mathematical-optimization - CPLEX OPL 中的多期生产批量模型

问题描述

我是一名最后一年的学生，也是 CPLEX OPL 编程语言的新手。我一直在研究一家饮料公司的批量产品模型。我考虑了一个基于 GLSP 的小桶模型，每个宏观周期都有基于设置变化的灵活的微观周期。

该模型假设一个具有 3 条生产线和 3 个混合罐的 2 阶段工艺。我未能将以下项目表示为 CPLEX 代码。

每个宏观周期中的一组微观周期
从产品i到产品j的转换
从 MS Excel 中读取一组字符串数据。

我在模型中引用的学术论文可在此处找到模型的学术论文

有没有人可以帮我将论文中的数学翻译成 CPLEX OPL？

以下是我迄今为止声明模型变量和参数的代码

/*********************************************
 * OPL 12.5 Model
 * Author: HENRY
 * Creation Date: Mar 9, 2019 at 7:30:27 PM
 *********************************************/
//Model Parameters
int M = 4;
int F = 4;
int T = 5;
int N = 15;
int J = 4;
{int} micro_periods = {1,2,3};
{int} periods = {1,2,3,4,5};

tuple micro_period {
   int micro1;
   int micro2;
   int micro3;
}

micro_period St[periods] = ...;//set of microperiods in period t

{string}machnes = {"line1","line2","line3"};

{string} lambda_j = ...;//set of lines that can produce item j
{string} alpha_m = ...;//set of items that can be produced on line m
{string} beta_m = ...;//set of liquid flavours that can be produced on tank m
{string} gamma_ml = ...;//set of items that can be produced on line m and need liquid flavour l

//ranges in the model
range products = 1..J;
range machines = 1..M;
range flavours = 1..F;
//range periods = 0..T-1;
//range micro_periods = 1..N; //total number of setups

tuple itm {
    string line1;
    string line2;
    string line3;
  }
//itm lambda_j[products] = ...;

tuple flvr {
    string O;
    string A;
    string F;
    string P;
  }
//flvr beta_m[machines] = ...;

tuple lin {
    string MA;
    string MO;
    string MF;
    string PP;
  }
//lin alpha_m[machines] = ...;

tuple gamma {
    string line;
    string flavor;
  }

//gamma gamma_ml[machines][flavours] = ...;//revist this one

//Data Variables
float djt[products][periods] = ...;
float hj[products] = ...;
float gj[products] = ...;
float aIImj[machines][products] = ...;
float KIm[machines] = ...;//total capacity of tank m
float KIImt[machines][periods] = ...;//total available time 
float rjl[products][flavours] = ...;
float qIm[machines] = ...;
float Ipj[products] = ...;//initial inventory of item j
float Imj[products] = ...;//inital back order of item j
//boolean yIml0[machines][flavours] = ...;
//boolean yIImj0[machines][products] = ...;

tuple flvr_edge {
    int k;
    int l;  
}
tuple prdt_edge {
    int i;
    int j;  
}
//sets in the model
setof(flvr_edge)flvrs = {<k,l> | k,l in flavours : k!=l};
float sIkl[flvrs] = ...;
float bIkl[flvrs] = ...;

setof(prdt_edge) prdts = {<i,j> | i,j in products: i!=j};
float sIIij[prdts] = ...;
float bIIij[prdts] = ...;

//Decision variables
dvar float+ Ipjt[products][periods];
dvar float+ Imjt[products][periods];
dvar float+ xIImjs[machines][products][micro_periods];
dvar float+ vIIms[machines][micro_periods];
dvar boolean yImls[machines][flavours][micro_periods];
dvar boolean yIImjs[machines][products][micro_periods];
dvar boolean zImkls[machines][flvrs][micro_periods];
dvar boolean zIImijs[machines][prdts][micro_periods];

标签： mathematical-optimizationcplexopl

mathematical-optimization - CPLEX OPL 中的多期生产批量模型

问题描述

解决方案

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