NL2030763B1 - Method for planning origin-based pre-cooling system considering investment of multi-type facilities - Google Patents
Method for planning origin-based pre-cooling system considering investment of multi-type facilities Download PDFInfo
- Publication number
- NL2030763B1 NL2030763B1 NL2030763A NL2030763A NL2030763B1 NL 2030763 B1 NL2030763 B1 NL 2030763B1 NL 2030763 A NL2030763 A NL 2030763A NL 2030763 A NL2030763 A NL 2030763A NL 2030763 B1 NL2030763 B1 NL 2030763B1
- Authority
- NL
- Netherlands
- Prior art keywords
- cooling
- path
- service
- station
- chromosome
- Prior art date
Links
- 238000001816 cooling Methods 0.000 title claims abstract description 326
- 238000000034 method Methods 0.000 title claims abstract description 35
- 210000000349 chromosome Anatomy 0.000 claims abstract description 107
- 230000035772 mutation Effects 0.000 claims abstract description 33
- 108090000623 proteins and genes Proteins 0.000 claims description 84
- 239000011159 matrix material Substances 0.000 claims description 81
- 238000004364 calculation method Methods 0.000 claims description 11
- 238000010276 construction Methods 0.000 claims description 9
- 230000001186 cumulative effect Effects 0.000 claims description 6
- 230000008093 supporting effect Effects 0.000 claims description 6
- 238000003306 harvesting Methods 0.000 claims description 3
- 239000013598 vector Substances 0.000 claims description 3
- 241001580033 Imma Species 0.000 claims 1
- 235000006679 Mentha X verticillata Nutrition 0.000 claims 1
- 235000002899 Mentha suaveolens Nutrition 0.000 claims 1
- 235000001636 Mentha x rotundifolia Nutrition 0.000 claims 1
- 230000003247 decreasing effect Effects 0.000 claims 1
- 238000009434 installation Methods 0.000 claims 1
- 230000004048 modification Effects 0.000 claims 1
- 238000012986 modification Methods 0.000 claims 1
- 238000005204 segregation Methods 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 8
- 238000009317 small scale farming Methods 0.000 abstract description 7
- 238000005457 optimization Methods 0.000 abstract description 5
- 230000002068 genetic effect Effects 0.000 abstract description 4
- 230000001737 promoting effect Effects 0.000 abstract description 3
- 239000003981 vehicle Substances 0.000 description 109
- 230000000875 corresponding effect Effects 0.000 description 30
- 239000000047 product Substances 0.000 description 23
- 235000013350 formula milk Nutrition 0.000 description 16
- 229910052729 chemical element Inorganic materials 0.000 description 4
- 235000014676 Phragmites communis Nutrition 0.000 description 3
- 229920000136 polysorbate Polymers 0.000 description 3
- 230000002311 subsequent effect Effects 0.000 description 3
- -1 if not Proteins 0.000 description 2
- 230000000977 initiatory effect Effects 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 241000689227 Cora <basidiomycete fungus> Species 0.000 description 1
- 241000282320 Panthera leo Species 0.000 description 1
- 229920002494 Zein Polymers 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000012271 agricultural production Methods 0.000 description 1
- 229940000425 combination drug Drugs 0.000 description 1
- 230000001934 delay Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000000796 flavoring agent Substances 0.000 description 1
- 235000019634 flavors Nutrition 0.000 description 1
- 235000012055 fruits and vegetables Nutrition 0.000 description 1
- 231100000350 mutagenesis Toxicity 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
- 239000005019 zein Substances 0.000 description 1
- 229940093612 zein Drugs 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0832—Special goods or special handling procedures, e.g. handling of hazardous or fragile goods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0835—Relationships between shipper or supplier and carriers
- G06Q10/08355—Routing methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Development Economics (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Biophysics (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Genetics & Genomics (AREA)
- Agronomy & Crop Science (AREA)
- Animal Husbandry (AREA)
- Marine Sciences & Fisheries (AREA)
- Mining & Mineral Resources (AREA)
- Primary Health Care (AREA)
- Educational Administration (AREA)
Abstract
The present invention relates to a method for planning an origin— based pre—cooling system considering an investment of multi—type facilities, which is proposed in consideration of applying both fixed and Hwbile pre—cooling methods. In the present invention, firstly, a siting—path optimization model of multi—type pre— cooling facilities for agricultural products in villages and towns is created with an objective of minimizing a system cost; secondly, an improved genetic algorithm is designed for solving, and chromosomes and rules for crossover and mutation are redesigned to optimize both siting and paths. The present invention can effectively improve the current situations of unavailable pre—cooling and, poor‘ pre—cooling effects for small— scale farming, with an important practical significance for perfecting and building a cooling system for agricultural products, optimizing a "first kilometer" process in cold chains for agricultural products, and promoting a socialized service process for villages and towns in China.
Description
METHOD FOR PLANNING ORIGIN-BASED PRE-COOLING SYSTEM CONSIDERING
INVESTMENT OF MULTI-TYPE FACILITIES
The present invention belongs to the field of cold chain lo- gistics, and relates to a method for planning an origin-based pre- cooling system considering an investment of multi-type facilities.
Origin-based pre-cooling is the first link in a cold chain of agricultural products. Delayed pre-cooling will accelerate the ma- turity, aging and spoilage of agricultural products so as to ac- cordingly affect their quality and flavor, shorten their shelf life, and result in inconvenience in subsequent transportation and sales. However, at present, the "first kilometer" cold chain in- frastructure for agricultural products is not perfect enough in
China, and pre-cooling systems have not yet been formed at ori- gins, resulting in that a pre-cooling preservation rate of agri- cultural products at origins is only 30%, which is far lower than 80% in European and American developed countries. Thus it is immi- nent to optimize origin-based pre-cooling systems.
Current, pre-cooling facilities in China are mainly pre- cooling stations relying on fixed buildings. However, agricultural products have a certain time limitation for pre-cooling treatment, that is, the longer the pre-cooling delay after picking is, the shorter the freshing period and the shelf life of agricultural products are. Whereas fixed pre-cooling stations can easily cause pre-cooling delays for agricultural products in remote origins, and it is unable to achieve an ideal pre-cooling effect. On the other hand, the harvest time of agricultural products is relative- ly concentrated only in some specific months, so there is general- ly a lower utilization rate of the fixed pre-cooling stations.
Compared with the fixed pre-cooling stations, mobile pre-cooling devices are more flexible with a higher utilization rate and a shorter payback period, and they are more suitable for China's current situations, such as dispersed agricultural production, small-scale farming in domination, and rural e-commerce as a sales method. However, the application of the mobile pre-cooling devices in China is still at an exploratory stage, and its operation mode and its cooperation mode with the fixed pre-cooling stations, etc. are still unclear. Therefore, it is necessary to actively explore the application of the mobile pre-cooling devices in the "first kilometer" system of the cold chain in China, so that it can ef- fectively improve the current situations of unavailable pre- cooling and poor pre-cooling effects for small-scale farming, with an important practical significance for perfecting and building a cooling system for agricultural products, optimizing a "first kil- ometer" process in cold chains for agricultural products, and pro- moting a socialized service process for villages and towns in Chi- na.
The present invention provides a method for planning an origin-based pre-cooling system considering an investment of mul- ti-type facilities, that is, forming a network layout of fixed pre-cooling facilities (represented by pre-cooling stations) and mobile pre-cooling facilities (represented by pre-cooling vehi- cles), and optimizing paths for vehicles supporting two types of pre-cooling facilities, to maximize an overall profit of a pre- cooling facility system in villages and towns. Decision-making contents include: 1) a pre-cooling method for each farmer; 2) a quantity, siting and capacity of pre-cooling stations, as well as a quantity and path of supporting transport vehicles; 3) a quanti- ty, capacity and path of pre-cooling vehicles. In the present in- vention, firstly, a siting-path optimization model of multi-type pre-cooling facilities for agricultural products in villages and towns is established with an objective of minimizing a system cost; secondly, an improved genetic algorithm is designed for so- lution, and chromosomes and rules for crossover and mutation are redesigned to optimize both siting and the paths. By implementing the present invention, the current situations of unavailable pre- cooling and poor pre-cooling effects for China's small-scale farm- ing can be effectively improved, and the establishment of an origin-based pre-cooling system in China is promoted.
In order to solve the problem of unavailable pre-cooling or poor pre-cooling effects for China's small-scale farming, the pre- sent invention proposes a method for planning an origin-based pre- cooling system considering an investment of multi-type facilities in consideration of using a fixed pre-cooling station and mobile pre-cooling vehicles simultaneously, with an objective of minimiz- ing an overall cost of pre-cooling facilities in villages and towns, so as to give full play to the advantages of various types of pre-cooling facilities and consummate the establishment of origin-based pre-cooling systems in China.
To achieve the above-mentioned objective, the present inven- tion provides the following technical solutions: a method for planning an origin-based pre-cooling system con- sidering an investment of multi-type facilities includes the fol- lowing steps:
Step I, designing an organizational form for a pre-cooling system considering an investment of multi-type facilities
The present invention considers an investment in two types of pre-cooling facilities, that is, a pre-cooling station and pre- cooling vehicles, which cover all the pre-cooling needs in rural areas. An organization form of fixed pre-cooling served by a pre- cooling station is designed as follows (see FIG. 1): after being picked by farmers, fruits and vegetables are transported to the pre-cooling station for uniform pre-cooling, and then transported back to the farmers by the transport vehicles after pre-cooling, and the farmers arrange subsequent delivery by themselves accord- ing to the selling situation. This method may lead to a pre- cooling delay. In order to ensure a good effect of the pre-cooling service and avoid the occurrence of the pre-cooling delay, a maxi- mum pre-cooling delay time is used as a constraint for a path transport time of each transport vehicle. The organizational form of mobile pre-cooling by a pre-cooling vehicle is designed as fol- lows (see FIG. 2): a pre-cooling vehicle equipped with mobile pre- cooling devices departs from a parking lot and goes to various service stations for pre-cooling services, and the pre-cooled products are returned to farmers upon completion of pre-cooling.
Compared with the fixed pre-cooling, this method enables lower goods damage in the process of loading and unloading without any pre-cooling delay. The two methods influence each other: the more farmers choose to send the harvested agricultural products to the pre-cooling station for pre-cooling, the larger pre-cooling sta- tion is required, and thus the construction cost, the natural goods damage, and the goods damage caused by pre-cooling delay will be increased; if farmers choose to rent a mobile pre-cooling vehicle for pre-cooling, refrigerating houses can be reduced, but more mobile pre-cooling vehicles will be purchased.
Step II, modeling
The problem is described as follows: in a rural area, there are m pre-cooling service stations, n optional pre-cooling sta- tions (parking lots), ¢ transport vehicles shuttling between the pre-cooling stations and the service stations, and © mobile pre- cooling vehicles; M={1, 2,.., m} is a set of service stations,
N=l{m+1, mt2,.., mtn} is a set of pre-cooling stations (parking lots), 8={1, 2,.., plis a set of transport vehicles, ={1, 2; oo} is a set of pre-cooling vehicles, V=MUN is a set of all points,
E={ (1,7) |m jeV}N{(i,j) | 4, JEN} is a set of sides, each side corre- sponds to a distance of d;;, and g, is a pre-cooling demand of the service station m(meM).
The modeling has an objective of minimizing the total cost of a pre-cooling system, and decision-making contents include: 1) a pre-cooling method for each farmer; 2) a siting and capacity of pre-cooling stations, as well as a quantity, type and path of sup- porting transport vehicles; 3) a quantity, type and path of pre- cooling vehicles. Variables and meanings thereof in the model are as follows:
Cp: a unit construction cost of a pre-cooling station; cs: a monthly unit operating cost of a pre-cooling station; cS: a purchase cost of a s-type transport vehicle; cPe a monthly operating cost of a s-type transport vehicle; cl": a unit driving cost of a s-type transport vehicle; ds: a maximum travel distance of a s-type transport vehicle;
vs: a traveling speed of a s-type transport vehicle; cf": a purchase cost of a u-type pre-cooling vehicle; cipe: a monthly operating cost of a u-type pre-cooling vehi- cle; 5 cire: a unit driving cost of a u-type pre-cooling vehicle; d,: a maximum travel distance of a u-type pre-cooling vehi- cle; oy: a single pre-cooling service time of a u-type pre-cooling vehicle;
Vu: a traveling speed of a u-type pre-cooling vehicle;
Hs: a number of transport nodes on the ©" path for transport vehicles; for example, the 1°%* path for transport vehicles is "121512", then H=4; ni(2SkSH,-1): a serial number of a service station located at the £** node on the op“ path for transport vehicles; for example, the 1° path for transport vehicles is "1215512, then 9} =1, in- dicating that the 2° node by which a transport vehicle passes on the 1° path is a No. 1 service station;
G,: a number of transport nodes on the «™ path for pre- cooling vehicles;
AY: a serial number of a service station located at the I" node on the oo" path for pre-cooling vehicles; for example, the 2% ] BE =3 —_— path for pre-cooling vehicles is "12453512", then ‚ indicat- ing that the 3™ node by which a pre-cooling vehicle passes on the 2™ path is a No. 3 service station; 5: a type of a transport vehicle, which is represented by its carrying capacity, for example, s=500 kg, 1,000 kg, 1,500 kg..., and s€S, wherein, S is a set of the types of transport vehicles; u: a type of a pre-cooling vehicle, which is represented by its carrying capacity, for example, u=500 kg, 1,000 kg, 1,500 kg..., and u€U, where, U is a set of the types of pre-cooling ve- hicles; ty: a travel time of a transport vehicle ¢ from a service ; ; : 9 mnd station 1 to a service station j, then bp me = ej: a travel time of a pre-cooling vehicle w from a service daw 20 station i to a service station j, then Exo 10 = Ek; ti Vu of’: a dwell time of a pre-cooling vehicle w at a service sta- tion i; if a u-type pre-cooling vehicle i provides service for a service station i, then of = oy;
Ef: a moment when a pre-cooling vehicle © arrives at a ser- vice station i, then Eio = Eje, +070, +E gi 8: a unit price of an agricultural product; g: a loading and unloading loss rate of a fixed pre-cooling method; ò: a loading and unloading loss rate of a mobile pre-cooling method, and ò<g; tvorr! daily working hours of a pre-cooling vehicle; tasiay! @ maximum pre-cooling delay time; t: a number of working days for picking and harvesting in a year;
T: business accounting year;
Tu: a variable of 0-1; when an origin n is selected as a parking lot for all vehicles, 7,="1", otherwise 7,=0; if a pre- cooling station is to be built in the system, it is defaulted to select a pre-cooling station as a parking lot; p: a scale of a pre-cooling station; a,: a variable of 0-1; when a pre-cooling station is built at an origin n, o,=1, otherwise o,=0;
Xijg! a variable of 0-1, representing whether there is a di- rect path between the sides (i,j) and that a transport vehicle ¢ is used for service; if yes, then x;,,=1, otherwise x;;,=0;
Viso: a variable of 0-1, representing whether there is a di- rect path between sides (i,j) and that a pre-cooling vehicle o is used for service; if yes, then y:y,=1, otherwise y:4,=0;
Boo: a variable of 0-1; when a transport vehicle ¢ provides service for a service station m, Bm=l, otherwise Bng=0;
Omo: a variable of 0-1; when a pre-cooling vehicle © provides service for a service station m, Sm>1l; otherwise p,,=0;
Ys: a variable of 0-1; when a s-type transport vehicle is used, y.=1, otherwise vy, =0;
Yu: a variable of 0-1; when a u-type pre-ccoling vehicle is used, y,=1, otherwise vy, =0;
The model having an objective of minimizing a total cost Ci; of a rural pre-cooling system includes the costs of a fixed pre- cooling subsystem and a mobile pre-cooling subsystem, expressed as
Cire and Cup, respectively, and the objective of the model can be expressed as formula (1): min Ca117Cste+ Cron (1) a cost structure of the fixed pre-cooling system includes: 1) pre-cooling station related costs C,,, including a construction cost and an operating cost of a pre-cooling station; 2) supporting transport vehicle related costs C.., including the three parts: a purchase cost of a transport vehicle, an operating cost of a transport vehicle, and a transport cost of a transport vehicle; and 3) a loading and unloading loss cost Cas. Therefore, a cost model of the fixed pre-cooling system is constructed as follows:
Cop = Yen Cn Cp + PeoTt) (2)
Coru = Zeo ses Vs (C57 + csP°TE) + Dgeg Li jev Zses 60%, Vsdy j CST (3)
Cas = > > 3081p G GS TE
PED med ( 4 ) sto = Cap + Cig + as (5) a cost structure of the mobile pre-cooling system includes: 1) a fixed cost Cp, including a corresponding purchase cost and an operating cost of a pre-cooling vehicle; 2) a travel cost Cra of a pre-cooling vehicle; and 3) a loading and unloading loss cost
Cuwa- Therefore, a cost model of the mobile pre-cooling system is constructed as follows:
Coop = DD vale + FTE) wei well (6)
Coa =D > > 30wy Yad, CFT =r Fey = (7)
Lowe = NT NT SG GP TE wel mear (8)
Como = Coop + Cora + Cea (9)
By combining formulae (1 - 9), an optimized model of a rural multi-type pre-cooling facility structure can be cbtained as fol- lows: min 3 Con ey med {1 ) sl. > Bop + > Foes = 1. Fm EM
Sif j ved wel (10) + a <1
FEN (11 ) ) DEN 3 > Log 3 — = rads IE > Ky = 1 = (12) > >, 8 ARTE Hen = 2 eed med { 13 }
Kip = Xiio = OViE V‚Vp Ep, Vw EL (14)
Kijp = Xijo =O VLJEN, Vp €Q,Vw € {] (15) > Eigse == 3 Xie . Vi = ¥. Wen £ 3 = =
Ey ey (1 6)
Sy For = > Vries * Wi = ¥, Foo € £3 ed Di ed 3
EN TEY (17)
NN ay, Sz LYo es
FEY tend ( 18 ) >. >. Foe = 1, Voen
TEN EM (19)
Sy Tige SH, —1L,¥ped he (20)
> Vio = Gy — 1,Y0 € 2 {Fev (21)
NN Va = i 5 3 # = &
Ly” * zes (22) > Fone Gm = > ¥sS vg as) mes ses (23) > ted; Sd, Vp €8 ry (24) wv A i Fy YA
NT 3 Aspi —_— Lastayr Yio & & (ed EV (25)
Ny. =1, Ve E12
Wey (26)
Fed, Sd Vo En
Liev (27)
See (28)
Bmp €{0,1}, Pmo €{0,1}, VMEM, Vp EO, VwEn (29)
Xijp €{0,1}, Vije €{0,1}, VijEV, VEO, Voen (30) ys €{0,1}, y,€{0,1}, Vse€S§, vuelU (31)
In this model, an objective function represented by formula (1) is a minimum cost of a rural pre-cooling facility system, which is a sum of the costs of the fixed and the mobile pre- cooling subsystems; formula (10) represents that each service sta- tion has one and only one pre-cooling method, that is, each ser- vice station can be served by one and only one vehicle for once only; formulae (11) and (12) indicate that only one pre-cooling station can be built in an entire system at most, and if a pre- cooling station is built, it serves at least one service station; formula (13) indicates that the capacity of a pre-cooling station must meet a total output of the farmers covered; formula (14) ex- presses that there is no path for the same service station and pre-cooling station (parking lot); formula (15) indicates that there is no path between optional pre-cooling stations (parking lots); formulae (16) and (17) are constraints to a balance of ve- hicle entry and exit, ensuring that the numbers of the vehicles entering and leaving each service station are the same; formulae (18) and (19) ensure that there is at most one service path for each vehicle, and each vehicle departs from the same parking lot and returns to the original parking lot upon completion of service delivery; formulae (20) and (21) eliminate sub-loops; formula (22) means that each transport vehicle has one and only one type; for- mula (23) indicates that a total output of the farmers served by each transport vehicle does not exceed a maximum load capacity of a transport vehicle; formula (24) indicates that a mileage of each transport vehicle every time does not exceed its maximum mileage; formula (25) represents that the time for a transport vehicle to transport products from an origin to a refrigerating house does not exceed the maximum delay time; formula (26) indicates that each pre-cooling vehicle has one and only one type; formula (27) indicates that a mileage of each pre-cooling vehicle every time does not exceed its maximum mileage; formula (28) represents that a travel time of each pre-cooling vehicle every time does not ex- ceed the working hours for the current day; formulae (29) - (31) represent the attributes of decision variables.
Step III, solving the model
The present invention proposes an improved genetic algorithm to solve the model. Firstly, a chromosome capable of simultaneous- ly expressing the decision of pre-cooling methods, a siting and construction scale of a pre-cooling station, a type, quantity and path of transport vehicles, a model, quantity and path of pre- cooling vehicles is designed to realize the optimization of siting and paths simultaneously, rather than a separated decision of sit- ing before path optimization; secondly, rules for generating a feasible solution that retains a randomness are designed, and a probability of being chosen for each optional site for siting is measured based on a logistic capacity-distance product, and a gene sequence of a service path segment is generated according to an idea of "random generation before adjustment"; thirdly, a crosso- ver operator and three mutation operators are redesigned according to the rules for chromosome coding. The specific steps for the so- lution are as follows:
Sl: chromosome coding
A chromosome is composed of three parts: 1 bit for an option- al site of a pre-cooling station (parking lot), m bits for a pre- cooling service station for agricultural products and m bits for separators; a gene in the 1°% bit is a selected site for a pre- cooling station (parking lot), and when a pre-cooling station is not required to be built, the gene in the 1°" bit only represents a starting point for a vehicle; other non-0 genes represent a ser- vice station; 0 genes represent a separator, consecutive genes be- tween two separators represent the stations that are served se- gquentially on a path, and a gene in the 2° bit is a fixed separa- tor. In order to distinguish a pre-cooling method used by each service station, when a position of a non-0 gene in the 1%: bit on a path is set as an odd number, the mobile pre-cooling method is selected for the corresponding service station and all the service stations on the same path; on the contrary, if a position of a non-0 gene in the 1** bit on the path is an even number, the fixed pre-cooling method is selected for the corresponding service sta- tion and all the service stations on the same path; FIG. 3 dis- plays 1 chromosome that may appear.
S2: initialization of a population
S2.1: setting the size of a population as np, and setting the population X as a zero matrix of np rows and 2mt+l lines;
S2.2: sequentially assigning values to the vectors X; of each row in the population X 82.2.1: siting part of a pre-cooling station
S2.2.1.1: determining a probability u, of each optional site for a pre-cooling station (parking lot) being chosen according to a logistic capacity-distance product, wherein the calculation for- d, mula is as follows: uy, = zein
S2.2.1.2: randomly selecting a site by means of a roulette to determine a value of a gene in the 1°" bit of X;, that is, py. values are accumulated in sequence to obtain a cumulative probability A, and a random number a; is generated in an interval [0,1]; when a;SA;, a No. 1 optional site is selected as a pre-cooling station (parking lot), and when 24, .,<a;£4,, a No. b optional site is se- lected.
S2.2.2: valuing a gene in the 2™ bit as 0, which is a fixed separator; 52.2.3: randomly arranging the m-1 0 genes and No. 1 - m ser- vice stations and filling them in the 3% - 2m+1"™ bits of Xi;
S2.2.4: check and adjustment of feasibility
This problem includes two types of constraints, namely vehi- cle capacity and time, where a total output of a service path for the fixed pre-cooling method does not exceed a carrying capacity of a maximum type of a transport vehicle, and a transport time does not exceed a maximum pre-cooling delay time; a total time of a service path for each pre-cooling vehicle does not exceed the maximum working hours of a single day. Each service path is checked in sequence to confirm whether it meets the above con- straints; if not, the path is adjusted or vehicles are added for a service station until all paths meet the constraints.
S2.2.4.1: identifying a chromosome, separating the paths ac- cording to the pre-cooling methods, and obtaining a path matrix and a demand matrix corresponding to the two pre-cooling methods, respectively.
Firstly, the positions of all 0 genes are found in a chromo- some, and judgment is performed in sequence on whether there is a non-0 gene between every two 0 genes, if not, skip to the next 0 gene, and if yes, the non-0 gene is used as a service path; an odevity is judged for a position of a 1°% non-0 gene in the chromo- some; if it is in an even bit, a fixed pre-cooling station is cho- sen for pre-cooling on the path, the path is stored in a fixed pre-cooling path matrix L°; and meanwhile, the demand of each ser- vice station on the path also needs to be recorded and stored in the fixed demand matrix 9%, with the number of rows being n,; if it is in an odd bit, a mobile pre-cooling vehicle is chosen for pre- cooling on the path, and accordingly the path and the demand are stored in a mobile pre-cooling path matrix ZI" and a mobile demand matrix ©, with the number of rows being n4; this judgment will continue until the last separator. If there is still a non-0 gene after the last 0 gene, the non-0 gene is used as a service path, its pre-cooling method is judged according to the aforesaid steps, and the path and the demand are stored in the corresponding matri- ces; secondly, the gene in the 1°" bit of the chromosome is a starting point of all the recorded service paths, and added to the beginning and the end of each row of LF and Lv.
S2.2.4.2: sequentially checking whether the existing chromo- some meets the constraint conditions.
If L° is not null, judgment on the capacity constraint of the fixed pre-cooling method is performed, that is, the elements of
Us each row in O° are sequentially accumulated to obtain a matrix ees of €, d 3 ; for the first n.-1 rows, when exceeds a maximum vehicle carrying capacity Sux, the serial numbers of the service stations corresponding to the element and all the subsequent elements in the row are added into [° from the positions of L°(c¢c+1,2) sequen- tially, whereas the original elements in L° are moved backwards, and correspondingly the matrices Q° and Of are updated at the same time; for the last row n,, if the total path demand exceeds the capacity limit, the number of transport vehicles is increased mentele] 4 by mas ‚ that is, [Peen] rows of element 0 are added into the matrices I°, 9° and Q&c: respectively, the gene in the 1°" bit of the chromosome is added at the beginning and the end of each new row of I°; at this moment, the number of rows in the aforesaid matrix is n,’; then the judgment on capacity constraint and the path adjustment are continued; when Q5..(c’,d) exceeds the maximum vehicle carrying capacity Sm; the serial numbers of the service stations corresponding to the element and all the subse- quent elements in the row are inserted from L°(c’+1,2), the origi- nal elements in T° are moved backwards, and correspondingly the matrices Q° and QF. are updated at the same time.
Then, judgment on the time constraint of the fixed pre- cooling method is performed, that is, according to the service paths in Lf, the time required to reach the pre-cooling station from each service station in each path is calculated sequentially to obtain a matrix T°. For the first n,’-1 rows, whether each ele- ment in the matrix exceeds tg... is judged in sequence; if yes, the serial numbers of the service stations corresponding to the ele- ment and the subsequent elements are added into the last row of L° from the positions of L°(n.’,2) sequentially, whereas the original elements in L° are moved backwards, and correspondingly the matri- ces T°, ¢° and QZ are updated at the same time; for the last row ng’, if tues, is exceeded, the number of transport vehicles needs to be increased by [reed] rows of element 0, that is, [pel] rows of element O are added into the matrices L°, T°, 0° and Qj.., respectively, and the gene in the 1°" bit of the chro- mosome is added at the beginning and the end of each new row of LY, and at the moment, the number of rows in the aforesaid matrix is ny”; then the judgment on capacity constraint and the path adjust- ment are continued; when T° (c”+d”) exceeds tae1ay, the serial num- bers of the service stations corresponding to the element and all the subsequent elements in the row are inserted from L°(c”+1,2), the original elements in Z° are moved backwards, and corresponding- ly the matrices T°, 9° and Qj. are updated at the same time. By this time, except for the last row of L°, all other paths have met the capacity and time constraints at the same time, and thus the capacity constraint is checked again for the last line using the same steps above.
Finally, judgment on the time constraint of the mobile pre- cooling method is performed according to the aforesaid idea, that is, according to the service paths in LY, the accumulated service hours 7’ in each path is calculated sequentially; when the service hours for the path exceed the maximum working hours tworr; the seri- al numbers of the overtime service station and the subsequent ser- vice stations are moved to the next path, and correspondingly the matrices T° and OQO" are updated at the same time, for the last path, if the service hours for the path exceed tr; the number of pre- cooling vehicles needs to be increased by [meteen] 1, that is,
[reed] ows of element 0 are added into the matrices LY, T° and OQ’, respectively, and the gene in the 1°" bit of the chromosome is added at the beginning and the end of each new row of LY; and at this moment, the number of rows in the aforesaid matrix is n.’, and then the judgment on capacity constraint and the path adjustment are continued; when T'(c¢,d) exceeds tr; the serial numbers of the service stations corresponding to the element and all the subse- quent elements in the row are inserted from LY(c+l,2), the original elements in ZI" are moved backwards, and correspondingly the matri- ces T and OQ" are updated at the same time.
S2.2.5: restoring the chromosome based on the latest L° and LV to obtain a feasible solution X;;
S2.3: repeating the steps in S2.2 for np times to get an ini- tial population X.
S3: fitness calculation 53.1: assigning values to the parameters required for calcu- lation based on the actual situation;
S3.2: calculating a fitness value of each chromosome in X 83.2.1: identifying a chromosomes X;, and obtaining a total cost of the system according to the pre-cooling method
S3.2.1.1: according to S2.2.4.1, identifying a chromosomes, separating the paths according to the pre-cooling methods, and ob- taining the path matrix and the demand matrix corresponding to the two pre-cooling methods, respectively.
S3.2.1.2: calculating the cost Co of the fixed pre-cooling system
Firstly, the scale p of the pre-cooling station, the type s and the quantity n, of transport vehicles are determined, p is a sum Q; of all the elements of the demand matrix corresponding to the fixed pre-cooling method; s needs to be determined according to the demand matrix and the carrying capacity of each type of the transport vehicles, wherein each row of the demand matrix of the pre-cooling method is summed first to take the maximum value, then this maximum value is compared with the carrying capacity of each type of the transport vehicles, and the type corresponding to the capacity that is greater than and the closest to this maximum val-
ue is the required type; n, is the number of rows in the demand matrix of the pre-cooling method; secondly, a total transport dis- tance D. of the transport vehicles needs to be calculated, that is, the distances of all paths are summed according to the path matrix and the distance between the points. Therefore, the construction cost of the pre-cooling station with a scale p is p-¢, and the operating cost is p-¢, tT; the purchase cost of the transport ve- hicles is n,'c{%, the operating cost is ng°c;°°-t:T, and the transport cost is 2:D;:c{T%:30:t:T; and the loading and unloading cost is g-Qs-8; and finally, the cost of the fixed pre-cooling system is a sum of the above-mentioned items, i.e., Cgg =P Cp +p cy: t-T+ng ST ng tT +2 Dg cP 30 T+g 00.
S3.2.1.3: calculating the cost Cms of the mobile pre-cooling system
Firstly, the type u and the quantity n, of the pre-cooling vehicles are determined, u needs to be determined according to the maximum element value in the demand matrix corresponding to the mobile method and the carrying capacity of each type of the pre- cooling vehicles, and the type corresponding to the carrying ca- pacity that is greater than and the closest to the maximum element value in the demand matrix is the required type; m, is the number of rows in the demand matrix of the pre-cooling method; secondly, a total transport distance D, of the transport vehicles needs to be calculated, that is, the distances of all paths are summed accord- ing to the path matrix and the distance between the points. Then, a total demand D, of the service stations that adopt the mobile pre-cooling method is calculated, that is, the elements of the de- mand matrix are summed. Therefore, the purchase cost of pre- cooling vehicles is ny, -cS%, the operating cost is nyc” :t:T, and the transport cost is 2:D,:ct7%-30:t:T; and the loss cost is 6:0,: 8; finally, the cost of the mobile pre-cooling system is a sum of the above-mentioned items, i.e.,
Cmop = Tu CE + ny rcpt T +2 Dy ca 30 tT +6: 040.
S3.2.1.4: adding Cs, and Cup to obtain the total cost Cu; of the chromosome system, and assigning a value to f(X);
S3.2.2: recording an optimal solution
S3.2.2.1: repeating the steps in 83.2.1, sequentially calcu- lating the total cost of each chromosome system in the matrix X to obtain a matrix f(X) of objective function values, and recording the minimum value as fu, and recording the chromosome correspond- ing to the minimum value as Xmiaj
S3.2.2.2: comparing the optimal objective function value of this generation with the currently obtained optimal minf; when fa <minf, minf=rf,;,; and updating the optimal chromosome minX into Xin at the same time; $53.3: calculating a fitness value of each chromosome accord- ing to the following formula to obtain a matrix F of population fitness values: mt
FX) = fun +1
S4: selection operation
Selecting a population by means of a roulette, namely:
S4.1: calculating a probability of each chromosome X; being chosen according to the following formula:
Fy
PTET
$4.2: accumulating p; values to obtain a cumulative probabil- ity Pr, and generating a random number a; in an interval [0,1]; when a,<P;, Xi is inherited into a new population X’; when Pii < a:<P;, X; 1s inherited into the new population X’ until the number of chromosomes in the new population X’ reaches np;
S5: crossover operation 35.1: randomly pairing chromosomes in the population X/’;
S5.2: judging in sequence whether each pair of chromosomes are subject to the crossover operation, that is, a random number is firstly generated in an interval [0,1], when the random number is less than or egual to a crossover probability p., go to $5.3; otherwise, keep the pair of original genes and go to S5.5; 35.3: performing the crossover operation by using a strategy of crossing a separator part and a non-separator part, respective- ly, that is, firstly inheriting the position and number of the separators of Parent 1 to Offspring 1, and then filling the non-
separator elements of Parent 2 sequentially in the Offspring 1 chromosome, as shown in FIG. 4; similarly, for Offspring 2, first- ly inheriting the position and number of the separator of Parent 2 to Offspring 2, and then filling the non-separator elements of
Parent 1 in Offspring 2. That is, the offspring inherits the par- ent’s pre-cooling method and the quantities of the two types of vehicles, as well as the siting of the pre-cooling station (park- ing lot) of the other parent and a certain degree of service path orders.
S5.4: checking and adjusting the feasibility for the crossed chromosomes according to 52.2.4; 35.5: performing the operations in S5.2-S5.4 for all chromo- somes to obtain a new population X7;
S6: mutation operation
The present invention uses a total of 3 mutation operators: a first mutation is a mutation of a gene in the 1°" bit for siting; a second mutation is a mutation of a service path segment gene part; a third mutation is a mutation of the pre-cooling service method.
Therefore, judgment is performed on whether the mutation operation is applied to each chromosome for 3 times. If mutation is re- quired, perform the mutation, and otherwise, skip it and go to 56.5.
S6.1: generating a random number on [0,1]. When the random number is less than or equal to the mutation probability Da; ran- domly selecting another point in the set N as a new optional site for the pre-cooling station (parking lot); when the random number is greater than p,, directly going to S6.2;
S6.2: generating a random number in an interval [0,1]. When the random number is less than or equal to p,, randomly selecting two genes (not 0 at the same time) at the 3% - 2m+1° gene segments of the chromosome for crossover, as shown in FIG. 5; when the ran- dom number is greater than Dn, going to 6.3;
S6.3: generating a random number in an interval [0,1]. When the random number is less than or equal to Ds; randomly selecting a path in the chromosome to change its pre-cooling method by means of increasing or reducing separators, as shown in FIG. 6; when the random number is greater than Dm, going to 6.4;
S6.4: checking and adjusting the feasibility for the mutated chromosomes according to 82.2.4; 86.5: performing the operations in S6.1-S6.4 for all chromo- somes to obtain a new population X77.
S57: iteration
S3-S6 are repeated until a maximum number of iterations is reached. Besides, when the optimal value does not change for mul- tiple consecutive generations any more, or the chromosomes of the population are completely consistent, the iteration will directly end. After iteration, an optimal chromosome minX can be finally obtained, it is interpreted according to the coding method in S1, and then the final planning solution can be obtained.
The present invention has the following advantages: the present invention proposes a method for planning an origin-based pre-cooling system considering an investment of mul- ti-type facilities, in consideration of applying both fixed and mobile pre-cooling methods, to solve a problem of unavailable pre- cooling or poor pre-cooling effects for China's small-scale farm- ing. In the present invention, firstly, a siting-path optimization model of multi-type pre-cooling facilities for agricultural prod- ucts in villages and towns is established with an objective of minimizing a system cost; secondly, an improved genetic algorithm is designed for solution, and chromosomes and rules for crossover and mutation are redesigned to optimize both siting and paths. The present invention can effectively improve the current situations of unavailable pre-cooling and poor pre-cooling effects for small- scale farming, with an important practical significance for per- fecting and building a cooling system for agricultural products, optimizing a "first kilometer" process in cold chains for agricul- tural products, and promoting a socialized service process for villages and towns in China.
FIG. 1 shows an organizational form of fixed pre-cooling;
FIG. 2 shows an organizational form of mobile pre-cooling;
FIG. 3 is a schematic diagram of chromosome coding;
FIG. 4 is an example of a crossover operator;
FIG. 5 is an example of the second mutation operator;
FIG. 6 is an example of the third mutation operator.
The present invention will be further explained below in com- bination with the accompanying drawings of specification.
A population size is set as 50, a crossover probability is 0.5, a mutation probability is 0.1, and a maximum number of itera- tion times is 2,000; when an optimal value does not change for 200 consecutive generations any more, or chromosomes of the population are completely consistent, the iteration will directly end.
Sl: chromosome coding
A chromosome is composed of three parts: 1 bit of an optional site of a pre-cooling station (parking lot), m bits of a pre- cooling service station for agricultural products and m bits of separators; a gene in the 1°" bit is a selected site for a pre- cooling station (parking lot}; and when a pre-cooling station is not required to be built, a gene in the 1°° bit only represents a starting point for a vehicle; other non-0 genes represent a ser- vice station; 0 genes represent a separator, consecutive genes be- tween two separators represent the stations that are served se- quentially on a path, and a gene in the 2" bit is a fixed separa- tor. In order to distinguish a pre-cooling method used by each service station, when a position of a non-0 gene in the 1°*' bit on a path is set as an odd number, the mobile pre-cooling method is selected for the corresponding service station and all the service stations on the same path; on the contrary, if a position of a non-0 gene in the 1°" bit on the path is an even number, the fixed pre-cooling method is selected for the corresponding service sta- tion and all the service stations on the same path.
S52: initialization of a population 82.1: setting the size of a population as np, and setting the population X as a zero matrix of np rows and 2m+l lines; 82.2: sequentially assigning values to the vectors X; of each row in the population X 82.2.1: siting part of a pre-cooling station
S2.2.1.1: determining a probability up, of each optional site for a pre-cooling station (parking lot) being chosen according to a logistic capacity-distance product, wherein the calculation for- . d mula 1s as follows: py = pmetintn
S2.2.1.2: randomly selecting a site by means of a roulette to determine a value of a gene in the 1°% bit of X;, that is, u, values are accumulated in sequence to obtain a cumulative probability A, and a random number a; is generated in an interval [0,1]; when a;<A;, a No. 1 optional site is selected as a pre-cooling station (parking lot}, and when 4, ;<a;f3,, a No. b optional site is se- lected. $2.2.2: valuing a gene in the 2™ bit as 0, which is a fixed separator; 82.2.3: randomly arranging the m-1 0 genes and No. 1 - m ser- vice stations, and filling them in the 3™-2m+1™ bits of Xi; 52.2.4: check and adjustment of feasibility
Each service path is checked in sequence whether it meets the above constraints; if not, the path is adjusted or vehicles are added for the service station until all paths meet the con- straints.
S2.2.4.1: identifying a chromosome, separating the paths ac- cording to the pre-cooling methods, and obtaining a path matrix and a demand matrix corresponding to the two pre-cooling methods, respectively.
Firstly, the positions of all 0 genes are found in a chromo- some, and judgment is performed in sequence on whether there is a non-0 gene between every two 0 genes, if not, skip to the next 0 gene, and if yes, the non-0 gene is used as a service path; an odevity is judged for a position of a 1°% non-0 gene in the chromo- some; if it is in an even bit, a fixed pre-cooling station is cho- sen for pre-cooling on the path, and the path is stored in a fixed pre-cooling path matrix L°; and meanwhile, the demand of each ser- vice station on the path also needs to be recorded and stored in the fixed demand matrix 9%, with the number of rows being ng; if it is in an odd bit, a mobile pre-cooling vehicle is chosen for pre- cooling on the path, and accordingly the path and the demand are stored in a mobile pre-cooling path matrix LY and the mobile demand matrix OQ", with the number of rows being n,; this judgment will continue until the last separator. If there is still a non-0 gere after the last 0 gene, the non-0 gene is used as a service path, its pre-cooling method is judged according to the aforesaid steps, and the path and the demand are stored in the corresponding matri- ces; secondly, the gene in the 1°" bit of the chromosome is a starting point of all the recorded service paths, and added to the beginning and the end of each row of I° and ZD".
S52.2.4.2: sequentially checking whether the existing chromo- some meets the constraint conditions
If Lf is not null, judgment on the capacity constraint of the fixed pre-cooling method is performed, that is, the elements of each row in Q° are sequentially accumulated to obtain a matrix es ; for the first n,-1 rows, when Q}.(c,d) exceeds a maximum vehicle carrying capacity Sp.., the serial numbers of the service stations corresponding to the element and all the subsequent elements in the row are added into ZI’ from the positions of L°(¢+l,2) sequen- tially, whereas the original elements in L° are moved backwards, and correspondingly the matrices ° and Qj. are updated at the same time; for the last row n,, if the total path demand exceeds the capacity limit, the number of transport vehicles is increased by Se that is, zee rows of element 0 are added into the matrices T°, @° and Qi: respectively, the gene in the 1°% bit of the chromosome is added at the beginning and the end of each new ow of 1°; at this moment, the number of rows in the aforesaid matrix is ng’, and then the judgment on capacity con- straint and the path adjustment are continued; when Q}..(c’,d) ex- ceeds the maximum vehicle carrying capacity Saxr the serial num- bers of the service stations corresponding to the element and all the subsequent elements in the row are inserted from L°(¢’+1,2), the original elements in L° are moved backwards, and corresponding- ly the matrices Q° and Qj. are updated at the same time.
Then, judgment on the time constraint of the fixed pre- cooling method is performed, that is, according to the service paths in 1°, the time required to reach the pre-cooling station from each service station in each path is calculated sequentially to obtain a matrix T°. For the first n,’-1 rows, whether each ele- ment in the matrix exceeds tg... is judged in sequence; if yes, the serial numbers of the service stations corresponding to the ele- ment and the subsequent elements are added into the last row of L° from the positions of L°(n.’,2) sequentially, whereas the original elements in L° are moved backwards, and correspondingly the matri- ces T°, ¢° and QZ are updated at the same time; for the last row ng’, if tues, is exceeded, the number of transport vehicles needs to be increased by [reed] rows of element 0, that is, [pel] rows of element O are added into the matrices L°, T°, 0° and Qj.., respectively, and the gene in the 1°" bit of the chro- mosome is added at the beginning and the end of each new row of LY; and at the moment, the number of rows in the aforesaid matrix is n,”, and then the judgment on capacity constraint and the path ad- justment are continued; when T° (c¢”,d”) exceeds tss; the serial numbers of the service stations corresponding to the element and all the subsequent elements in the row are inserted from
IF{¢”+1,2), the original elements in Z° are moved backwards, and correspondingly the matrices 7°, 9° and Qi are updated at the same time. By this time, except for the last row of L7, all other paths have met the capacity and time constraints at the same time, and thus the capacity constraint is checked again for the last line using the same steps above.
Finally, judgment on the time constraint of the mobile pre- cooling method is performed according to the aforesaid idea, that is, according to the service paths in LY, the accumulated service hours 7’ in each path is calculated sequentially; when the service hours for the path exceed the maximum working hours tworr; the seri- al numbers of the overtime service station and the subsequent ser- vice stations are moved to the next path, and correspondingly the matrices T° and OQO" are updated at the same time, for the last path, if the service hours for the path exceed tr; the number of pre- cooling vehicles needs to be increased by [mee], that is,
bs rows of element 0 are added into the matrices LY, TV and OQ’, respectively, and the gene in the 1°" bit of the chromosome is added at the beginning and the end of each new row of LY; at the moment, the number of rows in the aforesaid matrix is n.”, and then the judgment on capacity constraint and the path adjustment are continued; when T'(c¢,d) exceeds t,., the serial numbers of the service stations corresponding to the element and all the subse- quent elements in the row are inserted from LY(c+1,2), the original elements in ZI" are moved backwards, and correspondingly the matri- ces T and OQ" are updated at the same time.
S2.2.5: restoring the chromosome based on the latest L° and LV to obtain a feasible solution X;;
S2.3: repeating the steps in S2.2 for np times to get an ini- tial population X.
S3: fitness calculation 53.1: assigning values to the parameters required for calcu- lation based on the actual situation;
S3.2: calculating a fitness value of each chromosome in X 83.2.1: identifying a chromosomes X;, and obtaining a total cost of the system according to the pre-cooling method
S3.2.1.1: according to S2.2.4.1, identifying a chromosome, separating the paths according to the pre-cooling methods, and ob- taining the path matrix and the demand matrix corresponding to the two pre-cooling methods, respectively.
S3.2.1.2: calculating the cost Co of the fixed pre-cooling system
Firstly, the scale p of the pre-cooling station, the type s of the transport vehicles and the quantity n, of the transport ve- hicles are determined, p is a sum Q, of all the elements of the demand matrix corresponding to the fixed pre-cooling method; s needs to be determined according to the demand matrix and the car- rying capacity of each type of the transport vehicles, wherein each row of the demand matrix of the pre-cooling method is summed first to take the maximum value, then this maximum value is com- pared with the carrying capacity of each type of the transport ve- hicles, and the type corresponding to the capacity that is greater than and the closest to this maximum value is the required type; n, is the number of rows in the demand matrix of the pre-cooling method; secondly, a total transport distance D. of the transport vehicles needs to be calculated, that is, the distances of all paths are summed according to the path matrix and the distance be- tween the points. Therefore, the construction cost of the pre- cooling station with a scale p is p'C;,; and the operating cost is prc, tT; the purchase cost of the transport vehicles is ng-ci?, the operating cost is nge tT, and the transport cost is 2-Dg-cf-30-t-T; and the loading and unloading cost is g-Q,-8; and finally, the cost of the fixed pre-cooling system is a sum of the above-mentioned items, i.e.,
Csto =P Cp +p Cot THng cs Ang ET +2 Dg PBO tT +g
Qs-6.
S3.2.1.3: calculating the cost Cs of the mobile pre-cooling system
Firstly, the type corresponding to the carrying capacity that is greater than and the closest to the maximum element value in the demand matrix is type u of pre-cooling vehicles; n, is the num- ber of rows in the demand matrix of the pre-cooling method; sec- ondly, a total transport distance D, of the transport vehicles needs to be calculated, that is, the distances of all paths are summed according to the path matrix and the distance between the points. Then, the total demand D, of the service stations that adopt the mobile pre-cooling method is calculated, that is, the elements of the demand matrix are summed. Therefore, the purchase cost of pre-cooling vehicles is ny 'c{%, the operating cost is nu cp -t:T, and the transport cost is 2:Dp:c7%:30:t:T; and the loss cost is §-Q,-68; finally, the cost of the mobile pre-cooling system is a sum of the above-mentioned items, i.e.,
Crop = Tu CT tn tT +2 Dy cl 30 T+6: 080.
S3.2.1.4: adding C+, and Caos to obtain the total cost C,;; of the chromosome system, and assigning a value to f(X;); 83.2.2: recording an optimal solution
S3.2.2.1: repeating the steps in S3.2.1, sequentially calcu- lating the total cost of each chromosome system in the matrix X to obtain a matrix f(X) of objective function values, and recording the minimum value as f,;,; and recording the chromosome correspond- ing to the minimum value as Xin?
S3.2.2.2: comparing the optimal objective function value of this generation with the currently obtained optimal minf; when fun <minf, minf=f,:,, and updating the optimal chromosome minX into Xuan at the same time; 83.3: calculating a fitness value of each chromosome accord- ing to the following formula to obtain a matrix F of population fitness value:
Fo 1 te FX) == [min +1
S4: selection operation
Selecting a population by means of a roulette, namely: 84.1: calculating a probability of each chromosome X; being chosen according to the following formula: bi Zio Fy
S4.2: accumulating p; values to obtain a cumulative probabil- ity Pr, and generating a random number a; in an interval [0,1]; when a:SP:, X. is inherited into a new population X’; when Pi < a:SP;, X: is inherited into the new population X’ until the number of chromosomes in the new population X; reaches np.
S5: crossover operation
S5.1: randomly pairing chromosomes in the population X’;
S5.2: judging in sequence whether each pair of chromosomes are subject to the crossover operation, that is, a random number is firstly generated in an interval [0,1], when the random number is less than or equal to a crossover probability p., go to $5.3; otherwise, keep the pair of original genes and go to $5.5; 55.3: performing the crossover operation by using a strategy of crossing the separator part and a non-separator part, respec- tively, that is, firstly inheriting the position and number of separators of Parent 1 to Offspring 1, and then filling the non- separator elements of Parent 2 sequentially in the Offspring 1 chromosome, as shown in FIG. 4; similarly, for Offspring 2, first-
ly inheriting the position and number of the separator of Parent 2 to Offspring 2, and then filling the non-separator elements of
Parent 1 in Offspring 2. 55.4: checking and adjusting the feasibility for the crossed chromosomes according to S2.2.4; $5.5: performing the operations in S5.2-S5.4 for all chromo- somes to obtain a new population X”.
S6: mutation operation
The present invention uses a total of 3 mutation operators: the first mutation is a mutation of the gene in the 1°" bit for siting; the second mutation is a mutation of service path segment gene part; the third mutation is a mutation of the pre-cooling service method. Therefore, it shall be judged whether the mutation operation is performed for each chromosome for 3 times. If muta- tion is required, perform the mutation, and otherwise, skip it and go to S6.5.
S6.1: generating a random number in an interval [0,1]. When the random number is less than or equal to the mutation probabil- ity Dx randomly selecting another point in the set N as a new op- tional site for the pre-cooling station (parking lot}; when the random number is greater than Dx, directly going to S6.2;
S6.2: generating a random number in an interval [0,1]. When the random number is less than or equal to p,, randomly selecting two genes (not 0 at the same time) at the 3*%-2m+1*® gene segments of the chromosome for crossover, as shown in FIG. 5; when the ran- dom number is greater than p,, going to S6.3;
S6.3: generating a random number in an interval [0,1]. When the random number is less than or equal to pm randomly selecting a path in the chromosome to change its pre-cooling method by means of increasing or reducing separators, as shown in FIG. 6; when the random number is greater than Dm; going to S6.4;
S6.4: checking and adjusting the feasibility for the mutated chromosomes according to S2.2.4; 56.5: performing the operations in S6.1-S6.4 for all chromo- somes to obtain a new population X/Y.
S7: iteration
S3-S6 are repeated until a maximum number of iterations is reached.
Besides, when the optimal value does not change for mul- tiple consecutive generations any more, or the chromosomes of the population are completely consistent, the iteration will directly end.
After iteration, an optimal chromosome minX can be finally obtained, it is interpreted according to the coding method in £1, and then the final planning solution can be obtained.
Claims (4)
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110366073.2A CN113516272B (en) | 2021-04-06 | 2021-04-06 | Production place precooling system planning method considering multi-type facility investment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| NL2030763A NL2030763A (en) | 2022-10-19 |
| NL2030763B1 true NL2030763B1 (en) | 2023-04-17 |
Family
ID=78062182
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| NL2030763A NL2030763B1 (en) | 2021-04-06 | 2022-01-28 | Method for planning origin-based pre-cooling system considering investment of multi-type facilities |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN113516272B (en) |
| NL (1) | NL2030763B1 (en) |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104808627A (en) * | 2015-04-20 | 2015-07-29 | 海安县申菱电器制造有限公司 | Workshop layout method of discrete manufacturing system |
| CN110245779A (en) * | 2019-05-10 | 2019-09-17 | 杭州电子科技大学 | A kind of public transport dynamic based on genetic algorithm is dispatched a car method for optimizing scheduling |
| CN112598258B (en) * | 2020-12-17 | 2023-04-07 | 西北农林科技大学 | Multi-type precooling service resource scheduling optimization method in first kilometer |
-
2021
- 2021-04-06 CN CN202110366073.2A patent/CN113516272B/en active Active
-
2022
- 2022-01-28 NL NL2030763A patent/NL2030763B1/en active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113516272B (en) | 2023-09-01 |
| CN113516272A (en) | 2021-10-19 |
| NL2030763A (en) | 2022-10-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mogale et al. | Grain silo location-allocation problem with dwell time for optimization of food grain supply chain network | |
| Sahebjamnia et al. | Optimization of multi-period three-echelon citrus supply chain problem | |
| Lamsal et al. | Sugarcane harvest logistics in Brazil | |
| An et al. | Robust grain supply chain design considering post-harvest loss and harvest timing equilibrium | |
| Lamsal et al. | Harvest logistics in agricultural systems with multiple, independent producers and no on-farm storage | |
| Galetti et al. | Palm heart harvesting in the Brazilian Atlantic forest: changes in industry structure and the illegal trade | |
| Dutta et al. | The design and planning of an integrated supply chain for perishable products under uncertainties: A case study in milk industry | |
| CN107944598A (en) | A kind of logistics route collocation method and system | |
| Shilpi et al. | Market facilities and agricultural marketing: Evidence from Tamil Nadu, India | |
| JP2003535416A (en) | Transaction supply chain system and method | |
| Yadav et al. | Design of multi-objective sustainable food distribution network in the Indian context with multiple delivery channels | |
| Kyomugisha et al. | Potato market access, marketing efficiency and on-farm value addition in Uganda | |
| CN110147974A (en) | Inventory's sharing method and device between distributed warehouse | |
| US20020095232A1 (en) | Transactional supply chain system and method | |
| CN107274014A (en) | A kind of processing method, system and the terminal of the dispatching task based on standard container | |
| CN111768052A (en) | Automatic planning method for whole-vehicle factory approach logistics route based on algorithm model | |
| CN107274084A (en) | A kind of order intelligent dispatching method, system and terminal based on standard container | |
| Nasr et al. | An efficient solution method for an agri-fresh food supply chain: hybridization of Lagrangian relaxation and genetic algorithm | |
| CN111178591A (en) | Cold chain logistics product refrigeration transportation quality optimization management system based on big data | |
| NL2030763B1 (en) | Method for planning origin-based pre-cooling system considering investment of multi-type facilities | |
| Ambekar et al. | Structural mapping of public distribution system using multi-agent systems | |
| Patidar et al. | Restructuring the Indian agro-fresh food supply chain network: a mathematical model formulation | |
| CN116432880B (en) | Intelligent selection and freight quotation system for shared cloud warehouse logistics city distribution route | |
| Chen et al. | Integrated online order picking and vehicle routing of food cold chain with demand surge | |
| CN114418653B (en) | Anti-channel conflict processing method based on NFC (near field communication) label |