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:
341
server/utils/solver/modelBuilder.ts
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341
server/utils/solver/modelBuilder.ts
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import type { SolveInput } from '~/shared/types/schedule'
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import { getDaysInRange, getWeekday } from '~/app/utils/dateHelpers'
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import type { ConstraintJSON } from '~/shared/types/constraint'
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export function buildModel(input: SolveInput) {
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const { employees, framework, period_start, period_end, constraints } = input
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const days = getDaysInRange(period_start, period_end)
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const E = employees.length
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const S = framework.shifts.length
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const D = days.length
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// Variable naming: x_e_s_d = 1 if employee e works shift s on day d
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const varNames: string[] = []
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const varIndex: Record<string, number> = {}
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for (let e = 0; e < E; e++) {
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for (let s = 0; s < S; s++) {
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for (let d = 0; d < D; d++) {
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const name = `x_${e}_${s}_${d}`
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varIndex[name] = varNames.length
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varNames.push(name)
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}
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}
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}
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// Penalty variables for soft constraints
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const penaltyVarStart = varNames.length
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let penaltyVarCount = 0
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const softConstraintPenalties: Array<{ varIdx: number; weight: number }> = []
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function addPenaltyVar(weight: number): number {
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const idx = penaltyVarStart + penaltyVarCount++
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varNames.push(`penalty_${idx}`)
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softConstraintPenalties.push({ varIdx: idx, weight })
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return idx
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}
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// Objective: minimize sum of soft penalties
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const objectiveCoeffs: Record<string, number> = {}
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for (const p of softConstraintPenalties) {
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objectiveCoeffs[varNames[p.varIdx]] = p.weight
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}
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const bounds: Record<string, [number, number]> = {}
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const binaries = new Set<string>()
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for (const name of varNames) {
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bounds[name] = [0, 1]
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if (name.startsWith('x_')) binaries.add(name)
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}
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const rows: Array<{
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name: string
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vars: Record<string, number>
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lb: number
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ub: number
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}> = []
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let rowIdx = 0
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function addRow(vars: Record<string, number>, lb: number, ub: number) {
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rows.push({ name: `r${rowIdx++}`, vars, lb, ub })
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}
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// Structural constraint 1: at most 1 shift per employee per day
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for (let e = 0; e < E; e++) {
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for (let d = 0; d < D; d++) {
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const vars: Record<string, number> = {}
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for (let s = 0; s < S; s++) {
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vars[`x_${e}_${s}_${d}`] = 1
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}
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addRow(vars, 0, 1)
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}
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}
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// Structural constraint 2: coverage requirements
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for (let s = 0; s < S; s++) {
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const shift = framework.shifts[s]
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for (let d = 0; d < D; d++) {
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const weekday = getWeekday(days[d])
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const applicable = shift.days_applicable.length === 0 || shift.days_applicable.includes(weekday)
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if (!applicable) {
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// Force all x_e_s_d = 0 for this day
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for (let e = 0; e < E; e++) {
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addRow({ [`x_${e}_${s}_${d}`]: 1 }, 0, 0)
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}
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continue
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}
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const coverageVars: Record<string, number> = {}
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for (let e = 0; e < E; e++) {
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coverageVars[`x_${e}_${s}_${d}`] = 1
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}
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addRow(coverageVars, shift.workers_required, E)
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}
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}
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// Structural constraint 3: availability
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for (let e = 0; e < E; e++) {
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const emp = employees[e]
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for (let d = 0; d < D; d++) {
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const date = days[d]
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// Block unavailable dates
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if (emp.unavailable_dates.includes(date)) {
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for (let s = 0; s < S; s++) {
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addRow({ [`x_${e}_${s}_${d}`]: 1 }, 0, 0)
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}
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continue
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}
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// Block periods not available
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for (let s = 0; s < S; s++) {
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const shift = framework.shifts[s]
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const period = framework.periods.find(p => p.id === shift.period_id)
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if (period && emp.available_periods.length > 0 && !emp.available_periods.includes(period.id)) {
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addRow({ [`x_${e}_${s}_${d}`]: 1 }, 0, 0)
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}
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}
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}
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}
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// Process constraints from ConstraintJSON
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for (const c of constraints) {
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const cj = c.constraint_json as ConstraintJSON
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const params = cj.params as Record<string, unknown>
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// Determine affected employees
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let affectedEmployees: number[] = Array.from({ length: E }, (_, i) => i)
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if (cj.scope.type === 'employee') {
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const empIdx = employees.findIndex(e => e.id === (cj.scope as { type: 'employee'; employee_id: string }).employee_id)
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if (empIdx >= 0) affectedEmployees = [empIdx]
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else continue
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} else if (cj.scope.type === 'role') {
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const role = (cj.scope as { type: 'role'; role: string }).role
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affectedEmployees = employees
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.map((e, i) => ({ e, i }))
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.filter(({ e }) => e.roles.includes(role))
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.map(({ i }) => i)
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}
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switch (cj.type) {
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case 'max_consecutive_shifts': {
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const max = params.max_count as number
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for (const e of affectedEmployees) {
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for (let d = 0; d <= D - (max + 1); d++) {
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const vars: Record<string, number> = {}
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for (let dd = d; dd <= d + max; dd++) {
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for (let s = 0; s < S; s++) {
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vars[`x_${e}_${s}_${dd}`] = 1
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}
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}
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addRow(vars, 0, max)
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}
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}
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break
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}
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case 'max_consecutive_shift_type': {
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const max = params.max_count as number
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const periodId = params.period_id as string
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const shiftIndices = framework.shifts
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.map((sh, i) => ({ sh, i }))
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.filter(({ sh }) => sh.period_id === periodId)
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.map(({ i }) => i)
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if (shiftIndices.length === 0) break
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for (const e of affectedEmployees) {
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for (let d = 0; d <= D - (max + 1); d++) {
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const vars: Record<string, number> = {}
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for (let dd = d; dd <= d + max; dd++) {
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for (const s of shiftIndices) {
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vars[`x_${e}_${s}_${dd}`] = 1
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}
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}
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addRow(vars, 0, max)
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}
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}
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break
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}
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case 'forbidden_shift_sequence': {
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const firstPeriod = params.first_period_id as string
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const secondPeriod = params.second_period_id as string
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const firstShifts = framework.shifts
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.map((sh, i) => ({ sh, i }))
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.filter(({ sh }) => sh.period_id === firstPeriod)
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.map(({ i }) => i)
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const secondShifts = framework.shifts
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.map((sh, i) => ({ sh, i }))
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.filter(({ sh }) => sh.period_id === secondPeriod)
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.map(({ i }) => i)
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for (const e of affectedEmployees) {
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for (let d = 0; d < D - 1; d++) {
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for (const s1 of firstShifts) {
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for (const s2 of secondShifts) {
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addRow({
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[`x_${e}_${s1}_${d}`]: 1,
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[`x_${e}_${s2}_${d + 1}`]: 1
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}, 0, 1)
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}
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}
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}
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}
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break
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}
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case 'employee_avoids_period': {
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const periodId = params.period_id as string
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const shiftIndices = framework.shifts
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.map((sh, i) => ({ sh, i }))
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.filter(({ sh }) => sh.period_id === periodId)
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.map(({ i }) => i)
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if (cj.hard) {
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for (const e of affectedEmployees) {
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for (let d = 0; d < D; d++) {
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for (const s of shiftIndices) {
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addRow({ [`x_${e}_${s}_${d}`]: 1 }, 0, 0)
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}
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}
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}
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} else {
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// Soft: penalty per assignment
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const weight = (c.weight || 70) * 10
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for (const e of affectedEmployees) {
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for (let d = 0; d < D; d++) {
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for (const s of shiftIndices) {
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const pv = addPenaltyVar(weight)
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objectiveCoeffs[varNames[pv]] = weight
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bounds[varNames[pv]] = [0, 1]
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addRow({
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[varNames[pv]]: 1,
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[`x_${e}_${s}_${d}`]: -1
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}, 0, 1)
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}
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}
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}
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}
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break
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}
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case 'max_weekend_shifts_per_month': {
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const max = params.max_count as number
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const weekendDays = days
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.map((day, i) => ({ day, i }))
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.filter(({ day }) => {
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const wd = getWeekday(day)
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return wd === 5 || wd === 6
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})
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.map(({ i }) => i)
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for (const e of affectedEmployees) {
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const vars: Record<string, number> = {}
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for (const d of weekendDays) {
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for (let s = 0; s < S; s++) {
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vars[`x_${e}_${s}_${d}`] = 1
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}
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}
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if (Object.keys(vars).length > 0) {
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addRow(vars, 0, max)
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}
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}
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break
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}
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case 'min_rest_between_shifts':
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// Already handled implicitly by "max 1 shift per day"
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break
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default:
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break
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}
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}
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// Rebuild objective with finalized penalty vars
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for (const p of softConstraintPenalties) {
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objectiveCoeffs[varNames[p.varIdx]] = p.weight
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}
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// Initialize bounds for penalty vars
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for (let i = penaltyVarStart; i < varNames.length; i++) {
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if (!bounds[varNames[i]]) {
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bounds[varNames[i]] = [0, 1]
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}
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}
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return {
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name: 'ShiftCraft',
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sense: 'minimize' as const,
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offset: 0,
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col_lower: varNames.map(n => bounds[n]?.[0] ?? 0),
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col_upper: varNames.map(n => bounds[n]?.[1] ?? 1),
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col_cost: varNames.map(n => objectiveCoeffs[n] ?? 0),
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col_names: varNames,
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row_lower: rows.map(r => r.lb),
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row_upper: rows.map(r => r.ub),
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row_names: rows.map(r => r.name),
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a_matrix: {
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format: 'colwise' as const,
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start: buildColwiseStart(varNames, rows),
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index: buildColwiseIndex(varNames, rows),
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value: buildColwiseValues(varNames, rows),
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},
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integrality: varNames.map(n => binaries.has(n) ? 1 : 0),
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}
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}
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function buildColwiseStart(varNames: string[], rows: Array<{ vars: Record<string, number> }>): number[] {
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const starts: number[] = [0]
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let count = 0
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for (const name of varNames) {
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for (const row of rows) {
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if (name in row.vars) count++
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}
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starts.push(count)
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}
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return starts
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}
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function buildColwiseIndex(varNames: string[], rows: Array<{ vars: Record<string, number> }>): number[] {
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const indices: number[] = []
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for (const name of varNames) {
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rows.forEach((row, i) => {
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if (name in row.vars) indices.push(i)
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})
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}
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return indices
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}
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function buildColwiseValues(varNames: string[], rows: Array<{ vars: Record<string, number> }>): number[] {
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const values: number[] = []
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for (const name of varNames) {
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for (const row of rows) {
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if (name in row.vars) values.push(row.vars[name])
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}
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}
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return values
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}
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