[英]Using forall() predicate in minizinc as assignment statement without 'constraint'
I have a Minizinc program for generating the optimal charge/discharge schedule for a grid-connected battery, given a set of prices by time-interval.我有一个 Minizinc 程序,用于为并网电池生成最佳充电/放电时间表,给定一组按时间间隔的价格。
My program works (sort of; subject to some caveats), but my question is about two 'constraint' statements which are really just assignment statements:我的程序有效(有点;受到一些警告),但我的问题是关于两个“约束”语句,它们实际上只是赋值语句:
constraint forall(t in 2..T)(MW_SETPOINT[t-1] - SALE[t] = MW_SETPOINT[t]);
constraint forall(t in 1..T)(PROFIT[t] = SALE[t] * PRICE[t]);
These just mean Energy SALES
is the delta in MW_SETPOINT
from t-1
to 1
, and PROFIT
is SALE
* PRICE
for each interval.这些只是意味着 Energy
SALES
是MW_SETPOINT
从t-1
到1
的增量, PROFIT
是每个间隔的SALE
* PRICE
。 So it seems counterintuitive to me to declare them as 'constraints'.因此,将它们声明为“约束”对我来说似乎违反直觉。 But I've been unable to formulate them as assignment statements without throwing syntax errors.
但是我一直无法将它们表述为赋值语句而不会引发语法错误。
Question:问题:
constraint
s the recommended/idiomatic way to do it in Minizinc?constraint
s 的推荐/惯用方式? Full program for context:上下文的完整程序:
% PARAMS
int: MW_CAPACITY = 10;
array[int] of float: PRICE;
% DERIVED PARAMS
int: STARTING_MW = MW_CAPACITY div 2; % integer division
int: T = length(PRICE);
% DECISION VARIABLE - MW SETPOINT EACH INTERVAL
array[1..T] of var 0..MW_CAPACITY: MW_SETPOINT;
% DERIVED/INTERMEDIATE VARIABLES
array[1..T] of var -1*MW_CAPACITY..MW_CAPACITY: SALE;
array[1..T] of var float: PROFIT;
var float: NET_PROFIT = sum(PROFIT);
% CONSTRAINTS
%% If start at 5MW, and sell 5 first interval, setpoint for first interval is 0
constraint MW_SETPOINT[1] = STARTING_MW - SALE[1];
%% End where you started; opt schedule from arbitrage means no net MW over time
constraint MW_SETPOINT[T] = STARTING_MW;
%% these are really justassignment statements for SALE & PROFIT
constraint forall(t in 2..T)(MW_SETPOINT[t-1] - SALE[t] = MW_SETPOINT[t]);
constraint forall(t in 1..T)(PROFIT[t] = SALE[t] * PRICE[t]);
% OBJECTIVE: MAXIMIZE REVENUE
solve maximize NET_PROFIT;
output["DAILY_PROFIT: " ++ show(NET_PROFIT) ++
"\nMW SETPOINTS: " ++ show(MW_SETPOINT) ++
"\nMW SALES: " ++ show(SALE) ++
"\n$/MW PRICES: " ++ show(PRICE)++
"\nPROFITS: " ++ show(PROFIT)
];
It can be run with它可以运行
minizinc opt_sched_hindsight.mzn --solver org.minizinc.mip.coin-bc -D "PRICE = [29.835, 29.310470000000002, 28.575059999999997, 28.02416, 28.800690000000003, 32.41052, 34.38542, 29.512390000000003, 25.66587, 25.0499, 26.555529999999997, 28.149440000000002, 30.216509999999996, 32.32415, 31.406609999999997, 36.77642, 41.94735, 51.235209999999995, 50.68137, 64.54481, 48.235170000000004, 40.27663, 34.93675, 31.10404];"```
You can play with Array Comprehensions : (quote from the docs)您可以使用Array Comprehensions :(引自文档)
Array comprehensions have this syntax:
数组推导具有以下语法:
<array-comp>::= "[" <expr> "|" <comp-tail> "]"
For example (with the literal equivalents on the right):
例如(右边的文字等价物):
[2*i | i in 1..5] % [2, 4, 6, 8, 10]
Array comprehensions have more flexible type and inst requirements than set comprehensions (see Set Comprehensions ).
数组推导比集合推导具有更灵活的类型和 inst 要求(请参阅Set Comprehensions )。
Array comprehensions are allowed over a variable set with finite type, the result is an array of optional type, with length equal to the cardinality of the upper bound of the variable set.
数组推导允许对具有有限类型的变量集进行数组推导,结果是一个可选类型的数组,其长度等于变量集上限的基数。 For example:
例如:
var set of 1..5: x; array[int] of var opt int: y = [ i * i | i in x ];
The length of array will be 5.
数组的长度为 5。
Array comprehensions are allowed where the where-expression is a
var bool
.当 where 表达式为
var bool
时,允许使用数组推导。 Again the resulting array is of optional type, and of length equal to that given by the generator expressions.同样,结果数组是可选类型,并且长度等于生成器表达式给定的长度。 For example:
例如:
var int x; array[int] of var opt int: y = [ i | i in 1..10 where i;= x ];
The length of the array will be 10.
数组的长度为 10。
The indices of an evaluated simple array comprehension are implicitly
1..n
, wheren
is the length of the evaluated comprehension.评估的简单数组推导的索引隐含为
1..n
,其中n
是评估推导的长度。
Example:例子:
int: MW_CAPACITY = 10;
int: STARTING_MW = MW_CAPACITY div 2;
array [int] of float: PRICE = [1.0, 2.0, 3.0, 4.0];
int: T = length(PRICE);
array [1..T] of var -1*MW_CAPACITY..MW_CAPACITY: SALE;
array [1..T] of var 0..MW_CAPACITY: MW_SETPOINT = let {
int: min_i = min(index_set(PRICE));
} in
[STARTING_MW - sum([SALE[j] | j in min_i..i])
| i in index_set(PRICE)];
array [1..T] of var float: PROFIT =
[SALE[i] * PRICE[i]
| i in index_set(PRICE)];
solve satisfy;
Output: Output:
~$ minizinc test.mzn
SALE = array1d(1..4, [-10, -5, 0, 0]);
----------
Notice that index_set(PRICE)
is nothing else but 1..T
and that min(index_set(PRICE))
is nothing else but 1
, so one could write the above array comprehensions also as请注意,
index_set(PRICE)
只不过是1..T
,而min(index_set(PRICE))
只不过是1
,因此可以将上述数组推导式也写为
array [1..T] of var 0..MW_CAPACITY: MW_SETPOINT =
[STARTING_MW - sum([SALE[j] | j in 1..i])
| i in 1..T];
array [1..T] of var float: PROFIT =
[SALE[i] * PRICE[i]
| i in 1..T];
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