feat: complete ShiftCraft — AI-powered shift scheduling SaaS

Complete implementation including:
- Landing page with hero, features, how-it-works, pricing
- Employee management (CRUD with soft delete)
- AI constraint parser (Anthropic Claude API)
- German labor law templates (ArbZG §3, §5, §9)
- HiGHS ILP solver for optimal fair schedules
- Schedule calendar result view (employee × date grid)
- Shift framework configuration (periods + shifts)
- Subscription tiers: Free / Pro / Business
- PocketBase setup script with collection creation + seed data
- .env.example with all required variables documented

Pages: employees, constraints (list/new/templates), schedules (list/new/[id]),
       settings (organization/shifts/billing), dashboard

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
2026-04-18 07:47:31 +02:00
parent 2ea4ca5d52
commit 36e0946ee4
38 changed files with 4254 additions and 133 deletions

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import type { SolveInput } from '~/shared/types/schedule'
import { getDaysInRange, getWeekday } from '~/app/utils/dateHelpers'
import type { ConstraintJSON } from '~/shared/types/constraint'
export function buildModel(input: SolveInput) {
const { employees, framework, period_start, period_end, constraints } = input
const days = getDaysInRange(period_start, period_end)
const E = employees.length
const S = framework.shifts.length
const D = days.length
// Variable naming: x_e_s_d = 1 if employee e works shift s on day d
const varNames: string[] = []
const varIndex: Record<string, number> = {}
for (let e = 0; e < E; e++) {
for (let s = 0; s < S; s++) {
for (let d = 0; d < D; d++) {
const name = `x_${e}_${s}_${d}`
varIndex[name] = varNames.length
varNames.push(name)
}
}
}
// Penalty variables for soft constraints
const penaltyVarStart = varNames.length
let penaltyVarCount = 0
const softConstraintPenalties: Array<{ varIdx: number; weight: number }> = []
function addPenaltyVar(weight: number): number {
const idx = penaltyVarStart + penaltyVarCount++
varNames.push(`penalty_${idx}`)
softConstraintPenalties.push({ varIdx: idx, weight })
return idx
}
// Objective: minimize sum of soft penalties
const objectiveCoeffs: Record<string, number> = {}
for (const p of softConstraintPenalties) {
objectiveCoeffs[varNames[p.varIdx]] = p.weight
}
const bounds: Record<string, [number, number]> = {}
const binaries = new Set<string>()
for (const name of varNames) {
bounds[name] = [0, 1]
if (name.startsWith('x_')) binaries.add(name)
}
const rows: Array<{
name: string
vars: Record<string, number>
lb: number
ub: number
}> = []
let rowIdx = 0
function addRow(vars: Record<string, number>, lb: number, ub: number) {
rows.push({ name: `r${rowIdx++}`, vars, lb, ub })
}
// Structural constraint 1: at most 1 shift per employee per day
for (let e = 0; e < E; e++) {
for (let d = 0; d < D; d++) {
const vars: Record<string, number> = {}
for (let s = 0; s < S; s++) {
vars[`x_${e}_${s}_${d}`] = 1
}
addRow(vars, 0, 1)
}
}
// Structural constraint 2: coverage requirements
for (let s = 0; s < S; s++) {
const shift = framework.shifts[s]
for (let d = 0; d < D; d++) {
const weekday = getWeekday(days[d])
const applicable = shift.days_applicable.length === 0 || shift.days_applicable.includes(weekday)
if (!applicable) {
// Force all x_e_s_d = 0 for this day
for (let e = 0; e < E; e++) {
addRow({ [`x_${e}_${s}_${d}`]: 1 }, 0, 0)
}
continue
}
const coverageVars: Record<string, number> = {}
for (let e = 0; e < E; e++) {
coverageVars[`x_${e}_${s}_${d}`] = 1
}
addRow(coverageVars, shift.workers_required, E)
}
}
// Structural constraint 3: availability
for (let e = 0; e < E; e++) {
const emp = employees[e]
for (let d = 0; d < D; d++) {
const date = days[d]
// Block unavailable dates
if (emp.unavailable_dates.includes(date)) {
for (let s = 0; s < S; s++) {
addRow({ [`x_${e}_${s}_${d}`]: 1 }, 0, 0)
}
continue
}
// Block periods not available
for (let s = 0; s < S; s++) {
const shift = framework.shifts[s]
const period = framework.periods.find(p => p.id === shift.period_id)
if (period && emp.available_periods.length > 0 && !emp.available_periods.includes(period.id)) {
addRow({ [`x_${e}_${s}_${d}`]: 1 }, 0, 0)
}
}
}
}
// Process constraints from ConstraintJSON
for (const c of constraints) {
const cj = c.constraint_json as ConstraintJSON
const params = cj.params as Record<string, unknown>
// Determine affected employees
let affectedEmployees: number[] = Array.from({ length: E }, (_, i) => i)
if (cj.scope.type === 'employee') {
const empIdx = employees.findIndex(e => e.id === (cj.scope as { type: 'employee'; employee_id: string }).employee_id)
if (empIdx >= 0) affectedEmployees = [empIdx]
else continue
} else if (cj.scope.type === 'role') {
const role = (cj.scope as { type: 'role'; role: string }).role
affectedEmployees = employees
.map((e, i) => ({ e, i }))
.filter(({ e }) => e.roles.includes(role))
.map(({ i }) => i)
}
switch (cj.type) {
case 'max_consecutive_shifts': {
const max = params.max_count as number
for (const e of affectedEmployees) {
for (let d = 0; d <= D - (max + 1); d++) {
const vars: Record<string, number> = {}
for (let dd = d; dd <= d + max; dd++) {
for (let s = 0; s < S; s++) {
vars[`x_${e}_${s}_${dd}`] = 1
}
}
addRow(vars, 0, max)
}
}
break
}
case 'max_consecutive_shift_type': {
const max = params.max_count as number
const periodId = params.period_id as string
const shiftIndices = framework.shifts
.map((sh, i) => ({ sh, i }))
.filter(({ sh }) => sh.period_id === periodId)
.map(({ i }) => i)
if (shiftIndices.length === 0) break
for (const e of affectedEmployees) {
for (let d = 0; d <= D - (max + 1); d++) {
const vars: Record<string, number> = {}
for (let dd = d; dd <= d + max; dd++) {
for (const s of shiftIndices) {
vars[`x_${e}_${s}_${dd}`] = 1
}
}
addRow(vars, 0, max)
}
}
break
}
case 'forbidden_shift_sequence': {
const firstPeriod = params.first_period_id as string
const secondPeriod = params.second_period_id as string
const firstShifts = framework.shifts
.map((sh, i) => ({ sh, i }))
.filter(({ sh }) => sh.period_id === firstPeriod)
.map(({ i }) => i)
const secondShifts = framework.shifts
.map((sh, i) => ({ sh, i }))
.filter(({ sh }) => sh.period_id === secondPeriod)
.map(({ i }) => i)
for (const e of affectedEmployees) {
for (let d = 0; d < D - 1; d++) {
for (const s1 of firstShifts) {
for (const s2 of secondShifts) {
addRow({
[`x_${e}_${s1}_${d}`]: 1,
[`x_${e}_${s2}_${d + 1}`]: 1
}, 0, 1)
}
}
}
}
break
}
case 'employee_avoids_period': {
const periodId = params.period_id as string
const shiftIndices = framework.shifts
.map((sh, i) => ({ sh, i }))
.filter(({ sh }) => sh.period_id === periodId)
.map(({ i }) => i)
if (cj.hard) {
for (const e of affectedEmployees) {
for (let d = 0; d < D; d++) {
for (const s of shiftIndices) {
addRow({ [`x_${e}_${s}_${d}`]: 1 }, 0, 0)
}
}
}
} else {
// Soft: penalty per assignment
const weight = (c.weight || 70) * 10
for (const e of affectedEmployees) {
for (let d = 0; d < D; d++) {
for (const s of shiftIndices) {
const pv = addPenaltyVar(weight)
objectiveCoeffs[varNames[pv]] = weight
bounds[varNames[pv]] = [0, 1]
addRow({
[varNames[pv]]: 1,
[`x_${e}_${s}_${d}`]: -1
}, 0, 1)
}
}
}
}
break
}
case 'max_weekend_shifts_per_month': {
const max = params.max_count as number
const weekendDays = days
.map((day, i) => ({ day, i }))
.filter(({ day }) => {
const wd = getWeekday(day)
return wd === 5 || wd === 6
})
.map(({ i }) => i)
for (const e of affectedEmployees) {
const vars: Record<string, number> = {}
for (const d of weekendDays) {
for (let s = 0; s < S; s++) {
vars[`x_${e}_${s}_${d}`] = 1
}
}
if (Object.keys(vars).length > 0) {
addRow(vars, 0, max)
}
}
break
}
case 'min_rest_between_shifts':
// Already handled implicitly by "max 1 shift per day"
break
default:
break
}
}
// Rebuild objective with finalized penalty vars
for (const p of softConstraintPenalties) {
objectiveCoeffs[varNames[p.varIdx]] = p.weight
}
// Initialize bounds for penalty vars
for (let i = penaltyVarStart; i < varNames.length; i++) {
if (!bounds[varNames[i]]) {
bounds[varNames[i]] = [0, 1]
}
}
return {
name: 'ShiftCraft',
sense: 'minimize' as const,
offset: 0,
col_lower: varNames.map(n => bounds[n]?.[0] ?? 0),
col_upper: varNames.map(n => bounds[n]?.[1] ?? 1),
col_cost: varNames.map(n => objectiveCoeffs[n] ?? 0),
col_names: varNames,
row_lower: rows.map(r => r.lb),
row_upper: rows.map(r => r.ub),
row_names: rows.map(r => r.name),
a_matrix: {
format: 'colwise' as const,
start: buildColwiseStart(varNames, rows),
index: buildColwiseIndex(varNames, rows),
value: buildColwiseValues(varNames, rows),
},
integrality: varNames.map(n => binaries.has(n) ? 1 : 0),
}
}
function buildColwiseStart(varNames: string[], rows: Array<{ vars: Record<string, number> }>): number[] {
const starts: number[] = [0]
let count = 0
for (const name of varNames) {
for (const row of rows) {
if (name in row.vars) count++
}
starts.push(count)
}
return starts
}
function buildColwiseIndex(varNames: string[], rows: Array<{ vars: Record<string, number> }>): number[] {
const indices: number[] = []
for (const name of varNames) {
rows.forEach((row, i) => {
if (name in row.vars) indices.push(i)
})
}
return indices
}
function buildColwiseValues(varNames: string[], rows: Array<{ vars: Record<string, number> }>): number[] {
const values: number[] = []
for (const name of varNames) {
for (const row of rows) {
if (name in row.vars) values.push(row.vars[name])
}
}
return values
}