2f:Td09, ### Background One of India's leading employee commute service providers has the requirement to improve their services through best routing solutions for their employees picking and dropping facilities from office to home and vice versa. Nextbillion's Route Optimisation API generates optimal routes for the commute service, provided with the right inputs and desired constraints. The goal of this POC is to demonstrate a working solution on optimal routing of the employee commute services given the employee pickup location, drop location, type of vehicles with their maximum capacity, service time and login and logout time as input to the route optimiser. The Route optimiser automatically grouped and assigned the employees to vehicles with respect to their location and vehicle capacity. An optimal route was generated which meet the following criteria: * _"The employees should spend minimal time in the vehicle during the commute."_ * _"The employees should reach the office before login time"_ Note this notebook runs real code against NextBillion.ai' API. The purpose is to demonstrate one possible approach to solve the employee commute service use-case given a set of constraints.
### Problem Statement The employee commute service provider renders services for employee commute to home to office and vice versa. The employees are spread across the cities who have to reach office at different time shifts and there are also employees in office who have to reach home once their shift ends. It is considered that the vehicle used for employee commute service will start from the office. This POC considers 50 vehicles, which has the capacity to accomodate N employees (values configurable), driver excluded. The key criteria is to generate an optimised route for employee commute and also the following criteria were taken into account while planning, * The employees must reach office within the shift time (which will be given as delivery time slot in the input) * There should not be longer wait time for the employees and the service time is considered to be 5 mins
### Approach The approach we will follow to propose one solution to this problem is described below: * The employee pickup locations (latitude and longitude) were taken into account and given as inputs. the constraint considered here is the shift time i.e., the time slot to reach the office. This is given as delivery start time and delivery end time as input. * The vehicles for the employees commute are considered to start from the office location, which is given as the start location of the vehicle. The vehicle can accommodate N employees excluding the driver, which is given as constraint in the input. * The route optimiser will group the employees in the count of N to each available vehicle and compute the optimised route based on employee location * Run the Route optimiser within the employee group who have the same shift timing with the required 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/optimization/api/route-optimization-v2) 30:T91f,OptimizationProblem = { let shipments = []; let vehicles = []; let shipment = {}; let vehicle = {}; let location = []; let employee_index = 1; let vehicle_index = 1; let loc_index = 0; let locations = {}; let start_time = null; let end_time = null; let end_index = null; let options = {}; location.push(officeLocation); loc_index++; employees_test.forEach((row) => { shipment = { amount: [parseInt(`${row.capacity}`)] }; shipment.delivery = { location: `${row.to_location}`, id: employee_index++, description: `${row.employee_id}, ${row.gender}` }; shipment.pickup = { location: `${row.from_location}`, id: employee_index++, description: `${row.employee_id}, ${row.gender}` }; if (trip_type == "pickup") { start_time = toEpoch(delivery_start_time); end_time = toEpoch(delivery_end_time); shipment.delivery.time_windows = [[start_time, end_time]]; shipment.pickup.service = parseInt(`${row.service}`); shipment.delivery.location_index = 0; location.push(`${row.from_location}`); shipment.pickup.location_index = loc_index++; } else if (trip_type == "drop") { start_time = toEpoch(pickup_start_time); end_time = toEpoch(pickup_end_time); shipment.pickup.time_windows = [[start_time, end_time]]; shipment.pickup.location_index = 0; location.push(`${row.to_location}`); shipment.delivery.location_index = loc_index++; } shipments.push(shipment); }); vehicles_test.forEach((row) => { vehicle = { id: parseInt(`${row.vehicle_id}`), description: `${row.vehicle_model}`, capacity: [parseInt(`${row.capacity}`)], max_travel_cost: `${inputs.maxDriveTime.value}` * 60, start_index: 0, end_index: 0 }; vehicles.push(vehicle); }); locations = { id: 1, location: location }; options = { objective: { travel_cost: "duration", routing: { mode: "car", traffic_timestamp: toEpoch(traffic_timestamp) - 3600 // use traffic 1 hour before delivery } } }; let result = { vehicles: vehicles, shipments: shipments, locations: locations }; let jsonString = JSON.stringify(result); console.log(jsonString); return result; }31:Tc0c,OptimizationProblem2 = { let shipments = []; let vehicles = []; let shipment = {}; let vehicle = {}; let locations = {}; let location = []; let options = {}; let employee_index = 1; let vehicle_index = 1; let loc_index = 0; let start_time = null; let end_time = null; let delivery_location = null; let employeesDict = {}; let end_index = null; selectedRoute[0].steps.forEach((step) => { if (step.type == "pickup" || step.type == "delivery") { if (!employeesDict.hasOwnProperty(step.description)) { employeesDict[step.description] = [step.location]; } else { employeesDict[step.description].push(step.location); } } }); console.log("EMPLOYEES DICT: ", employeesDict); for (let key in employeesDict) { shipment = { amount: [1] }; shipment.delivery = { location: employeesDict[key][1], id: employee_index++, location_index: loc_index++, description: key }; location.push(employeesDict[key][1].join(",")); shipment.pickup = { location: employeesDict[key][0], id: employee_index++, location_index: loc_index++, description: key }; location.push(employeesDict[key][0].join(",")); if (trip_type == "pickup") { start_time = toEpoch(delivery_start_time); end_time = toEpoch(delivery_end_time); shipment.delivery.time_windows = [[start_time, end_time]]; shipment.pickup.service = service_time; } if (trip_type == "drop") { start_time = toEpoch(pickup_start_time); end_time = toEpoch(pickup_end_time); shipment.pickup.time_windows = [[start_time, end_time]]; } shipments.push(shipment); } locations = { id: 1, location: location }; let vehicleEndIndex = null; let vehicleStartIndex = null; if (trip_type == "pickup") { location.push(furthestEmployee); vehicleStartIndex = loc_index++; location.push(officeLocation); vehicleEndIndex = loc_index; } if (trip_type == "drop") { location.push(officeLocation); vehicleStartIndex = loc_index++; location.push(furthestEmployeeDrop); vehicleEndIndex = loc_index; } vehicle = { id: selectedRoute[0].vehicle, description: selectedRoute[0].description, capacity: [parseInt(vehicle_capacity)], max_travel_cost: inputs.maxDriveTime.value * 60, start_index: vehicleStartIndex, end_index: vehicleEndIndex }; vehicles.push(vehicle); options = { objective: { travel_cost: "duration", routing: { mode: "car", traffic_timestamp: toEpoch(traffic_timestamp) - 3600 // use traffic 1 hour before delivery } } }; //console.log("SHIPMENTS: ", shipments); //console.log("LOCATIONS: ", locations); //console.log("VEHCILES: ", vehicles); //console.log("OPTIONS: ", options); let result = { vehicles: vehicles, shipments: shipments, locations: locations, options: options }; //let jsonString = JSON.stringify(result); //console.log(jsonString); return result; }32:T45d,Inputs.table(selectedRoute2[0].steps, { columns: [ "type", "load", "description", "arrival", "duration", "service", "location" ], header: { description: "ID, Gender", type: "Task", arrival: "Arrival Time", load: "Nbr Loaded", description: "Employee", duration: "Duration", location: "Location" }, width: { description: 70, type: 50, arrival: 100, load: 80, duration: 100, location: 200 }, //format: { // type: (x) => x.toUpperCase(), // arrival: (x) => // new Date(x * 1000).toLocaleTimeString([], { // hour: "2-digit", // minute: "2-digit", // hour12: false // }), // duration: (x) => (x / 60).toFixed(1) + " mn" //}, format: { type: (x) => x.toUpperCase(), arrival: (x) => new Date(x * 1000).toLocaleTimeString("en-US", { hour: "2-digit", minute: "2-digit", hour12: false, timeZone: "Asia/Kolkata" }), duration: (x) => (x / 60).toFixed(1) + " mn" }, rows: 10, maxWidth: 800, multiple: false, required: false })33:T90c,EscortGroups = { function checkTimeRange(epochString) { var epochMilliseconds = parseInt(epochString) * 1000; var date = new Date(epochMilliseconds); var options = { timeZone: "Asia/Kolkata", hour: "2-digit", minute: "2-digit", hour12: false }; var timeString = date.toLocaleTimeString("en-IN", options); var timeParts = timeString.split(":"); var hours = parseInt(timeParts[0]); var minutes = parseInt(timeParts[1]); // Check if time falls within the specified ranges (18:00 - 23:59 or 00:01 - 06:00) if ( (hours >= 18 && hours < 24) || (hours >= 0 && hours < 6 && !(hours === 0 && minutes === 0)) ) { return true; } return false; } let female_group = []; let male_group = []; if (trip_type == "pickup") { let furthest_emp_gender = furthestEmployeeStep.description.split(", ")[1]; var epochString = selectedRoute2[0].steps[selectedRoute2[0].steps.length - 1].arrival; if (furthest_emp_gender == "F" && checkTimeRange(epochString)) { let gender; for (var i = 0; i < selectedRoute2[0].steps.length; i++) { if (selectedRoute2[0].steps[i].type == "pickup") { gender = selectedRoute2[0].steps[i].description.split(", ")[1]; if (gender == "F") { female_group.push(selectedRoute2[0].steps[i]); } else { male_group.push(selectedRoute2[0].steps[i]); } } } } } if (trip_type == "drop") { let furthest_emp_gender = furthestEmployeeStepDrop.description.split(", ")[1]; var epochString = selectedRoute2[0].steps[selectedRoute2[0].steps.length - 1].arrival; if (furthest_emp_gender == "F" && checkTimeRange(epochString)) { let gender; for (var i = 0; i < selectedRoute2[0].steps.length; i++) { if (selectedRoute2[0].steps[i].type == "delivery") { // CHANGE gender = selectedRoute2[0].steps[i].description.split(", ")[1]; if (gender == "F") { female_group.push(selectedRoute2[0].steps[i]); } else { male_group.push(selectedRoute2[0].steps[i]); } } } } } let result = { female_group: female_group, male_group: male_group }; return result; }34:T1122,OptimizationProblemEscort = { let shipments = []; let vehicles = []; let shipment = {}; let vehicle = {}; let locations = {}; let location = []; let options = {}; let employee_index = 1; let vehicle_index = 1; let loc_index = 0; let start_time = null; let end_time = null; let delivery_location = null; let employeesDict = {}; let end_index = null; if (splitEmployees == false) { //less than 4 passengers if (EscortGroups.female_group.length > 0) { if (trip_type == "pickup") { employeesDict["999, E"] = furthestEmployee.split(",").map(Number); } else { employeesDict["999, E"] = furthestEmployeeDrop.split(",").map(Number); } } selectedRoute2[0].steps.forEach((step) => { if (step.type == "pickup" && trip_type == "pickup") { employeesDict[step.description] = [step.location]; } if (step.type == "delivery" && trip_type == "drop") { employeesDict[step.description] = [step.location]; } }); } else { // more than 4 passengers if (EscortGroups.female_group.length > 0 && trip_type == "pickup") { employeesDict["999, E"] = furthestEmployee.split(",").map(Number); } else { employeesDict["999, E"] = furthestEmployeeDrop.split(",").map(Number); } console.log("FEMALE GROUP: ", EscortGroups.female_group); EscortGroups.female_group.forEach((female_employee) => { let employee_id = female_employee.description; if (!employeesDict.hasOwnProperty(employee_id)) { employeesDict[employee_id] = [female_employee.location]; } else { employeesDict[employee_id].push(female_employee.location); } }); } for (let key in employeesDict) { shipment = { amount: [1] }; let delivery_location; let pickup_location; if (trip_type == "pickup") { pickup_location = employeesDict[key].join(","); delivery_location = officeLocation; } else { pickup_location = officeLocation; delivery_location = employeesDict[key].join(","); } shipment.delivery = { location: delivery_location, id: employee_index++, location_index: loc_index++, description: key }; location.push(delivery_location); shipment.pickup = { location: pickup_location, id: employee_index++, location_index: loc_index++, description: key }; location.push(pickup_location); if (trip_type == "pickup") { start_time = toEpoch(delivery_start_time); end_time = toEpoch(delivery_end_time); shipment.delivery.time_windows = [[start_time, end_time]]; if (key != "999, E") { shipment.pickup.service = service_time; } else { shipment.pickup.service = 0; } } if (trip_type == "drop") { start_time = toEpoch(pickup_start_time); end_time = toEpoch(pickup_end_time); shipment.pickup.time_windows = [[start_time, end_time]]; } shipments.push(shipment); } locations = { id: 1, location: location }; let vehicleEndIndex = null; let vehicleStartIndex = null; if (trip_type == "pickup") { location.push(furthestEmployee); vehicleStartIndex = loc_index++; location.push(officeLocation); vehicleEndIndex = loc_index; } if (trip_type == "drop") { location.push(officeLocation); vehicleStartIndex = loc_index++; location.push(furthestEmployeeDrop); vehicleEndIndex = loc_index; } vehicle = { id: selectedRoute[0].vehicle, description: selectedRoute[0].description, capacity: [parseInt(vehicle_capacity)], max_travel_cost: inputs.maxDriveTime.value * 60, start_index: vehicleStartIndex, end_index: vehicleEndIndex }; vehicles.push(vehicle); options = { objective: { travel_cost: "duration", routing: { mode: "car", traffic_timestamp: toEpoch(traffic_timestamp) - 3600 // use traffic 1 hour before delivery } } }; //console.log("SHIPMENTS: ", shipments); //console.log("LOCATIONS: ", locations); //console.log("VEHCILES: ", vehicles); //console.log("OPTIONS: ", options); let result = { vehicles: vehicles, shipments: shipments, locations: locations, options: options }; //let jsonString = JSON.stringify(result); //console.log(jsonString); return result; }35:T462,Inputs.table(selectedRouteEscort[0].steps, { columns: [ "type", "load", "description", "arrival", "duration", "service", "location" ], header: { description: "ID, Gender", type: "Task", arrival: "Arrival Time", load: "Nbr Loaded", description: "Employee", duration: "Duration", location: "Location" }, width: { description: 70, type: 50, arrival: 100, load: 80, duration: 100, location: 200 }, //format: { // type: (x) => x.toUpperCase(), // arrival: (x) => // new Date(x * 1000).toLocaleTimeString([], { // hour: "2-digit", // minute: "2-digit", // hour12: false // }), // duration: (x) => (x / 60).toFixed(1) + " mn" //}, format: { type: (x) => x.toUpperCase(), arrival: (x) => new Date(x * 1000).toLocaleTimeString("en-US", { hour: "2-digit", minute: "2-digit", hour12: false, timeZone: "Asia/Kolkata" }), duration: (x) => (x / 60).toFixed(1) + " mn" }, rows: 10, maxWidth: 800, multiple: false, required: false })36:T55f,randomVehicle = { if (splitEmployees == true) { function randomVehicle() { let available_vehicles = []; let assigned_vehicles = []; vehicles_test.forEach((row) => { let vehicle = { id: parseInt(`${row.vehicle_id}`), description: `${row.vehicle_model}`, capacity: [parseInt(`${row.capacity}`)], max_travel_cost: `${inputs.maxDriveTime.value}` * 60, start_index: 0, end_index: 0 }; available_vehicles.push(vehicle); }); checkStatus.result.routes.forEach((route) => { let vehicle = { id: route.vehicle }; assigned_vehicles.push(vehicle); }); console.log(available_vehicles); console.log(assigned_vehicles); // Filter out vehicles that are already assigned let unassigned_vehicles = available_vehicles.filter( (av) => !assigned_vehicles.some((asv) => asv.id === av.id) ); // Randomly select one vehicle from unassigned_vehicles if (unassigned_vehicles.length > 0) { let random_vehicle = unassigned_vehicles[ Math.floor(Math.random() * unassigned_vehicles.length) ]; return random_vehicle; } else { return null; } } let selectedVehicle = randomVehicle(); return selectedVehicle; } }37:T5a8,distanceMatrixMale = { if (splitEmployees == true) { console.log(selectedRoute[0].steps); let destinations = ""; let origins = officeLocation; let job_index = 0; let dm_index = 0; let dm_dict = {}; selectedRoute[0].steps.forEach((step) => { let gender; if (step.type == "pickup" || step.type == "delivery") gender = step.description.split(", ")[1]; else gender = ""; if (trip_type == "pickup" && step.type == "pickup" && gender == "M") { if (job_index != 0) { destinations += "|"; } destinations += step.location[0] + "," + step.location[1]; dm_dict[dm_index] = step.location[0] + "," + step.location[1]; job_index++; dm_index++; } if (trip_type == "drop" && step.type == "delivery" && gender == "M") { if (job_index != 0) { destinations += "|"; } destinations += step.location[0] + "," + step.location[1]; job_index++; } }); console.log("DISTANCE MATRIX: ", dm_dict); let dmRetVal = null; try { let dmRetVal = axios({ method: "GET", url: `https://api.nextbillion.io/distancematrix/json-concise?key=${apiKey}&mode=4w&origins=${origins}&destinations=${destinations}`, headers: { "Content-Type": "application/json" } }).then((res) => res.data); return dmRetVal; } catch (err) {} } }38:T443,distanceMatrixMaleIdx = { if (splitEmployees == true) { let destinations = ""; let origins = officeLocation; let job_index = 0; let dm_index = 0; let dm_dict = {}; selectedRoute[0].steps.forEach((step) => { let gender; if (step.type == "pickup" || step.type == "delivery") gender = step.description.split(", ")[1]; else gender = ""; if (trip_type == "pickup" && step.type == "pickup" && gender == "M") { if (job_index != 0) { destinations += "|"; } destinations += step.location[0] + "," + step.location[1]; dm_dict[dm_index] = step.location[0] + "," + step.location[1]; job_index++; dm_index++; } if (trip_type == "drop" && step.type == "delivery" && gender == "M") { if (job_index != 0) { destinations += "|"; } destinations += step.location[0] + "," + step.location[1]; dm_dict[dm_index] = step.location[0] + "," + step.location[1]; job_index++; dm_index++; } }); return dm_dict; } }39:Te23,OptimizationProblemMale = { if (splitEmployees == true) { let shipments = []; let vehicles = []; let shipment = {}; let vehicle = {}; let locations = {}; let location = []; let options = {}; let employee_index = 1; let vehicle_index = 1; let loc_index = 0; let start_time = null; let end_time = null; let delivery_location = null; let employeesDict = {}; let end_index = null; EscortGroups.male_group.forEach((male_employee) => { let employee_id = male_employee.description; if (!employeesDict.hasOwnProperty(employee_id)) { employeesDict[employee_id] = [male_employee.location]; } else { employeesDict[employee_id].push(male_employee.location); } }); for (let key in employeesDict) { shipment = { amount: [1] }; let delivery_location; let pickup_location; if (trip_type == "pickup") { pickup_location = employeesDict[key].join(","); delivery_location = officeLocation; } else { pickup_location = officeLocation; delivery_location = employeesDict[key].join(","); } shipment.delivery = { location: delivery_location, id: employee_index++, location_index: loc_index++, description: key }; location.push(delivery_location); shipment.pickup = { location: pickup_location, id: employee_index++, location_index: loc_index++, description: key }; location.push(pickup_location); if (trip_type == "pickup") { start_time = toEpoch(delivery_start_time); end_time = toEpoch(delivery_end_time); shipment.delivery.time_windows = [[start_time, end_time]]; if (key != "999, E") { shipment.pickup.service = service_time; } else { shipment.pickup.service = 0; } } if (trip_type == "drop") { start_time = toEpoch(pickup_start_time); end_time = toEpoch(pickup_end_time); shipment.pickup.time_windows = [[start_time, end_time]]; } shipments.push(shipment); } locations = { id: 1, location: location }; let vehicleEndIndex = 0; let vehicleStartIndex = 0; if (trip_type == "pickup") { console.log("LOC INDEX 1: ", loc_index); location.push(furthestEmployeePickupMale); vehicleStartIndex = loc_index++; console.log("LOC INDEX 2: ", loc_index); location.push(officeLocation); vehicleEndIndex = loc_index; } if (trip_type == "drop") { location.push(officeLocation); vehicleStartIndex = loc_index++; location.push(furthestEmployeePickupMale); vehicleEndIndex = loc_index; } vehicle = randomVehicle; vehicle.start_index = vehicleStartIndex; vehicle.end_index = vehicleEndIndex; console.log(JSON.stringify(vehicle)); vehicles.push(vehicle); options = { objective: { travel_cost: "duration", routing: { mode: "car", traffic_timestamp: toEpoch(traffic_timestamp) - 3600 // use traffic 1 hour before delivery } } }; //console.log("SHIPMENTS: ", shipments); //console.log("LOCATIONS: ", locations); //console.log("VEHCILES: ", vehicles); //console.log("OPTIONS: ", options); let result = { vehicles: vehicles, shipments: shipments, locations: locations, options: options }; //let jsonString = JSON.stringify(result); //console.log(jsonString); return result; } }3a:T465,Inputs.table(selectedRouteMaleGroup[0].steps, { columns: [ "type", "load", "description", "arrival", "duration", "service", "location" ], header: { description: "ID, Gender", type: "Task", arrival: "Arrival Time", load: "Nbr Loaded", description: "Employee", duration: "Duration", location: "Location" }, width: { description: 70, type: 50, arrival: 100, load: 80, duration: 100, location: 200 }, //format: { // type: (x) => x.toUpperCase(), // arrival: (x) => // new Date(x * 1000).toLocaleTimeString([], { // hour: "2-digit", // minute: "2-digit", // hour12: false // }), // duration: (x) => (x / 60).toFixed(1) + " mn" //}, format: { type: (x) => x.toUpperCase(), arrival: (x) => new Date(x * 1000).toLocaleTimeString("en-US", { hour: "2-digit", minute: "2-digit", hour12: false, timeZone: "Asia/Kolkata" }), duration: (x) => (x / 60).toFixed(1) + " mn" }, rows: 10, maxWidth: 800, multiple: false, required: false })3b:T680,inputs = { let tomorrowStart = new Date(); tomorrowStart.setDate(new Date().getDate() + 1); tomorrowStart.setHours(6); tomorrowStart.setMinutes(0); tomorrowStart.setSeconds(0); tomorrowStart.setMilliseconds(0); let tomorrowEnd = new Date(); tomorrowEnd.setDate(new Date().getDate() + 1); tomorrowEnd.setHours(9); tomorrowEnd.setMinutes(0); tomorrowEnd.setSeconds(0); tomorrowEnd.setMilliseconds(0); let startISOTime = new Date(tomorrowStart).toISOString().slice(0, -1) + "-04:00"; let endISOTime = new Date(tomorrowEnd).toISOString().slice(0, -1) + "-04:00"; const inputs = { city: Inputs.select(cities, { label: "Select city", format: (x) => x.name, value: cities.find((t) => t.name === "Paris") }), jobs: Inputs.select(jobs, { label: "Number of Employees", value: "25" }), vehicles: Inputs.select(vehicles, { label: "Number of vehicles", value: "10" }), maxDriveTime: Inputs.number({ label: "Max Drive Time (in minutes)", value: 160 }), shiftEnd: Inputs.datetime({ label: "Shift End", value: endISOTime }), 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: "Capacity", value: "6" }) }; return inputs; }3c:T550,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 epochGMT = st.arrival; let ISTOffset = 3.5 * 60 * 60; let epochIST = epochGMT + ISTOffset; let stop = { type: "Feature", properties: { id: rte.vehicle, vin: rte.description, description: st.description, stop_idx: stIdx, arrival: epochIST, 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; }3d:T802,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: ", rte); //console.log("LENGTH: ", rte.pickup[0]); 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 epochGMT = st.arrival; let ISTOffset = 3.5 * 60 * 60; let epochIST = epochGMT + ISTOffset; let colorCode = null; if (trip_type == "drop") { if (st.type == "start" || st.type == "pickup") { colorCode = 16; } else if (st.type == "end" || st.type == "delivery") { colorCode = 255; } } else if (trip_type == "pickup") { if (st.type == "start" || st.type == "pickup") { colorCode = 255; } else if (st.type == "end" || st.type == "delivery") { colorCode = 16; } } let stop_number = stIdx; if (trip_type == "drop") { stop_number = stop_number - parseInt(rte.pickup[0]); } //console.log("STOP NUMBER: ", stop_number); let stop = { type: "Feature", properties: { id: rte.vehicle, vin: rte.description, description: st.description, stop_idx: stop_number, arrival: epochIST, type: st.type, pointType: colorCode }, geometry: { coordinates: [st.location[1], st.location[0]], type: "Point" } }; routesGeoJson2.features.push(stop); }); }); console.log(routesGeoJson2); return routesGeoJson2; }3e:T853,routesGeoJson3 = { let routesGeoJson3 = { 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 checkStatusMVRPEscort.result.routes.forEach((rte, idx) => { console.log("IDX: ", idx); console.log("RTE VEHICLE: ", rte.vehicle); console.log("RTE DESCRIPTION: ", rte.description); 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" } }; console.log(feature); console.log(JSON.stringify(feature)); routesGeoJson3.features.push(feature); rte.steps.forEach((st, stIdx) => { let epochGMT = st.arrival; let ISTOffset = 3.5 * 60 * 60; let epochIST = epochGMT + ISTOffset; let colorCode = null; if (trip_type == "drop") { if (st.type == "start" || st.type == "pickup") { colorCode = 16; } else if (st.type == "end" || st.type == "delivery") { colorCode = 255; } } else if (trip_type == "pickup") { if (st.type == "start" || st.type == "pickup") { colorCode = 255; } else if (st.type == "end" || st.type == "delivery") { colorCode = 16; } } let stop_number = stIdx; if (trip_type == "drop") { stop_number = stop_number - parseInt(rte.pickup[0]); } let stop = { type: "Feature", properties: { id: rte.vehicle, vin: rte.description, description: st.description, stop_idx: stop_number, arrival: epochIST, type: st.type, pointType: colorCode }, geometry: { coordinates: [st.location[1], st.location[0]], type: "Point" } }; routesGeoJson3.features.push(stop); }); }); console.log(routesGeoJson3); return routesGeoJson3; }3f:T88b,routesGeoJson4 = { let routesGeoJson4 = { 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 checkStatusMVRPMaleGroup.result.routes.forEach((rte, idx) => { console.log("IDX: ", idx); console.log("RTE VEHICLE: ", rte.vehicle); console.log("RTE DESCRIPTION: ", rte.description); 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" } }; console.log(feature); console.log(JSON.stringify(feature)); routesGeoJson4.features.push(feature); rte.steps.forEach((st, stIdx) => { let epochGMT = st.arrival; let ISTOffset = 3.5 * 60 * 60; let epochIST = epochGMT + ISTOffset; let colorCode = null; if (trip_type == "drop") { if (st.type == "start" || st.type == "pickup") { colorCode = 16; } else if (st.type == "end" || st.type == "delivery") { colorCode = 255; } } else if (trip_type == "pickup") { if (st.type == "start" || st.type == "pickup") { colorCode = 255; } else if (st.type == "end" || st.type == "delivery") { colorCode = 16; } } let stop_number = stIdx; if (trip_type == "drop") { stop_number = stop_number - parseInt(rte.pickup[0]); } //console.log("STOP NUMBER: ", stop_number); let stop = { type: "Feature", properties: { id: rte.vehicle, vin: rte.description, description: st.description, stop_idx: stop_number, arrival: epochIST, type: st.type, pointType: colorCode }, geometry: { coordinates: [st.location[1], st.location[0]], type: "Point" } }; routesGeoJson4.features.push(stop); }); }); //console.log(routesGeoJson4); return routesGeoJson4; }40:[{"id":"5ed262c38a66f085183bc100afd3197fb87637f5f713e6ba34ff332b6e2adfa6eb83bc2fc7cd76bb6a14e75c3102174a27da3646d06c53e9eca019cab14590ba","href":"https://static.observableusercontent.com/files/5ed262c38a66f085183bc100afd3197fb87637f5f713e6ba34ff332b6e2adfa6eb83bc2fc7cd76bb6a14e75c3102174a27da3646d06c53e9eca019cab14590ba","name":"vehicles_test (1).csv","type":"text/csv","lastModified":1695209137497.821,"size":1850}] 2d:{"title":"Employees Transportation","cells":[{"id":0,"value":"![text](https://nextbillion.ai/wp-content/uploads/2022/01/logo-nb-colored.png)\n\n# Employees Transportation\n# Proof Of Concept\n# Use-Case 1: Pickup & Drop (+ Escort Service)\n","pinned":false,"hidden":false,"mode":"md"},{"id":2518,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":2517,"value":"toc(\"h2, h3, h4\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2516,"value":"---\n","pinned":false,"hidden":false,"mode":"md"},{"id":508,"value":"$2f","pinned":false,"hidden":false,"mode":"md"},{"id":3715,"value":"viewof apiKey = secret2(\"apiKey\", {\n title: \"NextBillion.ai API Key\",\n submit: \"Set\",\n description:\n \"Contact support@nextbillion.ai to get your API key\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":525,"value":"### Loading Custom Data\nBelow we are loading the employee data with the pick up location and shift time along with the Vehicle data with the start location and capacity constraints.\n

","pinned":false,"hidden":false,"mode":"md"},{"id":2024,"value":"viewof panel_random = Inputs.form(inputs, {\n template: (v) => htl.html`\n \n`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3078,"value":"viewof employeesFile = Inputs.file({\n label: \"Employees File\",\n accept: \".csv\",\n required: true\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3082,"value":"employees_test = employeesFile.csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":1236,"value":"---\n","pinned":false,"hidden":false,"mode":"md"},{"id":3105,"value":"viewof table = Inputs.table(employees_test)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2936,"value":"vehicles_test = await FileAttachment(\"vehicles_test (1).csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2490,"value":"### Problem Constraints\n\nHere we are setting a few constrains including the driver maximum drive time and objective function to use.\n","pinned":false,"hidden":false,"mode":"md"},{"id":2038,"value":"viewof panel_mvrp = Inputs.form(inputs, {\n template: (v) => htl.html`\n

\n ${v.maxDriveTime}\n ${v.objectiveFunction}\n

\n\n`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3434,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":2042,"value":"{\n const button_mvrp = html` `;\n button_mvrp.onclick = () => {\n mutable run_optimization = true;\n mutable inputData_a = panel_mvrp;\n button_mvrp.disabled = true;\n };\n return html`${button_mvrp}`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":206,"value":"---\n### Build the Problem Inputs\nWe start to build the needed JSON objects that will be submitted to the MVRP endpoint. In this section, we are building the `jobs` which each consist of employee's pickup locations in the city region. We also create the list of locations that will be referenced by index. Browse the temporary object that will be used further down the page to create the final input object. You can also expand the block to see the Javascript code that created the temporary object.\n\n","pinned":false,"hidden":false,"mode":"md"},{"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"},{"id":221,"value":"mvrpID = {\n let shipments = OptimizationProblem.shipments;\n let vehicles = OptimizationProblem.vehicles;\n let locations = OptimizationProblem.locations;\n let options = OptimizationProblem.options;\n const mvrpRequest = {};\n\n mvrpRequest.description = `MVRP Tutorial ${new Date().toISOString()}`;\n mvrpRequest.locations = locations;\n mvrpRequest.vehicles = vehicles;\n mvrpRequest.shipments = shipments;\n mvrpRequest.options = options;\n console.log(mvrpRequest);\n\n try {\n let resp = await axios.post(\n `https://api.nextbillion.io/optimization/v2?key=${apiKey}`,\n mvrpRequest\n );\n return resp.data;\n } catch (error) {\n return {\n status: \"Error\",\n error: JSON.stringify(error)\n };\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":348,"value":"viewof jobID = htl.html`MVRP Request ID: ${\n mvrpID ? mvrpID.id : mvrpID\n}`","pinned":false,"hidden":false,"mode":"ojs"},{"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

","pinned":false,"hidden":false,"mode":"md"},{"id":720,"value":"html`IMPORTANT
If you get a status message indicating Job still processing just wait a few seconds and press this button.

${viewof requestResult}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":242,"value":"checkStatus = {\n mvrpID;\n await visibility();\n requestResult;\n yield Promises.delay(5000).then(() => {\n let mvrpResult = axios({\n method: \"GET\",\n url: `https://api.nextbillion.io/optimization/v2/result?id=${mvrpID.id}&key=${apiKey}`,\n headers: {}\n })\n .then((res) => res.data)\n .catch((error) => {\n let mvrpResult = {\n status: \"Error\",\n error: JSON.stringify(error)\n };\n });\n return mvrpResult;\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":631,"value":"kpiStats = {\n let deliveryCount = 0;\n checkStatus.result.routes.forEach((rte, rteidx) => {\n rte.steps.forEach((step, index) => {\n if (step.id && step.id != 99) {\n ++deliveryCount;\n }\n });\n });\n return {\n totalDistance: `${(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 unassigned: checkStatus.result.summary.unassigned\n };\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2414,"value":"### KPIs for all Vehicles","pinned":false,"hidden":false,"mode":"md"},{"id":645,"value":"| KPI | Value |\n| ------ | -------- |\n| Total Distance | ${kpiStats.totalDistance} |\n| Total Time | ${kpiStats.totalTime} |\n| Number of Routes | ${kpiStats.nbrRoutes} |\n| Average Employee Per Vehicle | ${kpiStats.avgStops/2}|\n| Unassigned Employee | ${kpiStats.unassigned}","pinned":false,"hidden":false,"mode":"md"},{"id":605,"value":"---\n### Render the intermediate Solution on a Map\nThis map shows groups of employees in the count of 4 assigned to each vehicle and the optimised route generated to pick them up from home and drop at the office within the shift time which is given as a constraint.","pinned":false,"hidden":false,"mode":"md"},{"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"},{"id":1676,"value":"green marker is the vehicle start location (the office) and red markers are employees pickup locations. You can click on the markers to get details.","pinned":false,"hidden":false,"mode":"md"},{"id":997,"value":"{\n await visibility();\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"},{"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: \"ID, Gender\",\n duration: \"Duration\"\n },\n width: {\n type: 80,\n arrival: 120,\n load: 80,\n duration: 100\n },\n format: {\n type: (x) => x.toUpperCase(),\n arrival: (x) =>\n new Date(x * 1000).toLocaleTimeString(\"en-US\", {\n hour: \"2-digit\",\n minute: \"2-digit\",\n hour12: false,\n timeZone: \"Asia/Kolkata\"\n }),\n duration: (x) => (x / 60).toFixed(1) + \" mn\"\n },\n\n rows: 11,\n maxWidth: 600,\n multiple: false,\n required: false\n})\n\n//.toLocaleString(\"en-US\", { timeZone: \"Asia/Calcutta\" });","pinned":false,"hidden":false,"mode":"ojs"},{"id":1898,"value":"selectedRoute = checkStatus.result.routes.filter(f => f.description === routeSelector.properties.vin)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2359,"value":"selectedRoute2 = checkStatus2.result.routes.filter(\n (f) => f.description === routeSelector2.properties.vin\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2743,"value":"### Visualize All Routes Using Optimizer \nIf you want to visualize the optimized routing solution along with the sequencing of the stops, the NB.ai Optimization Tester tool is available at NB.AI MVRP Playground App","pinned":false,"hidden":false,"mode":"md"},{"id":2164,"value":"---\n### Journey Optimisation\n\nHere we want to set the furthest employee from the office to be the start location of the driver's 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 vehicle.","pinned":false,"hidden":false,"mode":"md"},{"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 employees. 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"},{"id":2191,"value":"distanceMatrix = {\n let destinations = \"\";\n let origins = officeLocation;\n let job_index = 0;\n selectedRoute[0].steps.forEach((step) => {\n if (trip_type == \"pickup\" && step.type == \"pickup\") {\n if (job_index != 0) {\n destinations += \"|\";\n }\n destinations += step.location[0] + \",\" + step.location[1];\n job_index++;\n }\n if (trip_type == \"drop\" && step.type == \"delivery\") {\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"},{"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"},{"id":2218,"value":"furthestEmployeeStep = {\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}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":3532,"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].location.join(\",\");\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4034,"value":"furthestEmployeeStepDrop = {\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 let start_index = selectedRoute[0].steps.length / 2;\n step_index = highest_dist_index + start_index;\n return selectedRoute[0].steps[step_index];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3591,"value":"furthestEmployeeDrop = {\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 let start_index = selectedRoute[0].steps.length / 2;\n step_index = highest_dist_index + start_index;\n return selectedRoute[0].steps[step_index].location.join(\",\");\n}","pinned":false,"hidden":false,"mode":"ojs"},{"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 `shipments` which each consist of employee pickup locations in the city region with the main difference that we apply the optimization 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 (for pickups).\n* the driver start location is the office (for drops).","pinned":false,"hidden":false,"mode":"md"},{"id":3075,"value":"inputData_a","pinned":false,"hidden":false,"mode":"ojs"},{"id":3457,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":2289,"value":"mvrpID2 = {\n const selecteMvrpRequest = {\n description: `MVRP Tutorial ${new Date().toISOString()}`,\n locations: OptimizationProblem2.locations,\n vehicles: OptimizationProblem2.vehicles,\n shipments: OptimizationProblem2.shipments,\n options: {\n objective: {\n minimise_num_depots: false\n }\n }\n };\n console.log(JSON.stringify(selecteMvrpRequest));\n\n let resp = axios\n .post(\n `https://api.nextbillion.io/optimization/v2?key=${apiKey}`,\n selecteMvrpRequest\n )\n .then((response) => response.data)\n .catch((response) => response.data);\n return resp;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2292,"value":"viewof jobID2 = htl.html`
Selected MVRP Request ID: ${mvrpID2?mvrpID2.id:mvrpID2}
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2313,"value":"html`
If you get a status message indicating Job still processing just wait a few seconds and press this button.

${viewof requestResult}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2341,"value":"checkStatus2 = {\n mvrpID2;\n //await visibility();\n requestResult;\n yield Promises.delay(5000).then(() => {\n let mvrpResult2 = axios({\n method: \"GET\",\n url: `https://api.nextbillion.io/optimization/v2/result?id=${mvrpID2.id}&key=${apiKey}`,\n headers: {}\n })\n .then((res) => res.data)\n .catch((error) => {\n let mvrpResult = {\n status: \"Error\",\n error: JSON.stringify(error)\n };\n });\n return mvrpResult2;\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2344,"value":"kpiStats2 = {\n let deliveryCount = 0;\n checkStatus2.result.routes.forEach((rte, rteidx) => {\n rte.steps.forEach((step, index) => {\n if (step.id && step.id != 99) {\n ++deliveryCount;\n }\n });\n });\n return {\n totalDistance: `${(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"},{"id":2346,"value":"---\n### Render Improved Solution","pinned":false,"hidden":false,"mode":"md"},{"id":2348,"value":"viewof routeSelector2 = Inputs.select(\n routesGeoJson2.features.filter((f) => f.geometry.type === \"LineString\"),\n {\n multiple: false,\n label: \"Selected route to view\",\n format: (x) => x.properties.vin,\n value: (t) => t.properties.vin\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2354,"value":"{\n await visibility();\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"},{"id":2361,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":2467,"value":"viewof driverDeparture = htl.html`The driver will need to start from the office at ${driverDepartureTime}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2746,"value":"### Visualize Single Route Using Optimizer \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)","pinned":false,"hidden":false,"mode":"md"},{"id":3772,"value":"---\n### Female Escort\nWhen the first pickup from employee's home or last drop to employee's home happen to be between 6pm and 6am and the employee is a female, an escort should be dispatched. We can differentiate between two use-cases: \n\n1- The vehicle is not at full passenger capacity (less than 4 employees). In this case, we add an escort to the vehicle. \n\n2- The vehicle is already at full passenger capacity (4 employees). In this case we create two groups, respectively one group of male employees and one group of female employees to which we assign an escort.\n\n","pinned":false,"hidden":false,"mode":"md"},{"id":4167,"value":"Need to split employees in two vehicles?","pinned":false,"hidden":false,"mode":"md"},{"id":4165,"value":"splitEmployees = {\n if (selectedRoute2[0].steps.length < 10) return false;\n else return true;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3775,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":3852,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":3862,"value":"mvrpEscortID = {\n const selecteMvrpRequest = {\n description: `MVRP Tutorial ${new Date().toISOString()}`,\n locations: OptimizationProblemEscort.locations,\n vehicles: OptimizationProblemEscort.vehicles,\n shipments: OptimizationProblemEscort.shipments,\n options: {\n objective: {\n minimise_num_depots: false\n }\n }\n };\n\n let resp = axios\n .post(\n `https://api.nextbillion.io/optimization/v2?key=${apiKey}`,\n selecteMvrpRequest\n )\n .then((response) => response.data)\n .catch((response) => response.data);\n return resp;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3865,"value":"viewof jobEscort = htl.html`
Selected MVRP Request ID: ${\n mvrpEscortID ? mvrpEscortID.id : mvrpEscortID\n}
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3868,"value":"checkStatusMVRPEscort = {\n mvrpEscortID;\n requestResult;\n yield Promises.delay(5000).then(() => {\n let mvrpResultEscort = axios({\n method: \"GET\",\n url: `https://api.nextbillion.io/optimization/v2/result?id=${mvrpEscortID.id}&key=${apiKey}`,\n headers: {}\n })\n .then((res) => res.data)\n .catch((error) => {\n let mvrpResult = {\n status: \"Error\",\n error: JSON.stringify(error)\n };\n });\n return mvrpResultEscort;\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3885,"value":"kpiStatsEscort = {\n let deliveryCount = 0;\n checkStatusMVRPEscort.result.routes.forEach((rte, rteidx) => {\n rte.steps.forEach((step, index) => {\n if (step.id && step.id != 99) {\n ++deliveryCount;\n }\n });\n });\n return {\n totalDistance: `${(\n checkStatusMVRPEscort.result.summary.distance / 1000\n ).toFixed(1)} km`,\n totalTime: `${(checkStatusMVRPEscort.result.summary.duration / 60).toFixed(\n 1\n )} mn`,\n nbrRoutes: checkStatusMVRPEscort.result.summary.routes,\n avgStops: (\n deliveryCount / checkStatusMVRPEscort.result.summary.routes\n ).toFixed(1)\n };\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3890,"value":"selectedRouteEscort = checkStatusMVRPEscort.result.routes.filter(\n (f) => f.description === routeSelector3.properties.vin\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3925,"value":"### Render Escort Group","pinned":false,"hidden":false,"mode":"md"},{"id":3893,"value":"viewof routeSelector3 = Inputs.select(\n routesGeoJson3.features.filter((f) => f.geometry.type === \"LineString\"),\n {\n multiple: false,\n label: \"Selected route to view\",\n format: (x) => x.properties.vin,\n value: (t) => t.properties.vin\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3897,"value":"{\n return geoviewer(routesFiltered3, {\n width: \"100%\",\n height: \"550\",\n col: \"#8058a0\",\n fillOpacity: 0.9,\n lineWidth: 3,\n style: \"nbai\",\n apiKey: apiKey\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3902,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":3940,"value":"---\n### Assign Male Group to another vehicle (ONLY if full vehicle capacity)\nWe now should assign the male employees to a non assigned vehicle and re-run optimization.","pinned":false,"hidden":false,"mode":"md"},{"id":3945,"value":"### Find available vehicles","pinned":false,"hidden":false,"mode":"md"},{"id":3950,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":3956,"value":"### Build the Problem Statement for the Male Group","pinned":false,"hidden":false,"mode":"md"},{"id":4055,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":4124,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":4045,"value":"furthestEmployeePickupMale = {\n if (splitEmployees == true) {\n if (EscortGroups.male_group.length <= 1) {\n return EscortGroups.male_group[0].location.join(\",\");\n }\n\n let highest_dist = 0;\n let dm_index = distanceMatrixMaleIdx;\n let dm = distanceMatrixMale;\n\n let el1;\n let highest_dist_index;\n for (var i = 0; i < Object.keys(dm_index).length; i++) {\n console.log(\"LOG: \", dm.rows[0][i]);\n el1 = dm.rows[0][i][1];\n if (el1 > highest_dist) {\n highest_dist = el1;\n highest_dist_index = i;\n }\n }\n\n return distanceMatrixMaleIdx[parseInt(highest_dist_index)];\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3960,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":3963,"value":"mvrpMaleGroupID = {\n if (splitEmployees == true) {\n const selecteMvrpRequest = {\n description: `MVRP Tutorial ${new Date().toISOString()}`,\n locations: OptimizationProblemMale.locations,\n vehicles: OptimizationProblemMale.vehicles,\n shipments: OptimizationProblemMale.shipments,\n options: {\n objective: {\n minimise_num_depots: false\n }\n }\n };\n console.log(JSON.stringify(selecteMvrpRequest));\n\n let resp = axios\n .post(\n `https://api.nextbillion.io/optimization/v2?key=${apiKey}`,\n selecteMvrpRequest\n )\n .then((response) => response.data)\n .catch((response) => response.data);\n return resp;\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3966,"value":"viewof jobMaleGroup = htl.html`
Selected MVRP Request ID: ${\n mvrpMaleGroupID ? mvrpMaleGroupID.id : mvrpMaleGroupID\n}
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3974,"value":"checkStatusMVRPMaleGroup = {\n if (splitEmployees == true) {\n mvrpMaleGroupID;\n\n requestResult;\n yield Promises.delay(5000).then(() => {\n let mvrpResultMaleGroup = axios({\n method: \"GET\",\n url: `https://api.nextbillion.io/optimization/v2/result?id=${mvrpMaleGroupID.id}&key=${apiKey}`,\n headers: {}\n })\n .then((res) => res.data)\n .catch((error) => {\n let mvrpResult = {\n status: \"Error\",\n error: JSON.stringify(error)\n };\n });\n return mvrpResultMaleGroup;\n });\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3977,"value":"kpiStatsMaleGroup = {\n if (splitEmployees == true) {\n let deliveryCount = 0;\n checkStatusMVRPMaleGroup.result.routes.forEach((rte, rteidx) => {\n rte.steps.forEach((step, index) => {\n if (step.id && step.id != 99) {\n ++deliveryCount;\n }\n });\n });\n return {\n totalDistance: `${(\n checkStatusMVRPMaleGroup.result.summary.distance / 1000\n ).toFixed(1)} km`,\n totalTime: `${(\n checkStatusMVRPMaleGroup.result.summary.duration / 60\n ).toFixed(1)} mn`,\n nbrRoutes: checkStatusMVRPMaleGroup.result.summary.routes,\n avgStops: (\n deliveryCount / checkStatusMVRPMaleGroup.result.summary.routes\n ).toFixed(1)\n };\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3980,"value":"selectedRouteMaleGroup = checkStatusMVRPMaleGroup.result.routes.filter(\n (f) => f.description === routeSelector4.properties.vin\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3983,"value":"### Render Male Group","pinned":false,"hidden":false,"mode":"md"},{"id":3985,"value":"viewof routeSelector4 = Inputs.select(\n routesGeoJson4.features.filter((f) => f.geometry.type === \"LineString\"),\n {\n multiple: false,\n label: \"Selected route to view\",\n format: (x) => x.properties.vin,\n value: (t) => t.properties.vin\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3991,"value":"{\n return geoviewer(routesFiltered4, {\n width: \"100%\",\n height: \"550\",\n col: \"#8058a0\",\n fillOpacity: 0.9,\n lineWidth: 3,\n style: \"nbai\",\n apiKey: apiKey\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4025,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":2443,"value":"### Conclusion\n\nUsing a multi-steps approach where the optimizer is first used as a pre-processing step to create employee groups, running the distance matrix inside each group to assess who is the furthest employee and finally running the optimization again inside each group, we manage to achieve the base requirements. we were able to:\n* Provide a time to each employee for the pickup.\n* Minimize the time spend in the vehicle.\n* Arrive a few minutes before the actual office shift starts.\n\nAdditionally, we provide a solution to the edge case where a woman is pickup first or dropped last by adding an escort.","pinned":false,"hidden":false,"mode":"md"},{"id":1000,"value":"\n\n\n\n---\n### Additional Tools \nIf you want to visualize the optimized routing solution along with the sequencing of the stops, the NB.ai Optimization Tester tool is available. \n\nNB.AI MVRP Playground App (should automatically load the current solution)\n- Select the `SOLUTION` tab\n- Enter the same NB.ai API key used for the MVRP solution `${apiKey}`\n- Paste the MVRP Request ID into the input box `${mvrpID.id}`\n- Press the `Load` button","pinned":false,"hidden":false,"mode":"md"},{"id":341,"value":"### Other Dependencies","pinned":false,"hidden":false,"mode":"md"},{"id":1139,"value":"import {nextbillion,cities,secret2} from \"@yvannbai/nextbillion-ai-helpers\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":334,"value":"axios = {\n let axios = await require('axios');\n return axios;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":352,"value":"styling = html`\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":849,"value":"geoviewer = require(\"https://static.nextbillion.io/nbai-demo/nb_geoverview2.js\").then(\n (f) => f.view\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1613,"value":"html`${viewof requestResult}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1005,"value":"viewof requestResult = Inputs.button(\"Request Result\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1686,"value":"mutable inputData_a = null","pinned":false,"hidden":false,"mode":"ojs"},{"id":2051,"value":"mutable run_optimization = false;","pinned":false,"hidden":false,"mode":"ojs"},{"id":1098,"value":" jobs = [\"5\",\"10\",\"25\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1111,"value":"vehicles = [\"5\",\"10\",\"20\",\"50\",\"100\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1163,"value":"useCases = [\n {name: \"Delivery\", value: \"delivery\"},\n {name: \"Service Worker\", value: \"service\"}\n ]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1175,"value":"objectives = [\n {name: \"Minimize Time\", value: \"duration\"},\n {name: \"Minimize Distance\", value: \"distance\"}\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":2241,"value":"officeLocation = {\n return \"12.978527,77.665694\";\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3441,"value":"traffic_timestamp = {\n if (employees_test[0][\"trip_type\"] == \"pickup\")\n return employees_test[0][\"delivery_end_time\"];\n else return employees_test[0][\"pickup_end_time\"];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3462,"value":"trip_type = {\n //console.log(\"TEST: \", employees_test[1][\"trip_type\"]);\n return employees_test[0][\"trip_type\"];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3472,"value":"delivery_start_time = {\n return employees_test[0][\"delivery_start_time\"];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3476,"value":"delivery_end_time = {\n return employees_test[0][\"delivery_end_time\"];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3480,"value":"pickup_start_time = {\n return employees_test[0][\"pickup_start_time\"];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3484,"value":"pickup_end_time = {\n return employees_test[0][\"pickup_end_time\"];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3519,"value":"service_time = {\n return employees_test[1][\"service\"];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3529,"value":"vehicle_capacity = {\n return vehicles_test[1][\"capacity\"];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3624,"value":"function toEpoch(timeString) {\n // Split date and time\n const [date, time] = timeString.split(\" \");\n\n // Split year, month, day\n const [year, month, day] = date.split(\"-\");\n\n // Split hour, minute, second\n const [hour, minute, second] = time.split(\":\");\n\n // Create date object in UTC\n // Note: JavaScript's month is 0-indexed\n const dateObj = new Date(\n Date.UTC(year, month - 1, day, hour - 5, minute - 30, second)\n );\n\n // Convert to epoch time (in seconds)\n const epoch = Math.floor(dateObj.getTime() / 1000);\n\n return epoch;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2473,"value":"driverDepartureTime = {\n let departureEpoch =\n parseInt(selectedRoute2[0].steps[0].arrival) -\n parseInt(furthestEmployeeStep.duration);\n let humanReadableTime = new Date(departureEpoch * 1000).toLocaleString(\n \"en-US\",\n { timeZone: \"Asia/Kolkata\" }\n );\n return humanReadableTime;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1523,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":924,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":2351,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":3895,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs"},{"id":3988,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs"},{"id":1279,"value":"routesFiltered = Object({\n type: 'FeatureCollection',\n features: routesGeoJson.features.filter(f => f.properties.vin === routeSelector.properties.vin)\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":2356,"value":"routesFiltered2 = Object({\n type: 'FeatureCollection',\n features: routesGeoJson2.features.filter(f => f.properties.vin === routeSelector.properties.vin)\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":3899,"value":"routesFiltered3 = Object({\n type: \"FeatureCollection\",\n features: routesGeoJson3.features.filter(\n (f) => f.properties.vin === routeSelector.properties.vin\n )\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3996,"value":"routesFiltered4 = Object({\n type: \"FeatureCollection\",\n features: routesGeoJson4.features\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2511,"value":"import { toc } from \"@nebrius/indented-toc\" // This component generates the Table of Contents","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W40","stdlib":"1","titling":"auto"}

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

Selected MVRP Request ID: ${\n mvrpEscortID ? mvrpEscortID.id : mvrpEscortID\n}

Selected MVRP Request ID: ${\n mvrpMaleGroupID ? mvrpMaleGroupID.id : mvrpMaleGroupID\n}