kaiwen_test: modification to cntnv.py

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "esbonio.sphinx.confDir": ""
+}

demo/demo_model.py

@@ -29,9 +29,9 @@
 
 # Working example for MM1 model.
 # -----------------------------------------------
-from simopt.models.mm1queue import MM1Queue
-fixed_factors = {"lambda": 3.0, "mu": 8.0}
-mymodel = MM1Queue(fixed_factors)
+from simopt.models.cntnv import CntNV
+fixed_factors = {}
+mymodel = CntNV(fixed_factors)
 # -----------------------------------------------
 
 # The rest of this script requires no changes.

demo/demo_problem.py

@@ -43,9 +43,9 @@
 # Working example for CntNVMaxProfit problem.
 # -----------------------------------------------
 from simopt.models.cntnv import CntNVMaxProfit
-fixed_factors = {"initial_solution": (2,), "budget": 500}
+fixed_factors = {}
 myproblem = CntNVMaxProfit(fixed_factors=fixed_factors)
-x = (3,)
+x = (5, 5, 5, 5)
 mysolution = Solution(x, myproblem)
 # -----------------------------------------------
 

demo/demo_problem_solver.py

@@ -36,10 +36,10 @@
 print(f"Results will be stored as {file_name_path}.")
 
 # Initialize an instance of the experiment class.
-myexperiment = ProblemSolver(solver_name, problem_name)
+myexperiment = ProblemSolver(solver_name, problem_name, solver_fixed_factors={"sample_size": 10})
 
 # Run a fixed number of macroreplications of the solver on the problem.
-myexperiment.run(n_macroreps=20)
+myexperiment.run(n_macroreps=5)
 
 # If the solver runs have already been performed, uncomment the
 # following pair of lines (and uncommmen the myexperiment.run(...)
@@ -48,9 +48,9 @@
 
 print("Post-processing results.")
 # Run a fixed number of postreplications at all recommended solutions.
-myexperiment.post_replicate(n_postreps=200)
+myexperiment.post_replicate(n_postreps=20)
 # Find an optimal solution x* for normalization.
-post_normalize([myexperiment], n_postreps_init_opt=200)
+post_normalize([myexperiment], n_postreps_init_opt=20)
 
 # Log results.
 myexperiment.log_experiment_results()

docs/cntnv.rst

@@ -5,45 +5,158 @@ Model: Continuous Newsvendor Problem (CNTNV)
 Description:
 ------------
 
-A vendor orders a fixed quantity of liquid at the beginning of a day to be
-sold to customers throughout the day. The vendor pays a per-unit order cost
-:math:`c` for the initial inventory and sells it the product to customers at a per-unit price
-:math:`s`. At the end of the day, any unsold liquid can be salvaged at a per-unit price, :math:`w`.
+A vendor orders a fixed quantity of materials at the beginning of a day. A per-unit-material
+cost :math:`c_{m}` is assessed for each type of material. As the demand is observed, the vendor 
+optionally orders recourse material at a premium price :math:`c_{r}` and processes all materials 
+into a variety of finished goods in a manner that maximizes profit. The finished goods are sold 
+to customers at price :math:`p_{s}`. At the end of the day, any unprocessed materials can be 
+salvaged at price :math:`p_{v}`.
 
 Sources of Randomness:
 ----------------------
 
-Each day's random demand for liquid product follows Burr Type XII distribution and is denoted by :math:`D`.
-The parameters of the Burr Type XII distribution are :math:`α` and :math:`β` so that its cumulative
-distribution function is given by :math:`F(x) = 1 - (1+x^α)^{-β}` where :math:`x, α,` and
-:math:`β` are all positive.
+Each day's random demand for discrete product quantities follows Poisson distribution and 
+is denoted by :math:`D`. The parameters of the Poisson distribution is :math:`λ`. Its 
+cumulative distribution function is given by :math:`F(x; λ) = \sum_{k=0}^{x} \frac{e^{-λ}λ^k}{k!}` 
+where :math:`x` and :math:`λ` are positive.
 
 Model Factors: 
 --------------
 
-* Cost (:math:`c`): The price at which the newsvendor purchases one unit volume of liquid.
+* num_material (:math:`n_{m}`): The number of material types.
 
-    * Default: 5
+    * Default: 4
 
-* Price (:math:`s`): The price at which the newsvendor sells one unit volume of liquid.
+* num_product (:math:`n_{p}`): The number of product types.
 
-    * Default: 9 
+    * Default: 3
 
-* Salvage Price (:math:`w`): The price at which any unsold liquid is sold for salvage.
+* mat_to_prod (:math:`A`): :math:`n_{p}` by :math:`n_{m}` matrix, mapping :math:`n_{m}` materials to :math:`n_{p}` products.
 
-    * Default: 1
+    * Default: [[1, 2, 1, 3], [1, 1, 3, 1], [2, 0, 4, 1]]
 
-* Alpha (:math:`α`): Parameter for the demand distribution.
+* material_cost (:math:`c_{m}`): Purchasing cost per material unit. :math:`n_{m}` sized array.
 
-    * Default: 2
+    * Default: [1, 1, 1, 1]
 
-* Beta (:math:`β`): Parameter for the demand distribution.
+* recourse_cost (:math:`c_{r}`): Recourse purchasing cost per material unit. :math:`n_{m}` sized array. 
+
+    * Default: [2, 2, 2, 2]
+
+* process_cost (:math:`c_{p}`): Processing cost per product unit. :math:`n_{p}` sized array.
+
+    * Default: [0.1, 0.1, 0.1]
+
+* order_cost (:math:`c_{o}`): Fixed one-time ordering cost.
 
     * Default: 20
 
-* Quantity of Liquid (:math:`x`): Amount (volume) of liquid ordered at the beginning of the day.
+* purchase_yield (:math:`Y`): Yield rates for initially purchased materials. :math:`n_{m}` sized array. 
+
+    * Default: [0.9, 0.9, 0.9, 0.9]
+
+* total_budget (:math:`b`): Total budget for all materials, processing, ordering and recourse purchases.
+
+    * Default: 600
+
+* sales_price (:math:`p_{s}`): Sales price per product unit. :math:`n_{p}` sized array. 
+
+    * Default: [12, 12, 12]
+
+* salvage_price (:math:`p_{v}`): Salvage price per material unit. :math:`n_{m}` sized array. 
+
+    * Default: [0.6, 0.6, 0.6, 0.6]
+
+* order_quantity (:math:`x`): Initial order quantity per material. :math:`n_{m}` sized array. 
+
+    * Default: [20, 20, 20, 20]
+
+* poi_mean (:math:`λ`): Mean parameter for demand's poisson distribution. :math:`n_{p}` sized array. 
+
+    * Default: [15, 15, 15]
+
+Model variations:
+----------------------
+
+This newsvendor model is adaptable to various cases of the traditional model. Adjustment of
+model factors is necessary if simulating a particular case. 
+
+1) Retail vendor (traditonal): 
+
+    The newsvendor purchases products and sells them directly to the customers. There 
+    is no production involved. The decision variable in this problem is order quantity
+    of *products*. This variation is subject to below factor settings and intepretations: 
+
+    * :math:`n_{m}` = :math:`n_{p}` (there can be multiple products)
+    * :math:`A` = identity matrix of size :math:`n_{p}`
+    * :math:`c_{m}` = cost of each product type
+    * :math:`c_{r}` = recourse cost of each product type
+    * :math:`p_{v}` = salvage price of each product type
+    * :math:`Y` = yield rates of each product type
 
-    * Default: 0.5
+2) Factory vendor:
+
+    Initially, the newsvendor purchases materials instead of products. After demand is
+    observed, materials are processed into products. This is done using an integer program
+    with an objective of maximizing profit. Additionally, in this variation, materials 
+    can be salvaged but products can not be salvaged. The decision variable in this problem 
+    is order quantity of *materials*. This variation is subject to below factor settings 
+    and intepretations:
+
+    * :math:`n_{m}` :math:`⊥` :math:`n_{p}`. Independence between the number of materials and the number of products
+    * :math:`A` = Resource requirements to produce each product. :math:`n_{p}` by :math:`n_{m}` matrix.
+
+3) Multi-product:
+
+    Either in a factory vendor or retail vendor setting, there can exist multiple products
+    to be procured/produced and sold to customers. Previous constraints from retail or factory 
+    models still hold. This variation is subject to below factor settings and intepretations:
+
+    * :math:`n_{p}` > 1
+    * len(:math:`c_{p}`) > 1
+    * len(:math:`p_{s}`) > 1
+
+4) Single-product:
+
+    Similarly, a factory vendor or a retail vendor can sell one product only. Note that a single-
+    product retail vendor presents the same senario as the traditional newsvendor problem. A single-
+    product factory vendor can have multiple materials or one single material, in which case it will
+    behave as the traditional newsvendor problem. This variation is subject to below factor settings 
+    and intepretations:
+
+    * :math:`n_{p}` = 1 
+    * :math:`c_{p}` still exists as an array. len(:math:`c_{p}`) = 1.
+    * :math:`p_{s}` still exists as an array. len(:math:`p_{s}`) = 1.
+    * :math:`λ` stil exists as an array. len(:math:`λ`) = 1.
+
+    In other words, the factors' datatype should not alter, regardless of the model being the 
+    multi-product/single-product or retail/factory. 
+
+5) Recourse:
+
+    The recourse case does not change the nature of newsvendor's operations. It is an additional 
+    feature can co-exist with all previous variations of the model. Recourse refers to the newsvendor's 
+    decision to procure materials/products after demand is observed. Economically speaking, a vendor
+    would chooses to do so only if the return is worth the premium prices paid for the recourse. 
+
+    Recourse is disabled by default. In other words, its default input is None. 
+
+    In the retail newsvendor setting, the recourse refers to product recourse. 
+
+    In the factory newsvendor setting, the recourse refers to material recourse. 
+
+    In either case, len(:math:`c_{r}`) = :math:`n_{m}`. 
+
+
+6) Random yield:
+
+    This case refers to indepedent yields of procurement. Such applies to material in the factory 
+    newsvendor and products in the retail newsvendor. The randomness of yields comes from a binomial 
+    distribution using a random number generator. Yield rates (in array form) are inputed as a model 
+    factor. This variation is subject to below factor setting:
+
+    * len(:math:`Y`) = :math:`n_{m}` 
+    * 0 < :math:`Y_{i}` <= 1, for all :math:`Y_{i}` in :math:`Y`.
 
 Responses:
 ----------
@@ -57,14 +170,21 @@ Evan L. Porteus. Stochastic inventory theory. In D. P. Heyman and M. J. Sobel, e
 Stochastic Models, volume 2 of Handbooks in Operations Research and Management Science,
 chapter 12, pages 605–652. Elsevier, New York, 1990.
 
+Gallego, G., & Moon, I. (1993). The distribution free newsboy problem: Review and extensions.
+The Journal of the Operational Research Society, 44(8), 825.
+https://doi.org/10.2307/2583894
+
+Ding, X., Puterman, M. L., & Bisi, A. (2002). The censored Newsvendor and the optimal
+acquisition of Information. Operations Research, 50(3), 517–527.
+https://doi.org/10.1287/opre.50.3.517.7752
 
 Optimization Problem: Maximize Profit
 =====================================
 
 Decision Variables:
 -------------------
 
-* Quantity of Liquid (:math:`x`): Amount (volume) of liquid ordered at the beginning of the day.
+* order_quantity (:math:`x`): Amount of raw material to be purchased at the beginning of the day. :math:`n_{m}` sized array.
 
 Objectives: 
 -----------
@@ -74,14 +194,16 @@ Maximizes the vendor's expected profit.
 Constraints: 
 ------------
 
-Quantity of Liquid must be non-negative: :math:`x > 0`
+order_quantity must be an array of non-negative integers
+
+* :math:`x_{i}` >= 0, for all :math:`x_{i}` in :math:`x`.
 
 Problem Factors:
 ----------------
 
 * Budget: Max # of replications for a solver to take.
 
-  * Default: 1000
+  * Default: 3000
 
 Fixed Model Factors:
 --------------------
@@ -91,21 +213,21 @@ Fixed Model Factors:
 Starting Solution:
 ------------------
 
-* :math:`x = 0`
+* [40, 40, 100, 60]
 
 
 Random Solutions: 
 -----------------
 
-If random solutions are needed, generate :math:`x` from an Exponential distribution with mean 1.
+If random solutions are needed, generate :math:`x` from continous_random_vector_from_simplex 
+function in mrg32k3a.
 
 Optimal Solution:
 -----------------
 
-Global minimum at :math:`x^* = (1/((1-r)^{1/β})-1)^{1/α}`.
-For the default factors, the optimal solution is :math:`x^*` = 0.1878.
+* [82, 60, 144, 115]
 
 Optimal Objective Function Value:
 ---------------------------------
 
-For the default factors, the maximum expected profit is 0.4635.
+For the default factors, the maximum expected profit is 343.19

docs/simopt.models.rst

@@ -52,6 +52,14 @@ simopt.models.dynamnews module
    :undoc-members:
    :show-inheritance:
 
+simopt.models.example module
+----------------------------
+
+.. automodule:: simopt.models.example
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 simopt.models.facilitysizing module
 -----------------------------------