Adding an optimization engine on top of Joblogic

What Joblogic documents about routing and scheduling

Joblogic’s product pages describe the scheduler clearly. The vocabulary is workflow-first, route-second:

• Scheduling software. “Joblogic’s drag-and-drop calendar makes job scheduling and dispatch far more efficient.” The route layer beneath it is described as: “Schedule, manage and optimise the daily routes of your field engineers” with the ability to “dispatch tasks to nearby team members, reassign jobs to reduce transit time, and automatically schedule jobs to follow a more efficient travel route”.
• Multi-engineer and multi-client. “Organise multiple visits for multiple clients simultaneously” with bulk reassignment and batch deployment.
• Recurring contracts and PPM. “Schedule jobs as either one-off visits or as recurring contracts”; PPM schedules at site and asset level.
• Manual rebalancing. The “shunt” function pushes or pulls subsequent visits for the selected engineer or team – a documented primitive for moving the day forwards or backwards by hand.

What none of those pages describe is a constraint-based optimization engine: an input model of jobs, engineers, vehicles, depots, skills, time windows, SLAs and recurring cadences, and a documented optimization API that produces assignments under an objective. That layer is not where Joblogic competes. It is what eLogii adds.

Where drag-and-drop scheduling reaches its boundary

The pattern is consistent across operations where this comes up:

• Planner-as-optimizer. The planner spends the morning hand-balancing jobs across engineers, depots and skill sets. The route calculation between the jobs they place is fast and correct; the placement itself is the bottleneck. The shunt function helps with one engineer’s sequence; it doesn’t solve cross-engineer or cross-depot reassignment.
• Multi-depot rebalancing. A regional contractor or distribution arm with three or four depots needs the optimizer to treat all depots as part of the same problem. Joblogic treats depot as a per-engineer starting point, so cross-depot rebalancing is operator work.
• Recurring PPM and compliance at scale. Quarterly compliance, monthly PPM, F-Gas, gas-cert and boiler cadences across a property portfolio. Joblogic generates the schedules and tracks the certificates; the optimization across the generated stops, interacting with reactive work, is its own problem.
• Constraint-heavy commercial work. A two-engineer install over three days with a customer-confirmed start window, a skill that needs the same engineer back on day three, an overnight stop. The constraints are real and there are usually hundreds of jobs to satisfy them across.
• SLA-locked work mixed with flexible. The optimizer needs to protect the locked stops, balance the flexible ones, and re-optimize on the fly when a no-access comes in. Drag-and-drop plus shunt catches up to this slowly.

At a glance: a 60-engineer regional contractor

A regional plumbing and heating contractor running three depots across the South-East. Sixty engineers in the field. Two planners. The book splits roughly 40% PPM under SLA contracts (gas safety, boiler servicing, F-Gas) and 60% reactive call-outs that come in through phones, email and Joblogic’s customer portal. Joblogic covers the FSM workflow cleanly: mobile engineer app on the van, F-Gas with bottle tracking, gas certificates, job costing, BI dashboards, accounts integration with Sage.

The bottleneck shows up in the planning room each morning. The two planners spend an hour hand-balancing reactive against PPM, reconciling yesterday’s customer-booked slots against today’s actual routes, and working around the engineer off sick at depot 2 by using the shunt function and manual moves to depots 1 and 3. The route calculation between assigned jobs is fast. The placement of those jobs is the bottleneck. Adding eLogii compresses that morning hour into a 10-minute review of a constraint-aware optimization run; Joblogic continues to own everything else.

The workaround in Joblogic and where it breaks

The workaround is the planner. Joblogic’s drag-and-drop board plus the shunt function is good at what it’s built for, and an experienced planner can carry a real operation on it. The friction shows up at scale: time spent on planning grows non-linearly with the number of engineers and depots; the bus-factor of the operation is the one planner who knows where everything goes; cross-day and cross-depot constraints get carried in heads and spreadsheets, not in the system. When the planner takes leave, planning quality drops visibly. When the operation grows past the planner’s capacity, the team adds a second planner, then a third, and the coordination tax climbs again.

None of this means Joblogic is the wrong tool. It means there is an optimization layer below the scheduling board that Joblogic does not claim to be, and eLogii does.

How eLogii’s optimization engine handles this

eLogii’s engine takes a constraint model as input and produces both assignments and routes as output. The planner steers it with rules they can see; the engineer executes the plan in the field app they already use.

• Two engines. The Default engine optimizes 100 tasks in under 10 seconds for high-throughput daily planning. The Advanced engine takes more factors into account and is the choice for multi-depot, multi-day, long-haul and constraint-heavy operations.
• Three assignment modes. Optimize Everything (creates fresh routes including all assignments), Add to Routes, Keep Existing Assignments (incorporates new tasks while preserving driver assignments), and Add to Routes, Keep Existing Assignments and ETAs (inserts new tasks into available slots without modifying existing stop sequences or ETAs).
• Three load-balancing modes. Most Efficient Routes (fewest vehicles), Balance the Minimum Number of Routes (across load, time, distance or job count), and Use All Vehicles / Finish as Soon as Possible (maximize speed).
• REST-callable. All six modes are programmatic. Planners can lock specific routes, manually reorder stops, or re-run with new constraints. The optimizer is not a black box.
• Rule-based re-optimization. Re-route a no-access visit to the nearest engineer with the right skill, without moving any customer-confirmed slots in the next 90 minutes. Visible to the planner.

How the integration sits with Joblogic

Joblogic stays in place as the system of record. The connector between the two products is custom-built; there is no published eLogii-Joblogic integration on either side. Joblogic exposes an API to customers and partners against plan tier, and eLogii’s REST API has 70+ endpoints including the optimization endpoints. The flow:

1. Read from Joblogic. eLogii reads jobs, customers, engineers, vehicles, sites and skill sets from Joblogic’s API. Recurring PPM templates and compliance cadences flow in alongside the daily job queue.
2. Optimize in eLogii. The run produces routes with assignments, start times, end times, overnight stops, depot start/end, SLA respect and recurring-cadence respect. Planner reviews in eLogii’s dispatch desk or accepts an Auto run.
3. Write back to Joblogic. Routes and ETAs are written back to Joblogic. The mobile engineer app picks up the planned assignments. Completion data flows back to Joblogic for invoicing, compliance documentation and BI.
4. Engineer experience unchanged. The engineer opens Joblogic’s mobile app on site. The routing underneath is the one eLogii planned.

Most teams complete the connector build in 3 to 5 weeks. Typical first wave: the multi-depot regional book, a large PPM or compliance program, or the business unit where Joblogic’s drag-and-drop scheduler is leaking the most.

Frequently asked questions

Does Joblogic have an optimization engine?

Joblogic has a route calculation feature in its scheduler. The features page describes it as: “Schedule, manage and optimise the daily routes of your field engineers” with the ability to “dispatch tasks to nearby team members, reassign jobs to reduce transit time, and automatically schedule jobs to follow a more efficient travel route”. Those are route-level calculations once the planner has placed jobs via drag-and-drop and the shunt function. What Joblogic’s published docs do not describe is a constraint-based optimization engine that takes jobs, engineers, vehicles, depots, skills, time windows and SLAs as inputs and produces the assignments themselves. That layer is what eLogii adds.

What is the difference between Joblogic’s route calculation and an optimization engine?

Route calculation: given assignments, find the fastest path between the stops. Optimization engine: given a constraint model (skills, capacity, time windows, SLAs, depots, recurring cadences, cross-day dependencies), produce the assignments that minimize or maximize an objective (drive time, vehicles used, balance across crews). Joblogic does the first inside the scheduler. eLogii does both, exposed as a REST API. The two layer cleanly: Joblogic keeps the scheduling board, mobile engineer app, asset and PPM, compliance documentation and FSM workflow; eLogii sits underneath as the engine the planner doesn’t have to be.

When is Joblogic’s built-in scheduler enough?

When the planner can comfortably make the assignments by hand and Joblogic’s route calculation plus the shunt function cover the rebalancing the operation needs. This covers a wide band of UK trade and field service work across Joblogic’s 30+ supported verticals: HVAC, plumbing, electrical, gas, pest control, drainage, cleaning, landscaping, fire and safety, elevator maintenance, facilities management. Where the operation outgrows it is the optimization layer underneath – when assignment becomes a constraint-satisfaction problem across multi-depot, recurring PPM and reactive work rather than a planner judgement call.

How does eLogii’s optimization engine integrate with Joblogic?

Custom integration against both products’ APIs. Joblogic exposes an API to customers and partners against plan tier; eLogii’s REST API has 70+ endpoints including the optimization endpoints, ApiKey auth and a full-parity sandbox. Once the connector is built: eLogii reads jobs, engineers, vehicles, sites and PPM templates from Joblogic, runs the optimization, writes optimized routes and ETAs back to Joblogic. The engineer opens Joblogic’s mobile app on site; the route underneath is the one eLogii planned.

What does eLogii’s optimization engine look like in product terms?

Two engines and six configurable modes, all REST-callable. The Default engine optimizes 100 tasks in under 10 seconds for high-throughput daily planning. The Advanced engine handles multi-depot, multi-day, long-haul and constraint-heavy operations. Three assignment modes: Optimize Everything, Add to Routes Keep Existing Assignments, Add to Routes Keep Existing Assignments and ETAs. Three load-balancing modes: Most Efficient Routes, Balance the Minimum Number of Routes, Use All Vehicles / Finish as Soon as Possible. Each is callable programmatically and visible to the planner as a control they can see and steer.