2d:null 2e:["$","title",null,{"children":"Auto-Carrier Transportation Problem Study (ATP) | NB.AI Solutions Team | Observable"}] 33:T724,function generateAlternativeCapacities(compartments, fuelTypeCount) { const sum = compartments.reduce((a, b) => a + b, 0); const perms = (arr) => { if (arr.length <= 1) return [arr]; const result = []; for (let i = 0; i < arr.length; i++) { const rest = [...arr.slice(0, i), ...arr.slice(i + 1)]; for (const p of perms(rest)) { result.push([arr[i], ...p]); } } return result; }; const unique = new Set(); const output = []; const addUnique = (config) => { const key = config.join(","); if (!unique.has(key)) { unique.add(key); output.push(config); } }; // A. Permutations across all fuel types (1-to-1 fuel type per compartment) const compPerms = perms(compartments); compPerms.forEach((perm) => { if (perm.length <= fuelTypeCount) { const config = new Array(fuelTypeCount).fill(0); perm.forEach((vol, i) => { config[i] = vol; }); addUnique(config); } }); // B. All compartments same fuel type for (let i = 0; i < fuelTypeCount; i++) { const config = new Array(fuelTypeCount).fill(0); config[i] = sum; addUnique(config); } // C. Two compartments same fuel type, one different for (let i = 0; i < compartments.length; i++) { for (let j = i + 1; j < compartments.length; j++) { const k = compartments.findIndex((_, idx) => idx !== i && idx !== j); const vol1 = compartments[i] + compartments[j]; const vol2 = compartments[k]; for (let a = 0; a < fuelTypeCount; a++) { for (let b = 0; b < fuelTypeCount; b++) { if (a === b) continue; const config = new Array(fuelTypeCount).fill(0); config[a] = vol1; config[b] = vol2; addUnique(config); } } } } return output; }34:T9a5,md`

Sample Data

Focusing on a regional solution for simplicity i.e. US Midwest

Vehicles to Transport

Consists of a random set of 200 vehicles to be delivered from random locations and random number of vehicles (2-4) at each location. Each vehicle is set with random profiles of weight, height, length and width. In this study we will simplify the vehicles to 3 OEM's (Honda, Toyota, Ford) and 3 models each (Truck, Sedan, Hatchback).

Consider setup time at each location and service time to load each vehicle.

Consider pickup time window(s) for each location

| | |Honda ||| Toyota ||| Ford | |----------------|------------------------------|------------------------------|------------------------------| | | Ridgeline | Accord | Fit | Tundra | Camry | Yaris | F350 | Focus | Fiesta | | | Truck | Sedan | HB | Truck | Sedan | HB | Truck | Sedan | HB | | **Weight (lbs)** | 6,050 | 3,216 | 2,496 | 6,800 | 3,190 | 2,295 | 9,900 | 2,097 | 3,620 | | **Length (inches)** | 207 | 195 | 162 | 229 | 189 | 154 | 233 | 179 | 160 | | **Height (inches)** | 70 | 58 | 60 | 76 | 58 | 59 | 77 | 58 | 58 | | **Width (inches)** | 78 | 73 | 67 | 80 | 72 | 67 | 80 | 72 | 68 |

Delivery Location Data

Dealer locations for the vehicle Delivery time window(s)

Auto-Carrier (truck) Data

Define the start and ending location for the auto-carrier. Define the planning period for the truck Define the hours-of-service requirements Capacity configuration, alternative capacity configurations Empty auto-carrier weight ~40,000 lbs and max weight 80,000 lbs

`35:T573,viewof kpiTable = { let kpis = []; if (!mvrpID) { return; } mvrpID.forEach((sol) => { kpis.push(sol.kpis); }); const kpiTable = Inputs.table(kpis, { columns: [ "nbrRoutes", "unassigned", "totalTime", "totalDistance", "avgStops" ], header: { nbrRoutes: "Routes", unassigned: "Unassigned", totalTime: "Total Time", totalDistance: "Total Distance", avgStops: "Avg. Stops" }, width: { nbrRoutes: 100, unassigned: 80, totalTime: 120, totalDistance: 120, avgStops: 120 }, format: { unassigned: (x) => html`

${ x / 2 }

`, nbrRoutes: (x) => html`

${x}

`, totalTime: (x) => html`

${secondsToHHmm( parseInt(x.split(" ")[0] * 60) )}

`, totalDistance: (x) => html`

${parseInt( x.split(" ")[0] * 0.62 )} miles

` }, maxWidth: 1100, multiple: false, required: false }); return kpiTable; }36:T9b3,viewof theRouteTable = { if (!mvrpID) { return; } mvrpID[0].result.routes.forEach((r) => { r.cost += (r.distance / 1000) * inputData_a.costPerMile; }); const myTable = Inputs.table(mvrpID[0].result.routes, { columns: [ "vehicle", "steps", "description", "duration", "distance", "setup", "service", "waiting_time", "cost" ], header: { vehicle: "Veh", steps: "Stops", duration: "Drive Time", setup: "Setup Time", service: "Service Time", waiting_time: "Wait Time", distance: "Driving Distance", description: "Start Location", cost: "Cost" }, width: { vehicle: 50, steps: 45, duration: 120, distance: 120, setup: 120, service: 120, waiting_time: 120, description: 150, cost: 60 }, format: { cost: (x) => html`

$${x.toFixed( 0 )}

`, steps: (x) => html`

${x.length}

`, duration: (x) => html`

${secondsToHHmm( x )}

`, service: (x) => html`

${secondsToHHmm( x )}

`, setup: (x) => html`

${secondsToHHmm( x )}

`, waiting_time: (x) => html`

${secondsToHHmm( x )}

`, vehicle: (x) => html`

${x}

`, description: (x) => html`

${ x.split("-")[1] }

`, distance: (x) => html`

${( x * 0.000621371 ).toFixed(0)}

` }, rows: 5, maxWidth: 1200, multiple: false, required: true, value: mvrpID[0].result.routes[0] }); return myTable; }37:T97f,viewof theStepTable = { if (!mvrpID) { return; } let idx = 0; let transformedData = theRouteTable.steps.map((step) => { const [vin, make, location] = step.description ? step.description.split("|") : ["", "", ""]; idx += 1; return { ...step, // Keep existing properties idx, vin, make, location // Add new fields }; }); let myTable = Inputs.table(transformedData, { columns: [ "type", "arrival", "departure", "vin", "make", "location", "load" ], header: { type: "Task", arrival: "Arrival", departure: "Departure", vin: "VIN", make: "Make/Model", location: "Location", load: "Load" }, width: { type: 50, vin: 60, arrival: 120, departure: 120, location: 450 }, format: { type: (x) => html`

${x.toUpperCase()}

`, arrival: (x) => html`

${new Date( x * 1000 ).toLocaleTimeString([], { month: "2-digit", day: "2-digit", hour: "2-digit", minute: "2-digit", hour12: false })}

`, vin: (x) => html`

${x}

`, make: (x) => html`

${x}

`, location: (x) => html`

${x}

`, description: (x) => html`

${x}

`, load: (x) => html`

${ x[0] } ${x[1].toLocaleString()}lbs

`, departure: (x) => html`

${new Date( x * 1000 ).toLocaleTimeString([], { month: "2-digit", day: "2-digit", hour: "2-digit", minute: "2-digit", hour12: false })}

` }, rows: 5, value: transformedData[0], maxWidth: 1200, multiple: false, required: false }); return myTable; }38:T493,viewof theUnassignedTable = { if (!mvrpID || !mvrpID[0].result.unassigned) { return; } const myTable = Inputs.table(mvrpID[0].result.unassigned, { columns: ["id", "type", "reason"], header: { id: "Desc.", type: "Type", reason: "Unassigned Reason" }, width: { id: 350, type: 60, reason: 250, setup: 120, service: 120, waiting_time: 120, description: 200 }, format: { id: (x) => html`

${x}

`, type: (x) => html`

${x}

`, reason: (x) => html`

${x}

`, distance: (x) => html`

${( x * 0.000621371 ).toFixed(0)}

` }, rows: 10, maxWidth: 1200, multiple: false, required: true, value: mvrpID[0].result.unassigned[0] }); return myTable; }39:T90e,selectedStop = { if (!mvrpID) { return; } if (theStepTable) { // Remove existing stop layers and sources if they exist if (nbmap.map.getLayer("stop-layer")) { nbmap.map.removeLayer("stop-layer"); } if (nbmap.map.getLayer("stop-label-layer")) { nbmap.map.removeLayer("stop-label-layer"); } if (nbmap.map.getSource("stop-source")) { nbmap.map.removeSource("stop-source"); } // Define GeoJSON source with properties for labeling nbmap.map.addSource("stop-source", { type: "geojson", data: { type: "FeatureCollection", features: [ { type: "Feature", geometry: { coordinates: [ theStepTable.snapped_location[1], theStepTable.snapped_location[0] ], type: "Point" }, properties: { stopIdx: theStepTable.idx, stopType: theStepTable.type } } ] }, cluster: false }); // Add circle marker layer nbmap.map.addLayer({ id: "stop-layer", type: "circle", source: "stop-source", paint: { "circle-color": [ "case", ["==", ["get", "stopType"], "pickup"], "#1cc117", // Green for pickup ["==", ["get", "stopType"], "delivery"], "#ff0000", // Red for delivery "#1cc117" // Default green ], "circle-radius": 15, "circle-stroke-width": 1.5, "circle-stroke-color": "#000" } }); // Add text label layer nbmap.map.addLayer({ id: "stop-label-layer", type: "symbol", source: "stop-source", layout: { "text-field": ["get", "stopIdx"], // Get stopType from properties "text-size": 14, "text-font": ["Open Sans Bold", "Arial Unicode MS Bold"], "text-anchor": "center" }, paint: { "text-color": "#000", "text-halo-color": "#fff", "text-halo-width": 2 } }); return theStepTable; } else { if (nbmap.map.getLayer("stop-layer")) { nbmap.map.removeLayer("stop-layer"); } if (nbmap.map.getLayer("stop-label-layer")) { nbmap.map.removeLayer("stop-label-layer"); } } }3a:Tcc2,function updateRouteOnMap() { if (!mvrpID) { return; } // Remove existing route layer if it exists if (nbmap.map.getLayer("route-layer")) { nbmap.map.removeLayer("route-layer"); nbmap.map.removeSource("route-source"); } // Ensure we have a valid route if (!theRouteTable) return; let unescapedPoly = theRouteTable ? theRouteTable.geometry : ""; let decodedPoly = { type: "FeatureCollection", features: [ { type: "Feature", properties: {}, geometry: { coordinates: nextbillion.utils.polyline .decode(unescapedPoly, 5) .map((c) => c.reverse()), type: "LineString" } } ] }; const firstLabelLayerId = nbmap.map .getStyle() .layers.find((layer) => layer.type === "symbol").id; if (!nbmap.map.getSource("route-source")) { nbmap.map.addSource("route-source", { type: "geojson", data: null }); } // Add new route layer nbmap.map.getSource("route-source").setData(decodedPoly); // Remove the route layers if they already exist if (nbmap.map.getLayer("route-casing")) { nbmap.map.removeLayer("route-casing"); } if (nbmap.map.getLayer("route-inner")) { nbmap.map.removeLayer("route-inner"); } if (nbmap.map.getLayer("route-arrows")) { nbmap.map.removeLayer("route-arrows"); } nbmap.map.addLayer( { id: "route-casing", type: "line", source: "route-source", layout: { "line-join": "round", "line-cap": "round" }, paint: { "line-color": "#cf9ae2", // Outer color (casing) "line-width": 10 // Outer line thickness } }, firstLabelLayerId ); // Add inner route line (on top of casing) nbmap.map.addLayer( { id: "route-inner", type: "line", source: "route-source", layout: { "line-join": "round", "line-cap": "round" }, paint: { "line-color": "#ffffff", // Inner color (white) "line-width": 6 // Inner line thickness } }, firstLabelLayerId ); // Add directional arrows along the route nbmap.map.addLayer({ id: "route-arrows", type: "symbol", source: "route-source", layout: { "symbol-placement": "line", // Place arrows along the line "symbol-spacing": 250, // Adjust the spacing between arrows "icon-image": "arrow-icon", // Use a custom arrow icon "icon-size": 0.35, // Adjust the size of the arrows "icon-allow-overlap": true, // Allow the arrows to overlap with other features "icon-rotation-alignment": "map", // Align arrows to follow the route direction "icon-rotate": 0 // Use the route's bearing to rotate arrows } }); let coordinates = decodedPoly.features[0].geometry.coordinates; let bounds = coordinates.reduce(function (bounds, coord) { return bounds.extend(coord); }, new nextbillion.maps.LngLatBounds(coordinates[0], coordinates[0])); // Ensure the bounds have valid data before fitting if (!bounds.isEmpty()) { nbmap.map.fitBounds(bounds, { padding: 50, // Adds padding around the route for better visibility duration: 1000 // Smooth animation duration in milliseconds }); } }3b:T701,function addAuctionLocations(nbmap) { if (nbmap.map.getLayer("auctions-layer")) { nbmap.map.removeLayer("auctions-layer"); } // Remove old source if exists if (nbmap.map.getSource("auctions")) { nbmap.map.removeSource("auctions"); } const filteredAuctionHouses = { type: "FeatureCollection", features: auctionHouses.features.filter( (feature) => inputData_a.depotCities.includes(feature.properties.name) || inputData_a.destinationDepots.includes(feature.properties.name) ) }; nbmap.map.addSource("auctions", { type: "geojson", data: auctionHouses, cluster: false }); // Add clustered points layer nbmap.map.addLayer({ id: "auctions-layer", type: "circle", source: "auctions", paint: { "circle-color": "#1cc117", "circle-radius": 8, "circle-stroke-width": 1.5, "circle-stroke-color": "#000" } }); // Add popups on hover for unclustered yards let popup = new nextbillion.maps.Popup({ closeButton: false, closeOnClick: false }); function showPopup(event) { const feature = event.features[0]; popup .setLngLat(feature.geometry.coordinates) .setHTML( `

${feature.properties.name}
${feature.properties.location}

` ) .addTo(nbmap.map); } function hidePopup() { popup.remove(); } nbmap.map.on("mouseenter", "auctions-layer", showPopup); nbmap.map.on("mouseleave", "auctions-layer", hidePopup); }3c:Tc5d,function addDealerLocations(nbmap) { if (nbmap.map.getLayer("dealers-layer")) { nbmap.map.removeLayer("dealers-layer"); } const dealersGeoJSON = { type: "FeatureCollection", features: midwestDealers.map((y) => { return { type: "Feature", properties: { type: "dealer", id: y.id, name: y.name, address: `${y.address.streetAddress} ${y.address.addressLocality}, ${y.address.addressRegion}` }, geometry: { type: "Point", coordinates: [parseFloat(y.geo.longitude), parseFloat(y.geo.latitude)] } }; }) }; // Remove old source if exists if (nbmap.map.getSource("dealers")) { nbmap.map.removeSource("dealers"); } nbmap.map.addSource("dealers", { type: "geojson", data: dealersGeoJSON, cluster: false }); // Add clustered points layer nbmap.map.addLayer({ id: "dealers-layer", type: "circle", source: "dealers", paint: { "circle-color": "#ea4335", "circle-radius": 4, "circle-stroke-width": 1.5, "circle-stroke-color": "#fff" } }); // Add popups on hover for unclustered dealers let popup = new nextbillion.maps.Popup({ closeButton: false, closeOnClick: false }); function showPopup(event) { const feature = event.features[0]; const iconUrl = feature.properties.name.includes("Ford") ? "https://brandlogos.net/wp-content/uploads/2015/01/ford-logo.png" : feature.properties.name.includes("Toyota") ? "https://e7.pngegg.com/pngimages/116/77/png-clipart-toyota-corolla-car-toyota-rav4-honda-logo-toyota-emblem-text-thumbnail.png" : "https://www.honda.com/-/media/Honda-Homepage/Images/Logos/svg/Honda_Logo.svg?sc_lang=en"; popup .setLngLat(feature.geometry.coordinates) .setHTML( `

${feature.properties.name}
${feature.properties.address}

` ) .addTo(nbmap.map); } function hidePopup() { popup.remove(); } nbmap.map.on("mouseenter", "dealers-layer", showPopup); nbmap.map.on("mouseleave", "dealers-layer", hidePopup); // Adjust map to center on the middlemost point with zoom level 8 if (dealersGeoJSON.features.length > 0) { // Calculate the average latitude and longitude (geometric center) const totalPoints = dealersGeoJSON.features.length; const sumCoordinates = dealersGeoJSON.features.reduce( (sum, feature) => { sum[0] += feature.geometry.coordinates[0]; // Sum of longitudes sum[1] += feature.geometry.coordinates[1]; // Sum of latitudes return sum; }, [0, 0] ); const centerLng = sumCoordinates[0] / totalPoints; const centerLat = sumCoordinates[1] / totalPoints; // Set the map center and zoom level nbmap.map.easeTo({ center: [centerLng, centerLat], zoom: 4, // Set initial zoom to 8 for better clustering view duration: 1000 // Smooth transition }); } }3d:T18ad,mvrpID = { function generateTimeSpans(timestamps) { const CST_OFFSET = -6 * 60 * 60; // CST is UTC-6 const startHour = 13; const shiftDuration = 10; const spans = []; // Get the start and end timestamps const startDate = new Date(timestamps[0] * 1000); const endDate = new Date(timestamps[1] * 1000); // Get the UTC midnight for the first and last days let currentDayUTC = timestamps[0]; const lastDayUTC = timestamps[1]; // Iterate through each day from start to end while (currentDayUTC <= lastDayUTC) { const startTime = currentDayUTC; const endTime = currentDayUTC + shiftDuration * 60 * 60; spans.push([startTime, endTime]); // Move to the next day currentDayUTC = endTime + (24 - shiftDuration) * 60 * 60; } return spans; } if (inputData_a) { let location = []; //V2 API let loc_index = 0; let shipments = []; let jobs = []; let vehicles = []; let depots = []; let locations = []; let bodyData = []; let nbrVehicles = 1; vehicles = []; shipments = []; depots = []; let relations = []; let end_depot_ids = []; locations = []; location = []; loc_index = 0; if (inputData_a.destinationDepots) { let depotIdx = 99; inputData_a.destinationDepots.forEach((d) => { const filteredDepot = auctionHouses.features.filter((feature) => [d].includes(feature.properties.name) ); if (filteredDepot) { let depot = { id: depotIdx.toString(), location_index: loc_index++, description: filteredDepot[0].properties.location }; depots.push(depot); end_depot_ids.push(depotIdx.toString()); depotIdx++; location.push( `${filteredDepot[0].geometry.coordinates[1].toString()},${filteredDepot[0].geometry.coordinates[0].toString()}` ); } }); } let currentDepotId = 0; trucks.forEach((v, idx) => { if (v.depot != currentDepotId) { currentDepotId = v.depot; let depot = { id: currentDepotId.toString(), location_index: loc_index++, description: v.location }; depots.push(depot); location.push(`${v.latitude.toString()},${v.longitude.toString()}`); } let vehicleCost = { fixed: parseInt(inputData_a.fixedCosts) }; if (inputData_a.objectiveFunction.name === "Minimize Time") { vehicleCost.per_hour = parseInt(inputData_a.costPerHour); } else { vehicleCost.per_km = parseInt(inputData_a.costPerMile); } let vehicle = { id: v.id, description: `${v.id}-${v.location}`, start_depot_ids: [currentDepotId.toString()], end_depot_ids: end_depot_ids, capacity: [9, 40000], costs: vehicleCost, max_tasks: inputData_a.maxTasks === "0" ? null : parseInt(inputData_a.maxTasks) + 1, max_distance: inputData_a.maxDistance === "0" ? null : parseInt(inputData_a.maxDistance) * 1609.34, max_working_time: inputData_a.maxWorkingHrs === "0" ? null : parseInt(inputData_a.maxWorkingHrs) * 3600, time_window: [ Date.parse(inputData_a.shiftStart) / 1000, Date.parse(inputData_a.shiftEnd) / 1000 ] }; vehicles.push(vehicle); }); const timeWindows = inputData_a.customConstraints.includes("Time Windows") ? generateTimeSpans(vehicles[0].time_window) : [[]]; midwestDealers.forEach((d) => { d.orders.forEach((o, idx) => { const shipment = { id: `${d.name}-${idx}`, description: `${o.name}-${idx}`, //revenue: o.revenue, pickup: { id: `Load: ${o.origin.name} - ${o.id}`, description: `${o.id}|${o.make} ${o.model}|${o.origin.name}`, location_index: loc_index++, service: 1800, setup: 3600, time_windows: timeWindows }, delivery: { id: `Drop: ${o.destination.name} - ${o.id}`, description: `${o.id}|${o.make} ${o.model}|${o.destination.name}-${o.destination.address}`, location_index: loc_index++, service: 1800, time_windows: timeWindows }, amount: [1, o.weight], follow_lifo_order: inputData_a.customConstraints.includes("LIFO") ? true : false }; location.push( `${o.origin.location.latitude},${o.origin.location.longitude}` ); location.push( `${o.destination.location.latitude},${o.destination.location.longitude}` ); shipments.push(shipment); }); }); locations = { id: 12, location: location }; bodyData.push({ description: `Auto-Carrier Test ${new Date().toISOString()}`, locations: { id: 1, location: location }, vehicles: vehicles, shipments: shipments, depots: depots, options: { travel_cost: inputData_a.objectiveFunction, minimise_num_depots: false, routing: { mode: "truck" } } }); jobs = []; vehicles = []; location = []; //} //if (inputData_a.customObjectives) { // if (inputData_a.customObjectives[""]) //} let options = { travel_cost: inputData_a.objectiveFunction, minimise_num_depots: inputData_a.customObjectives.includes( "Minimize_Depots" ) ? true : false, objective: { solver_mode: "internal", travel_cost: "duration" //custom: { // type: "min-max", // value: "tasks" //} }, //grouping: { // order_grouping: { // diameter: 250 // } //}, routing: { mode: "truck", avoid: inputData_a.avoids //traffic_timestamp: Date.parse(inputData_a.shiftEnd) / 1000 } }; const result = await Promise.all( bodyData.map(async (data) => { return mvrp(data, options); }) ); return result; } }3e:T71c,function generateRandomOrders(dealer, allDealers) { const orders = []; const numOrders = Math.floor(Math.random() * 4) + 1; // 1-4 orders // Vehicle models and their corresponding weights const modelWeights = { Ridgeline: 6050, Accord: 3216, Fit: 2496, Tundra: 6800, Camry: 3190, Yaris: 2295, F350: 9900, Focus: 2097, Fiesta: 3620 }; // Vehicle makes and models mapping const modelMap = { Ford: ["F350", "Focus", "Fiesta"], Toyota: ["Tundra", "Camry", "Yaris"], Honda: ["Ridgeline", "Accord", "Fit"] }; for (let i = 0; i < numOrders; i++) { const destinationIndex = Math.floor(Math.random() * allDealers.length); const make = faker.helpers.arrayElement(Object.keys(modelMap)); const model = faker.helpers.arrayElement(modelMap[make]); const weight = modelWeights[model]; // Get weight for the selected model const revenue = Math.floor(Math.random() * (20000 - 15500)) + 15500; const auctionIdx = Math.floor(Math.random() * 10) + 1; orders.push({ id: faker.vehicle.vin(), make, model, weight, revenue, origin: { location: { latitude: auctionHouses.features[auctionIdx].geometry.coordinates[1], longitude: auctionHouses.features[auctionIdx].geometry.coordinates[0] }, name: `${auctionHouses.features[auctionIdx].properties.name} - ${auctionHouses.features[auctionIdx].properties.location}` }, destination: { location: allDealers[destinationIndex].geo, name: allDealers[destinationIndex].name, address: `${allDealers[destinationIndex].address.streetAddress} ${allDealers[destinationIndex].address.addressLocality}, ${allDealers[destinationIndex].address.addressRegion}` } }); } return orders; }3f:T458,auctionHouses = { let ah = await FileAttachment("auction_houses@2.json").json(); async function geocodedAuctionHouses(ah) { let geocodes = []; for (let i = 0; i < ah.length; i++) { let resp = await fetch( `https://api.nextbillion.io/geocode?key=${apiKey}&q=${ah[i].location}&at=32.77886290735464,-96.80654219674012&in=countryCode:USA&limit=1` ).then(); let geo = await resp.json(); let result = { geocode: `${geo.items[0].position.lat},${geo.items[0].position.lng}`, ...ah[i] }; geocodes.push(await result); } return geocodes; } const ahGeocoded = await geocodedAuctionHouses(ah); return { type: "FeatureCollection", features: ahGeocoded.map((auction) => { const [lat, lng] = auction.geocode.split(",").map(Number); return { type: "Feature", geometry: { type: "Point", coordinates: [lng, lat] // GeoJSON format uses [longitude, latitude] }, properties: { name: auction.name, location: auction.location } }; }) }; }40:T67c,async function mvrp(data, options) { let url = ""; url = `https://api.nextbillion.io/optimization/v2`; progress.value = 0; const optimizeRequest = await axios .post(`${url}` + `?key=${apiKey}`, { ...data, options }) .then((res) => res.data); const mvrpRequestId = optimizeRequest.id; let attempts = 0; while (attempts < 150) { const result = await axios({ url: `${url}/result?id=${mvrpRequestId}&key=${apiKey}`, method: "GET", headers: {} }); if ( result.data && result.data.status === "Ok" && result.data.message == "" ) { result.data.id = mvrpRequestId; // KPIs let deliveryCount = 0; result.data.result.routes.forEach((rte, rteidx) => { rte.steps.forEach((step, index) => { step.departure = step.arrival + step.service; if (step.id && step.id != 99) { ++deliveryCount; } }); }); result.data.kpis = { totalDistance: `${(result.data.result.summary.distance / 1000).toFixed( 1 )} km`, totalTime: `${(result.data.result.summary.duration / 60).toFixed( 1 )} mn`, nbrRoutes: result.data.result.summary.routes, avgStops: (deliveryCount / result.data.result.summary.routes).toFixed( 1 ), unassigned: result.data.result.summary.unassigned, frontSeatWeight: result.data.description.split("|")[1] }; progress.value = 0; return result.data; } attempts++; progress.value += 2; await new Promise((resolve) => setTimeout(resolve, 5 * 1000)); } }41:T744,styling = html` `42:T1376,inputs = { function parseDateStringWithOffset(dateString, offsetMinutes) { const [datePart, timePart] = dateString.split(" "); const [day, month, year] = datePart.split("/").map(Number); const [hours, minutes] = timePart.split(":").map(Number); // Create a Date object in the local time zone const localDate = new Date(year, month - 1, day, hours, minutes); // Convert local time to UTC const utcDate = new Date( localDate.getTime() - localDate.getTimezoneOffset() * 60000 ); // Apply the desired offset const offsetDate = new Date(utcDate.getTime() + offsetMinutes * 60000); return offsetDate; } const offset = new Date().getTimezoneOffset(); let newBase = new Date(new Date().getTime() + 86400 * 1000); let endBase = new Date(new Date().getTime() + 5 * 86400 * 1000); let tmrwStart = new Date( `${newBase.toLocaleDateString("en-US")} 12:00:00 GMT${ offset >= 0 ? "+" : "" }${Math.round(offset / 60)}${ Math.abs(offset % 60) ? Math.abs(offset % 60) : "" }` ); const tmrwEnd = new Date( `${endBase.toLocaleDateString("en-US")} 23:00:00 GMT${ offset >= 0 ? "+" : "" }${Math.round(offset / 60)}${ Math.abs(offset % 60) ? Math.abs(offset % 60) : "" }` ); const inputs = { depotCities: Inputs.checkbox( [ "Chicago", "Cincinnati", "Cleveland", "Columbus", "Detroit", "Indianapolis", "Kansas City", "Milwaukee", "Minneapolis", "Pittsburgh", "St. Louis" ], { label: "Depot Locations", value: ["Chicago", "Columbus", "Indianapolis", "Pittsburgh"] } ), destinationDepots: Inputs.checkbox( ["Chicago", "Columbus", "Detroit", "Indianapolis", "Pittsburgh"], { label: "Destination Depots", value: [] } ), trucksAtDepot: Inputs.text({ label: "Trucks per depot", value: "5", width: 45 }), shiftStart: Inputs.datetime({ label: "Shift Start", value: tmrwStart.toISOString().slice(0, -1) }), shiftEnd: Inputs.datetime({ label: "Shift End", value: `${tmrwEnd.toISOString().slice(0, -1)}` }), 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: "900", width: 45 }), nbrDealers: Inputs.text({ readonly: false, label: "Nbr of Dealers", value: 15, width: 45 }), fixedCosts: Inputs.text({ readonly: false, label: "Fixed Cost for Vehicle", value: 1000, width: 45 }), costPerHour: Inputs.text({ readonly: false, label: "Cost per Hour", value: 150, width: 45 }), costPerMile: Inputs.text({ readonly: false, label: "Cost per Km", value: 3, width: 45 }), avoids: Inputs.checkbox( ["toll", "highway", "left_turn", "sharp_turn", "right_turn", "uturn"], { label: "Avoid on Route", value: [] } ), customObjectives: Inputs.checkbox( ["Even_Distribution", "Minimize_Depots"], { label: "Objectives", value: [] } ), customConstraints: Inputs.checkbox(["LIFO", "Time Windows"], { label: "Constraints", value: ["LIFO", "Time Windows"] }), remainingDriveTimeUntilBreak: Inputs.text({ readonly: false, label: "Remaining Drive Time Until Break (The available driving hours at origin before the driver needs to take a rest break) (hrs)", value: 8, width: 45 }), remainingDriveTime: Inputs.text({ readonly: false, label: "Remaining Drive Time (The available driving hours at origin until the end of duty) (hrs)", value: 11, width: 45 }), shortBreakDuration: Inputs.text({ readonly: false, label: "Short Break Duration (hrs)", value: 0.5, width: 45 }), endOfDutyBreakDuration: Inputs.text({ readonly: false, label: "End of Duty Break Duration (hrs)", value: 10, width: 45 }), maxDriveTimeBeforeBreak: Inputs.text({ readonly: false, label: "Max Drive Time Before Break (hrs)", value: 8, width: 45 }), maxDriveTimeBeforeLongBreak: Inputs.text({ readonly: false, label: "Max Drive Time Before Long Break (hrs)", value: 11, width: 45 }), maxTasks: Inputs.text({ label: "Maximum nbr of tasks", value: "0", width: 45 }), maxDistance: Inputs.text({ label: "Maximum distance (miles)", value: "0", width: 45 }), maxWorkingHrs: Inputs.text({ label: "Maximum working hrs", value: "0", width: 45 }) }; 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Auto-Carrier Transportation Problem Study (ATP)

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":147,"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","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":990,"value":"altenative_capacity_array = {\n const compartments = [2000, 2000, 1500];\n const fuelTypes = 3;\n return generateAlternativeCapacities(compartments, fuelTypes);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":988,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"md`

Problem Statement

\n\n

This notebook studies a problem statement as follows:
\n\nGiven a **heterogeneous fleet of auto-carriers** in a **distribution center**, a **region** consisting of a set of **dealers**, each requiring a set of **vehicles**, determine the **route** for the auto-carriers based on **user’s inputs** and **load the vehicles** onto the auto-carriers to serve all the dealers at a **minimum cost**. \n
`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"md`

Auto-carrier with nine (9) loading slots and ramps

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"screenshot20250226At141206 = FileAttachment(\"auto-carrier-9-slot.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"md`

Loading/Unloading Graph

\n\n

Ramp **9** is the exit ramp. The nodes represent ramps/slots. In order to load/unload to a given node the prior nodes in the graph need to be clear. For example, for a vehicle to load to slot **4**, slots {**7,8,9**} need to be empty. For vehicle **3** to unload, slots {**7,8,9**} need to be clear.

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":83,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":159,"value":"viewof panel2 = Inputs.form(inputs, {\n template: (v) => htl.html`\n

\n ${v.shiftStart}\n ${v.shiftEnd}\n ${v.objectiveFunction}\n ${v.maxTasks}\n ${v.maxDistance}\n ${v.maxWorkingHrs}\n ${v.fixedCosts}\n ${v.costPerMile}\n ${v.costPerHour}\n ${v.trucksAtDepot}\n ${v.nbrDealers}\n ${v.ordersPerDealer}\n

\n ${v.avoids}
\n ${v.customObjectives}
\n ${v.customConstraints}\n

\n ${v.depotCities}
\n ${v.destinationDepots}\n

\n`\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":156,"value":"{\n const button = html` `;\n button.onclick = () => {\n mutable inputData_a = panel2;\n button.disabled = true;\n };\n return html`${button}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":153,"value":"progress = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":716,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":175,"value":"{\n const container = html`\n

\n `;\n yield container;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":647,"value":"md`

Planned Routes

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":606,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":650,"value":"md`

Stops

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":413,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":743,"value":"md`

Unassigned

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createMap(\"result-map\", 10);\n return nbmap;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":180,"value":"function createMap(id, zoom = 10) {\n let startPoint = null;\n\n const themap = new nextbillion.maps.Map({\n container: document.getElementById(id),\n center: {\n lat: 40.1177,\n lng: -83.1265\n },\n zoom,\n maxZoom: 19,\n minZoom: 0,\n style: `https://api.nextbillion.io/maps/streets/style.json&key=${apiKey}`,\n scrollZoom: true\n });\n\n themap.map.on(\"load\", async function (e) {\n addDealerLocations(themap);\n addAuctionLocations(themap);\n\n themap.map.loadImage(\n \"https://d12qcqjlhp2ahm.cloudfront.net/arrow.png\",\n function (error, image) {\n if (error) throw error;\n themap.map.addImage(\"arrow-icon\", image);\n }\n );\n });\n return themap;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":482,"value":"{\n if (!mvrpID) {\n return;\n }\n if (nbmap && theRouteTable) {\n updateRouteOnMap(nbmap);\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":476,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":200,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":183,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":216,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":261,"value":"function getValidPolygon(geojson) {\n if (!geojson.features || geojson.features.length === 0) {\n console.error(\"GeoJSON is empty or invalid.\");\n return null;\n }\n return (\n geojson.features.find(\n (f) => f.geometry.type === \"Polygon\" || f.geometry.type === \"MultiPolygon\"\n ) || null\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":114,"value":"// Main function to get random dealers within the geojson boundary\nfunction getRandomDealers(dealers, geojson, count = 200) {\n const boundary = getValidPolygon(geojson);\n if (!boundary) return [];\n\n // Filter dealers inside the boundary\n const insideDealers = dealers.filter((dealer) =>\n turf.booleanPointInPolygon(\n turf.point([\n parseFloat(dealer.geo.longitude),\n parseFloat(dealer.geo.latitude)\n ]),\n boundary\n )\n );\n\n console.log(`Total dealers inside boundary: ${insideDealers.length}`);\n\n // Shuffle and select random dealers\n const selectedDealers = d3.shuffle(insideDealers).slice(0, count);\n\n // Assign random orders to each selected dealer\n selectedDealers.forEach((dealer) => {\n dealer.orders = generateRandomOrders(dealer, selectedDealers);\n });\n\n return selectedDealers;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":258,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":124,"value":"midwestDealers = getRandomDealers(\n dealers,\n regionDefinition,\n inputData_a.nbrDealers\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":110,"value":"dealers = {\n const ford = await FileAttachment(\"normalized-ford.json\").json();\n const honda = await FileAttachment(\"normalized-acura.json\").json();\n const toyota = await FileAttachment(\"normalized-toyota.json\").json();\n return [...ford, ...honda, ...toyota];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":219,"value":"trucks = {\n let trucks = [];\n let truckCount = 0;\n auctionHouses.features.forEach((d, idx) => {\n if (inputData_a.depotCities.includes(d.properties.name)) {\n for (let i = 0; i < inputData_a.trucksAtDepot; i++) {\n const t = {\n id: ++truckCount,\n location: `${d.properties.name} - ${d.properties.location}`,\n latitude: d.geometry.coordinates[1],\n longitude: d.geometry.coordinates[0],\n depot: idx + 1\n };\n trucks.push(t);\n }\n }\n });\n return trucks;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":190,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":119,"value":"regionDefinition = {\n const midwest = await FileAttachment(\"midwest_region.geojson\").json();\n return midwest;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":116,"value":"turf = require(\"https://unpkg.com/@turf/turf@6/turf.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":137,"value":"$40","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":140,"value":"import { nextbillion, secret2 } from \"@nbai/nextbillion-ai-dependencies\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":161,"value":"axios = {\n let axios = await require(\"axios\");\n return axios;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":133,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":144,"value":"$41","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":164,"value":"mutable inputData_a = null","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":167,"value":"$42","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":169,"value":"objectives = [\n { name: \"Minimize Time\", value: \"duration\" },\n { name: \"Minimize Distance\", value: \"distance\" }\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":232,"value":"faker = (await import(\"https://esm.run/@faker-js/faker@latest\")).faker","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":807,"value":"avoidInput = {\n if (inputData_a) {\n let avoids = \"\";\n inputData_a.avoids.forEach((av) => (avoids += `${av},`));\n return avoids.length ? avoids.slice(0, -1) : \"none\";\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":724,"value":"function secondsToHHmm(seconds) {\n const hours = Math.floor(seconds / 3600);\n const minutes = Math.floor((seconds % 3600) / 60);\n\n return `${String(hours).padStart(2, \"0\")}H ${String(minutes).padStart(\n 2,\n \"0\"\n )}MN`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":150,"value":"{\n nextbillion.setApiKey(apiKey);\n return html`Set API key
nextbillion.setApiKey(${apiKey.substr(\n 0,\n 4\n )}....)`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":277,"value":"apiKey","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":307,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":8,"value":"[Venkatachalam, Saravanan & Sundar, Kaarthik. (2016). Branch-and-price algorithm for an auto-carrier transportation problem. 10.48550/arXiv.1605.09030. ](https://www.researchgate.net/publication/303681493_Branch-and-price_algorithm_for_an_auto-carrier_transportation_problem/citation/download)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"}],"files":"$W43","stdlib":"1","titling":"auto"}} 32:"$31:body"