## Friday, June 17, 2022

### Partitioning an Interval

I recently saw a question on a help forum that fits the following pattern. The context is an optimization model (mixed integer program). There is a continuous variable $x$ and a constant value $a$ (frequently but not always 0), and we want to distinguish among three cases (with different consequences in the model): $x<a;$ $x=a;$ or $x>a.$ Each case has separate consequences within the model, which I will call $C_1,$ $C_2$ and $C_3$ respectively. Variations on this question arise frequently. For instance, make $x$ the difference of two variables, set $a=0$ and you are distinguishing whether the difference is negative, zero or positive.

To make things work properly, we need $x$ to be bounded, say $L\le x \le U.$ The goal is to break the feasible range of $x$ into three intervals. To do so, we will introduce a trio of binary variables, $y_1, y_2, y_3$ together with the constraint $$y_1 + y_2 + y_3 = 1.$$ We will come up with constraints such that fixing $y_1 = 1$ puts you in the left portion of the domain, fixing $y_2 = 1$ puts you in the middle portion, and fixing $y_3 = 1$ puts you in the right portion. The $y$ variables are then used elsewhere in the model to enforce whichever condition $C$ applies.

I'm using three variables for clarity. You can get by with two binary variables (changing the equality constraint above to $\le$), where both variables equaling zero defines one of the domain intervals. Figure 1 shows what the user wants and how the $y$ variables relate to it.

 Figure 1

The intervals for $x$ are $[L, a)$ when $y_1 = 1$, the singleton $[a, a]$ when $y_2 = 1,$ and $(a, U]$ when $y_3 = 1.$ Unfortunately, the user cannot have what the user wants because the feasible region of a MIP model must be closed, and the first and third intervals in Figure 1 are not closed. Closing them results in Figure 2.

 Figure 2

The intervals for $x$ are now $[L, a],$ $[a, a]$ and $[a, U].$ The good news is that this decomposition is easy to accomplish. The bad news is that the user likely will not accept it. If $x=a$, any one of the $y$ variables could take the value 1, so any of the three conditions could be triggered.

This brings us to the first (and more common) of two possible compromises. We can change the strict inequality $x > a$ to the weak inequality $x \ge a + \epsilon$ where $\epsilon$ is some small positive number, and do something analogous with the left interval. The result is Figure 3.

 Figure 3

Now $y_1=1 \implies x \le a-\epsilon,$ $y_2 = 1 \implies x=a,$ and $y_3=1 \implies x \ge a + \epsilon.$ The good news is that all three intervals are closed. The bad news is that values of $x$ between $a-\epsilon$ and $a$ or between $a$ and $a + \epsilon$ are suddenly infeasible, whereas they were feasible in the original problem. Also note that any tiny deviation from $a$ will throw $x$ into no man's land. That leads to one more possibility, involving yet another positive constant $\delta < \epsilon$ and shown in Figure 4.

 Figure 4
In this version, the domain of $x$ is broken into the closed intervals $[L, a-\epsilon],$ $[a-\epsilon +\delta, a+\epsilon - \delta]$ and $[a+\epsilon, U].$ There are still values of $x$ that were feasible in the original model but no longer feasible, but now you get to count values of $x$ "close" to $a$ as signalling the second of your three conditions.

The algebra to put either Figure 3 or Figure 4 into force is actually pretty straightforward. You just need to write a pair of inequalities $\dots \le x \le \dots$ where the left side expression is the sum of each $y$ variable times the lower bound that would apply when that variable is 1 and the right expression is the sum of each $y$ variable times the upper bound that would apply. So for Figure 3, where the intervals are $[L, a-\epsilon],$ $[a,a]$ and $[a+\epsilon, U]$, we would have $$Ly_1 + a y_2 + (a+\epsilon)y_3 \le x \le (a-\epsilon)y_1 + a y_2 + Uy_3.$$ For Figure 4, the constraints would be $$Ly_1 + (a-\epsilon+\delta)y_2 + (a+\epsilon)y_3 \le x \le (a-\epsilon)y_1 + (a+\epsilon-\delta) y_2 + Uy_3.$$

There is one "tactical" point to consider. When this is first explained to someone who was unaware that strict inequalities are verboten, they are often tempted to set $\epsilon = 10^{-G}$ where $G$ is the gross domestic product of their native country. The problem is that the solver is using finite precision computer arithmetic, and every solver has a tolerance value $\tau$ (small but not zero) such that coming within $\tau$ of satisfying a constraint is "close enough". If you pick $\epsilon$ (or $\delta$) too small, it has the same consequence as setting it equal to 0 in terms of how valid the solutions are. You can end up with a value of $a$ for $x$ (to within rounding error) triggering consequence $C_1$ or $C_3$ rather than $C_2$, or a value of $x$ strictly greater than $a$ triggering $C_2$ (or possibly even $C_1$), and so on. So $\epsilon$ (and, if needed, $\delta$) must be greater than the rounding tolerance used by the solver.

## Tuesday, May 24, 2022

### Allocating Resource Sets

The following problem was posted on Operations Research Stack Exchange. A system contains $K$ users and $N$ resources. Each unit of resource may be allocated to at most one user. Each user $k$ requires a specified number $d_k$ of resource units, which must be consecutive. Not all resource sequences of the proper length will be acceptable to a user. Citing an example from the original post, you might have $d_1=3,$ with user 1 accepting only one of following sets: $\{1, 2, 3\},$ $\{7, 8, 9\},$ $\{11, 12, 13\},$ or $\{17, 18, 19\}.$ The objective is to assign resources to as many users as is possible.

Formulating this as an integer linear program is straightforward, and I coded the IP model in Java (using CPLEX as the solver) to provide a benchmark. The author of the question, however, was looking for heuristic solutions. I suggested a few possibilities, and decided to code two of them just to see how well they did. The setup for all of them is identical. You start with the following elements:

• the set of unsatisfied users (initially, all users);
• an enumeration of all resource sets compatible with any user (with each such set represented by an integer ID number);
• a mapping of each user ID to the set of IDs of all compatible resource sets;
• a mapping of each resource set ID to the set of IDs of all users who would accept that set; and
• a mapping of each resources set ID to the set of IDs of all other resources sets that conflict with that set.

Two resource sets conflict if they intersect, in which case allocating one of them precludes allocating the other since each unit of resource can be used at most once.

All the heuristics I suggested work by finding a valid allocation (an unused resource set that does not conflict with any resource set already used and an user who has not yet been served and will accept that resource set), making that allocation, and then updating the mappings described above. Updating the mappings means removing the user who just received resources, removing the resource set just assigned, and removing any other unassigned resource sets that conflict with the set just assigned. Those changes can have "ripple effects": removing a user may result in a surviving resource set having no compatible users left (in which case the resource set can be removed), and removing a resource set (whether used or due to a conflict) may leave a user with no surviving resource sets that are compatible, in which case the user can be removed. So the update step involves some looping that must be coded carefully.

The various heuristics I suggested are distinguished by two fundamental choices. First, in each iteration do you pick an available resource set and then look for a user who will accept it, or do you pick a user who has not yet been served and then look for a compatible resource set that is still available? Second, regardless of order, what criteria do you use for selecting users and resource sets?

There are a lot of ways to make those choices, and I home in on two. My first approach starts by selecting the available resource set with the fewest remaining conflicts and then chooses the compatible user having the fewest surviving options for resource sets. My second approach starts by selecting the remaining user with the fewest surviving options for resource sets and then choosing the compatible resource set with the fewest remaining conflicts. In all cases, ties are broken randomly. My logic is that picking the resource set with the fewest conflicts will leave the most surviving resource sets and (hopefully) allow more users to be served, and picking the user with the fewest remaining options will (hopefully) reduce the number of users who are frozen out because all compatible resource choices have already been allocated.

I'll take a moment to note here that the two approaches I just stated are intended to be one-pass heuristics (find a feasible allocation and stop). You could rerun them with different random seeds, but that would only change the results (not necessarily for the better) if one or more ties had occurred during the first pass. Another possibility, which I did not bother to code, would be to select either resource set or user randomly at each stage, then select the other half of the assignment randomly from the compatible choices. I suspect that any single run of the random approach would likely do worse than what I described above, but you could keep rerunning the purely random heuristic for a specific number of iterations (or with a time limit) and track the best solution ever found. That might improve over the versions I tested.

Using the dimensions specified (in a comment, replying to me) by author of the question, I did a small number of tests. In those tests, the resource set first heuristic consistently beat the user first heuristic, and both exhibited what I would consider to be decent results. On three test runs with different seeds for the random problem generator, I got results of 14/13/11, 15/14/11 and 14/13/10, where the first number is the optimal number of customers served (from the IP model), the second is the number served by the resource-first heuristic, and the third is the number served by the customer-first heuristic. Run times for the heuristics on my humble PC (including setup times) were minuscule (under 20 milliseconds).

My Java code can be found here.

## Sunday, March 13, 2022

### Wolf, Goat and Cabbage Part II

This continues my previous post about the "Wolf, Goat and Cabbage" logic puzzle. Using a MIP model to solve it strikes me as somewhat analogous to hammering in a nail with the butt end of a screwdriver handle: it works, but maybe it's not the ideal tool. To me, a constraint solver would make more sense, so I coded up a model using IBM's CP Optimizer (henceforth "CPO", part of the CPLEX Optimization Suite). Unsurprisingly, it worked (after some debugging) and produced the same solution.

MIP solvers largely share the same "vocabulary" for building models (real / integer / binary variables, linear and maybe quadratic expressions, equality and inequality constraints). From my limited exposure to constraint solvers, there is significant variation in both what things the solver does and does not understand. Some of that is variable types. A very basic solver may understand logical (true/false) and integer variables, and maybe real-valued variables (although I'm not sure handling real variables is anywhere near universal). CPO (and presumably some other solvers) understand "interval variables", which as the name suggests represent intervals of discrete values (usually time intervals, though possibly location or some other aspect). Similarly, different solvers will understand different types of global constraints. I suspect that every CP solver worth mentioning understands "all different" (no two variables in a bunch of integer variables can take the same value), but some solvers will implement "no overlap" constraints (the time interval during which I am eating and the time interval during which I am showering cannot overlap) or precedence constraints (this job has to end before this other job can start). Those kinds of constraints make certain scheduling problems easier to solve with CP than with a MIP model.

Anyway, I'm not entirely new to CPO, though far from proficient, and I tripped over a few "features" while coding the puzzle. I wanted to use boolean (true/false) variables for certain things, such as whether an item had made it to the far side of the river (true) or was stuck on the near side (false). CPO lets you declare a boolean variable but then treats it as an integer variable, meaning that you need to think in terms of 0 and 1 rather than false and true. So you can't say "if $x$ then ..."; instead, you need to say "if $x = 1$ then ..." (and trust me, the syntax is clunkier than what I'm typing here). When you go to get the value of your boolean variable $x$ after solving the model, CPO returns a double precision value. CPLEX users will be used to this, because in a MIP model even integer variables are treated as double-precision during matrix computations. CP solvers, though, like to do integer arithmetic (as far as I know), so it's a bit unclear why my boolean variable has to be converted from double precision to integer (or boolean). Even odder is that, at least in the Java API, there is a method that returns the value of an integer variable as an integer if you pass the name of the variable as the argument, but if you pass the actual variable you are going to get a double. (Did a federal government panel design this?)

In any event, logic of the CPO model is moderately straightforward, with constraints like "you can't carry something in the boat if it isn't on the bank the boat departs from" and "if the wolf and goat end up in the same place at the end of a period, the farmer better end up there too". Some things are bit less clunky with CPO than with CPLEX. For instance, to figure out what (if anything) is in the boat at a given time, the MIP model requires binary variables subscripted by time and item index (1 if that item is in the boat on that trip 0 if not). The CPO model just needs an integer variable for each time period whose value is either the index of the thing in the boat or a dummy value if the boat is empty. Furthermore, the nature of the variable automatically takes care of a capacity constraint. Since there is only one variable for what's in the boat, at most one thing (whatever that variable indicates) can be in the boat.

Some (most?) constraint solvers, including CPO, provide a way to use a variable as an index to another variable. In my code, the integer variable indicating what's in the boat at time $t$ is used to look up the location variable (near or far bank) for that item at time $t$ from a vector of location variables for all items at time $t$.

Anyway, the code in my repository has been updated to include the CPO model, and it's heavily commented in case you wanted to compare it to the MIP model.

## Friday, March 11, 2022

### Wolf, Goat and Cabbage

On Operations Research Stack Exchange, someone asked about a possible connection between the "wolf, goat and cabbage" logic puzzle and Monge's optimal transport problem. In the logic puzzle, a farmer has to get a wolf, a goat and a cabbage across a river using a boat that can only accommodate one of them (plus the farmer) at a time. If you leave the wolf and goat alone together at any point, the wolf eats the goat. If you leave the goat and cabbage alone together at any point, the goat eats the cabbage. Fortunately, the wolf has no appetite for cabbage and the cabbage does not seem to want to eat anything, else the problem would be infeasible.

Neither Monge's transport problem nor the more commonly taught (in my opinion) Hitchcock transportation problem directly apply, although you can (almost) treat the puzzle as a multiperiod commodity flow with an "inventory" of each item (wolf, goat, cabbage) on each side of the river. The "almost" part is that you need some of the variables to be integer, for two reasons. One is that the LP relaxation of the logic constraints (e.g., inventory of wolf + inventory of goat $\le 1$ on this side of the river at this time) will result in fractional values (we'll leave half a wolf and half a goat here and carry the other halves across the river) (which would greatly diminish the values of both wolf and goat). The other is that the objective is to minimize the number of trips made. It would be tempting to just assign a cost of 1 to each movement of an object in either direction, but the catch is that you will occasionally cross with nothing in the boat (besides the farmer). Those "deadheading" trips count toward the objective, but it's tricky to assign a cost to a zero flow.

To fill in some idle time, I coded up a MIP model. Mind you, I am not advocating MIP as a way to solve problems like this; I just wanted to confirm my thinking (in particular, that an LP commodity flow model would have fractional solutions). Assume that the farmer arrives at the left bank at time 0 with all three items, and that each trip (in either direction) counts as one time unit, with the first trip occurring at time 1. We need to set an upper bound $T$ on the number of trips. Since the state of the system is described by the location of four things (counting the farmer), with each have two possible locations (left bank, right bank), $T=2^4 =16$ works. The set of items will be denoted $I=\lbrace W, G, C\rbrace.$ My formulation uses the following variables.

• $L_{i,t}\in [0,1]$ and $R_{i,t}\in [0,1]$ are the inventories of item $i\in I$ at time $t\in \lbrace 0,\dots,T$ on the left and right banks respectively, after any trip occurring in that period.
• $x_{i,t}\in \lbrace 0,1 \rbrace$ and $y_{i,t}\in \lbrace 0,1 \rbrace$ are the amount of item $i$ crossing the river at time $t$ from left to right or right to left respectively.
• $z_t\in \lbrace 0,1 \rbrace$ is 1 if transport is ongoing and 0 if we are done (the farmer and all three items are on the right bank).

It would be perfectly fine (but unnecessary) to make the inventory variables integer-valued, and we could also make the inventory variables integer-valued and drop the integrality restrictions on the cartage variables ($x$ and $y$).

Some of the variables can be fixed at the outset.

• We start with all inventory on the left bank, so $L_{i,0}=1$ and $R_{i,0}=0$ for all $i\in I.$
• There is no trip at time 0, so $z_0=0$ and $x_{i,0}=y_{i,0}$ for all $i\in I$.
• Trips alternate left-to-right (odd time periods) and right-to-left (even time periods), so $x_{i,t}=0$ for all $i\in I$ and for all even $t$ and $y_{i,t}=0$ for all $i\in I$ and for all odd $t$.

The objective function is to minimize the number of trips required. $$\min \sum_{t=1}^T z_t.$$

The constraints are rather straightforward.

• The inventory on either bank in any period is the inventory on that bank from the preceding period, plus any arriving inventory and minus any departing inventory. So for $t\ge 1$ $$L_{i,t} = L_{i, t-1} - x_{i,t} + y_{i,t}\quad \forall i\in I$$ and $$R_{i,t} = R_{i,t-1} + x_{i,t} - y_{i,t}\quad \forall i\in I.$$
• In an odd numbered period (where the farmer ends up on the right bank), neither wolf and goat nor goat and cabbage can be on the left bank. So for odd $t$ $$L_{W,t} + L_{G,t} \le 1$$ and $$L_{G,t} + L_{C,t}\le 1.$$
• In an even numbered period (where the farmer ends up on the left bank), neither wolf and goat nor goat and cabbage can be on the right bank unless the problem is completed ($z_t = 0$), in which case the farmer remains on the right bank. So for even $t$ $$R_{W,t}+R_{G_t} + z_t \le 2$$ and $$R_{G,t} + R_{C,t} + z_t \le 2.$$
• Transport continues until the left bank is empty. $$3z_t \ge \sum_{i\in I} L_{i,t - 1}\quad \forall t\ge 1.$$

It does indeed produce a correct solution, using seven trips (see the Wikipedia page for the solution) ... and with integrality conditions dropped it does indeed produce a nonsense solution with fractions of items being transported.

Java code for this model (using CPLEX) is in my Git repository.

## Thursday, March 3, 2022

### Finding Almost All Paths

A question posted on Stack Overflow (and subsequently deleted) led to a blog post by Erwin Kalvelagen on how to find all paths between two nodes in a directed graph (possibly with self-loops, i.e. arcs from a node to itself) subject to two constraints: no arc can be used more than once in a path; and there is an upper limit $M$ on the number of arcs used. Note that a path might visit a *node* more than once. It just cannot repeat an arc. The original question seems to have referred to an undirected graph, but Erwin's post works with directed graphs and so will I.

Erwin explored some mixed integer linear programming (MIP) models in his post, and a subsequent post on OR Stack Exchange led to more proposals of MIP models (including one from me). I also suggested that a "brute force" approach might be faster than any of the MIP models. In what follows, I will spell out both my MIP model and the brute force approach I used. Java code for both (which requires CPLEX and the Apache Commons Collections library) are in my code repository.

In what follows $A$ is the set of arcs in the graph, $s$ is the origin node for all paths, $t$ is the destination node for all paths, and $M$ is the maximum number of arcs to include in a path.

### MIP model

The MIP model uses the following variables.
• $u_{a}\in\left\{ 0,1\right\}$ is 1 if and only if arc $a$ is used on the path.
• $f_{a}\in\left\{ 0,1\right\}$ is 1 if and only if arc $a$ is the first arc on the path.
• $\ell_{a}\in\left\{ 0,1\right\}$ is 1 if and only if arc $a$ is the last arc on the path.
• $y_{ab}\in\left\{ 0,1\right\}$ is 1 if and only if arc $b$ immediately follows arc $a$ on the path.
• $z_{a}\in\left[0,M\right]$ will be the number of arcs preceding arc $a$ on the path (0 if $a$ is not on the path).

Some of the variables can be eliminated (fixed at 0) at the outset.

• $f_{a}=0$ if node $s$ is not the tail of arc $a.$
• $\ell_{a}=0$ if node $t$ is not the head of arc $a.$
• $y_{ab}=0$ if the head of arc $a$ is not the tail of arc $b.$

Since we are just interested in finding feasible solutions, the objective is to minimize 0.

Use of the $z$ variables mimics the Miller-Tucker-Zemlin approach to avoiding subtours in the traveling salesman problem. A common knock on the MTZ formulation for the TSP is that it has a somewhat loose continuous relaxation. Since we have a trivial objective (all feasible solutions are optimal), that is not a concern here.

The constraints are as follows.

• There must be one first arc and one last arc.
$$\sum_{a\in A}f_{a}=1.$$$$\sum_{a\in A}\ell_{a}=1.$$
• At most $M$ arcs can be used.$$\sum_{a\in A}u_{a}\le M.$$
• An arc is used if and only if it is either the first arc or follows another arc on the path.$$f_{a}+\sum_{b\in A}y_{ba}=u_{a}\quad\forall a\in A.$$
• The last arc must be a used arc.$$\ell_{a}\le u_{a}\quad\forall a\in A. (1)$$
• The sequence value of an unused arc is 0.$$z_{a}\le Mu_{a}\quad\forall a\in A.$$
• No arc can follow the last arc.$$\ell_{a}+\sum_{b\in A}y_{ab}\le1\quad\forall a\in A. (2)$$
• If an arc is used, either it is the last arc or another arc follows it.$$\ell_{a}+\sum_{b\in A}y_{ab}=u_{a}\quad\forall a\in A. (3)$$
• If an arc $b$ follows arc $a$, the sequence number of arc $b$ must be one higher than the sequence number of arc $a.$ $$z_{a}-z_{b}+\left(M+1\right)y_{ab}\le M\quad\forall a,b\in A,a\neq b.$$

Over on OR Stack Exchange, Rob Pratt correctly pointed out that constraint (3) implies constraints (1) and (2). I've left them in the Java code anyway. The CPLEX presolver removes them automatically.

#### Finding all solutions

To find all solutions, one possible approach is to solve whatever MIP model you choose, then add a "no-good" constraint that eliminates the solution just found (and only that one) and solve again, until eventually the aggregation of "no-good" constraints makes the model infeasible. What I did instead was to use the "populate" command in CPLEX, which accumulates a pool of solutions. Besides a time limit, I used two non-default parameter settings: I cranked up the solution pool capacity (the maximum number of solutions to find) to something high enough to let it find all solutions; and I set the solution pool intensity to its highest value (4), which tells CPLEX to aggressively seek out all feasible solutions.

### Brute force approach

The brute force approach is remarkably straightforward. We will use a queue of (incomplete) paths that I will call the "to-do list". Start by creating a length one path for each arc with tail $s$ and add them to the to-do list. We now proceed in a loop until the to-do list is empty. At each iteration, we pull a path $P$ off the to-do list, identify the arcs whose tails match the head of the last arc in $P$, remove any arcs already on $P$, and for each surviving arc $a$ create a new path $P'$ by extending $P$ with $a$. If the head of arc $a$ is $t$, $P'$ is a new $s-t$ path, which we record. Either way, if $P'$ has length less than $M$, we add it to the to-do list, and eventually try to extend it further.

### Do they work?

Would I be posting this if they didn't. :-) I tested both methods on the digraph from Erwin's post, which has 10 nodes and 22 arcs (with two self-loops). The source and sink are nodes 1 and 10 respectively. With $M=3$ there are four paths (which both methods find), and with $M=4$ there are nine paths (which both methods find). In both cases, brute force took about 1 ms. on my PC (using non-optimized Java code with a single thread). CPLEX times were rather stochastic, but I think it fair to say that building the models took around 55+ ms. and solving them typically took 20 ms. or more.
When I set a maximum length ($M$) of 10, things got interesting. The brute force approach found 268 paths (in about 6 ms), while CPLEX found only 33. Neither time limit nor pool size were a factor, so I assume that this is just a limitation of the aggressive setting for pool intensity. That means that to find all possible solutions using CPLEX, the solve / add constraint / solve again approach would be necessary.

## Tuesday, February 22, 2022

Someone posted a question on OR Stack Exchange about a problem with a "nonseparable bilinear objective function" with "decoupled constraints". The problem has the following form:

\begin{alignat*}{1} \min & \sum_{i=1}^{m}\sum_{j=1}^{n}a_{ij}x_{i}y_{j}+b^{\prime}x+c^{\prime}y\\ \textrm{s.t. } & x\in X\subset\mathbb{R}^{m}\\ & y\in Y\subset\mathbb{R}^{n} \end{alignat*}

where $X$ and $Y$ are polytopes (i.e., defined by linear constraints, and bounded) in the positive orthants of their respective spaces (i.e., $x\ge 0$ and $y\ge 0$) and $a$, $b$ and $c$ are all nonnegative. So besides everything being nonnegative, the key things here are that the objective contains bilinear terms (always involving the product of an $x$ and a $y$) and each constraint involves either only $x$ or only $y$. The author was looking for a way to exploit the separability of the constraints in solving the problem.

It occurred to me that a heuristic approach might be to solve a sequence of linear programs, alternating between $X$ and $Y$. Let $h(x,y)$ be the objective function of the original problem, and let $$f(x;y) = \sum_i\sum_j a_{ij}x_i y_j + b^\prime x$$and $$g(y;x)=\sum_i\sum_j a_{ij}x_i y_j + c^\prime y$$ be respectively the portions of the objective dependent on $x$ with $y$ fixed or dependent on $y$ with $x$ fixed. Each is linear in one set of variables (the other set being fixed). So we could proceed as follows:

1. Minimize $f(x;0)$ over $X$ (a linear program) to get a starting value $x^0\in X.$
2. Minimize $g(y;x^0)$ over $Y$ to get a starting value $y^0\in Y.$ We now have an incumbent solution with objective value $h(x^0,y^0)=b^\prime x^0 + g(y^0;x^0).$ Set $t=0$.
3. Repeat the following until the first time that the incumbent does not improve.
1. Minimize $f(x;y^t)$ over $X$ to get $x^{t+1}.$ If $f(x^{t+1},y^t) + c^\prime y^t$ is less than the incumbent value, make $(x^{t+1}, y^t)$ the new incumbent, else punt.
2. Minimize $g(y;x^{t+1})$ over $Y$ to get $y^{t+1}.$ If $g(y^{t+1};x^{t+1}) + b^\prime x^{t+1}$ is less than the incumbent value, make $(x^{t+1},y^{t+1})$ the new incumbent and increment $t$, else punt.

This will generate a sequence of solutions, each a corner point of the feasible region $X\times Y,$ with monotonically decreasing objective values.

Before continuing, it is worth noting that we are guaranteed that at least one corner of $X\times Y$ will be an optimum. This is of course always true when the objective is linear, but not always true with a quadratic objective. In our case, suppose that $(x^*, y^*)$ is an arbitrary optimum for the original problem. Then $x^*$ must minimize $f(x;y^*)$ over $X$, so either $x^*$ is a corner of $X$ of (if there are multiple optimal) $x^*$ can be replaced by a corner of $X$ with the same value of $f(x;y^*)$ (and thus the same value of $h(x;y^*)$, since the difference $c^\prime y^*$ does not depend on $x$). Apply the same logic on the other variable to show that $y*$ is either a corner of $Y$ or can be replaced by one.

I'm still calling this a heuristic, because there is one more piece to the puzzle. Could the procedure stop at a corner of $X\times Y$ that is a local but not global optimum? I'm not sure. Offhand, I do not see a way to prove that it will not, but I also cannot easily conjure up an example where it would.

To test this (and to confirm that I was not hallucinating, which has been known to happen), I wrote some Java code to generate random test problems and try the procedure. The code uses CPLEX to solve the LPs. In all test cases, it terminated with a solution (at least locally optimal) in a pretty small number of iterations.

As a benchmark, I tried solving the full QP models with CPLEX, setting its "optimality target" parameter to "OPTIMALGLOBAL", which tells CPLEX to search for a global optimum to a nonconvex problem. This causes CPLEX to turn the problem into a mixed-integer quadratic program, which shockingly takes longer to solve than an LP. In a limited number of test runs, I observed a surprisingly consistent behavior. At the root node, CPLEX immediately found a feasible solution and then immediately found a better solution that matched what my procedure produced. After than (and within a usually stingy time limit I set), CPLEX made progress on the bound but never improved on the incumbent. In two test runs, CPLEX actually reached proven optimality with that second incumbent, meaning my procedure had found a global optimum.

So perhaps my "heuristic" can actually be shown to be an exact algorithm ... or perhaps not. At least in my test runs, CPLEX working on the QP found the best solution about as fast as, or maybe slightly faster than, my procedure did. On the other hand, my procedure only requires an LP solver, so it will work with code that does not accept QPs.

My Java code (which requires CPLEX) is available here.

Addendum: User "Dusto" on the Discord Operations Research server posted a simple counterexample to global convergence. The example has no constraints other than bounds on the variables (from 0 to 10 in all cases). The $b$ and $c$ vectors are strictly positive, so the linear programs in my heuristic will start at the origin and get stuck there. The $A$ matrix is crafted so that a negative overall objective value is attainable.

## Thursday, February 10, 2022

### Tournament Scheduling: CPLEX v. CP Optimizer (Part 3)

This is the third and final chapter in my opus about scheduling "pods" (players) in a tournament. Reading the first post will be essential to understanding what is going on here. Reading the second post (in which I formulate an integer programming model for the problem) is definitely optional.

I will stick with the notation introduced in the prior post. As before, there are $N$ pods (players) being organized into teams of two to play in $G$ games (two teams per game, one in white, one in black). Each game has two slots corresponding to the two teams in the game.

• $t$ indexes a team;
• $p$ and $q$ index pods;
• $g$ indexes a game;
• $s$ indexes a slot (and $s'$ is the other slot of the same game);
• $c$ is a jersey color (0 for white, 1 for black).

For a slot $s$, I will use $G_s$ and $C_s$ to denote respectively the game in which the slot exists and the jersey color (0 or 1) of the team in the slot. For a team $t$, $P_{t,0}$ and $P_{t,1}$ will be the two pods on the team. (The order they are listed is immaterial.)

The model variables are almost (but not exactly) what they were in the IP model.

• $x_{t}\in \lbrace 0, \dots, 2G-1\rbrace$ is the index of the slot in which team $t$ plays (where slots 0 and $G$ are the white and black teams in the first game and slots $G-1$ and $2G-1$ are the white and black teams in the final game).
• $y_{pqg}\in \lbrace 0,1\rbrace$ is 1 if pods $p$ and $q\neq p$ are playing in game $g$ on opposing teams.
• $z_{pg}\in \lbrace 0,1\rbrace$ is the color jersey worn by pod $p$ at the conclusion of game $g$ (regardless of whether or not $p$ played in game $g$).
• $w_{pg}\in \lbrace 0,1\rbrace$ is 1 if pod $p$ changes jersey color going into game $g.$
• $v_{pg} \in \lbrace 0,1\rbrace$ is 1 if pod $p$ is playing in game $g.$

The changes from the IP model are that $x$ is general integer rather than binary (with dimension 1 rather than 2), the third index of $y$ is the game rather than the slot, and $v$ is a new variable. As in the IP model, the objective is to minimize $\sum_p \sum_g w_{pg}.$

The constraints are as follows. Note that the requirement that every team play exactly once is captured by the assignment of a single slot value to each team (via $x$).

• Two different teams cannot occupy the same slot: $$\textrm{allDiff}(x).$$
• Occupying a slot implies playing in the game: $$x_t = s \implies (v_{P_{t,1},G_s} = 1 \wedge v_{P_{t,2},G_s} = 1) \quad \forall t,s.$$
• Occupying a slot determines the color of the jersey worn in the game: $$x_t = s \implies (z_{P_{t,1},G_s} = C_s \wedge z_{P_{t,2},G_s} = C_s) \quad \forall t,s.$$
• Jersey color for a pod remains constant from game to game unless a change is recorded: $$z_{p,g} \neq z_{p,g-1} \iff w_{p,g} = 1 \quad \forall p, \forall g > 0.$$
• Playing in consecutive games precludes changing jerseys: $$(v_{p,g - 1} = 1 \wedge v_{p,g} = 1) \implies w_{p,g} = 0 \quad \forall p, \forall g > 0.$$
• Two pods are opponents if and only if they are playing in the same game with different colors: $$y_{pqg} = 1 \iff (v_{pg} = 1 \wedge v_{qg} = 1 \wedge z_{pg} \neq z_{qg} \quad \forall p, \forall q\neq p, \forall g.$$
• Every pair of pods faces each other exactly twice: $$\sum_g y_{pqg} = 2 \quad \forall p, \forall q\neq p.$$

Here "allDiff()" is the CPLEX notation for the "all different" constraint, a global constraint common to most constraint solvers. Given a collection of variables with the same range, the all different constraint says that no two variables in the collection can take the same value.

To put it mildly, I would not be shocked if there is a more efficient (easier to solver) way to write the problem as a CP model.

## Wednesday, February 9, 2022

### Tournament Scheduling: CPLEX v. CP Optimizer (Part 2)

In my previous post, I described a tournament scheduling problem and discussed my results using both an integer programming model (CPLEX) and a constraint programming model (CP Optimizer) to try to solve it. Here I will present the IP model. (This post will be gibberish unless you have read at least the start of the previous post.)

There are multiple ways to write an integer programming (IP) model for the tournament problem. My initial instinct was to think in terms of what games a pod plays in, which pods it plays with/against and so on, but I fairly quickly changed to thinking in terms of teams rather than pods. With $N=9$ pods and two pods to a team, there are $N(N-1)/2=36$ possible teams, which we can enumerate at the outset. There will be 18 games, each containing two teams, and every team will play exactly once. Each game contains two "slots", one for the team in white jerseys and the other for the team in black jerseys.

In what follows, I will (I hope) stick to the following notation:

• $t$ indexes a team;
• $p$ and $q$ index pods;
• $g$ indexes a game;
• $s$ indexes a slot (and $s'$ is the other slot of the same game);
• $c$ is a jersey color (0 for white, 1 for black).

Since I am using Java, all indexing is zero-based. Slots are numbered so that game 0 has slots 0 (white) and $G$ (black), game 1 has slots 1 (white) and $G+1$ (black), etc., where $G=N(N-1)/4$ is the number of games. I will denote by $T_p$ the set of teams $t$ containing pod $p$.

The model variables are as follows.

• $x_{ts}\in \lbrace 0,1\rbrace$ is 1 if team $t$ plays in slot $s,$ 0 if not.
• $y_{pqs}\in \lbrace 0,1\rbrace$ is 1 if pod $p$ is playing in slot $s$ and is opposed by pod $q.$
• $z_{pg}\in \lbrace 0,1\rbrace$ is the color jersey worn by pod $p$ at the conclusion of game $g$ (regardless of whether or not $p$ played in game $g$).
• $w_{pg}\in \lbrace 0,1\rbrace$ is 1 if pod $p$ changes jersey color going into game $g,$ 0 if not.

The objective is to minimize $\sum_p \sum_g w_{pg}.$ The constraints are the following.

• Every team plays exactly once. $$\sum_s x_{ts} = 1 \quad\forall t.$$
• Every schedule slot is filled exactly once. $$\sum_t x_{ts} = 1 \quad\forall s.$$
• Pods $p$ and $q$ oppose each other with $p$ in slot $s$ if and only if $p$ is playing in $s$ and $q$ is playing in $s'$. $$y_{pqs} \le \sum_{t\in T_p} x_{ts} \quad\forall p, q\neq p, s.$$ $$y_{pqs} \le \sum_{t\in T_q} x_{ts'} \quad\forall p, q\neq p, s.$$ $$y_{pqs} \ge \sum_{t\in T_p} x_{ts} + \sum_{t\in T_q} x_{ts'} - 1 \quad\forall p, q\neq p, s.$$
• Every pair of pods opposes each other exactly twice. $$\sum_s y_{pqs} = 2 \quad \forall p\neq q.$$
• No pod plays consecutive games in different jerseys.$$\sum_{t\in T_p}\left( x_{t,s-1} + x_{t,s'} \right) \le 1 \quad \forall p, \forall s\notin \lbrace 0, G\rbrace.$$
• A pod playing in a game has its jersey color determined by its team's slot. (This has the desirable side effect of preventing two teams containing the same pod from playing against each other.) $$\sum_{t\in T_p} x_{t, g+G} \le z_{pg} \le 1 - \sum_{t\in T_p} x_{t,g} \quad \forall p,g.$$(Note that for any game index $g$, slot $g$ is the white slot in game $g$ and slot $g+G$ is the black slot.)
• A pod's color for any game (playing or not) is the same as its color for the previous game, unless a change occurs. $$z_{p, g-1} - w_{pg} \le z_{pg} \le z_{p, g-1} + w_{pg} \quad\forall p, \forall g \ge 1.$$

In the next post, I will try to articulate my CP model for the problem.

## Tuesday, February 8, 2022

### Tournament Scheduling: CPLEX v. CP Optimizer (Part 1)

A recent question on Mathematics Stack Exchange asked about scheduling $N$ "pods" (players) in a tournament under the following conditions.

• There will be $N(N-1)/4$ games played sequentially (no games in parallel).
• Games pit teams of two pods each against each other. One team wears white and one team wears black.
• Each pod partners with every other pod once and plays against every other pod twice.
• No pod can play in consecutive games wearing different jersey colors.

The objective is find a schedule that minimizes the total number of times pods have to change their jersey color.

The question specifies $N=9$. Note that, since there are $N(N-1)/4$ games, a necessary condition for the problem to be feasible is that either $N$ or $N-1$ must be divisible by $4$. That condition is not, however, sufficient. An obvious counterexample is when $N=4$ and there are three games to be scheduled. The first game uses all four teams, as does the second game. Since no pod can be forced to wear different jerseys in consecutive games, all four teams would go into the second game wearing the same colors as in the first game ... which means being in the same teams, violating the rule about pods being teamed together exactly once.

I coded both an integer programming model (using CPLEX) and a constraint programming model (using CP Optimizer) in Java and tried to solve the tournament problem with $N=9$. Since everything is inherently integer and the constraints are more logical than arithmetic (the only real algebra is summing up the number of jersey changes), I had two initial expectations: that CPLEX would provide tighter lower bounds than CPO (because MIP models tend to do better with bounds than CP models); and that CPO would find feasible (and possibly optimal) schedules faster than CPLEX would, since the problem is inherently logic-based (and CP models often do better than MIP models on scheduling problems). I was half right. CPLEX did provide tighter lower bounds than CPO, at least within the time limits I was willing to use, although I don't think either came remotely close to a "tight" lower bound. CPO, however, struggled massively to find feasible schedules while CPLEX did not have too much trouble doing so.

Before going any further, I need to issue three caveats. First, the longest I ran the IP model was 30 minutes and the longest I ran the CP model was an hour. Second, while I am pretty familiar with formulating IP models, I am much less familiar running CP models, so I may have missed opportunities to make the CP model more efficient. Third, while CPLEX gives the user a fair amount of indirect control over the search process (by setting branching priorities, MIP emphasis, how frequently to try certain kinds of cuts and so on), CP Optimizer may offer the user even more flexibility in how to tailor searches I am not yet familiar enough to do anything beyond trying to indicate which variables should be the first candidates for branching (and I'm not sure I got that right).

I'll end this installment with a synopsis of some runs.

• In a 30 minute run using the new setting 5 for the MIP emphasis parameter (stressing use of heuristics to improve the incumbent, at the cost of not paying too much attention to the lower bound), CPLEX found a feasible schedule with 14 jersey changes and a lower bound of 2.97 (a 79% gap). It evaluated somewhere around 18,500 nodes.
• In a 30 minute run, CP Optimizer branched over 957 million times but never found a feasible schedule and ended with a best bound of 0.
• Finally, I tried running CPLEX for three minutes (in which it found an incumbent with 19 jersey changes) and then used that incumbent as a starting solution for a one hour run of CP Optimizer. CPO accepted the incumbent solution, did a bit over 1.55 billion branch operations, and failed to improve on it. The best bound was again 0.

If you want to look at my code (which will make slightly more sense after my next couple of posts, where I will describe the models), it is available from my university GitLab repository.

## Sunday, February 6, 2022

### Taxi Dispatch

A question on OR Stack Exchange pertains to a taxi stand. The assumptions are as follows.

• There are $t$ taxis operating between points A and B, with all passenger traffic going from A to B. For concreteness, I'll refer to a "taxi stand" with a "line" of taxis, similar to what you might see at an airport or popular hotel.
• The round-trip travel time for a taxi (taking passengers from A to B and deadheading back from B to A) is deterministic with value $T$. (Note: Deterministic travel time is the author's assumption, not mine.)
• Each taxi has a capacity for $p$ passengers and is dispatched only when full.
• Passengers arrive according to a Poisson process with parameter (rate) $\lambda$.

The author was looking for the mean time between dispatches.

It has been a very long time since I taught queuing theory, and I have forgotten most of what I knew. My best guess for the mean time between dispatches, under the assumption that the system reached steady state, was $p/\lambda$, which would be the long-run average time required for $p$ passengers to arrive. To achieve steady-state, you need the average arrival rate ($\lambda$) to be less than the maximum full-blast service rate ($t p/T$). If arrivals occur faster than that, in the long term you will dispatching a taxi every $1/T$ time units while either queue explodes or there is significant balking/reneging.

To test whether my answer was plausible (under the assumption of a steady state), I decided to write a little discrete event simulation (DES). There are special purpose DES programs, but it's been way to long since I used one (or had a license), so I decided to try writing one in R. A little googling turned up at least a couple of DES packages for R, but I did not want to commit much time to learning their syntax for a one-off experiment, plus I was not sure if any of them handled batch processing. So I wrote my own from scratch.

My code is not exactly a paragon of efficiency. Given the simplicity of the problem and the fact that I would only be running it once or twice, I went for minimal programmer time as opposed to minimal run time. That said, it can run 10,000 simulated passenger arrivals in a few seconds. The program uses one data frame as what is known in DES (or at least was back in my day) an "event calendar", holding in chronological order three types of events: passengers joining the queue; taxis being dispatched; and taxis returning.

The simulation starts with all taxis in line and no passengers present. I did not bother with a separate "warm-up" period to achieve steady-state, which would be required if I were striving for more accuracy. For a typical trial run ($t=5$, $p=4$, $T=20$, $\lambda=0.7$), my guess at the mean time between dispatches worked out to 5.714 and the mean time computed from the run was 5.702. So I'm inclined to trust my answer (and to quit while I'm ahead).

If anyone is curious (and will forgive the lack of efficiency), you can take a look at my R notebook (which includes the code).

## Wednesday, January 26, 2022

### Balancing Machine Groups

This post continues my previous post about approximate equality of some value across multiple entities. The motivation was a question posed on OR Stack Exchange asking how to partition $M$ machines into $G$ groups so that each group would have approximately the same total productivity. The machines have specified productivity values $P_1,\dots,P_M \ge 0$, and the productivity of a group is just the sum of the productivity values of the group members. The question assumes that $G$ divides $M$ evenly, so that each group has the number $S = M / G$ of members.

The previous post covered the issue of what metric to use in the objective function. Here I will focus on what algorithm/heuristic to use to solve it.

The author of the question specified $M=21$, $G=7$, $S=3$ for problem dimensions, with productivity values $P_m \in [0,10].$ He also posed a heuristic that on the surface makes sense: assign machines sequentially (I assume in decreasing productivity order) to the group with lowest current productivity (and with space to accept another machine). A genetic algorithm with chromosomes representing permutations of $1,\dots,M$ can be applied to any of the objective functions. (Since GAs maximize fitness and the problem here is to minimize the dispersion measure, we would just maximize the difference between the dispersion measure and some fixed upper bound on it.)

Finally, we can find exact solutions using a mixed integer linear program and any objective except MSD. (The MSD would be a mixed integer quadratic program.) The MIP models would contain variables $x_{mg}\in \lbrace 0,1 \rbrace$ for each machine $m$ and group $g$, indicating membership of that machine in that group. They would also contain auxiliary variables $y_g \ge 0$ to indicate the productivity value for each group $g$. The common part of the models would involve constraints $$\sum_{g=1}^G x_{mg} = 1\quad\forall m$$ (every machine is assigned to exactly one group), $$\sum_{m=1}^M x_{mg} = S \quad\forall g$$ (every group contains $S$ machines) and $$y_g = \sum_{m=1}^M P_m x_{mg} \quad\forall g$$ (defining the group productivity variables in terms of the assignments). To minimize the range, we would add variables $z_0, z_1 \ge 0$ plus constraints $$z_0 \le y_g \quad\forall g$$ and $$z_1 \ge y_g \quad \forall g$$ and then minimize $z_1 - z_0$. To minimize the MAD, we would instead add variables $z_g \ge 0$ for $g=1,\dots,G$ with the constraints $$z_g \ge y_g - S\cdot \overline{P} \quad\forall g$$ and $$z_g \ge S\cdot \overline{P} - y_g \quad\forall g$$ (where $\overline{P} = (\sum_m P_m)/M$ is the mean productivity level of the machines, so that $S\cdot \overline{P}$ is the mean productivity per group regardless of how the groups are formed). The objective would then be to minimize $\sum_g z_g$.

I decided to code some of the alternatives in R (using CPLEX to solve the MIPs) and see how they did. The author's heuristic has the advantage of being very fast. The MIP models, not surprisingly, were much slower. I tried adding some antisymmetry constraints (since the objective value is unaffected if you permute the indices of the groups), but they did not seem to help run time, so I dropped them.

Here are the range and MAD values achieved by each method on a particular test run (reasonably representative of other runs).

Method Criterion Range % Gap  MAD % Gap
Author      None   381.02613 564.734106
GA     Range    52.04068  49.728175
MIP     Range     0.00000   2.215414

The gap measures are percentage above optimal. The author's method seems to be suboptimal by a wide margin, and the GAs are not doing particularly well on this example either.

We can visualize the group productivity levels for each solution in a bubble plot, where the size of a point is the productivity. The actual group productivity levels are close enough to each other that the plot is not helpful, so rather than plotting productivity I will plot the absolute percentage deviation from the overall mean productivity of all groups.

For the author's heuristic, group 3 has a productivity level close to the target mean and the other groups are pretty far off. (Remember that this is absolute deviation, so while we know group 1 for the author's solution is way off, we do not know from the plot whether it is above or below mean.) The MIP model that minimizes range looks the best to me, even though the MIP model that minimizes MAD is better with respect to overall absolute deviation.

There are three caveats to bear in mind. First, this is one test run, with randomly generated data. Altering the data affects all the results, although in multiple runs the author's heuristic always had gaps of 200% or more and the GAs never found optimal solutions. Second, performance of the GAs is affected by multiple parameters, in particular the population size and number of generations run. Third, we could adapt the author's heuristic by using the $G$ machines with highest productivity as "anchors" for the $G$ groups and then assigning the remaining machines in random order rather than in decreasing productivity order. That would allow us to run the heuristic multiple times with different sort orders for the machines, subject to time and/or iteration limits, and pick the best solution found.

My R notebook is available for download. Fair warning: it requires a bunch of packages, and requires a CPLEX installation if you plan to run the MIP models.

## Tuesday, January 25, 2022

### Quantifying Approximate Equality

A question posed on OR Stack Exchange asked how to partition $M$ machines with specified productivity values $P_1,\dots,P_M \ge 0$ into $G$ groups of $S$ machines each (where $G\cdot S = M$) so that each group would have approximately the same total productivity. This raises an interesting question about how one quantifies "approximate equality".

Some of the suggestions for metrics among the responses to the question included minimizing the maximum productivity of any group, maximizing the minimum productivity of any group, and minimizing the range (the difference between the maximum and minimum). I suspect that mathematical convenience at least partly motivated them, since they are all easy to incorporate in an optimization model. Of those three, minimizing the range would get my vote. At the same time, minimizing the range does not minimize the "lumpiness" of the distribution of values between the extremes. So I would also consider minimizing either the mean absolute deviation (MAD) or mean squared deviation (MSD) for the group productivity values, recognizing that either of those might result in a greater overall spread of group values.

At this point I will drop any references to machines and productivity and just look at the general problem of allocating a fixed amount of something to a fixed number of somethings. Let's suppose that I have 49 units of whatever and 7 entities to whom to distribute it. The mean number of units given to any entity will be 49 / 7 = 7, regardless of how I do the allocating. Consider the following plot showing three different possibilities.

The size of each bubble is the number of entities receiving an allocation of a given size. True equality would manifest as one large bubble at value 7.

Methods A and B have the same minimum and maximum (and hence the same range), but method B is clearly more concentrated (smaller MAD and MSD). I suspect that the author of the original question would prefer B to A. On the other hand, if we are allocating rewards to people, someone might possibly find A more egalitarian, in that the recipient of the smallest amount (1) is less disadvantaged relative to the recipient(s) of the second smallest amount (3 in A, 7 in B), and similarly the recipient of the largest amount (13) is less advantaged relative to the recipient(s) of the second largest amount (11 in A, 7 in B).

Now compare methods B and C. C has a larger minimum, smaller maximum and smaller range, but B has a smaller MAD. (They have the same MSD.) Which is closer to equality? I think that is a subjective question. In my next post, I will compare a few possible solutions to the original question.

My point here is that minimizing range and minimizing MAD are both valid choices, potentially leading to substantially different solutions.