Multi-objective optimization model construction method and system for deep and far sea culture material supply

Abstract

The invention belongs to the technical field of deep sea cultivation, and particularly relates to a deep sea cultivation material supply multi-objective optimization model construction method and system, and the method comprises the following steps: S1, analyzing the material supply mode, time, line, material type and quantity of each net cage; determining decision variables; s2, using decision variable information to construct an objective function and constraint conditions; the method has the advantages that under the condition that factors such as the relative position, the maximum storage capacity, the existing quantity of various materials, daily consumption, supply mode restriction and economic cost of each net cage are comprehensively considered, an optimized supply scheme of the storage materials of each net cage is given.

Description

Technical Field

[0001] The present invention belongs to the technical field of deep-sea aquaculture, and in particular relates to a method and system for constructing a multi-objective optimization model for replenishment of deep-sea aquaculture materials. Background Art

[0002] In recent years, my country's deep-sea aquaculture industry has developed rapidly, and large-scale aquaculture equipment such as cages and workboats are widely used in deep sea areas far from land. However, due to the long transportation distance and complex sea conditions in these sea areas, the current allocation of materials for deep-sea aquaculture mainly relies on experience and judgment, and lacks a scientific method that comprehensively considers multiple factors, resulting in high costs for marine material replenishment and relatively low efficiency. Therefore, the sustainable development of deep-sea aquaculture faces certain challenges. Summary of the invention

[0003] Based on the above problems, the present application provides a method and system for constructing a multi-objective optimization model for deep-sea aquaculture material replenishment, and its technical solution is:

[0004] A method for constructing a multi-objective optimization model for deep-sea aquaculture material replenishment includes the following steps:

[0005] S1. Analyze the material supply method, time, route, material type and quantity of each cage; clarify the decision variables;

[0006] S2. Use decision variable information to construct objective functions and constraints.

[0007] Preferably, in step S1,

[0008] (1) Supply method: whether to use live fish transport vessels or support vessels for supply;

[0009] (2) Resupply time: including the departure time of live fish transport vessels, support vessels, and the replenishment time of each cage;

[0010] (3) Types and quantities of supplies: including the types and quantities of supplies loaded on transport vehicles and the types and quantities of supplies for each cage;

[0011] (4) Supply route: the number of cages that can be supplied at one time by live fish transport vessels and support vessels, and route planning.

[0012] Preferably, in step S1, the decision variables include the following:

[0013] Number of times the support ship is used z ;

[0014] Number of times the live fish transport ship has been used b ;

[0015] Support the ship's loading capacity of feed and fuel;

[0016] The loading capacity of fry and feed on live fish transport vessels;

[0017] On the mth day, the live fish transport vessel and support vessel are cage W i The supply quantity of Category L materials.

[0018] Preferably, in step S2, the optimization objective function is set:

[0019] Support the use of support vessels and live fish transport vessels as little as possible;

[0020] N 1 =min{Num z}

[0021] N 2 =min{Num b}

[0022] Number of times the support ship is used z ;Num number of times the live fish transport ship is used b ;

[0023] When resupplying, you should replenish as much supplies as possible to delay the next resupply time:

[0024]

[0025] Among them, S bjl W i The supply volume of the L-th category material on the mth day, W i The supply volume of live fish transport ship for Category L materials on the mth day, The supply volume of the L-type material of cage Wi for the m-th day of the support and guarantee ship. i The Lth category of materials, L∈{fish fry, feed, oil}.

[0026] Preferably, in step S2, set the constraint condition: cage W i The daily consumption of the above materials shall not exceed their daily maximum consumption:

[0027] Q xhl ≤Q′ xh

[0028] Among them, Q xhl W i The consumption of the Lth category material on the mth day, Q′ xh W i Maximum daily consumption of Category L materials.

[0029] Preferably, in step S2, set constraints:

[0030] Cage W i The replenishment amount of each type of material shall not exceed its remaining storage amount:

[0031]

[0032] Among them, C ccl W i Maximum storage capacity of Category L materials;

[0033] Cage W i The storage volume of each type of material on day m shall not be less than r% of its maximum storage volume:

[0034]

[0035] C′ ccl W i The current storage capacity of Category L materials.

[0036] Preferably, in step S2, set constraints:

[0037] The volume constraints V of the live fish transport ship and the volume constraints V′ of the support ship are:

[0038]

[0039]

[0040] Among them, W wx W i The volume of Category L materials;

[0041] The load constraints W of the live fish transport ship and the load constraints W' of the support ship are:

[0042]

[0043]

[0044] Among them, W wx W i The weight of Category L materials;

[0045] Cage W i The supply volume S of the Lth category material on the mth day bjl :

[0046]

[0047] A multi-objective optimization model construction system for deep-sea aquaculture material supply, including an information collection module, a data processing module and a decision-making module;

[0048] The information collection module collects the material supply method, time, route, material type and quantity of each cage;

[0049] Data processing module: processing the information of the number of decision variables;

[0050] Decision-making module: outputs the optimal judgment result based on the objective function and constraints.

[0051] Preferably, the information collection module also collects data information such as the number, maximum load, cargo hold specifications and sailing speed of live fish transport ships and support and guarantee ships.

[0052] Preferably, the decision module comprehensively considers the relative position of each cage, the maximum storage capacity, the existing quantity of various materials, the daily consumption and the constraints of the replenishment method, and provides an optimized replenishment plan for the storage materials of each cage.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. According to the consumption of storage materials in each deep-sea cage, taking into account the relative position of each cage, the maximum storage capacity, the existing amount of various materials, the daily consumption, the constraints of the replenishment method and the economic cost, an optimized replenishment plan for the storage materials in each cage is given.

[0055] 2. Multiple deep sea cages W i Material replenishment is needed. The storage capacity and daily consumption of each deep-sea cage are different. It is required to replenish three types of materials: feed, fry and oil for each deep-sea cage. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is the flow chart of this application.

[0057] Figure 2 The transportation time for live fish transport vessels.

[0058] Figure 3 To support the guarantee of ship transportation time. DETAILED DESCRIPTION

[0059] In order to facilitate understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. Embodiments of the present application are provided in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0060] Question:

[0061] There are many deep sea cages W iMaterial replenishment is needed. The storage capacity and daily consumption of each deep-sea cage are different. It is required to replenish three types of materials: feed, fry and oil for each deep-sea cage.

[0062] In order to ensure the needs of aquaculture production operations, the storage volume of materials should not be less than r% of the storage capacity of the aquaculture cages. Each aquaculture cage has a material storage center. The supply methods include live fish transport ships and support ships, but the number, maximum load, cargo hold specifications and sailing speed of live fish transport ships and support ships are limited; live fish transport ships and support ships depart from the same material support center, and each support ship can only guarantee one supply transportation per day, and fuel can only be supplied by support ships. Now, according to the storage material consumption of each deep-sea cage, taking into account the relative position of each cage, the maximum storage capacity, the existing amount of various materials, the daily consumption, and the constraints of the supply method and economic costs, an optimized supply plan for the storage materials of each cage is given.

[0063] Figure 1 As shown, a method for constructing a multi-objective optimization model for deep-sea aquaculture material replenishment includes the following steps:

[0064] S1. Analyze the material supply method, time, route, material type and quantity of each cage; clarify the decision variables;

[0065] Problem Analysis:

[0066] The material supply plan for each cage involves the supply method, time, route, material type, quantity and loading method, etc., that is, the problem requires clear decision variables, including:

[0067] (1) Supply method, i.e., whether to use live fish transport vessels or support vessels for supply;

[0068] (2) Resupply time, including departure time of live fish transport vessels and support vessels, and replenishment time of each cage;

[0069] (3) Types and quantities of supplies, including the types and quantities of supplies loaded on transport vehicles and the types and quantities of supplies for each cage;

[0070] (4) Supply routes, i.e. the number of cages that can be supplied by live fish transport vessels and support vessels at one time, route planning, etc.

[0071] The goal of this problem solution is to minimize the cost of replenishment, that is, the minimum number of replenishments, while satisfying the task constraints and condition constraints.

[0072] Among the supply methods, support and guarantee ships have a slower speed but better carrying capacity, and are mainly used for conventional supply. Live fish transport ships have a weaker supply capacity but a faster transport speed, and can achieve rapid supply. Support and guarantee ships do not need to consider volume constraints. Among the three types of materials that need to be supplied, live fish supply requirements are usually more urgent, and live fish transport ships need to be used. Feed can be supplied by support and guarantee ship + live fish transport ship, but it mainly relies on support and guarantee ships for supply, and liquid fuel can only be supplied by support and guarantee ships.

[0073] Decision variables:

[0074] ①Num number of times the support ship is used z ;

[0075] ②Number of times the live fish transport ship is used h ;

[0076] ③ Support the loading capacity of feed and fuel on the ship;

[0077] ④ The loading capacity of fry and feed on live fish transport vessels;

[0078] ⑤ The transportation routes of live fish transport vessels and support vessels;

[0079] ⑥ On the mth day, the live fish transport ship and support ship are cage W i The supply of the Lth category of materials. S2. Use the decision variable information to construct the objective function and constraints.

[0080] (2) Model establishment

[0081] Based on the above analysis, it is assumed that: live fish transport ships and support and guarantee ships return immediately after completing the supply mission and do not stay at sea or in the cages; the time for live fish transport ships and support and guarantee ships to load materials at the supply port and unload materials at each cage is not considered; live fish transport ships can transport live fish and feed but not oil; support and guarantee ships can transport feed and oil but not live fish.

[0082] Optimization goal:

[0083] ① Support the use of support vessels and live fish transport vessels as little as possible;

[0084] N 1 =min{Num z}

[0085] N 2 =min{Num b}

[0086] 2 When replenishing, you should replenish as much supplies as possible and delay the next replenishment time:

[0087]

[0088] Among them, S bjl W i The supply volume of the L-th category material on the mth day, W i The supply volume of live fish transport ship for Category L materials on the mth day, W i The supply volume of the L-type material supported by the support ship on the mth day. i The Lth category of materials, L∈{fish fry, feed, oil}.

[0089] Constraints:

[0090] ①Network box W i The daily consumption of the above materials shall not exceed their daily maximum consumption:

[0091] Q xhl ≤Q′ xh

[0092] Among them, Q xhl W i The consumption of the Lth category material on the mth day, Q′ xh W i Maximum daily consumption of Category L materials;

[0093] ②Net Cage W i The replenishment amount of each type of material shall not exceed its remaining storage amount:

[0094]

[0095] Among them, C ccl W i Maximum storage capacity of Category L materials;

[0096] ③Network Cage W i The storage volume of each type of material on day m shall not be less than r% of its maximum storage volume:

[0097]

[0098] Among them, C′ ccl W i The current stockpile of Category L materials;

[0099] ④ The volume V constraints of the live fish transport ship and the volume V′ constraints of the support ship are:

[0100]

[0101]

[0102] Among them, Vwx W i The volume of Category L materials;

[0103] ⑤ The load constraints W of the live fish transport ship and the load constraints W′ of the support ship are:

[0104]

[0105]

[0106] Among them, W wx W i The weight of Category L materials;

[0107] ⑥Net Cage W i The supply volume S of the Lth category material on the mth day bjl :

[0108]

[0109] Example

[0110] Assume that there are three cages ABC that need to be supplied. With cage A as the origin and due north as the y-axis, a rectangular coordinate system is established. The coordinates of cages B and C are (400nmile, 0) and (300nmile, 300nmile) respectively, and the coordinates of the rear supply port D are (600nmile, 1200nmile); there are two live fish transport ships, each of which can only guarantee one transport per day, with a full load capacity of 40t, a speed of 16 knots, and a cargo hold specification of 20m long, 3m wide, and 3m high; there are two support and guarantee ships, each with a speed of 12 knots, which can carry 12,000t of oil and 10,000t of solid materials at a time; r = 50, that is, the materials in each cage are not less than 50% of the maximum storage capacity. The transit time of each live fish transport ship and support and guarantee ship from the supply port to each cage is as follows: Figure 2-3 shown.

[0111] (1) Calculation of fuel supply. Under the condition of maximum daily consumption, the minimum materials required for cage aquaculture production operations are retained to obtain the latest replenishment time of each cage. If the support and guarantee ship directly supplies the cage, the earliest replenishment time of each cage is obtained, see Table 1. As can be seen from Table 1, the maximum daily consumption is far less than the maximum storage capacity of fuel. A fully loaded support and guarantee ship can meet the cage aquaculture production operation needs with one replenishment and does not need to be replenished again. Regardless of the size difference of each cage, an equal distribution strategy is adopted for replenishment to extend the next replenishment time of the cage as much as possible, thereby obtaining 5,000 tons of fuel for each cage. Considering the latest replenishment time and the earliest replenishment time, the supply route of the support and guarantee ship can be arranged at an appropriate time, and the departure time is the first day.

[0112] Table 1 Analysis of oil replenishment time for each cage

[0113] Cage A Cage B Cage C Maximum storage capacity (t) 20000 17500 15000 Current storage capacity (t) 10000 9500 8800 Maximum daily consumption (t) 165 125 110 Earliest replenishment time (d) 2 2 2 Latest replenishment time (d) 3 6 8

[0114] (2) Calculation of fry supply. The storage capacity of fry in each cage and the maximum daily consumption are shown in Table 2. Fry need to be supplied by live fish transport ships. The maximum daily consumption is much smaller than the maximum storage capacity of fry. A live fish transport ship fully loaded with fry can meet the cage farming production operation needs with one supply. The equal distribution strategy is adopted for supply, and the next supply time of the cage is extended as much as possible. Thus, 50,000 fry are supplied to each cage. Considering the latest supply time and the earliest supply time, the supply route of the live fish transport ship can be arranged at an appropriate time, and the departure time is the first day.

[0115] Table 2 Analysis of the time for replenishing fry in each cage

[0116]

[0117]

[0118] (3) Calculation of feed replenishment. Feed needs to be replenished by live fish transport ships and support and security ships together. Limited by the sailing time of the support and security ship, the support and security ship must provide the supply for the first two days; after the support and security ship arrives, it will replenish according to the maximum storage capacity. Under the condition of maximum daily consumption, the minimum feed storage required for aquaculture production operations is retained to obtain the latest replenishment time of feed for each cage. The earliest time for the support and security ship to replenish the cages is the next day. Before the support and security ship arrives, the feed of each cage needs to be replenished by the live fish transport ship to meet its minimum aquaculture production needs. The support and security ship can directly replenish the feed of each cage to the maximum storage capacity in one replenishment. The maximum storage capacity of cage feed needs to meet the maximum daily consumption of feed within a week. On the first day, the live fish transport ship must replenish feed for the cages whose latest replenishment time is 0.

[0119] Table 3 Analysis of feed replenishment time for each cage

[0120]

[0121] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for constructing a multi-objective optimization model for deep-sea aquaculture material supply, characterized in that: The following steps are involved: S1. Analyze the material supply method, time, route, material type and quantity of each cage; Identify decision variables; S2. Use decision variable information to construct objective functions and constraints.

2. The method for constructing a multi-objective optimization model for deep-sea aquaculture material supply according to claim 1, characterized in that: In step S1, (1) Supply method: whether to use live fish transport vessels or support vessels for supply; (2) Resupply time: including the departure time of live fish transport vessels, support vessels, and the replenishment time of each cage; (3) Types and quantities of supplies: including the types and quantities of supplies loaded on transport vehicles and the types and quantities of supplies for each cage; (4) Supply route: the number of cages that can be supplied at one time by live fish transport vessels and support vessels, and route planning.

3. The method for constructing a multi-objective optimization model for deep-sea aquaculture material supply according to claim 1, characterized in that: In step S1, the decision variables include the following: Number of times the support ship is used z ; Number of times the live fish transport ship has been used b ; Support the ship's loading capacity of feed and fuel; The loading capacity of fry and feed on live fish transport vessels; On the mth day, the live fish transport vessel and support vessel are cage W i The supply quantity of Category L materials.

4. The method for constructing a multi-objective optimization model for deep-sea aquaculture material supply according to claim 3, characterized in that: In step S2, the optimization objective function is set: Support the use of support vessels and live fish transport vessels as little as possible; N1=min{Num z } N2=min{Num b } Number of times the support ship is used z ;Num number of times the live fish transport ship is used b ; When resupplying, you should replenish as much supplies as possible to delay the next resupply time: Among them, S bjl W i The supply volume of the L-th category material on the mth day, W i The supply volume of live fish transport ship for Category L materials on the mth day, W i The supply volume of the L-type material support ship on the mth day, net box W i The Lth category of materials, L∈{fish fry, feed, oil}.

5. The method for constructing a multi-objective optimization model for deep-sea aquaculture material supply according to claim 3, characterized in that: In step S2, set the constraint conditions: cage W i The daily consumption of the above materials shall not exceed their maximum daily consumption: Q xhl ≤Q’ xh Among them, Q xhl W i The consumption of the Lth category material on the mth day, Q' xh W i Maximum daily consumption of Category L materials.

6. The method for constructing a multi-objective optimization model for deep-sea aquaculture material supply according to claim 4, characterized in that: In step S2, set constraints: Cage W i The replenishment amount of each type of material shall not exceed its remaining storage amount: Among them, C ccl W i Maximum storage capacity of Category L materials; Cage W i The storage volume of each type of material on day m shall not be less than r% of its maximum storage volume: C' ccl W i The current storage capacity of Category L materials.

7. The method for constructing a multi-objective optimization model for deep-sea aquaculture material supply according to claim 4, characterized in that: In step S2, set constraints: The volume constraints V of the live fish transport ship and the volume constraints V' of the support ship are: Among them, V wx W i The volume of Category L materials; The load constraints W of the live fish transport ship and the load constraints W' of the support ship are: Among them, W wx W i The weight of Category L materials; Cage W i The supply volume S of the Lth category material on the mth day bjl :

8. A system for constructing a multi-objective optimization model for deep-sea aquaculture material replenishment according to any one of claims 1 to 7, characterized in that: It includes information collection module, data processing module and decision-making module; The information collection module collects the material supply method, time, route, material type and quantity of each cage; Data processing module: processing the information of the number of decision variables; Decision-making module: outputs the optimal judgment result based on the objective function and constraints.

9. The system according to claim 8, characterized in that The information collection module also collects data information such as the number, maximum load, cargo hold specifications and sailing speed of live fish transport ships and support and guarantee ships.

10. The system according to claim 8, characterized in that The decision module comprehensively considers the relative position of each cage, the maximum storage capacity, the existing quantity of various materials, the daily consumption and the constraints of the replenishment method, and provides an optimized replenishment plan for the storage materials of each cage.

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