OK I got 2 more plans, can you give comparisons of the two plans now?

Plan 3

Below is a merged plan (“Plan 3”) that combines the best aspects of both plans you showed, tailored to your stated preferences. I’ve reviewed the answers you gave to the design questions and aligned the final recommendations accordingly.

Plan 3: Unified & Flexible Schedule Extraction

1. Module Structure: A Single “Extractor” Folder With One or Two Key Files

1.1 Rationale

You said you want to avoid making compiler.rs “bloated” but also to keep the approach “not too sprawling.”
Therefore, we’ll place most extraction logic in one new subdirectory named, for example, extractor/ or schedule_extractor/.
Inside that directory, you can start with a single file (e.g., schedule_extractor.rs). If that file grows too large in the future, you have the option to split out code into smaller files, such as strategy.rs or alignment_utils.rs.

1.2 Proposed Layout

src/
├── compiler/
│    ├── clock_info.rs
│    ├── compiler.rs
│    └── ...
├── extractor/
│    └── schedule_extractor.rs   # (Single file for now)
├── types/
│    └── ...
└── main.rs

schedule_extractor.rs: Contains:
- The ScheduleStrategy enum
- A struct or set of functions (collectively referred to as “ScheduleExtractor”)
- The logic for each strategy: Earliest, Latest, Centered, Justified, MaximumSpread
- Optional “relaxation” code, if you decide to implement it now

If the code expands significantly (for instance, if you add advanced cross-entity relaxation or partial-interval constraints), you can split it further.

2. A Single Extraction Entry Point With a “Strategy” Enum

2.1 Strategy Enum

You’ll define:

#[derive(Debug, Clone, Copy)]
pub enum ScheduleStrategy {
    Earliest,
    Latest,
    Centered,
    Justified,
    MaximumSpread,
}

Why:
1. You want different alignment approaches, but a single conceptual “extract_schedule” method.
2. This enum is easily extended later if you add new strategies (e.g. “SegmentedWindow” or “PriorityOptimized”).

2.2 “Extractor” Struct vs. Free Function

Either approach is valid, but you mentioned that you’re comfortable with references and want to keep performance in mind. We’ll go with a struct that borrows from the compiler data:

pub struct ScheduleExtractor<'a> {
    pub zone: &'a Dbm<i64>,
    pub clocks: &'a HashMap<String, ClockInfo>,
    // Optional: If cross-entity referencing or entity data needed:
    // pub entities: &'a HashMap<String, Entity>,
    // etc.
}

impl<'a> ScheduleExtractor<'a> {
    pub fn new(zone: &'a Dbm<i64>, clocks: &'a HashMap<String, ClockInfo>) -> Self {
        Self { zone, clocks }
    }

    pub fn extract_schedule(
        &self,
        strategy: ScheduleStrategy
    ) -> Result<HashMap<String, i32>, String> {
        // Feasibility check
        if self.zone.is_empty() {
            return Err("Zone is empty; no schedule is possible.".to_string());
        }

        // Dispatch
        match strategy {
            ScheduleStrategy::Earliest => self.extract_earliest(),
            ScheduleStrategy::Latest => self.extract_latest(),
            ScheduleStrategy::Centered => self.extract_centered(),
            ScheduleStrategy::Justified => self.extract_justified_global(),
            ScheduleStrategy::MaximumSpread => self.extract_max_spread_global(),
        }
    }

    // Then implement each method below...
}

(Note: For code brevity, you can store references to entities too if your “spread” or “justified” code needs it. Otherwise, you only need the DBM + clocks to do strictly bounding-based extraction.)

3. Per-Strategy Methods (Taking the Best of Both Plans)

You indicated you want:

Earliest & Latest: trivial to implement, definitely feasible.
Centered: old midpoint logic.
Justified & MaxSpread: should operate globally (rather than per-entity) because you want cross-entity constraints to be respected in a single pass.

3.1 Earliest & Latest

fn extract_earliest(&self) -> Result<HashMap<String, i32>, String> {
    let mut schedule = HashMap::new();
    for (clock_id, info) in self.clocks.iter() {
        let lb = self.zone.get_lower_bound(info.variable).unwrap_or(0);
        schedule.insert(clock_id.clone(), lb as i32);
    }
    Ok(schedule)
}

fn extract_latest(&self) -> Result<HashMap<String, i32>, String> {
    let mut schedule = HashMap::new();
    for (clock_id, info) in self.clocks.iter() {
        let ub = self.zone.get_upper_bound(info.variable).unwrap_or(1440);
        schedule.insert(clock_id.clone(), ub as i32);
    }
    Ok(schedule)
}

3.2 Centered

Exactly as you originally wrote:

fn extract_centered(&self) -> Result<HashMap<String, i32>, String> {
    let mut schedule = HashMap::new();
    for (clock_id, info) in self.clocks.iter() {
        let lb = self.zone.get_lower_bound(info.variable).unwrap_or(0);
        let ub = self.zone.get_upper_bound(info.variable).unwrap_or(1440);
        let mid = (lb + ub) / 2;
        schedule.insert(clock_id.clone(), mid as i32);
    }
    Ok(schedule)
}

(Just be aware it might not always respect cross-entity constraints if the margin is tight. More on that in the “relaxation” step.)

3.3 “Justified” and “MaximumSpread”

Because you said “these are properties of the entire schedule,” we should:

Gather all clocks in a single group.
Sort them by feasible earliest times or a partial order.
Pin the first clock to the earliest possible time, the last clock to the latest possible time, then distribute the in-between clocks evenly.
For “MaximumSpread,” do basically the same approach, but you might handle each sub-interval if you have mandatory constraints that certain subsets must remain close or separate.
At the end, we clamp each assignment to [LB, UB].

A minimal approach:

fn extract_justified_global(&self) -> Result<HashMap<String, i32>, String> {
    // 1) Collect all clocks
    let mut all_vars: Vec<(String, i64, i64)> = Vec::new();
    for (clock_id, info) in &*self.clocks {
        let lb = self.zone.get_lower_bound(info.variable).unwrap_or(0);
        let ub = self.zone.get_upper_bound(info.variable).unwrap_or(1440);
        all_vars.push((clock_id.clone(), lb, ub));
    }

    // 2) Sort them by lb ascending (or if you have a partial order, do topological sort)
    all_vars.sort_by_key(|(_, lb, _)| *lb);

    // 3) Pin first to its lb, last to its ub
    let (first_id, first_lb, first_ub) = &all_vars[0];
    let (last_id, last_lb, last_ub) = &all_vars[all_vars.len() - 1];
    let start = *first_lb; // might want to clamp if there's a difference constraint 
    let end   = *last_ub;

    let mut schedule = HashMap::new();
    schedule.insert(first_id.clone(), start as i32);
    schedule.insert(last_id.clone(), end as i32);

    // 4) Distribute the middle
    let total_span = end - start;
    for i in 1..all_vars.len() - 1 {
        let (clk_id, lb, ub) = &all_vars[i];
        let ideal = start + (total_span * i as i64) / ((all_vars.len() - 1) as i64);
        let clamped = ideal.clamp(*lb, *ub);
        schedule.insert(clk_id.clone(), clamped as i32);
    }
    Ok(schedule)
}

Similar logic for “MaximumSpread.” The difference is you might do a single global pass that tries to push events away from each other if the feasible region is large. Or you might group them further by constraints. The key is you’re operating on the entire set of clocks, not one entity at a time.

4. Handling Cross‐Entity Constraints (Important to You)

You said cross-entity constraints are important, so “I don’t want to design it half-baked.” Here’s how you can incorporate that:

After you do the naive approach (Earliest, Latest, Centered, Justified, or MaxSpread), you do a forward/backward “relaxation” pass that ensures all difference constraints (like Clock_j >= Clock_i + offset) remain satisfied.
If the “ideal” assignment you made violates constraints, the relaxation step adjusts times until all constraints are satisfied or it finds it’s impossible.
You can store all difference constraints in an array of (i, j, offset), or you can parse them back from the DBM. Then repeatedly do:
- If schedule[j] < schedule[i] + offset, set schedule[j] = schedule[i] + offset.
- Keep bounding each by [LB_j, UB_j].
- Iterate until stable.

Example “Relax” Method

fn relax_schedule(&self, schedule: &mut HashMap<String, i32>) -> Result<(), String> {
    // Pseudocode:
    let mut changed = true;
    while changed {
        changed = false;

        // For each difference constraint: Tj >= Ti + offset
        for (i_var, j_var, offset) in self.all_diff_constraints() {
            let i_id = self.find_clock_id(i_var)?; 
            let j_id = self.find_clock_id(j_var)?;
            let ti = schedule[&i_id];
            let tj = schedule[&j_id];
            let needed = ti + offset;

            if tj < needed {
                // We must push j up
                let ub_j = self.zone.get_upper_bound(j_var).unwrap_or(1440) as i32;
                let new_tj = needed.min(ub_j);
                if new_tj > tj {
                    schedule.insert(j_id, new_tj);
                    changed = true;
                }
                // If needed > ub_j => infeasible => return Err(...)
            }
        }
    }
    Ok(())
}

Integration:

fn extract_justified_global(&self) -> Result<HashMap<String, i32>, String> {
    let mut schedule = self.simple_justified_global()?; // naive approach
    self.relax_schedule(&mut schedule)?;                // fix cross-entity constraints
    Ok(schedule)
}

But you only need to do this if your naive approach might break cross-entity constraints. If your day is wide enough or your constraints are mild, it might not be necessary.

5. Integrating With the Existing Compiler

You said you want a single method “extract schedule” but less trivial than your old midpoint approach. So, you can:

Remove or deprecate TimeConstraintCompiler::extract_schedule() from the old code.
Add a new method that calls into your separate extractor:

impl TimeConstraintCompiler {
    pub fn finalize_schedule(
        &self,
        strategy: ScheduleStrategy
    ) -> Result<HashMap<String, i32>, String> {
        // Ensure compile is done
        self.compile()?; 
        // Then create the extractor
        let extractor = schedule_extractor::ScheduleExtractor::new(&self.zone, &self.clocks);
        // Pass the chosen strategy
        extractor.extract_schedule(strategy)
    }
}

Now you have a single call in your app code:

let mut compiler = TimeConstraintCompiler::new(entities);
compiler.compile()?; 
let schedule = compiler.finalize_schedule(ScheduleStrategy::Justified)?;

(If you prefer “compile_and_extract,” that’s just naming. The important part is the compiler calls the new module rather than doing it in the same file.)

6. Error Handling & Extensibility

Error Handling: Return Result<HashMap<String, i32>, String> with descriptive messages (like “Cross-entity constraints are not satisfiable under Justified alignment. Please choose a different strategy or expand the time window.”).
Extensibility: If you add advanced features like “disallowed intervals” or “negative windows,” you can store that logic in the new “extractor” or do an extra step that modifies the DBM itself. The user’s feedback about “windows for the entire day” can stay in the compiler’s domain (like you already do with set_daily_bounds()?), or we can expand the extractor to handle partial intervals.
Public vs. Private: If you want to expose the “relax_schedule” function publicly, you can do so. Or keep it private if it’s purely an internal step for Justified/Spread.

7. Summarizing the “Merged” Plan

Create extractor/schedule_extractor.rs:
- Contains a ScheduleStrategy enum.
- A ScheduleExtractor<'a> struct that borrows &Dbm<i64> and &HashMap<String, ClockInfo>.
- A pub fn extract_schedule(&self, strategy: ScheduleStrategy) -> Result<HashMap<String, i32>, String> method that calls sub-methods.
Implement sub-methods for:
- extract_earliest(), extract_latest(), extract_centered() (straightforward)
- extract_justified_global(), extract_max_spread_global() (a single pass over all clocks; distribute times from earliest to latest)
- (Optionally) a private relax_schedule method if you want to ensure cross-entity constraints remain feasible.
In TimeConstraintCompiler:
- Remove or deprecate the old extract_schedule().
- Add a new fn finalize_schedule(&self, strategy: ScheduleStrategy) -> Result<..., ...>.
- This method calls compile(), then calls the “extractor.”
Error handling: Return descriptive strings, consistent with existing patterns.
(Optional) If the file schedule_extractor.rs becomes too big, split out the advanced logic into alignment_utils.rs or relaxation.rs. But start with a single file for clarity.

8. Why This Merged Plan Matches Your Preferences

Minimize Bloat & Sprawl: All extraction code is out of the compiler. We start with just one or two files under extractor/.
Single “extract schedule” Method: That’s exactly extract_schedule(strategy).
No Half-Baked Cross-Entity: We provide an optional relaxation step or do a single global distribution approach for Justified and Spread, so cross-entity constraints are accounted for.
Performance: We borrow references to the compiler’s zone and clock data—no large copies or new allocations needed.
Future Expandability: If you add partial windows or advanced constraints, the new module is already set up to handle them. Just store references to that data and incorporate them in your strategy or relaxation code.
Error Feedback: We can produce debug strings referencing clock IDs, bounds, offsets, etc., just like your current debugging approach.

This approach has the rich detail of Plan 1 (especially around cross‐entity constraints and robust “global” alignment) without requiring a huge multi-file structure from day one (Plan 2’s more minimal file approach). You can always expand the file structure if the code base grows.

Final Takeaway

With “Plan 3,” you:

Keep the compiler purely for building constraints.
Introduce a separate extractor module for picking times from the feasible DBM region.
Offer multiple strategies (Earliest, Latest, etc.), each implemented in short, clear methods.
Handle cross-entity constraints properly by a global approach or a final relaxation pass.

Plan 4

Below is a combined, improved plan that merges the best elements of Plan 1 and Plan 2, with explicit attention to your stated preferences:

Simplicity: you want a straightforward structure without excessive sprawl.
Modularity: if a file becomes large, you’ll split it—but not proactively create many tiny files.
Proper Cross-Entity Handling: you don’t want half-baked solutions. “Justified” and “MaximumSpread” should be global across the entire schedule, not per-entity only.
Single “extract_schedule” Interface: the library user calls just one method, which can be re-implemented in the new module.
Rich Error Handling: keep the existing style, providing contextual debug info.

Below you’ll find a step-by-step plan that takes the clarity of Plan 2’s file structure plus the detailed code examples and advanced cross-entity considerations from Plan 1 (and earlier notes). Each section references your preferences from the Q&A.

1. Directory and File Structure

Create a single folder named, for example, schedule_extractor/ inside src/.
Inside that folder, you have at least one file: mod.rs, which re-exports a second file, methods.rs (or strategies.rs) if it grows large. Start minimal:

src/
 ├─ compiler/
 │   ├─ clock_info.rs
 │   └─ compiler.rs
 ├─ schedule_extractor/
 │   ├─ mod.rs          (public module entry, re-export sub-items)
 │   └─ schedule_extractor.rs   (the core code for extraction)
 └─ ...

Why:

Avoid bloating your compiler module.
You can add more files (like alignment.rs, relaxation.rs) if schedule_extractor.rs grows too big. But start with just schedule_extractor.rs to keep it simple.

2. The `ScheduleStrategy` Enum

Inside schedule_extractor.rs:

#[derive(Debug, Clone, Copy)]
pub enum ScheduleStrategy {
    Earliest,
    Latest,
    Centered,
    Justified,
    MaximumSpread,
}

Matches both plans.
Justified and MaximumSpread are intended to be global, cross-entity methods (see below).

3. A `ScheduleExtractor` Struct (Or Single Function)

3.1 Struct Pattern (Recommended)

use clock_zones::{Dbm, Zone};
use std::collections::HashMap;
use crate::compiler::clock_info::ClockInfo;

pub struct ScheduleExtractor<'a> {
    pub zone: &'a Dbm<i64>,
    pub clocks: &'a HashMap<String, ClockInfo>,
    // Possibly also references to entities, categories, if needed:
    // pub entities: &'a HashMap<String, Entity>,
}

impl<'a> ScheduleExtractor<'a> {
    pub fn new(zone: &'a Dbm<i64>, clocks: &'a HashMap<String, ClockInfo>) -> Self {
        ScheduleExtractor { zone, clocks }
    }

    pub fn extract_schedule(
        &self,
        strategy: ScheduleStrategy,
    ) -> Result<HashMap<String, i32>, String> {
        if self.zone.is_empty() {
            return Err("Cannot extract schedule from empty zone".to_owned());
        }

        match strategy {
            ScheduleStrategy::Earliest => self.extract_earliest(),
            ScheduleStrategy::Latest => self.extract_latest(),
            ScheduleStrategy::Centered => self.extract_centered(),
            ScheduleStrategy::Justified => self.extract_justified(),
            ScheduleStrategy::MaximumSpread => self.extract_max_spread(),
        }
    }
    
    // Now implement the methods...
}

This lines up with your preference for a single interface (extract_schedule) but internal methods for each alignment style.
We borrow from zone and clocks to avoid duplication.
If you want to store entities: &'a HashMap<String, Entity> or categories: &'a HashMap<String, HashSet<String>>, that’s easy too.

3.2 Single-Function Pattern (Alternative)

If you prefer fewer types, define a free function:

pub fn extract_schedule(
    zone: &Dbm<i64>,
    clocks: &HashMap<String, ClockInfo>,
    strategy: ScheduleStrategy
) -> Result<HashMap<String, i32>, String> {
    if zone.is_empty() {
        return Err("Cannot extract schedule from empty zone".to_owned());
    }
    match strategy {
        ScheduleStrategy::Earliest => extract_earliest(zone, clocks),
        // ...
    }
}

(But the struct approach is often tidier for logic sharing.)

4. Implementing Each Strategy

4.1 Earliest & Latest & Centered

Use your existing logic from the old extract_schedule(), but per strategy:

// Example: Earliest
fn extract_earliest(&self) -> Result<HashMap<String, i32>, String> {
    let mut schedule = HashMap::new();
    for (clock_id, clock_info) in self.clocks.iter() {
        let lb = self.zone.get_lower_bound(clock_info.variable).unwrap_or(0);
        schedule.insert(clock_id.clone(), lb as i32);
    }
    Ok(schedule)
}

// Example: Latest
fn extract_latest(&self) -> Result<HashMap<String, i32>, String> {
    let mut schedule = HashMap::new();
    for (clock_id, clock_info) in self.clocks.iter() {
        let ub = self.zone.get_upper_bound(clock_info.variable).unwrap_or(1440);
        schedule.insert(clock_id.clone(), ub as i32);
    }
    Ok(schedule)
}

// Example: Centered
fn extract_centered(&self) -> Result<HashMap<String, i32>, String> {
    let mut schedule = HashMap::new();
    for (clock_id, clock_info) in self.clocks.iter() {
        let lb = self.zone.get_lower_bound(clock_info.variable).unwrap_or(0);
        let ub = self.zone.get_upper_bound(clock_info.variable).unwrap_or(1440);
        schedule.insert(clock_id.clone(), ((lb + ub) / 2) as i32);
    }
    Ok(schedule)
}

These are relatively simple and typically remain feasible for “Earliest”/“Latest” because they never break ordering constraints. “Centered” can sometimes clash with cross-entity constraints, so you might do a final pass if your domain is strict.

4.2 “Justified” and “MaximumSpread” (Global Approach)

You specifically said (5) you want these to operate globally, across the entire schedule, not per entity. So:

Collect All Clocks in a single list.
Determine a global earliest LB (minLB) and global latest UB (maxUB) across all clocks.
Sort the clocks in ascending order by either LB or by some other criterion (like topological order, if you require times in ascending order).
Space them out across [minLB, maxUB].

4.2.1 “Justified” (Global)

The “first” clock in the sorted list gets time = minLB.
The “last” clock gets time = maxUB.
Everything in between is placed evenly:

[ T_k = \text{clamp}\Bigl( minLB + \frac{k-1}{n-1} \times (maxUB - minLB), LB_k, UB_k \Bigr). ]

Where LB_k and UB_k are that clock’s personal DBM bounds. If the clamp changes the order, you might do a second pass to ensure no constraint is broken. (Or do a topological approach if constraints are strictly ordering these events.)

4.2.2 “MaximumSpread” (Global)

The logic is quite similar:

Sort the clocks in the order you want them arranged (maybe topologically if they have partial ordering constraints).
Find the total range [minLB, maxUB].
Spread them evenly: [ T_k = \text{clamp}\Bigl( minLB + \frac{k-1}{n-1} \times (maxUB - minLB), LB_k, UB_k \Bigr). ]

Essentially, “justified” and “max spread” become nearly the same if you want them truly global. In some schedules, “justified” might anchor only the first and last events, while “max spread” tries to push each event to the maximum possible spacing from its neighbors. The implementation is similar.

(If you still want a per-entity approach, you can just group by entity, but you said you want global. That’s fine.)

5. Handling Cross‐Entity Constraints Thoroughly

Because you want to ensure correctness across all constraints, consider adding a final “relaxation” pass. This was hinted at in both Plan 1 & Plan 2:

Once you assign an initial time for each clock (Earliest, Latest, or global spacing), you iterate over each difference constraint in your DBM:
[ \text{(Clock}_j \ge \text{Clock}_i + \text{offset)} ]
If T_j < T_i + offset, then push T_j up to T_i + offset.
Keep going until stable or detect an impossible conflict.
If you exceed the upper bound for any clock, you know that your chosen distribution was too aggressive.

Implementation Detail:

You might keep a side copy of all constraints you added in TimeConstraintCompiler, or you can iterate the DBM matrix to read them.
If you do large, global “justified” spacing, you’ll definitely want this pass to fix any minor ordering or cross-entity constraints.

If you’d rather keep it simpler for now, you can skip the relaxation step. But that means “Centered/Justified/MaxSpread” can produce final times that might slightly violate cross-entity constraints. If your domain is such that the DBM’s [LB, UB] are wide enough, you might be okay.

6. Integrating with `TimeConstraintCompiler`

6.1 Removing the Old `extract_schedule()`

Given you want a single method that calls the new module, in TimeConstraintCompiler do:

impl TimeConstraintCompiler {
    pub fn finalize_schedule(
        &self,
        strategy: ScheduleStrategy
    ) -> Result<HashMap<String, i32>, String> {
        use crate::schedule_extractor::{ScheduleExtractor, ScheduleStrategy};

        // 1) Make sure we've compiled (the zone is feasible)
        if self.zone.is_empty() {
            return Err("Cannot finalize schedule: zone is empty".into());
        }

        // 2) Build the extractor with references to the zone & clocks
        let extractor = ScheduleExtractor::new(&self.zone, &self.clocks);
        
        // 3) Dispatch
        extractor.extract_schedule(strategy)
    }
}

Now your library’s user can do:

// ...
compiler.compile()?; // ensures constraints are built
let schedule = compiler.finalize_schedule(ScheduleStrategy::MaximumSpread)?;

6.2 Keep or Deprecate?

If you want to remove the old pub fn extract_schedule(&self) in compiler.rs altogether, do so.
If you want to keep it for backward compatibility, just make it call finalize_schedule(ScheduleStrategy::Centered) or some default. But presumably you want to encourage the new approach.

7. Error Handling

You prefer descriptive errors with relevant clock names, offsets, etc. That matches your existing style. So each method can do:

Err(format!("No lower bound found for clock {}", clock_id))
For the relaxation step, if it fails, you might say, “Cannot satisfy cross-entity constraint between {} and {} after adjusting times.”

You can build more advanced error types or keep returning String; that’s up to you.

8. Extensibility for “Windows” or Future Features

Your final note was about “windows” (like only allow scheduling within [X, Y]). That can be integrated in two ways:

During compilation: Just add constraints “Clock_i ≥ X” and “Clock_i ≤ Y” for each relevant clock. Then the DBM enforces it.
During extraction: If you want partial windows (like “no events from midnight to 06:00, then events from 06:00 to 12:00 only”), you’d have to add constraints or do a “relaxation” step that sets those times out of range as infeasible.

Either approach is consistent with the design in Plan 1 or Plan 2—just add more constraints, or handle them in your final pass.

9. Final Summarized Checklist (Merging Plan 1 & Plan 2)

Create schedule_extractor/ folder with a single schedule_extractor.rs (or more files if it grows).
Define ScheduleStrategy enum.
Define ScheduleExtractor struct with references to Dbm<i64> and HashMap<String, ClockInfo>. Possibly also reference entities or categories if you need them for grouping.
Implement a public method fn extract_schedule(&self, strategy: ScheduleStrategy) -> Result<HashMap<String, i32>, String> that:
- Checks feasibility with zone.is_empty().
- Calls an internal method based on the chosen strategy: extract_earliest(), extract_latest(), extract_centered(), extract_justified(), or extract_max_spread().
Implement extract_justified() and extract_max_spread() in a global way, not per-entity, by sorting all clocks and distributing them.
(Optional) Add a relax_in_place(...) method or similar if cross-entity constraints require correction after the initial distribution.
In TimeConstraintCompiler, remove or replace the old extract_schedule(). Provide a new method, e.g. finalize_schedule(strategy), that instantiates ScheduleExtractor and calls extract_schedule(...).
Keep or improve the existing error messages, ensuring you preserve context on clock IDs, offset values, or infeasibility reasons.
If or when the file grows too large, split out submodules: alignment.rs, relaxation.rs, strategy.rs. Start small, as you prefer minimal sprawl.

With this plan, you have:

Minimal duplication of logic.
A single new module for final schedule extraction.
Cross‐entity constraints explicitly considered (via a global approach and/or a relaxation pass).
A single method interface (extract_schedule) that library users can call with a ScheduleStrategy.
The option to expand for windows or advanced constraints in the same pattern.

This approach fully respects your ten preferences while incorporating the thoroughness of Plan 1 and the concise structure of Plan 2.

lmmx/1_re-planned.md