All Problems
Category-indexed directory of operational optimization problems.
A New Part Order Arrives — How Do I Get the Optimal Operation Sequence + Machine Choice + Setup?
If you are an SMB CNC manufacturer, tooling shop or engineering workshop producing 50-500 different parts, every new order puts the same decision in front of you: you take the customer's CAD model and have to work out on which machine, in which order, with which tool and fixture, and in how many setups the part will be made. If you simply write down the first feasible sequence that comes to mind, setup time grows 3-5x and parts that miss tolerance are reworked; finding the right sequence means comparing several alternative routings for the same part. When the decision lives only in one engineer's head, the similar-part memory walks out of the door when that engineer leaves, and routings of older parts are not refreshed when a new machine is bought. This page is for production-engineering teams who want to make the operation sequence and the machine assignment written and comparable as the bridge from design to make.
Backward from the Customer Order — Which Raw Material, When, and How Much Should I Order?
For mid-size manufacturing SMBs running 50-500 end items and 200-2,000 raw materials and sub-components (automotive Tier-2, white-goods component shops, furniture and assembly, machinery). With each product's parts list (BOM) and each part's procurement or production lead time on file, the question is: a customer orders 200 units of product X with a fixed delivery week — which raw materials in what quantity must be ordered which week, and which sub-parts must enter assembly when? Material requirements planning (academic name MRP) answers this question backwards from the delivery date and is the core logic underneath every ERP. Done on paper or by 'my supplier always takes 3 weeks' rules of thumb, you either run out and stop the line, or burn cash on excess stock.
Five Suppliers Quote the Same Part — How Much Do I Buy from Each?
You run a manufacturer with 20-200 staff and buy the same raw material or component from 4-8 different suppliers. Each supplier differs on price, quality, lead time, capacity, payment terms, and financial stability; deciding who stays on the approved list and how much of each order goes to whom are two separate calls. A wrong pick can trigger a months-long quality crisis or stop the line when a single supplier fails; the 'cheapest bid wins' approach hides quality, late-delivery, and compliance costs and ends up 15-40% more expensive. Scoring 5-15 bids across 8-12 criteria by hand in a spreadsheet becomes inconsistent fast.
How Do I Balance Production Rate, Inventory, Overtime and Subcontracting Over the Next 12 Months?
You run a mid-size manufacturer with 10-50M TRY monthly revenue, 5-30 product families and 30-200 workers; sales hands you a one-year demand forecast where peaks and troughs vary sharply month to month. Each month for each product family you must make five interlocking calls at once: run at a steady rate and build inventory, hire and fire workers, cover peaks with overtime, subcontract to sub-tier manufacturers, or push orders into next month. Get the mix wrong and average inventory bloats 20-40%, overtime breaks the 270 hours per year legal cap, last-minute subcontracting closes 20-40% above market price, and late deliveries lose customers. Splitting these five decisions across sales, production and HR with three separate intuitions burns 4-25M TRY of operating margin each year.
How Many Items Do I Sample From an Incoming Lot, and How Many Defects Do I Allow — Inspection Cost vs Customer Complaint Trade-off?
If you run incoming inspection at an automotive tier-1 supplier, the entry-control desk of an export textile mill, or quality control at a food or pharmaceutical plant, weekly lots of 100-5,000 parts arrive from your suppliers; testing each lot 100% is expensive, sometimes destructive, or delays production. For every lot you have to decide two numbers: how many parts to sample at random, and how many defects to allow before rejecting the whole lot. Too few samples plus a loose limit lets bad lots through and into customer complaints; too many samples plus a tight limit rejects good lots, picks unnecessary fights with the supplier, and delays deliveries. No matter how numeric the quality target in the contract looks, balancing those two risks by intuition gradually erodes both your quality memory and your cost control.
How Many Orders Per Year, How Much Per Order — So Setup + Holding Cost Is Minimum?
For a mid-size wholesaler with 5-50M TRY revenue, an SMB manufacturer buying packaging or raw materials, or a warehouse/purchasing lead managing 50-500 SKUs, two decisions repeat on every SKU: how large should each order be, and how often. Order too large and capital sits in the warehouse, storage fills up, spoilage + obsolescence + financing costs rise; order too small and per-order transport + customs + processing fees crush the unit cost as order frequency explodes. The practical 'buy one month's stock' or 'fill the truck' rule typically deviates 20-50% from the optimum — about 10-25% extra in total annual cost. The right balance per SKU comes from annual demand, fixed cost per order, capital-tying rate (25-45% per year in Turkey over the last 5 years), and unit storage cost; supplier volume discounts require a tier-by-tier comparison. On 200M TRY of annual purchases, doing this right is worth 300K-2M TRY of operating margin per year.
How Much of Each Product Should I Make for the Most Profit?
You run a 5-50-worker shop making 3-15 products. The same raw material, the same machines and the same workers are shared across products. Each week sales says 'we can sell 200 more of this', production says 'but that machine is full', accounting says 'product A has the highest margin, push that one'. Each product has a different margin, a different machine-hour consumption, a different raw-material usage and a different demand ceiling. Decide by gut and you usually load the product with the highest sale price — yet the product that **eats the least bottleneck-machine hour** can be more profitable. Take this call by intuition and 10-25% of the monthly margin you could earn from the same capacity stays on the table.
How Much Should I Produce In One Batch, and When Do I Start Again?
One of the basic questions a manufacturer faces: there is demand data for some period, every new production batch carries a setup cost (mold change, line setup, calibration), and what gets produced but not sold immediately incurs inventory cost (warehouse, capital tied up, obsolescence). The schedule of how much to produce each week must be planned in advance; the mathematical name for this is the Lot Sizing Problem. The single-product, deterministic-demand, uncapacitated variant was solved by Wagner and Whitin in 1958 via dynamic programming. Multi-product, capacitated, multi-stage variants grew into MRP, Capacitated Lot Sizing (CLSP), and Economic Lot Scheduling (ELSP).
Which Equipment Should I Service, and How Often?
Setting the preventive maintenance schedule for a production line with 5–30 major pieces of equipment, a fleet of 10–100 vehicles, or a facility with 50–300 machines/assets (hotel, hospital, factory, mall). Each asset has a different failure tendency, usage intensity, maintenance duration, and downtime cost. The decision: which asset, when, with which crew, in which sequence, so that unplanned failures are minimal, planned downtime is short, and the maintenance workload stays balanced. Manual planning works up to 20–30 assets; above that, either too-early maintenance wastes parts and labor, or too-late maintenance triggers failures and lost production.
From One Node to Another — How Do I Compute the Shortest Path on a Weighted Graph?
For SMBs that need to compute the fastest or shortest route between two points: 10-50-vehicle field-service teams (plumbing, electrical, appliance repair), urban courier/parcel operations, or dispatch centres coordinating emergency response. Every day brings hundreds of 'how do I get from A to B fastest right now' questions; the answer shifts with traffic, road closures and vehicle type. A wrong route costs the technician one or two jobs missed for the day, the courier a late delivery, and the firm a customer. Manual or by-eye routing typically leaves 20-60 wasted minutes per vehicle per day on the table compared with a network-aware route calculation.
Multiple Plants, Multiple Customers — How Much Does Each Plant Ship to Each Customer to Minimise Total Freight?
For food, packaging or textile producers shipping weekly from 3-8 plants or regional warehouses to 20-100 customers. The weekly decision is: which plant ships how much to which customer, given fixed plant capacities, stated customer demands, and a different per-unit cost (distance + vehicle + contract terms) for each plant-customer pair. The goal is the lowest total freight bill across the network. A 'nearest plant' or 'we've always done it this way' habit typically leaves 10-20% extra fuel and vehicle cost on the table compared with a systematic allocation.
One Vehicle, Many Stops — In What Order Should I Visit Them All to Minimize Total Distance?
You run a field technician visiting 8-15 customers a day (HVAC, lift servicing, white-goods repair), a single-vehicle supplier tour by a sales rep, or a PCB drilling machine sequencing 500-5,000 holes. All of them face the same core call: given N points, in what order should a single vehicle or head visit each one and return to the start. Get the order wrong and a field service vehicle burns 80-200 TRY/day extra in fuel and driver hours, a PCB line takes 15-30% longer per part, and the last customer of the day misses their delivery window. At 50 stops, a hand-built sequence runs 20-40% above the true minimum; as the number of stops grows, the gap from intuitive ordering compounds.
Several Vehicles, Many Customers — Which Vehicle in Which Sequence, Capacity Not Exceeded, Total Distance Minimum?
A distributor or supplier delivering daily from one depot to 10-100 customers (food, beverage, water, B2B spare parts); fixed vehicle capacity (2-5 tonnes, 30 m³), known order quantities per customer, flexible delivery times. Every morning three questions: how many vehicles dispatch today, which vehicle visits which customers, in what sequence — capacity not exceeded, total distance minimised. A dispatcher handles 15-25 customers mentally; beyond that, route quality drops, customers in the same area split across two vehicles, and 1-2 extra vehicles hit the road each day. 10-25% of total distance and 1-2 vehicles per day depend on planning quality; fuel + driver cost is 30-50% of operating expense.
What Fits Into a Truck or Container, and In What Order Do You Load It?
Given a truck, container, or cargo vehicle of fixed dimensions, what placement of boxes (or pallets) with varying sizes, weights, and stacking rules gives the highest utilization rate? The mathematical name for this question is the three-dimensional bin packing problem (3D-BPP) or container loading problem (CLP). Methods that simultaneously solve volume-fit, weight limits, stacking rules, weight distribution (balance), and delivery sequence (multi-drop) have been studied since the 1990s. Even a 5% utilization improvement materially raises deliveries-per-vehicle for an SMB.
Where Should I Open the New Warehouse?
A distributor, e-commerce operator, or SMB manufacturer is planning to open 1–5 new warehouses, branches, or distribution centers over the next 2–5 years. The decision: which city or region, how many facilities, what size, and which existing warehouses transfer which customer or order volume to which new facility. A bad location means 5–10 years of high transport cost, late deliveries, and lost customers; a good location means $300K–1.5M annual savings over the same period. When the decision is made on gut feel (e.g. 'put it next to the factory, the workers live nearby'), it rarely lands on the optimum — because transport cost, rent, taxes, labor, and service time are constraints that must be balanced together.
Which Van to Which Customer, at What Time?
A local delivery fleet of 5–30 vans planning daily routes. Each customer has a delivery time window (a shop accepts deliveries between 09:00–12:00; a restaurant only before 14:00). The decision: which customer goes on which van, in which order, so every window holds, fuel and driver hours stay low, and no van runs over capacity. A dispatcher can hand-plan 30–50 stops; past that, plan quality drops — empty kilometers, late deliveries, second runs, and driver overtime.
Dozens of SKUs from the Same Supplier — At What Frequency Do I Order Each So Trucks and Stock Cost Are Minimum Together?
This page is for an SMB wholesaler, importer or manufacturer pulling 50-500 SKUs from the same supplier — typically across 3-15 main suppliers. The weekly question is the same: from this supplier, which products should ship today and which can wait until next week? If every product triggers its own order, the same supplier ends up sending three trucks and three customs filings a week; one truck, one customs filing and one setup cost can cover all of it if the groups are right. Manual planning falls apart past 50 SKUs: some weeks a half-empty truck, others three back-to-back orders — fixed costs come back as 5-15% of unit product cost.
How Much of Each Product Should I Order So I Neither Run Out Nor Pile Up?
Written for retail, e-commerce, or manufacturing SMBs managing 500-5,000 SKUs. If your purchasing team still places orders every month from a spreadsheet using last-three-months average plus ten percent, and yet you see empty shelves and clogged warehouses at the same time, you are in this problem. When the same company carries both stockouts (5-15% lost sales) and dead inventory (10-25% capital tied up), intuition has hit its limit: seasonality, lead-time uncertainty, and slow-moving SKUs cannot be corrected by hand item-by-item. Systematic forecasting paired with an inventory policy typically frees 1.8-5.4M TRY/year of working capital on 50M TRY revenue and lifts the service level from roughly 85% to 95%.
Stock Is Not Clearing as the Season Ends — When and How Deep Should I Mark Down So That Margin Holds and Dead Stock Stays Low?
This page is for you if you run a seasonal-collection fast-fashion retailer, a 30-100 store apparel/shoes/accessories chain, or a fresh-food chain (greengrocer, bakery, butcher). The classic pain: a 200-1,000 SKU collection sells for 4-12 weeks and then the unsold stock becomes a problem — discount too early and you erode the gross margin, discount too late and you sit on a pile of dead stock at the end of the season. The decision: which item, when, and by what percentage to mark down? In a mid-size fashion chain run on gut-feel rules, 20-35% of end-of-season stock ends up as salvage; a proper markdown schedule lifts the gross margin by 5-15%, cuts dead stock by 20-30%, and adds 15-60 million TRY of operating margin a year on a typical revenue base.
When to Reorder, and How Much?
A retailer, e-commerce store, or distributor holding 200–5,000 SKUs. For each SKU the question is: when do I reorder from the supplier, and how much? Suppliers deliver 3–21 days after the order (lead time), demand swings day to day, some products expire, warehouse space is finite, and most suppliers impose a minimum order quantity (MOQ). The decision: which SKU to order, when, in what quantity, so that 'out of stock' (lost sales) and 'over-stocked' (cash trapped, expiry) are both kept in check. Manual tracking works up to ~50–100 SKUs; above that, 'I keep it in my head' breaks down — either you over-order or you go out of stock on something critical.
Which Products Stay on the List, and How Much Shelf Do They Each Get?
Written for an SMB retail chain with 30-200 stores or an e-commerce operator running 500-5,000 SKUs. If every month you fight over which products stay on the shelf, how much space each one gets, and which existing item to drop to fit a new supplier, this is your problem. Intuitive listing and hand-drawn planograms quietly erode category margin: bestsellers get too little facing, dead SKUs hog space, and the substitution effect (a customer leaving for a competitor when their pick is missing) stays invisible. A systematic store- and category-level decision process typically swings annual margin by 2-6M TRY on a 50M TRY category.
Which SKU on Which Shelf, and Along What Route Should the Picker Walk?
An e-commerce warehouse or omnichannel retail DC stores 15,000-200,000 product codes (SKUs) and picks 2K-30K order lines a day. A picker walks 200-800 m per order and 15-30 km a day in total. Fifty to seventy percent of that walking is non-value-adding. Two coupled decisions must be made: (a) **slotting** — which SKU is stored on which shelf and at which height, given pick frequency, co-order pattern and ergonomic load; (b) **picking routing** — for a given pick list, the picker's aisle sequence and the chosen routing rule (S-shape, return, midpoint, largest-gap, combined). Add-on decisions: collect multiple orders in one trip (batch picking), split the warehouse into zones (zone picking), merge at a sort station (sort-merge). When both decisions are solved together, walking distance per picker can drop 25-40%.
Fixed Budget, Many Candidate Projects — Which Subset Should I Pick So That Total Return Is Maximised?
A mid-size holding or SMB investment committee faces 50-200 candidate projects every year (factory capacity, new line, warehouse, IT modernisation, digital transformation) against a fixed annual budget (50-500M TRY): which subset gets picked so the budget is respected and total return (NPV) is maximised? The intuitive 'sort by NPV / investment ratio, take from the top' rule misses small but high-return projects that fit the last 5-10% of the budget — empirically it deviates from the optimum by 5-15%, and with multi-dimensional constraints (budget + labour + machine-hours) the gap widens to 10-25%. The same structure recurs in monthly marketing campaign selection, capacity-bounded cargo loading, and supplier subset selection. The widespread committee belief 'no mathematical optimum exists, we just pick by judgement' is wrong — an optimal solution for 200-1000 candidates is delivered in minutes.
How Much Cash in Which ATM, How Often to Refill — Balancing Empty-ATM Complaints Against High Immobilisation Cost
If you are a mid-size commercial or participation bank running 50-500 ATMs, every morning you must answer three questions: how much cash should each ATM hold, how often should each one be replenished, and which route should the armoured vehicle take. The wrong extremes are expensive: too much cash sitting inside an ATM inflates the annual 5-15% interest-opportunity cost plus the insurance premium; too little cash empties the machine, customers cannot withdraw, complaints and brand damage follow. Because a shopping centre, a bus stop, a campus and an office district all have very different withdrawal patterns, an intuitive 'same amount everywhere' rule hurts both ends at once. This page is for bank operations teams who want to take all three decisions together, driven by data.
Multiple Inputs + Multiple Outputs — How Do I Measure the Relative Efficiency of My Branches or Units?
This page is for you if you run a bank with 100-500 branches, a multi-site hospital chain with 200-1,500 beds, an education directorate with hundreds of schools, or a public body comparing performance province by province. The core question: which of your branches/hospitals/schools is efficient and which is not — and for those that are not, which 'peer' unit should they learn from and by how much? Each unit consumes several inputs at once (headcount, floor space, budget) and produces several outputs at once (revenue, customers/patients/students, quality); a single-ratio metric like 'revenue per employee' does not capture that and can flag an efficient unit as weak, or vice versa. Done properly — because the peer benchmark gives a concrete improvement reference — acceptance of improvement plans for weak units rises by 40-70%, which is worth roughly 10-50 million TRY a year in operating margin on a mid-size branch network.
What Share of My Money Should Go Into Which Investment?
One of the basic questions for an SMB or individual investor: there is a sum of capital, multiple investment options (stocks, bonds, FX, commodities, deposits, real estate, reinvestment in the business), each with different expected return and risk, and correlations among them (when one falls, another rises, and so on). What percent of the capital goes where? The mathematical name is the Portfolio Optimization Problem. In 1952 Harry Markowitz introduced the mean-variance framework — the Nobel-winning foundation of modern portfolio theory. Maximize expected return while minimizing variance (risk) is a quadratic-programming problem.
How Do I Lay Out Outpatient Appointments — Keeping Patient Wait and Doctor Idle Time Both Low?
This page is for the manager of a private clinic or hospital outpatient ward with 5-20 doctors handling 100-800 appointments per day. Two coupled decisions: how long should an appointment slot be (15 or 20 minutes, same for everyone or not), and should some slots take two patients to absorb the no-shows? The wrong design triggers three things at once: patients wait 40-90 minutes and the complaint hotline fills up, the doctor sits idle 1-2 hours in the afternoon or runs until 8 pm, and a 20-30% no-show rate quietly erodes capacity. A fixed 15-minute template looks tidy on paper, but when real consultations swing between 5 and 25 minutes the afternoon queue always collapses.
Monthly Nurse Roster — Holding Burnout, Preference and Skill Together
This page is for the head nurse of a private hospital with 50-300 nurses staffing 24/7 wards (ICU, surgery, ER, internal medicine, maternity) and building the monthly shift roster. Every shift needs enough nurses and at least one senior qualified one, consecutive night shifts are capped, there must be at least 11 hours of rest between shifts, personal day and leave requests must be honoured, and night-shift load must be distributed fairly — all at the same time. Manual rostering past 50 nurses costs the head nurse 30-60 hours a month, and the 'who worked less' argument still opens every month. Nurses on unfair rosters quit 2-3 times more often; replacement cost is 50-150K TRY per nurse.
Which Patient to Which Ward and Bed, and When to Trigger Discharge?
For the management running a 50-500 bed private hospital or clinic with 6-15 services (cardiology, internal medicine, pediatrics, intensive care, oncology and similar). Every day two interlinked decisions must be made: which ward and bed at what hour an arriving patient is admitted to, and how many hours in advance the discharge decision is triggered for already admitted patients. When a ward fills up, the patient waits hours in the emergency department or is placed in the wrong service, which lengthens stay and raises clinical risk. If bed occupancy is tracked manually, emergency-elective collisions and weekend congestion are unavoidable.
Which Surgery in Which OR, at What Time?
Weekly surgical case scheduling for a 20–80 bed private hospital, a day-surgery center, or a clinic with 3–8 operating rooms. Every week 40–200 surgeries must be planned; each case has a different estimated duration (45 minutes to 6 hours), required surgeon and nursing team, anesthesia type, consumables and implants, and patient admission/recovery time. The decision: which case, which day, which room, in which order, with which team. Boosting room utilization while honoring surgeon off-days, patient wait time, and the emergency-reserve buffer is hard. Manual planning works up to 20–30 cases/week; above that, it takes the manager 4–8 hours a week and surgery delays and night-shift overtime rise.
How Do I Build Weekly Employee Patterns — Demand Met, Rest, Hours and Fairness All Holding Together?
The HR or operations manager of a 7-day 24-hour service (retail chain call center, hotel reception, security service, hospital cleaning) builds not individual shifts but **weekly patterns** for 100-500 employees: who works which days, in which shifts (morning/afternoon/night), with what days-off pattern — peak-hour demand covered, weekend and night load shared fairly. The intuitive plan bleeds from one of two ends: understaffed peaks (queue at the till, lost sales, abandoned calls) or overstaffed lulls (80-200 TRY/hour labour, roughly 30-60K TRY a month wasted on a 100-person operation). On top of that, contract breaches (45-hour weekly cap, 5 consecutive days, 7-10 nights per month) trigger payroll penalties and labour-law risk; without a written fairness metric, turnover climbs to 40-80% and every new hire costs 8-30K TRY in training. For a 200-person operation, annual payroll is in the 30-80M TRY range; a 10% improvement is a 3-8M TRY/year saving.
n Tasks + n People / Machines — Who Do I Assign To What So Total Cost or Time Is Minimum?
For service SMBs that start each week asking 'who should I put on what': engineering practices with 5-30 engineers, law offices distributing 20-80 files a week, facility-management firms with 10-50 field technicians, or hospitals matching surgeons to cases. Every person-task pair has a different real cost because skill, time, travel distance and personal preference mix together; the intuitive 'best person on the hardest job' rule cannot see those differences. Bad pairings show up as overtime, late deliveries and customer complaints by Friday. A systematic matching of the same team typically cuts total cost or time by 15-30%.
Which Technician to Which Customer, at What Time?
An HVAC service, elevator maintenance company, appliance service, ISP technician operator or agricultural machinery service with 5–50 field technicians faces a daily request list each morning: 30–150 customers seeking planned periodic maintenance, breakdown repair, or installation. The decision: which technician, which customer, in what order, at what time. Constraints to honor in parallel: customer time window (morning / afternoon / a specific slot), technician skill (HVAC brand A vs B, elevator type, internet infrastructure), travel time (20–90 minutes in-city), spare parts in the technician's van, urgent-job priority. Manual assignment is workable for 10–15 technicians; above that, the dispatch team spends 2–4 hours a day on the phone — slipped appointments, customer dissatisfaction, and idle technicians become routine.
Which Chain of Activities Drives the Project End Date, and Which Activity Can Slip Without Hurting It?
This page is for you if you run a mid-size construction, industrial-plant or enterprise software/IT project of 50-300 activities. The everyday questions: which tasks run sequentially, which can run in parallel; which chain is 'critical' — i.e. a one-day slip on it slips the whole project by one day; which tasks have slack; if your duration estimates are uncertain, what is the chance of hitting the target date. Public-works and large-investment contracts typically charge 0.5-1.0% of contract value per day of delay; managing those numbers with a proper critical-path and slack analysis instead of a gut-feel Gantt chart is worth 5-25 million TRY a year in operating profit for a typical mid-size contractor. Inside: the method family in plain terms, a step-by-step path for an SMB, and the technical questions to ask when buying a project-management tool.
Which Task on Which Day, with Which Crew?
On a $1–7M construction project (residential block, light commercial building, small industrial facility), or in a contractor running 3–10 projects in parallel, the problem of sequencing 100–500 tasks per project (formwork, rebar, concrete, MEP, finishing), assigning crews and machines, and tracking durations. The decision: which task starts which day, with which crew, with which machine, so precedence constraints hold (no slab formwork before the columns are poured), resource constraints hold (one crane on site, no two crews on the same floor), holidays and weather are respected, and the contractual delivery date is met. Manual planning works up to 30–50 tasks; above that, the daily 'who's where' tracking eats the manager's hours and slippage becomes inevitable.
Which Course at Which Hour, in Which Room?
Every term, a private school with 15–50 teachers, 8–30 classes, and 5–20 rooms, a 30–100-teacher learning center, or a 50–200-course university faculty must produce the weekly course timetable. The decision: which course is taught by which teacher, on which day, at which time, in which room. Constraints to honor in parallel: teacher availability (most teachers have certain hours when they can't teach), room capacity and type (lab, music room, gym), student-group non-overlap, limits on consecutive periods, lunch breaks, and pedagogical preferences (e.g. no academic class right after PE). Manual preparation for 50–100 courses takes 2–5 days of labor; above that, weeks of negotiation, teacher complaints, and last-minute revisions become routine.
Which Student to Which Stop, and Which Bus in Which Order?
For a private school transporting 200-1,500 students with 10-50 buses, running twice a day in the morning and afternoon. At the start of every term four nested decisions must be made: where to place stops, which student goes to which stop, which bus visits which stops and in what order, and how the same fleet is shared between a 7:30 primary and an 8:30 high school. Mistakes are costly: a 90-minute one-way ride for a 7-year-old loses parents, a half-empty bus burns fuel, and a late bus erases an hour of class. Routes drawn in a spreadsheet have to be reworked from scratch each time a new enrolment arrives.
PV, Battery, Generator, Load — Every 15 Minutes, How Much of Which Source Should I Run?
You operate a tourism site, university campus, small industrial estate, or a 100-500 household island/rural microgrid with 200-1500 kW of rooftop solar, a 100-500 kWh battery, and a 50-200 kW backup diesel generator. Every 5-15 minutes you must make four coupled calls at once: how much energy to import from the grid and whether to send surplus solar back to the grid or into the battery, whether to charge, discharge or hold the battery, whether to fire up the generator, and which flexible loads run now versus later. Get the order wrong and surplus solar is pushed back to the grid without a contract, peak-tariff electricity is bought at night, the generator burns fuel at the wrong hour, and when the grid drops it is unclear whether critical loads (cold chain, server room, lift) stay powered. Rule-of-thumb dispatch on a 1 MW PV + 500 kWh battery + 200 kW generator site burns 0.8-3M TRY of operating margin per year.
What Should I Consume When, to Drop the Bill?
Written for SMB manufacturers or industrial-zone facilities consuming 2-20 GWh per year (textile, foundry, plastics, food processing, cold storage, hotel). If half your bill is the day/peak/night tariff split or the connected-power (kW) charge, but you still decide intuitively which load runs at which hour, the leak compounds every month. Shifting time-flexible loads (melting furnace, compressors, cold storage, pumps) from peak hours to cheap windows typically cuts the bill by 12-30%; with on-site renewables or a battery the gap widens further. On a 5M TRY annual bill the typical savings band is 600K-1.5M TRY/year, and without a structured hourly plan you leave most of it on the table.