schedule join multiple tables Basically I need an scalable efficient way to select all scheduled items so it made sense that the main table is the schedule. The problem is that all the operation tables have different properties

I created a diagram which is obviously wrong but demonstrates what I am trying to achieve. 

I was thinking about having the join on the other side so that all three operations pointed to the schedule, but if I do that then to get a complete schedule I have to aggregate all the operation tables. What if I have loads more in the future?

Another option was to have the delivery, maintenance, loading columns in the schedule table but again there could be loads more operations. I'm just wondering of I am missing something or is this the best option?
You can have two fields in schedule table:


operation_type
operation_id


Then you can select everything like this:

SELECT s.*, l.weight, d.driver, d.fuel, mo.mechanic, mo.parts
  FROM schedule AS s
  LEFT JOIN loading_operation AS l ON s.operation_id=l.id
                                  AND s.operation_type='l'
  LEFT JOIN delivery_operation AS d ON s.operation_id=d.id
                                  AND s.operation_type='d'
  LEFT JOIN maintenance_operation AS mo ON s.operation_id=mo.id
                                  AND s.operation_type='m';


and the the output of this query would be something like:

id  date_from  date_to    operation_type operation_id weight driver fuel mechanic 
 1 2019-02-06 2019-03-14       m            5                               874  
 2 2019-01-16 2019-02-24       d            3                 431     838         
 3 2019-03-16 2019-03-03       m            5                               874  
 4 2018-12-16 2019-02-11       m            2                               975  
 5 2019-02-12 2019-03-03       d            1                 825     438         
 6 2019-02-12 2019-03-14       d            7                 604     534         
 7 2019-01-11 2019-02-25       l            9          304                       
 8 2019-03-15 2019-02-14       m            8                               188  
 9 2019-03-05 2007-04-20       m            2                               975  
10 2019-02-25 2007-04-22       m            5                               874