edits":null,"fork":{"id":"9f05f36c822642e4","slug":"employees-transportation","title":"Employees Transportation","owner":{"id":"05baed136f9134c1","login":"nbai","name":"NB.AI Solutions Team"}},"stars":0,"starred":false,"owner":{"type":"team","id":"05baed136f9134c1","login":"nbai","name":"NB.AI Solutions Team","avatar_url":"https://avatars-next.observableusercontent.com/7a25d1b5deb9c5290b12e4e0914aacdbd4196bb5c3a94c63a0fda0fe706d28b2","tier":"starter_2024"},"version":2762,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"bcb6b2e256659128","login":"jim-welch","name":"Jim Welch","avatar_url":"https://avatars-next.observableusercontent.com/6334ed2ccd004bd8ab34e9f05c047bf4ef291dea914767f26079d7756d073607","tier":null}],"members":[{"type":"individual","id":"bcb6b2e256659128","login":"jim-welch","name":"Jim Welch","avatar_url":"https://avatars-next.observableusercontent.com/6334ed2ccd004bd8ab34e9f05c047bf4ef291dea914767f26079d7756d073607","tier":null,"role":"editor"},{"type":"team","id":"05baed136f9134c1","login":"nbai","name":"NB.AI Solutions Team","avatar_url":"https://avatars-next.observableusercontent.com/7a25d1b5deb9c5290b12e4e0914aacdbd4196bb5c3a94c63a0fda0fe706d28b2","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":{"id":"73d557bcc8a3ebc8","slug":"solutions","title":"Solutions","owner":{"id":"05baed136f9134c1","login":"nbai","name":"NB.AI Solutions Team"}},"type":"notebook","body":{"title":"Employee Transportation Solution","cells":[{"id":0,"value":"![text](https://nextbillion.ai/wp-content/uploads/2022/01/logo-nb-colored.png)\n# Employee Transportation Solution","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2518,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2517,"value":"toc(\"h2, h3, h4\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2516,"value":"---\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":508,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$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":2630,"value":"{\n nextbillion.setApiKey(apiKey);\n return html`Set API key
nextbillion.setApiKey(${apiKey.substr(\n 0,\n 4\n )}....)`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2633,"value":"html`Add the stylesheet for the NB.ai SDK
<link href=\"https://maps-gl.nextbillion.io/maps/v2/api/css\" rel=\"stylesheet\">`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2635,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1236,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2490,"value":"### Problem Statement\n\nHere we are setting the overall problem statement including the minivan drivers shift, vehicle maximum capacity and objective function to use.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2038,"value":"viewof panel_mvrp = Inputs.form(inputs, {\n template: (v) => htl.html`\n

\n ${v.city}\n ${v.jobs}\n ${v.vehicles}\n ${v.objectiveFunction}\n

\n \n

\n\n ${v.capacity}\n ${v.workDropoffStart}\n ${v.workDropoffEnd}\n

\n`\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2042,"value":"{\n const button_mvrp = html` `;\n button_mvrp.onclick = () => {\n mutable run_optimization = true;\n mutable inputData_a = panel_mvrp;\n button_mvrp.disabled = true;\n };\n return html`${button_mvrp}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":534,"value":"{\n return html`

${md`### Random Data Visualization\nThe random data will contain the requested number of employees and will consist of a pickup location for the employee. Green points are vehicles which are coincident with the employer location and red points are employee pickup locations.`}

`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2638,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1912,"value":"randomVRPInputs = {\n let retval = null;\n let vehiclesLoc = inputData_a.city.centroid.split(\",\");\n try {\n let retval = await axios({\n method: \"GET\",\n url: `https://m4aqpzp5ah6n7ilzunwyo5ma2u0ezqcc.lambda-url.us-east-2.on.aws/?region=${\n inputData_a.city.value\n }&number=${inputData_a.jobs}&vehicles=${inputData_a.vehicles}&type=${\n inputData_a.useCase.value === \"delivery\" ? \"darp\" : \"service\"\n }`,\n headers: {}\n }).then((res) => res.data);\n let tmp = retval;\n\n tmp.vehicleArray.forEach((vehicle) => {\n vehicle.latitude = vehiclesLoc[0];\n vehicle.longitude = vehiclesLoc[1];\n });\n\n for (let i = 0; i < tmp.vehicleArray.length; i++) {\n tmp.vehGeoJson.features[i].geometry.coordinates[0] = vehiclesLoc[1];\n tmp.vehGeoJson.features[i].geometry.coordinates[1] = vehiclesLoc[0];\n }\n return tmp;\n } catch (err) {}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":594,"value":"---\n### Submit the Problem to the MVRP API Endpoint\nWe are now to the point where we can put together the full MVRP request containing all of our pickup locations, vehicles, and options for the solution.\n

\nThe MVRP endpoint is a non-blocking/asynchronous endpoint. We will receive back a reference ID for the optimization job and a status message as seen below. We've highlighted the reference ID in green.\n

\nNote: if you see an error in this section, more than likely you have an invalid API Key set in the input box above. \n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":221,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":348,"value":"viewof jobID = htl.html`
MVRP Request ID: ${mvrpID?mvrpID.id:mvrpID}
`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":601,"value":"---\n### Retrieve MVRP Result\nOnce we have the reference ID for the MVRP request, we can poll that MVRP status endpoint to get the solution. In this tutorial, we are waiting 10 seconds before polling for the solution. When you see a status message of `Ok` you can browse the result to see the optimized routing solution.\n

\nNote: if you see an error in this section, more than likely you have an invalid API Key set in the input box above or the API key is not valid for the city region selected.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":720,"value":"html`
If you get a status message indicating Job still processing just wait a few seconds and press this button.

${viewof requestResult}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":242,"value":"checkStatus = {\n mvrpID;\n //await visibility();\n requestResult;\n yield Promises.delay(5000).then(() => {\n let mvrpResult = axios({\n method: \"GET\",\n url: `https://api.nextbillion.io/optimise-mvrp/result?id=${mvrpID.id}&key=${apiKey}`,\n headers: {\n } \n }).then(res=>res.data)\n .catch(error => {\n let mvrpResult = {\n status: 'Error', \n error: JSON.stringify(error)\n }\n });\n return mvrpResult;\n })\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":631,"value":"kpiStats = {\n let deliveryCount = 0;\n checkStatus.result.routes.forEach((rte, rteidx) => {\n rte.steps.forEach((step, index) => {\n if (step.id && step.id != 99) {\n ++deliveryCount;\n }\n });\n });\n return {\n totalDistance: `${(checkStatus.result.summary.distance / 1000).toFixed(\n 1\n )} km`,\n totalTime: `${(checkStatus.result.summary.duration / 60).toFixed(1)} mn`,\n nbrRoutes: checkStatus.result.summary.routes,\n avgStops: (deliveryCount / checkStatus.result.summary.routes).toFixed(1),\n unassignedStops: checkStatus.result.summary.unassigned\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2414,"value":"### KPIs for all minivans","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":645,"value":"| KPI | Value |\n| ------ | -------- |\n| Total Distance | ${kpiStats.totalDistance} |\n| Total Time | ${kpiStats.totalTime} |\n| Number of Routes | ${kpiStats.nbrRoutes} |\n| Average Stops Per Vehicle | ${kpiStats.avgStops}|\n| Unassigned Employees | ${kpiStats.unassignedStops} |","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":605,"value":"---\n### Render Solution on a Map\nThis maps shows groups of employees assigned to each minivan. \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:\"100%\",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":1898,"value":"selectedRoute = checkStatus.result.routes.filter(f => f.description === routeSelector.properties.vin)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2359,"value":"selectedRoute2 = checkStatus2.result.routes.filter(f => f.description === routeSelector2.properties.vin)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2164,"value":"---\n### Optimize each driver journey to meet employees requirements\n\nHere we want to set the furthest employee from the office to be the start location of the driver journey. This will ensure the employees are picked in the appropriate order from the furthest to the closest, thus to minimize the time each employee spent in the minivan. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2173,"value":"### Distance Matrix Computation\n\nFor each optimized group of employees created by the optimizer (all the employees to be picked up by a given driver), we will assess who is the furthest employee. As such we compute a Distance Matrix where the origin is the office and the destinations are the employee locations.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2191,"value":"distanceMatrix = {\n let destinations = \"\";\n let officeLL = panel_mvrp.city.centroid.split(\",\");\n let origins = officeLocation;\n let job_index = 0;\n selectedRoute[0].steps.forEach((step) => {\n if (step.type == \"job\") {\n if (job_index != 0) {\n destinations += \"|\";\n }\n destinations += step.location[0] + \",\" + step.location[1];\n job_index++;\n }\n });\n\n let dmRetVal = null;\n try {\n let dmRetVal = axios({\n method: \"GET\",\n url: `https://api.nextbillion.io/distancematrix/json-concise?key=${apiKey}&mode=4w&origins=${origins}&destinations=${destinations}`,\n headers: {\n \"Content-Type\": \"application/json\"\n }\n }).then((res) => res.data);\n return dmRetVal;\n } catch (err) {}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2214,"value":"### Furthest Employee\n\nHere we apply some basic logic by parsing the distance matrix output to pick the employee for which the distance is the furthest from the office.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2218,"value":"furthestEmployee ={\n let step_index = 0;\n let highest_dist_index = 0;\n let highest_dist = 0;\n distanceMatrix.rows[0].forEach((el,idx) => {\n if (el[1] > highest_dist) {\n highest_dist = el[1];\n highest_dist_index = idx;\n }\n });\n \n step_index = highest_dist_index + 1;\n return selectedRoute[0].steps[step_index];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2418,"value":"---\n### Build the Problem Inputs for each Employee group\nNow we build the needed JSON objects that will be submitted to the MVRP endpoint for each employee group. We are as before building the `jobs` which each consist of employees pickup locations in the city region with the main difference that we apply the optimizer to a subset i.e one single employee group. In addition we set new constraints:\n* the driver start location is the furthest employee location to ensure the furthest employee is picked up first.\n* an artificial job (with no pickup) is created at the office location with a pickup time window constraint set from ${panel_mvrp.workDropoffStart.toLocaleTimeString(\"en-uk\")} to ${panel_mvrp.workDropoffEnd.toLocaleTimeString(\"en-uk\")}.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2287,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2289,"value":"mvrpID2 = {\n const selecteMvrpRequest = {\n description: `Route Optimization - Employee Transportation ${new Date().toISOString()}`,\n locations: selectedMVRPProblem.locations,\n vehicles: selectedMVRPProblem.vehicles,\n jobs: selectedMVRPProblem.jobs,\n options: {\n objective: {\n travel_cost: inputs.objectiveFunction.value.value,\n minimise_num_depots: false\n }\n }\n };\n\n let resp = axios\n .post(\n `https://api.nextbillion.io/optimise-mvrp?key=${apiKey}`,\n selecteMvrpRequest\n )\n .then((response) => response.data)\n .catch((response) => response.data);\n return resp;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2292,"value":"viewof jobID2 = htl.html`
Selected MVRP Request ID: ${mvrpID2?mvrpID2.id:mvrpID2}
`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2294,"value":"---\n### Retrieve MVRP Result\nOnce we have the reference ID for the MVRP request, we can poll that MVRP status endpoint to get the solution. Given the asynchronous nature of the endpoint, we wait 10 seconds before polling for the solution. When you see a status message of `Ok` you can browse the result to see the optimized routing solution.\n

\nNote: if you see an error in this section, more than likely you have an invalid API Key set in the input box above or the API key is not valid for the city region selected.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2313,"value":"html`
If you get a status message indicating Job still processing just wait a few seconds and press this button.

${viewof requestResult}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2341,"value":"checkStatus2 = {\n mvrpID2;\n //await visibility();\n requestResult;\n yield Promises.delay(5000).then(() => {\n let mvrpResult2 = axios({\n method: \"GET\",\n url: `https://api.nextbillion.io/optimise-mvrp/result?id=${mvrpID2.id}&key=${apiKey}`,\n headers: {\n } \n }).then(res=>res.data)\n .catch(error => {\n let mvrpResult = {\n status: 'Error', \n error: JSON.stringify(error)\n }\n });\n return mvrpResult2;\n })\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2344,"value":"kpiStats2 = {\n let deliveryCount = 0;\n checkStatus2.result.routes.forEach((rte, rteidx) => {\n rte.steps.forEach((step, index) => {\n if (step.id && step.id != 99) {\n ++deliveryCount;\n }\n });\n });\n return {\n totalDistance: `${(checkStatus2.result.summary.distance/1000).toFixed(1)} km`,\n totalTime: `${(checkStatus2.result.summary.duration/60).toFixed(1)} mn`,\n nbrRoutes: checkStatus2.result.summary.routes,\n avgStops: (deliveryCount/checkStatus2.result.summary.routes).toFixed(1)\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2346,"value":"---\n### Render Solution on a Map","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2348,"value":"viewof routeSelector2 = Inputs.select(routesGeoJson2.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":2354,"value":"{\n return geoviewer(routesFiltered2, {width:\"100%\",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":2361,"value":"Inputs.table(selectedRoute2[0].steps, {\n columns: [\"type\", \"arrival\", \"load\", \"description\", \"duration\"],\n header: {\n type: \"Task\",\n arrival: \"Arrival Time\",\n load: \"Nbr Loaded\",\n description: \"Account\",\n duration: \"Duration\"\n },\n width: {\n type: 60,\n arrival: 60,\n load: 40,\n description: 240,\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 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":2467,"value":"viewof driverDeparture = htl.html`
For the driver to arrive at the first pickup location (the furthest employee) at time indicated in the table above, he will need to start from the office at ${driverDepartureTime}
`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2443,"value":"### Conclusion\n\nUsing a multi-steps approach where the optimizer is first used as a pre-processing step to create employees groups, running the distance matrix inside each group to assess who is the furthest employee and finally running the optimization again inside each group, we manage to achieve the set requirements as highlighted in the table above, which ultimately should lead to employee satisfaction.\n\nBy following this multi-step approach we were able to:\n* Provide a time to each employee for the pickup.\n* Minimize the time spend in the minivan.\n* Arrive a few minutes before the actual shift starts.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1000,"value":"\n\n\n\n---\n### Additional Tools \nIf you want to visualize the optimized routing solution along with the sequencing of the stops, the NB.ai Optimization Tester tool is available. \n\nNB.AI MVRP Playground App (should automatically load the current solution)\n- Select the `SOLUTION` tab\n- Enter the same NB.ai API key used for the MVRP solution `${apiKey}`\n- Paste the MVRP Request ID into the input box `${mvrpID.id}`\n- Press the `Load` button","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","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":334,"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":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":1613,"value":"html`${viewof requestResult}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1005,"value":"viewof requestResult = Inputs.button(\"Request Result\")","pinned":false,"hidden":false,"mode":"ojs","author":"$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":"$undefined"},{"id":2051,"value":"mutable run_optimization = false;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1098,"value":"jobs = [\"5\", \"10\", \"25\", \"100\", \"200\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1111,"value":"vehicles = [\"5\",\"10\",\"20\",\"50\",\"100\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$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":"$undefined"},{"id":2241,"value":"officeLocation = {\n let officeLL = panel_mvrp.city.centroid.split(\",\");\n return officeLL[0] + \",\" + officeLL[1];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2473,"value":"driverDepartureTime = {\n let departureEpoch = parseInt(selectedRoute2[0].steps[0].arrival) - parseInt(furthestEmployee.duration);\n let humanReadableTime = (new Date( departureEpoch *1000)).toLocaleString();\n return humanReadableTime;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1523,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":924,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2351,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$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":"$undefined"},{"id":2356,"value":"routesFiltered2 = Object({\n type: 'FeatureCollection',\n features: routesGeoJson2.features.filter(f => f.properties.vin === routeSelector.properties.vin)\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2511,"value":"import { toc } from \"@nebrius/indented-toc\" // This component generates the Table of Contents","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W3a","stdlib":"1","titling":"auto"}} 32:"$31:body"

MVRP Request ID: ${mvrpID?mvrpID.id:mvrpID}

Selected MVRP Request ID: ${mvrpID2?mvrpID2.id:mvrpID2}

For the driver to arrive at the first pickup location (the furthest employee) at time indicated in the table above, he will need to start from the office at ${driverDepartureTime}