Stochastic Programming Test Problem: Inventory Model. More...

#include <inventory.h>

Inheritance diagram for pagmo::problem::inventory:

Public Member Functions
	inventory (int weeks=4, int sample_size=10, unsigned int seed=0)
	Constructor from weeks, sample size and random seed. More...

std::string	get_name () const
	Get problem's name. More...

base_ptr	clone () const
	Clone method. More...

Public Member Functions inherited from pagmo::problem::base_stochastic
	base_stochastic (int, unsigned int=0u)
	Constructor from global dimension and random seed. More...

	base_stochastic (int, int, int, int, int, const double &, unsigned int)
	Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

	base_stochastic (int, int, int, int, int, const std::vector< double > &, unsigned int)
	Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance vector. More...

unsigned int	get_seed () const
	Gets the pseudo random generator seed. More...

void	set_seed (unsigned int) const
	Sets the pseudo random generator seed. More...

Public Member Functions inherited from pagmo::problem::base
	base (int, int=0, int=1, int=0, int=0, const double &=0)
	Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

	base (int, int, int, int, int, const std::vector< double > &)
	Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

	base (const double &, const double &, int, int=0, int=1, int=0, int=0, const double &=0)
	Constructor from values for lower and upper bounds, global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

	base (const decision_vector &, const decision_vector &, int=0, int=1, int=0, int=0, const double &=0)
	Constructor from upper/lower bounds, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

template<std::size_t N>
	base (const double(&v1)[N], const double(&v2)[N], int ni=0, int nf=1, int nc=0, int nic=0, const double &c_tol=0)
	Constructor from raw arrays, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

template<class Iterator1 , class Iterator2 >
	base (Iterator1 start1, Iterator1 end1, Iterator2 start2, Iterator2 end2, int ni=0, int nf=1, int nc=0, int nic=0, const double &c_tol=0)
	Constructor from iterators, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

virtual	~base ()
	Trivial destructor. More...

std::string	human_readable () const
	Return human readable representation of the problem. More...

bool	operator== (const base &) const
	Equality operator. More...

bool	operator!= (const base &) const
	Inequality operator. More...

bool	is_compatible (const base &) const
	Compatibility operator. More...

bool	compare_x (const decision_vector &, const decision_vector &) const
	Compare decision vectors. More...

bool	verify_x (const decision_vector &) const
	Verify compatibility of decision vector x with problem. More...

bool	compare_fc (const fitness_vector &, const constraint_vector &, const fitness_vector &, const constraint_vector &) const
	Simultaneous fitness-constraint comparison. More...

virtual void	pre_evolution (population &) const
	Pre-evolution hook. More...

virtual void	post_evolution (population &) const
	Post-evolution hook. More...

virtual void	set_sparsity (int &lenG, std::vector< int > &iGfun, std::vector< int > &jGvar) const
	Sets the sparsity pattern of the gradient. More...

const decision_vector &	get_lb () const
	Lower bounds getter. More...

const decision_vector &	get_ub () const
	Upper bounds getter. More...

void	set_bounds (const decision_vector &, const decision_vector &)
	Bounds setter from pagmo::decision_vector. More...

template<class Iterator1 , class Iterator2 >
void	set_bounds (Iterator1 start1, Iterator1 end1, Iterator2 start2, Iterator2 end2)
	Bounds setter from iterators. More...

template<std::size_t N>
void	set_bounds (const double(&v1)[N], const double(&v2)[N])
	Bounds setter from raw arrays. More...

void	set_bounds (const double &, const double &)
	Set bounds to specified values. More...

void	set_bounds (int, const double &, const double &)
	Set bounds to specified values. More...

void	set_lb (const decision_vector &)
	Set lower bounds from pagmo::decision_vector. More...

void	set_lb (int, const double &)
	Set specific lower bound to value. More...

void	set_lb (const double &)
	Set all lower bounds to value. More...

template<class Iterator >
void	set_lb (Iterator start, Iterator end)
	Lower bounds setter from iterators. More...

template<std::size_t N>
void	set_lb (const double(&v)[N])
	Lower bounds setter from raw array. More...

void	set_ub (const decision_vector &)
	Set upper bounds from pagmo::decision_vector. More...

void	set_ub (int, const double &)
	Set specific upper bound to value. More...

void	set_ub (const double &)
	Set all upper bounds to value. More...

template<class Iterator >
void	set_ub (Iterator start, Iterator end)
	Upper bounds setter from iterators. More...

template<std::size_t N>
void	set_ub (const double(&v)[N])
	Upper bounds setter from raw array. More...

unsigned int	get_fevals () const
	Return number of function evaluations. More...

unsigned int	get_cevals () const
	Return number of constraints function evaluations. More...

size_type	get_dimension () const
	Return global dimension. More...

size_type	get_i_dimension () const
	Return integer dimension. More...

f_size_type	get_f_dimension () const
	Return fitness dimension. More...

c_size_type	get_c_dimension () const
	Return global constraints dimension. More...

c_size_type	get_ic_dimension () const
	Return inequality constraints dimension. More...

const std::vector< double > &	get_c_tol () const
	Return constraints tolerance. More...

double	get_diameter () const
	Get the diameter of the problem. More...

constraint_vector	compute_constraints (const decision_vector &) const
	Compute constraints and return constraint vector. More...

void	compute_constraints (constraint_vector &, const decision_vector &) const
	Compute constraints and write them into contraint vector. More...

bool	compare_constraints (const constraint_vector &, const constraint_vector &) const
	Compare constraint vectors. More...

bool	test_constraint (const constraint_vector &, const c_size_type &) const
	Test i-th constraint of c (using tolerance information). More...

bool	feasibility_x (const decision_vector &) const
	Test feasibility of decision vector. More...

bool	feasibility_c (const constraint_vector &) const
	Test feasibility of constraint vector. More...

fitness_vector	objfun (const decision_vector &) const
	Return fitness of pagmo::decision_vector. More...

void	objfun (fitness_vector &, const decision_vector &) const
	Write fitness of pagmo::decision_vector into pagmo::fitness_vector. More...

bool	compare_fitness (const fitness_vector &, const fitness_vector &) const
	Compare fitness vectors. More...

void	reset_caches () const
	Reset internal caches. More...

const std::vector< constraint_vector > &	get_best_c (void) const
	Get the best known constraint vector. More...

const std::vector< decision_vector > &	get_best_x (void) const
	Get the best known decision vector. More...

const std::vector< fitness_vector > &	get_best_f (void) const
	Get the best known fitness vector. More...

void	set_best_x (const std::vector< decision_vector > &)
	Sets the best known decision vectors. More...

Protected Member Functions
std::string	human_readable_extra () const
	Extra human readable info for the problem. More...

void	objfun_impl (fitness_vector &, const decision_vector &) const
	Objective function implementation. More...

Protected Member Functions inherited from pagmo::problem::base
virtual bool	equality_operator_extra (const base &) const
	Extra requirements for equality. More...

virtual bool	compare_fc_impl (const fitness_vector &, const constraint_vector &, const fitness_vector &, const constraint_vector &) const
	Implementation of simultaneous fitness-constraint comparison. More...

void	estimate_sparsity (const decision_vector &, int &lenG, std::vector< int > &iGfun, std::vector< int > &jGvar) const
	Heuristics to estimate the sparsity pattern of the problem. More...

void	estimate_sparsity (int &lenG, std::vector< int > &iGfun, std::vector< int > &jGvar) const
	Heuristics to estimate the sparsity pattern of the problem. More...

virtual void	compute_constraints_impl (constraint_vector &, const decision_vector &) const
	Implementation of constraint computation. More...

virtual bool	compare_constraints_impl (const constraint_vector &, const constraint_vector &) const
	Implementation of constraint vector comparison. More...

virtual bool	compare_fitness_impl (const fitness_vector &, const fitness_vector &) const
	Implementation of fitness vectors comparison. More...

Friends
class	boost::serialization::access

Additional Inherited Members
Public Types inherited from pagmo::problem::base
typedef decision_vector::size_type	size_type
	Problem's size type: the same as pagmo::decision_vector's size type.

typedef fitness_vector::size_type	f_size_type
	Fitness' size type: the same as pagmo::fitness_vector's size type.

typedef constraint_vector::size_type	c_size_type
	Constraints' size type: the same as pagmo::constraint_vector's size type.

Static Public Attributes inherited from pagmo::problem::base
static const std::size_t	cache_capacity = 5
	Capacity of the internal caches.

Protected Attributes inherited from pagmo::problem::base_stochastic
rng_double	m_drng
	Random number generator for double-precision floating point values.

rng_uint32	m_urng
	Random number generator for unsigned integer values.

unsigned int	m_seed
	Seed of the random number generator.

Detailed Description

Stochastic Programming Test Problem: Inventory Model.

This problem is a generalization of the simple inventory problem so-called of the "news-vendor", widely used to introduce the main tools and techniques of stochastic programming in general Assume you are a newsvendor and each week, for the next $ N$ weeks, you need to decide how many journals to order (indicated with the decision variable $ x_i $ ). The weekly journal demand is unknown to you and is indicated with the variable $d_i$ . The cost of ordering journals before the week starts is $ c$ , the cost of ordering journals during the week (in order to meet an unforeseen demand) is $ b $ and the cost of having to hold unsold journals is $ h $ . The inventory level of journals will be defined by the succession:

$I_i = [I_{i-1} + x_i - d_i]_+, I_1 = 0$

while the total cost of running the journal sales for $N$ weeks will be:

$J(\mathbf x, \mathbf d) = c \sum_{i=1}^N x_i+ b \sum_{i=1}^N [d_i - I_i - x_i]_+ + h \sum_{i=1}^N [I_i + x_i - d_i]_+$

See also: www2.isye.gatech.edu/people/faculty/Alex_Shapiro/SPbook.pdf

Author: Dario Izzo (dario.nosp@m..izz.nosp@m.o@esa.nosp@m..int)

Definition at line 65 of file inventory.h.

Constructor & Destructor Documentation

pagmo::problem::inventory::inventory	(	int	weeks = `4`,
		int	sample_size = `10`,
		unsigned int	seed = `0`
	)

Constructor from weeks, sample size and random seed.

Given the numer of weeks (i.e. prolem dimension), the sample size to approximate the expected value and a starting random seed, we contruct the inventory prolem

Parameters

[in]	weeks	integer dimension of the problem corresponding to the numer of weeks to plan the inventory for.
[in]	sample_size	integer dimension of the sample used to approximate the expected value
[in]	seed	unsigned integer used as starting random seed to build the pseudorandom sequences used to generate the sample

See also: problem::base constructors.

Definition at line 33 of file inventory.cpp.

Member Function Documentation

base_ptr pagmo::problem::inventory::clone ( ) const

virtual

Clone method.

Provided that the derived problem implements properly the copy constructor, virtually all implementations of this method will look like this:

return base_ptr(new derived_problem(*this));

               @return problem::base_ptr to a copy of this.

Implements pagmo::problem::base.

Definition at line 40 of file inventory.cpp.

std::string pagmo::problem::inventory::get_name ( ) const

virtual

Get problem's name.

Default implementation will return the problem's mangled C++ name.

Returns: name of the problem.

Reimplemented from pagmo::problem::base.

Definition at line 75 of file inventory.cpp.

std::string pagmo::problem::inventory::human_readable_extra ( ) const

protectedvirtual

Extra human readable info for the problem.

Will return a formatted string containing the values vector, the weights vectors and the max weight.

Reimplemented from pagmo::problem::base.

Definition at line 66 of file inventory.cpp.

void pagmo::problem::inventory::objfun_impl	(	fitness_vector &	f,
		const decision_vector &	x
	)		const

protectedvirtual

Objective function implementation.

Takes a pagmo::decision_vector x as input and writes its pagmo::fitness_vector to f. This function is not to be called directly, it is invoked by objfun() after a series of safety checks is performed on x and f.

Parameters

[out]	f	fitness vector into which x's fitness will be written.
[in]	x	decision vector whose fitness will be calculated.

Implements pagmo::problem::base.

Definition at line 45 of file inventory.cpp.

The documentation for this class was generated from the following files:

Public Member Functions

Protected Member Functions

Friends

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation