Thursday, July 25, 2013

Optimizing Schedules: QWL Considerations

A first blog post from India.
Impact of Decision Variables on Humans
This practice related comment was triggered by yet another useful 'Punk Rock OR' post  - on 'optimization and unhappy truckers', which briefly reviews a Tom Vanderbilt article that noted that mathematical optimization may having contributed to the incremental unhappiness of employees who were affected by the decisions prescribed by the model. TV's article also talks about the optimization of airline crew schedules, which is a useful example to analyze some of the side-effects of optimization.

Scheduling Objectives
The rules that govern the safe scheduling of airline crews are incredibly complex, and used to appear in a bound book form (every single one must be programmed into the optimization system, scarring an Operations Researcher for life). Additionally, there are hundreds of different 'cost' components that typically go into an airline crew scheduling system that is so neatly abstracted to "Min cx. Ax=1, x binary" in OR textbooks. Some of the objectives are listed below:
1. cost, utilization, efficiency
2. quality of work life (QWL)
3. schedule regularity
4. operational resilience
5. Downstream system compatibility
.. and many more..

Among these, a component that is most relevant to the discussion here is QWL, a non-negotiable component of "soft rules" that go beyond what the FAA prescribes and diligently adheres to the letter and spirit of a collective bargaining agreement (CBA) between the management and representatives of the crews. QWL metrics are audited and checked before schedules are published, and tracked over time. A drop in QWL metrics can result in followup phone calls from crew representatives, and keeping the call volume (and decibel) to a minimum is a clear and track-able goal.

Anonymous Schedules, Personal Impacts
While traveling on company flights, I initially used to strike up a conversation with flight-attendants (FAs) to get their opinions on their schedules, and any particular issues they had. There were some harsh complaints, but also the occasional compliment based on their feedback that compared their QWL to FAs in other carriers.  Nevertheless, schedules are initially anonymous, and thus indifferent to personal needs, while also being free of privacy concerns. It is safe to say that unless schedules are personalized, there's bound to be unhappy crews. Personalization is at odds with automation, and the task of optimally synchronizing and scheduling 30, 000 FAs and pilots, and hundreds of expensive aircraft that operate thousands of flights per day, while trying to keep costs down, reliability high, and crews happy is non-trivial. Luckily, the space of feasible schedules contains many trillions of possibilities, and is diverse enough to accommodate many, many management and crew objectives to produce tons of alternative near-optimal solutions. In fact, this feature plays a vital part in designing new and improved crew safety rules during CBA negotiations. To summarize, modern, large-scale industrial optimization systems are sophisticated, robust, and flexible enough to accommodate a myriad of human-impact objectives without breaking a sweat. Who knows, truly personalized schedules that sync with personal calendars, while also keeping utilization high, may well be technically feasible now. Preferential bidding systems (PBS) have already been in place for more than a decade now.

Actions Reflect Priorities
Some of my purely personal observations based on the data I have seen: the QWL metrics for a schedule is correlated to the negotiating clout of the organization for whom the scheduling is done, and the importance given by management to maintaining harmonious relations with them. Higher up the food chain, the better the QWL. Not surprisingly, some employee organizations may have their own optimization systems that enable them to evaluate their schedules (and also 'game' the system). 

In between the scheduler and the 'schedulee' is the OR layer, the secret sauce. I'd like to believe that OR'ers can make and have made a positive difference by paying attention to the net human impact of a binary variable changing from a 0 to 1 to find win-win stakeholder-friendly alternative optima. I've seen analysts devote many days trying to figure out how to make excruciatingly complex experimental QWL constraints work cost-effectively in the optimization system to break an ongoing CBA negotiation deadlock: for example, how to limit the flying done by west-coast based pilots when it is dawn, Eastern Standard Time (EST). Putting the plane on autopilot and going to sleep is not an option. I have even seen prototypes that used "crew happiness variables" :)

It is interesting to look at the optimized crew-aircraft schedules for fractional jets that ferry well-heeled folks and time-starved execs on Gulfstream-Vs to various parts of the world between tiny airports. Needless to say, non-bottomline 'costs' and degree of personalization play a prominent role in the objective function. The customer is both king and queen. In the end, a well-designed optimization system's objectives can accommodate the considerations of all the stakeholders to consistently (and merely) reflect their relative importance from a human decision-maker's perspective. Nothing more, nothing less. As Gandhi ji said, actions reflect priorities.

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