CN110046761A - A kind of ethyl alcohol inventory's Replenishment Policy based on multi-objective particle - Google Patents
A kind of ethyl alcohol inventory's Replenishment Policy based on multi-objective particle Download PDFInfo
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Abstract
The invention proposes a kind of ethyl alcohol inventory's Replenishment Policy based on multi-objective particle is difficult to optimize determining problem for ethyl alcohol quantity in stock, and realization makes rational planning for ethyl alcohol inventory's replenishment quantity, reduces ethyl alcohol inventory totle drilling cost.Ethyl alcohol inventory Replenishment Policy is by establishing warehouse cost model and purchase cost model, procurement cost and batch quantity, the relationship for starting Replenishment Policy inventory balance and delivery time；Optimize warehouse cost model and purchase cost model using multi-objective particle, the optimal setting for obtaining batch quantity fixed when signal period replenishes every time, starting Replenishment Policy inventory balance and delivery time, to make ethyl alcohol quantity in stock be maintained at a reasonable level, promote inventory optimization control, guarantees industrial process stable operation.
Description
Technical field
The present invention makes rational planning for ethyl alcohol inventory's replenishment quantity using multi-objective particle realization, obtains single
The optimal setting of the batch quantity, starting Replenishment Policy inventory balance and delivery time fixed when period replenishes every time, is based on
The ethyl alcohol inventory Replenishment Policy of multi-objective particle can not only reduce inventory's totle drilling cost, but also can be improved inventory
All transfer efficients.Multi-objective particle is applied to ethyl alcohol inventory Replenishment Policy, ethyl alcohol inventory's replenishment quantity is closed
Reason planning promotes inventory optimization control, is the important component of industrial process operation, belongs to process control field.
Background technique
Inventory's Replenishment Policy has important influence to the inventory levels of ethyl alcohol, if strategy is improper to be easy to cause ethyl alcohol
Hoard or shortage.The retardance of randomness, time of delivery that ethyl alcohol uses makes enterprise that must hold a certain amount of inventory with full
It is full to test demand, and due to the particularity of ethyl alcohol chemical property, ethyl alcohol storage quantity has defined upper limit value, but inventory in warehouse
Amount is not the smaller the better.Quantity in stock is too small, can increase purchase cost because of insufficient supply, urgent dispatching；Quantity in stock is excessive, meeting
Increase management cost and increases a possibility that peril occurs simultaneously.It is mended using ethyl alcohol inventory's Replenishment Policy after optimization
Goods can make ethyl alcohol quantity in stock be maintained at one reasonable horizontal, to reduce inventory's totle drilling cost, promote inventory optimization control.
Therefore, research achievement of the invention has broad application prospects.
Nowadays chemicals is replenished by the experience of administrator, very big to people's dependence, cannot be controlled and be replenished well
Quantity, be easy to cause the waste of resource.Therefore, optimal ethyl alcohol inventory's Replenishment Policy is selected, to reduction inventory's totle drilling cost, is promoted
Have great importance into inventory optimization control.Batch quantity, starting Replenishment Policy inventory balance, the setting value of delivery time,
It not only determines warehouse cost, but also also has important influence to the purchase cost of ethyl alcohol.Ethyl alcohol warehouse cost and purchase cost
Between it is interrelated, shifting, to reduce inventory's totle drilling cost, needing to carry out tradeoff therebetween keeps two objective functions most
May all it be optimal.Therefore, it is necessary to seek suitable optimization method, ethyl alcohol warehouse cost model and ethyl alcohol purchase cost are established
Model is considering also to want stock buildup amount appropriate to reduce purchase cost while reducing quantity in stock to reduce warehouse cost.
At this point, batch quantity fixed when signal period replenishes every time can be obtained by reasonable optimization method, start the plan that replenishes
The slightly optimal setting of inventory balance and delivery time makes ethyl alcohol quantity in stock be maintained at a reasonable level, surplus inventories is avoided to accumulate
Pressure and goods and materials shortage, the final target realizing inventory's totle drilling cost and minimizing.
The present invention devises a kind of ethyl alcohol inventory's Replenishment Policy based on multi-objective particle, passes through multiple target
Particle swarm optimization algorithm obtains batch quantity fixed when signal period replenishes every time, starting Replenishment Policy inventory balance and is sent to
The optimal setting of time.
Summary of the invention
Present invention obtains a kind of ethyl alcohol inventory's Replenishment Policy based on multi-objective particle, passes through multiple target
Particle swarm optimization algorithm obtains batch quantity fixed when signal period replenishes every time, starting Replenishment Policy inventory balance and is sent to
The optimal setting of time is optimal ethyl alcohol warehouse cost and purchase cost all as far as possible, realizes that inventory's totle drilling cost is minimum
The target of change；
Present invention employs the following technical solution and realize step:
1. a kind of ethyl alcohol inventory's Replenishment Policy based on multi-objective particle, which is characterized in that including following
Step:
(1) selection and ethyl alcohol warehouse cost and all relevant variable of purchase cost are as follows: batch quantity Q (t), starting replenish plan
Slightly inventory balance R (t), delivery time L (t)；
(2) ethyl alcohol warehouse cost and purchase cost model are established:
Wherein, f1(t) it is the ethyl alcohol warehouse cost model in t-th of period, comprising management cost, accident risk cost, becomes
Matter loses cost；f2(t) it is the ethyl alcohol purchase cost model in t-th of period, includes ordering cost, transportation cost, urgent dispatching
Strain cost；With 1 year for a cycle, Q (t) is fixation batch quantity when replenishing every time in t-th of period；R (t) is t-th
Period starts Replenishment Policy inventory balance every time；L (t) is each delivery time in t-th of period；M is ethyl alcohol daily demand amout
Desired value；M is the desired value of ethyl alcohol total demand in signal period；A is the warehouse cost in signal period of every bottle of ethyl alcohol；
A is the ordering cost of every bottle of ethyl alcohol；B is the transportation cost of every bottle of ethyl alcohol；CT0To transport fixed cost every time；L2For logistics distribution
Maximum duration interval；N is to shorten the logistics distribution time increased strain cost of needs；
(3) ethyl alcohol inventory Replenishment Policy optimization method is designed:
1. initializing Optimized Iterative step number K；
2. using the ethyl alcohol warehouse cost of foundation and purchase cost model as the optimization aim letter of multi-objective particle swarm algorithm
Number；
3. when kth step iterative calculation, by the position x of particlet(k)=[Qt(k), Rt(k), Lt(k)] objective function is substituted into,
Calculate target function value；Wherein, k is current iteration step number, xt(k) particle position of iteration is walked for t-th of period kth；Qt
(k) batch quantity of iteration is walked for t-th of period kth；Rt(k) the starting Replenishment Policy inventory of iteration is walked for t-th of period kth
Surplus；Lt(k) delivery time of iteration is walked for t-th of period kth；Of t-th of period kth step during acquisition evolutionary computation
Body optimal location pt(k), the personal best particle p t-th of period kth walkedt(k) with t-th -1 step knowledge base of period kth
Solve Ht(k-1) it is compared, Ht(k-1)=[h1,t(k-1), h2,t(k-1) ..., hι,t(k-1)], hι,tIt (k-1) is t-th of period
The ι optimal solution in -1 step knowledge base of kth updates the knowledge base H of t-th of period kth step by dominance relationt(k), it dominates and closes
It is that computing function is:
Ht(k)=Ht(k-1)∪pt(k-1), if f1(hι,t(k-1))≥f1(ptAnd f (k-1))2(hι,t(k-1))≥f2(pt
(k-1)) (4)
Wherein, ∪ be relationship simultaneously, by the personal best particle p of t-th of -1 step of period ktht(k-1) and hι,t(k-1) corresponding
Target function value be compared, if pt(k-1) target function value is less than hι,t(k-1) target function value, then by pt(k-
1) individual optimal solution as t-th of period is stored in knowledge base, otherwise saves hι,t(k-1) optimal for t-th of period individual
Solution, f1() is ethyl alcohol warehouse cost model, f2() is ethyl alcohol purchase cost model, according to densimetry from knowledge base Ht(k) in
Select globally optimal solution gt(k)；
4. the individual optimal solution p walked by t-th of period ktht(k) the globally optimal solution g walked with t-th of period ktht(k)
Update the particle position and speed, particle rapidity and location update formula of k+1 step are as follows:
vi,j,t(k+1)=ω vi,j,t(k)+c1r1(pi,j,t(k)-xi,j,t(k))+c2r2(gj,t(k)-xi,j,t(k))；
(5)
xi,j,t(k+1)=xi,j,t(k)+vi,j,t(k+1)； (6)
Wherein, vi,j,tIt (k) is speed of i-th of the particle of t-th of period kth step iteration on j dimension search space,
xi,j,t(k) be position of i-th of the particle of t-th period kth step iteration on j dimension search space, the value range of i be [1,
100], j=5, ω are inertia weight, ω=0.5, c1It is personal best particle accelerated factor, c1=0.4, c2It is global optimum position
Set accelerated factor, c2=0.3, r1It is personal best particle coefficient, r1=0.5, r2It is global optimum's position parameter, r2=0.6,
pi,j,tIt (k) is individual optimal solution of i-th of the particle of t-th of period kth step iteration on j dimension search space, gj,tIt (k) is t
Globally optimal solution when a period kth step iteration on j dimension search space；
5. going to step 6. if current iterative steps k is more than or equal to Optimized Iterative step number K；If current iteration
Step number k is less than greatest iteration step number K, and iterative steps k adds 1, goes to step 3.；
6. from knowledge base Ht(K) a globally optimal solution g is randomly choosed inj,t *(K), globally optimal solution g is savedj,t *(K),
Wherein gj,t *(K)=[Qt *(K), Rt *(K), Lt *(K)] for step 3. defined in optimal ethyl alcohol inventory Replenishment Policy, Qt *(K) it is
Optimal ethyl alcohol batch quantity, Rt *It (K) is the starting optimal ethyl alcohol inventory balance of Replenishment Policy, Lt *(K) when being sent to for optimal ethyl alcohol
Between；
(4) batch quantity fixed when signal period replenishes every time, starting are obtained based on multi-objective particle
The optimal setting of Replenishment Policy inventory balance and delivery time.
Creativeness of the invention is mainly reflected in:
(1) present invention studies ethyl alcohol inventory's Replenishment Policy, to make ethyl alcohol quantity in stock be maintained at one reasonably
Level reduces surplus inventories risk, avoids loss out of stock, under the premise of allowing to shorten the logistics distribution time, establishes ethyl alcohol storage
Cost model and purchase cost model, procurement cost and batch quantity, the pass for starting Replenishment Policy inventory balance and delivery time
System；
(2) interrelated, shifting between warehouse cost and purchase cost, to reduce inventory's totle drilling cost, need two
Tradeoff is carried out between person is optimal two objective functions all as far as possible, but computation complexity is higher, by being manually difficult
To optimal solution, present invention employs multi-objective particle swarm algorithms to optimize model, so as to fast and accurately obtain list
The optimal setting of the batch quantity, starting Replenishment Policy inventory balance and delivery time fixed when a period replenishes every time；
Detailed description of the invention
Fig. 1 is the Pareto optimal solution set result figure of ethyl alcohol inventory Replenishment Policy of the present invention
Specific embodiment
(1) selection and ethyl alcohol warehouse cost and all relevant variable of purchase cost are as follows: batch quantity Q (t), starting replenish plan
Slightly inventory balance R (t), delivery time L (t)；
(2) ethyl alcohol warehouse cost and purchase cost model are established:
Wherein, f1(t) it is the ethyl alcohol warehouse cost model in t-th of period, comprising management cost, accident risk cost, becomes
Matter loses cost；f2(t) it is the ethyl alcohol purchase cost model in t-th of period, includes ordering cost, transportation cost, urgent dispatching
Strain cost；With 1 year for a cycle, Q (t) is fixation batch quantity when replenishing every time in t-th of period；R (t) is t-th
Period starts Replenishment Policy inventory balance every time；L (t) is each delivery time in t-th of period；M is ethyl alcohol daily demand amout
Desired value, m=5 bottles；M be signal period in ethyl alcohol total demand desired value, M=2000 bottles；A is every bottle of ethyl alcohol single
Warehouse cost in period, A=200 member；A is the ordering cost of every bottle of ethyl alcohol, a=8 member；B is the transportation cost of every bottle of ethyl alcohol,
B=1 member；CT0To transport fixed cost, CT every time0=20 yuan；L2For the maximum duration interval of logistics distribution, L2=6 days；N is
Shorten the logistics distribution time increased strain cost of needs, n=60 member/day；
(3) ethyl alcohol inventory Replenishment Policy optimization method is designed:
1. initializing Optimized Iterative step number K, K=100；
2. using the ethyl alcohol warehouse cost of foundation and purchase cost model as the optimization aim letter of multi-objective particle swarm algorithm
Number；
3. when kth step iterative calculation, by the position x of particlet(k)=[Qt(k), Rt(k), Lt(k)] objective function is substituted into,
Calculate target function value；Wherein, k is current iteration step number, xt(k) particle position of iteration is walked for t-th of period kth；Qt
(k) batch quantity of iteration is walked for t-th of period kth；Rt(k) the starting Replenishment Policy inventory of iteration is walked for t-th of period kth
Surplus；Lt(k) delivery time of iteration is walked for t-th of period kth；Of t-th of period kth step during acquisition evolutionary computation
Body optimal location pt(k), the personal best particle p t-th of period kth walkedt(k) with t-th -1 step knowledge base of period kth
Solve Ht(k-1) it is compared, Ht(k-1)=[h1,t(k-1), h2,t(k-1) ..., hι,t(k-1)], hι,tIt (k-1) is t-th of period
The ι optimal solution in -1 step knowledge base of kth updates the knowledge base H of t-th of period kth step by dominance relationt(k), it dominates and closes
It is that computing function is:
Ht(k)=Ht(k-1)∪pt(k-1), if f1(hι,t(k-1))≥f1(ptAnd f (k-1))2(hι,t(k-1))≥f2(pt
(k-1)) (4)
Wherein, ∪ be relationship simultaneously, by the personal best particle p of t-th of -1 step of period ktht(k-1) and hι,t(k-1) corresponding
Target function value be compared, if pt(k-1) target function value is less than hι,t(k-1) target function value, then by pt(k-
1) individual optimal solution as t-th of period is stored in knowledge base, otherwise saves hι,t(k-1) optimal for t-th of period individual
Solution, f1() is ethyl alcohol warehouse cost model, f2() is ethyl alcohol purchase cost model, according to densimetry from knowledge base Ht(k) in
Select globally optimal solution gt(k)；
4. the individual optimal solution p walked by t-th of period ktht(k) the globally optimal solution g walked with t-th of period ktht(k)
Update the particle position and speed, particle rapidity and location update formula of k+1 step are as follows:
vi,j,t(k+1)=ω vi,j,t(k)+c1r1(pi,j,t(k)-xi,j,t(k))+c2r2(gj,t(k)-xi,j,t(k))；
(5)
xi,j,t(k+1)=xi,j,t(k)+vi,j,t(k+1)； (6)
Wherein, vi,j,tIt (k) is speed of i-th of the particle of t-th of period kth step iteration on j dimension search space,
xi,j,t(k) be position of i-th of the particle of t-th period kth step iteration on j dimension search space, the value range of i be [1,
100], j=5, ω are inertia weight, ω=0.5, c1It is personal best particle accelerated factor, c1=0.4, c2It is global optimum position
Set accelerated factor, c2=0.3, r1It is personal best particle coefficient, r1=0.5, r2It is global optimum's position parameter, r2=0.6,
pi,j,tIt (k) is individual optimal solution of i-th of the particle of t-th of period kth step iteration on j dimension search space, gj,tIt (k) is t
Globally optimal solution when a period kth step iteration on j dimension search space；
5. going to step 6. if current iterative steps k is more than or equal to Optimized Iterative step number K；If current iteration
Step number k is less than greatest iteration step number K, and iterative steps k adds 1, goes to step 3.；
6. from knowledge base Ht(K) a globally optimal solution g is randomly choosed inj,t *(K), globally optimal solution g is savedj,t *(K),
Wherein gj,t *(K)=[Qt *(K), Rt *(K), Lt *(K)] for step 3. defined in optimal ethyl alcohol inventory Replenishment Policy, Qt *(K) it is
Optimal ethyl alcohol batch quantity, Rt *It (K) is the starting optimal ethyl alcohol inventory balance of Replenishment Policy, Lt *(K) when being sent to for optimal ethyl alcohol
Between；
(4) batch quantity fixed when signal period replenishes every time, starting are obtained based on multi-objective particle
The optimal setting of Replenishment Policy inventory balance and delivery time.
A kind of ethyl alcohol inventory's Replenishment Policy based on multi-objective particle, is calculated by multi-objective particle swarm optimization
Method obtains batch quantity fixed when signal period replenishes every time, starting Replenishment Policy inventory balance and the optimal of delivery time and sets
The objective function of definite value, the objective function and purchase cost model that make ethyl alcohol warehouse cost model is all optimal as far as possible；Figure
It is middle display ethyl alcohol inventory Replenishment Policy Pareto optimal solution set, X-axis: year warehouse cost, unit be member, Y-axis: year buying at
This, unit is member, and wherein each scatterplot represents a kind of Pareto optimal solution of ethyl alcohol inventory Replenishment Policy.
Claims (1)
1. a kind of ethyl alcohol inventory's Replenishment Policy based on multi-objective particle, which comprises the following steps:
(1) it selects and ethyl alcohol warehouse cost and all relevant variable of purchase cost are as follows: batch quantity Q (t), starting Replenishment Policy library
Surplus amount R (t), delivery time L (t)；
(2) ethyl alcohol warehouse cost and purchase cost model are established:
Wherein, f1(t) it is the ethyl alcohol warehouse cost model in t-th of period, includes management cost, accident risk cost, deterioration losses
Cost；f2(t) be t-th of period ethyl alcohol purchase cost model, comprising ordering cost, transportation cost, urgent dispatching strain at
This；With 1 year for a cycle, Q (t) is fixation batch quantity when replenishing every time in t-th of period；R (t) is that t-th of period is every
Secondary starting Replenishment Policy inventory balance；L (t) is each delivery time in t-th of period；M is the desired value of ethyl alcohol daily demand amout；
M is the desired value of ethyl alcohol total demand in signal period；A is the warehouse cost in signal period of every bottle of ethyl alcohol；A is every bottle
The ordering cost of ethyl alcohol；B is the transportation cost of every bottle of ethyl alcohol；CT0To transport fixed cost every time；L2For the longest of logistics distribution
Time interval；N is to shorten the logistics distribution time increased strain cost of needs；
(3) ethyl alcohol inventory Replenishment Policy optimization method is designed:
1. initializing Optimized Iterative step number K；
2. using the ethyl alcohol warehouse cost of foundation and purchase cost model as the optimization object function of multi-objective particle swarm algorithm；
3. when kth step iterative calculation, by the position x of particlet(k)=[Qt(k), Rt(k), Lt(k)] objective function is substituted into, is calculated
Target function value；Wherein, k is current iteration step number, xt(k) particle position of iteration is walked for t-th of period kth；QtIt (k) is t
The batch quantity of a period kth step iteration；Rt(k) the starting Replenishment Policy inventory balance of iteration is walked for t-th of period kth；Lt
(k) delivery time of iteration is walked for t-th of period kth；The individual of t-th of period kth step is optimal during acquisition evolutionary computation
Position pt(k), the personal best particle p t-th of period kth walkedt(k) with the solution H of t-th of -1 step knowledge base of period ktht(k-
1) it is compared, Ht(k-1)=[h1,t(k-1), h2,t(k-1) ..., hι,t(k-1)], hι,tIt (k-1) is t-th of period kth -1
The ι optimal solution in knowledge base is walked, the knowledge base H of t-th of period kth step is updated by dominance relationt(k), dominance relation meter
Calculating function is:
Ht(k)=Ht(k-1)∪pt(k-1), if f1(hι,t(k-1))≥f1(ptAnd f (k-1))2(hι,t(k-1))≥f2(pt(k-
1)) (4)
Wherein, ∪ be relationship simultaneously, by the personal best particle p of t-th of -1 step of period ktht(k-1) and hι,t(k-1) corresponding mesh
Offer of tender numerical value is compared, if pt(k-1) target function value is less than hι,t(k-1) target function value, then by pt(k-1) make
Individual optimal solution for t-th of period is stored in knowledge base, otherwise saves hι,tIt (k-1) is t-th of period individual optimal solution,
f1() is ethyl alcohol warehouse cost model, f2() is ethyl alcohol purchase cost model, according to densimetry from knowledge base Ht(k) choosing in
Select globally optimal solution gt(k)；
4. the individual optimal solution p walked by t-th of period ktht(k) the globally optimal solution g walked with t-th of period ktht(k) it updates
The particle position and speed, particle rapidity and location update formula of k+1 step are as follows:
vi,j,t(k+1)=ω vi,j,t(k)+c1r1(pi,j,t(k)-xi,j,t(k))+c2r2(gj,t(k)-xi,j,t(k))； (5)
xi,j,t(k+1)=xi,j,t(k)+vi,j,t(k+1)； (6)
Wherein, vi,j,tIt (k) is speed of i-th of the particle of t-th of period kth step iteration on j dimension search space, xi,j,t(k)
It is position of i-th of the particle of t-th of period kth step iteration on j dimension search space, the value range of i is [1,100], j=
5, ω be inertia weight, ω=0.5, c1It is personal best particle accelerated factor, c1=0.4, c2Be global optimum position accelerate because
Son, c2=0.3, r1It is personal best particle coefficient, r1=0.5, r2It is global optimum's position parameter, r2=0.6, pi,j,t(k) it is
Individual optimal solution of i-th of the particle of t-th of period kth step iteration on j dimension search space, gj,tIt (k) is t-th of period kth
Globally optimal solution when walking iteration on j dimension search space；
5. going to step 6. if current iterative steps k is more than or equal to Optimized Iterative step number K；If current iterative steps k
Less than greatest iteration step number K, iterative steps k adds 1, goes to step 3.；
6. from knowledge base Ht(K) a globally optimal solution g is randomly choosed inj,t *(K), globally optimal solution g is savedj,t *(K), wherein
gj,t *(K)=[Qt *(K), Rt *(K), Lt *(K)] for step 3. defined in optimal ethyl alcohol inventory Replenishment Policy, Qt *It (K) is optimal
Ethyl alcohol batch quantity, Rt *It (K) is the starting optimal ethyl alcohol inventory balance of Replenishment Policy, Lt *It (K) is optimal ethyl alcohol delivery time；
(4) batch quantity fixed when signal period replenishes every time is obtained based on multi-objective particle, starting replenishes
The optimal setting of tactful inventory balance and delivery time.
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| CN113947341A (en) * | 2020-07-17 | 2022-01-18 | 上海顺如丰来技术有限公司 | Supply chain replenishment method and device, computer equipment and storage medium |
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| CN117114577B (en) * | 2023-09-08 | 2024-05-24 | 深圳市元亿通科技有限公司 | Self-adaptive inventory cost management method and system |
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