Develop a simulation of an elevator that travels between floors 1 and N where N is an input. Time is sequenced in discrete steps starting at 1; at each time step, the elevator may ascend one floor, descend one floor, or remain at its current floor, as determined by its strategy. The first line of the input file indicates the number N of floors. This is followed by one line per person using the elevator: her id, call time (when she calls for the elevator), origin floor (where she boards the
elevator), and destination floor (where she debarks the elevator). For example:

5
100 2 1 4
101 3 4 5
102 3 3 2
103 5 5 1
104 5 1 4

Here, individual 100 boards at time 2 from floor 1 with a destination of floor 4. Assume sensible inputs (e.g., times are positive integers, floors are integers in the range 1 through N).

Whenever the elevator stops at a floor from which people have called it (i.e., their origin floor but not before their call time), they all board the elevator. And whenever the elevator stops at a passengers’ destination floor, she gets off the elevator. (Everyone behaves so as to minimize their travel time.) The simulation stops when every individual has boarded and been brought to her destination floor.

The simulation proceeds in integer time steps, starting at time 1. At each time step t, where the elevator is on floor f, the elevator is notified of:

1. every person who calls at time t and the floor each is calling from;
2. every person who boards at floor f and each one’s destination floor; and
3. every person who gets off at floor f (which would be her destination).

A strategy determines the floor the elevator begins on at time 1, and a policy for moving to the next floor (up one floor, down one floor, or remaining at the current floor) from one time step to the next. Here is a simple strategy:

Strategy 1: Start at floor 1. In each time step, successively go up one floor until reaching the top floor N, then successively go down one floor until reaching the bottom floor 1, and continue this ‘up to the top floor then down to the bottom floor’ policy.

Note that Strategy 1, which is rather naïve, doesn’t use notifications 1-3 above; the elevator just moves up and down, receiving and discharging passengers, until the last scheduled passenger arrives at her destination and the simulation ends. Here we run the simulation using the input file 

data1.in (given above) and Strategy 1:

>> sim = Simulation.new
=> #<Simulation:0x27d3fc8>
>> sim.run('data1.in', :strategy1)
Time 1: Floor 1
Time 2: Floor 2
100 calls from floor 1
Time 3: Floor 3
101 calls from floor 4
102 calls from floor 3
102 boards from floor 3
Time 4: Floor 4
101 boards from floor 4
Time 5: Floor 5
103 calls from floor 5
104 calls from floor 1
103 boards from floor 5
101 debarks onto floor 5
Time 6: Floor 4
Time 7: Floor 3
Time 8: Floor 2
102 debarks onto floor 2
CISC 683: Object-Oriented Design Project 3
Time 9: Floor 1
100 boards from floor 1
104 boards from floor 1
103 debarks onto floor 1
Time 10: Floor 2
Time 11: Floor 3
Time 12: Floor 4
100 debarks onto floor 4
104 debarks onto floor 4
=> nil

We measure the efficiency of a strategy with respect to the passengers’ average wait time. Let’s say that someone’s wait time is the number of time steps from her call time until she reaches her destination floor. So wait time is the time waiting for the elevator to arrive at the origin floor plus the time traveling in the elevator to the destination floor. For example, the wait times for passengers 100 through 104 are 10, 2, 5, 4, and 7, respectively. Then we define the efficiency of a strategy (with respect to input) to be the average wait time over all passengers. Our simulation reports this value for the simulation just run:

>> puts sim.average_wait_time
5.6

Here is what you need to submit:
1. A sequence diagram for the case where a passenger calls the elevator, the elevator arrives, she boards, the elevator moves (over time) to her destination floor, and she debarks. You may devise other diagrams but this one is essential. You are welcome to discuss and work on your design on our discussion board and during class time (but you should implement the project on your own).

2. Implement Strategy 1 as part of your project. In addition, devise and implement a second strategy that is generally more efficient than Strategy 1. Where sim is a Simulation object, 

we run a simulation like this:

sim.run(filename_string, strategy_symbol)

The symbol :strategy2 should identify your (more efficient) strategy.

3. Write a method

sim.multirun(nbr_runs, filename_prefix, strategy)

that runs your program nbr_runs many times on the input files named by filename_prefix with suffixes 000.in, 001.in, …, and returns the average efficiency. For example:

sim.multirun(15, 'myelev', :strategy2)

runs your Strategy 2 on the files myelev000.in, …, myelev014.in and returns the average of the 15 efficiency values. I will give you a set of input files to test your program on, and you should report the average efficiency you obtain on these files for both strategies. You can also use these input files to compare your strategy to other strategies, with respect to efficiency. I’ll also give you a short Ruby program to generate your own test files.

4. Submit your Ruby code for this project, in addition to your scenario diagram.