© 2020 Opturion. All Rights Reserved

OPTURION WHITE PAPER: City West Water Dispatch Optimisation Proof of Concept

February 2019 

Introduction

City West Water (CWW) provides drinking water, sewerage, trade waste and recycled water services to customers in Melbourne’s central business district, inner and western suburbs.  Part of this business is to manage the maintenance of the required infrastructure. CWW contract their maintenance activities to the maintenance and facilities management services company, PFM.

There are two main kinds of maintenance cases: preventative and responsive.  Preventative maintenance (PM) cases are those that are scheduled in advance, often required to be repeated at regular intervals.  Responsive cases represent unplanned work, for example, a SCADA alarm for a well level. Responsive cases have varying levels of priority and target attendance times. Financial penalties are incurred if the average time-to-attendance for responsive cases exceeds a given target.

AIM

The aim of this project is to produce a proof of concept optimisation tool that will demonstrate the capability to automatically create and maintain the monthly schedule of preventative maintenance actions resulting from the preventative maintenance cases and allocate responsive actions to crews as the cases arise. The tool would ultimately be used to assist CWW’s planners and schedulers. The proof of concept will only consider those cases and crews in the mechanical and electrical (M&E) area of the business.

current process

In this section, we describe our understanding of the current scheduling and dispatch processes for M&E cases. PM cases are generated in CWW’s Oracle eAM and integrated into Focus 3. Responsive cases can be generated by the SCADA system and integrated into Focus 3 or created in Focus 3 by the CWW Operations Control Centre. Scheduling of the resulting actions for both PM and responsive cases for M&E is the responsibility of the PFM Operations Manager for M&E at the Brooklyn depot.

CREWS 

M&E crews are mix of PFM crews and subcontractors.  Crew shifts vary in length, but somewhere between 8 - 12 hours is usual.  For PFM employees the workday commences from their departure from home until the end of their shift. For subcontractors, the beginning of a shift is taken from the crew’s arrival on-site at their first action of the day.  Crews may work overtime. For M&E, a crew usually consists of a single member.

  

M&E crews are currently managed by associating each crew with a zone (i.e. a geographic region).  Actions within a zone will usually be assigned to that zone’s crew. Each crew has an in-tray that contains their scheduled PM actions for the month, as well as any responsive actions that have been assigned to them. Crews have differing levels of efficiency, which affects the amount of time that they require to complete an action. Crews may be accompanied by special equipment (e.g. an excavator or crane) for some, or all, of the duration of their shift.

Preventative Maintenance ACtions 

PM actions are allocated to crews at the beginning of each month, based on the type and location of each action and crew capability.  All of a crew’s PM actions for a month are placed in their in-tray at the beginning of the month and the crew choose the day and time when they will undertake the work.  PM actions must be completed by the end of the month. Some PM actions, those requiring special equipment, authorisations or multiple crews, may be scheduled to a greater degree of precision.   With that exception, there is currently no daily plan of a crew’s PM actions, beyond what the crew decide for themselves. For M&E, there are approximately 150-200 PM actions per month.

Preventative maintenance actions are allocated using according to the following criteria:

  • The zone in which the action is located and which crew belongs to the zone.

  • The capabilities and efficiency of the crew.

  • The amount of work that is allocated to that crew.

Forecasting

Vendor Managed Inventory can ultimately only be successful if we have an accurate forecast of future demand (sales). This allows for planning further ahead and reducing the number of visits or increasing the delivered quantities. Planners are typically informed by a basic forecast of the demand from the last 3 days and same time last week. We create and improve the forecast by using Machine Learning techniques and utilise characteristics of each day (day of the week, weekend day, public holiday, school holiday) and seasonal factors.

VMI and Route Optimisation

The final component uses the forecast and schedules and routes deliveries using the load building rules or a safe approximation of them. The forecast will tell us how much we must deliver to each site on each day, and how much we can deliver. The optimisation will then build routes for each day, allocating deliveries to the different sites onto those routes. They are to be built such that:

  • There are never any stockouts (assuming the future takes place as per the forecast)

  • We don’t deliver more than each tank will hold (as per the forecast)

  • We do not break any loading rules

In essence, the optimisation will combine three components:

  • Routing and Sequencing: subject to travel times, loading and unloading, fatigue management and vehicle availability

  • Vendor Managed Inventory: preventing stockouts and not delivering more than what will fit

  • Load Management: creating feasible loads with respect to the vehicle configuration and regulatory and business rules

The inputs to this optimisation are vehicle and driver availabilities, and a forecast for each site and tank. The output is a multi-day plan consisting of a run sheet for each driver and shift that lists all of the stops to be made, and all of the quantities to deliver (at a minimum).

Typical Benefits

Benefits include:

  • Reduction in number of vehicles and shifts by making maximum use of B-doubles and multi-drop deliveries. This can be up to 20%.

  • In the combined VMI/Route Optimisation scenario, we can reduce further reduce the number of shifts required and kms driven by approximately 10%

  • Increased payload by more accurate load planning. This increases revenue by around 1% on a consistent basis.

Use Cases

Here we will define a number of use cases for the proposed optimisation tools.

 Day Ahead Load Planning:  For this use case, we’d be given a vehicle configuration, and a sequence of site visits (one or more), as well as minimum and maximum volumes to be delivered to each tank at each of the sites. For the day ahead planning, we may use conservative product densities to be on the safe side. There is the option to play around with preferences, e.g. preferred compartments for particular fuels/tanks and the planner could run multiple optimisation scenarios to gauge the impact of these (e.g. first see how much can be delivered, and then see if we can deliver the same amount with preferential fuel to compartment allocation). Also, in case the order of the site visits is flexible, each order can be tried in a separate optimisation scenario.

Pre-Loading Re-Optimisation: Before loading the vehicle, we can do a quick re-optimisation taking into account the most recent product density readings. Because of time constraints, this is not possible with the current manual process, but the optimisation tool could achieve this task automatically in very little time, and as such increase delivery efficiency.

 Delivery Date and Route Optimisation:  This is the VMI/Route Optimisation case: the forecasting combined with a fleet and driver roster, is used to make the decision of which sites to visit on which days/shifts, and which routes to take for each vehicle. The exact delivery quantities can then be further optimised as a second stage process.

Bid Optimisation: The optimisation tools can be used to model the impact of taking on new business. Various scenarios can be modelled, giving the optimisation the flexibility to choose an appropriate vehicle mix and work pattern to achieve the transport requirements at the lowest cost.

Opturion 1584x287.png