WO2021232882A1 - 基于数学优化模型的片剂营养品生产现场计划与调度方法 - Google Patents

基于数学优化模型的片剂营养品生产现场计划与调度方法 Download PDF

Info

Publication number
WO2021232882A1
WO2021232882A1 PCT/CN2021/078637 CN2021078637W WO2021232882A1 WO 2021232882 A1 WO2021232882 A1 WO 2021232882A1 CN 2021078637 W CN2021078637 W CN 2021078637W WO 2021232882 A1 WO2021232882 A1 WO 2021232882A1
Authority
WO
WIPO (PCT)
Prior art keywords
production
tablet
scheduling
optimization model
site
Prior art date
Application number
PCT/CN2021/078637
Other languages
English (en)
French (fr)
Inventor
许晓云
蒋科伟
李海东
田伯凯
夏旭东
商琛栋
赵亚萍
李珍
王燕
Original Assignee
江苏艾兰得营养品有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 江苏艾兰得营养品有限公司 filed Critical 江苏艾兰得营养品有限公司
Priority to US17/459,109 priority Critical patent/US20210390480A1/en
Publication of WO2021232882A1 publication Critical patent/WO2021232882A1/zh

Links

Images

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
    • 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
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063118—Staff planning in a project environment
    • 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
    • G06Q10/06313—Resource planning in a project environment
    • 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
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • 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
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management
    • 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
    • G06Q10/067—Enterprise or organisation modelling
    • 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
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G06Q10/0875—Itemisation or classification of parts, supplies or services, e.g. bill of materials
    • 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
    • G06Q50/04—Manufacturing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the invention relates to the technical field of tablet nutrition production planning and scheduling optimization, in particular to a tablet nutrition production site planning and scheduling method based on a mathematical optimization model.
  • the technical problem to be solved by the present invention is to overcome the above-mentioned technical bottleneck problem, adopt methods such as operation research optimization theory and simulation experiment, design efficient optimization algorithm and simulation model, and carry out integrated modeling of the production site planning and scheduling of tablet nutritional products , Solve and optimize, quantitatively evaluate possible order expectations and the impact of on-site changes, and then further balance the production line, rationally lay out production capacity, strengthen agile production capabilities, improve the response speed of the supply chain, and provide enterprises with the upgrade of smart manufacturing Theory and data support.
  • the technical solution adopted by the present invention to solve its technical problems is: aiming at the complex and fast-changing tablet nutrition production site, relying on the relevant data of tablet nutrition production, using the theory of operation research and simulation optimization theory, from the core production link
  • On the basis of reducing the overall order delay it meets the requirements of seamless connection in time, intermediate product inventory and other aspects of the production process links, in order to achieve the goal of comprehensive optimization of the entire production process.
  • the specific implementation steps include the following:
  • Step 1 Sort out the production and operation process of tablet nutritional products
  • Step 2 Analyze the production scheduling situation of tablet nutritional products
  • Step 3 Diagnose the production and delivery of tablet nutrition
  • Step 4. Construct an optimization model and plan for tablet nutrition
  • Step 5 Perform simulation experiment verification on the constructed scheduling optimization plan
  • the tablet nutrition production site planning and scheduling method based on the mathematical optimization model of the present invention has the following beneficial effects: First, the present invention performs mathematical optimization modeling on the production process of tablet nutrition, and comprehensively utilizes big data and optimization methods. , To achieve the overall coordination of the enterprise supply chain, the results are quantifiable, implementable, and repeatable, and greatly improve customer order response capabilities; second, the present invention uses production optimization to drive the intelligent optimization of the entire industrial chain, and it can be promoted through the enterprise industrial Internet platform in the future Apply to related industries, comprehensively improve the business intelligence level of enterprises, comprehensively improve operational efficiency, reduce order fulfillment costs, and improve corporate profitability.
  • FIG. 1 is a flowchart of the present invention
  • Figure 2 is the main flow chart of tablet production
  • Figure 3 is the scheduling output sample table of the scheduling optimization model
  • Figure 4 is a summary table of manual scheduling and model scheduling conclusions.
  • Step 1 Sorting out the production and operation process of tablet nutraceuticals
  • the present invention will first sort out the production and operation process of tablet nutritional products.
  • the production process of the tablet is mainly a physical process, and the production process involves self-processed particles and raw materials purchased from other suppliers.
  • the particles used in it can be self-made or collected outside, and the types of self-made particles will be controlled in combination with factors such as cost and management complexity.
  • other raw and auxiliary materials need to be added, such as low-content functional ingredients, some excipients in the tableting process, flavors and fragrances, colors, and so on.
  • the fully formulated materials are mixed according to the process to ensure uniformity.
  • the production of tablets is carried out in batches. After the tableting is finished, some products need to be coated with a film coating on the surface of the tablets due to factors such as stability problems or customer requirements. After the production of the tablets is finished, the final packaging is carried out according to the sales channels.
  • Step 2 Analysis of the production scheduling situation of tablet nutrition
  • the business side provides product requirements to the production planning department.
  • the requirements are in various forms. They mainly include two modes: 1.
  • the product variety, quantity and delivery date are given.
  • the colleagues in the planning department directly schedule production accordingly. If the delivery date is really not met, then Feedback with the corresponding business department to confirm whether to postpone or cancel the order; 2.
  • Provide customer information, variety, and quantity (mainly for external customers), and the planning department will schedule the expected delivery date to the business department according to the customer’s priority and capacity. As the delivery time promised to customers.
  • Each product has a corresponding formula BOM, based on which the number of granular intermediates and the number of raw and auxiliary materials required can be decomposed. Particles and raw and auxiliary materials have corresponding inspection and release time, and the scheduling of tableting must be after the release of intermediates and raw materials.
  • On-site production scheduling is completed by listing the production capacity of all tablet presses, adhering to the principle of "first come first served" orders, and complete the "rough scheduling” of orders.
  • the so-called "rough production scheduling” means that the production planner lists the types of products and the number of batches that need to be completed every day for each production line in each workshop according to the order delivery status.
  • the on-site management personnel will also determine the order of production for the products on a production line according to the actual situation on the site, and form the final executable "fine scheduling" in the production workshop.
  • the rough scheduling cycle is generally fixed, but because customer orders are adjusted from time to time, especially for high-priority customers, there will be temporary orders and adjustments. Therefore, the rough scheduling of the production department is actually carried out every day.
  • Step 4 Construction of tablet nutrition optimization model and plan
  • the goal of the present invention is to satisfy all constraint conditions, optimize the production schedule of the product, design and deliver a fully automatic scheduling model for tablet production, and output available
  • the production scheduling information used in the production workshop minimizes the number of days of cumulative delay for orders.
  • the clearance time is related to factors such as the machine model, whether it involves changing the mold, and whether the color of the tablet changes from dark to light;
  • the type of particles needs to be cleared.
  • the time of clearing is related to the properties of the particles such as whether it is calcium or colored.
  • Work calendar no production will be arranged during the holiday period.
  • the customer's requirements for the shape of the tablet determine the choice of the mold; the mold and the model of the tablet machine also have a corresponding relationship: even if the same product is produced on a different model of tablet machine, the mold is different. 8) Limit on the number of molds: The sum of the number of a certain mold used on all machines on the same day cannot exceed the number of molds owned. 9) Inspection and release: The delivery time of the product order should be minus 7 days of inspection and release time, that is, the production of tablet products should be completed 7 days before the delivery time.
  • the variables of the tablet production scheduling model mainly cover material types (raw materials, intermediates, finished products), equipment types (quantity, corresponding clearance time), product correspondence (tablets and granules), clearance rules, priority levels, production efficiency, etc. .
  • the above variable information can be adjusted in the model in real time. If the number of equipment is increased or decreased, and the production efficiency is improved, the model can be updated immediately.
  • the specific model variables defined are as follows:
  • FX j release date of product raw materials, 1,...,J;
  • DM j the total demand for product j during the scheduling period
  • DMG k the total demand for particle k during the scheduling period
  • ⁇ kj the capacity loss coefficient when the k-th particle is used to produce product j
  • p j the production priority of product j (generally the same as the order priority, if two or more orders contain the same product, the priority value of all the products is the one with the highest priority value);
  • IGA k the initial inventory of particle k
  • IRA k the initial inventory of raw material k
  • RR kt the quantity of the k-th raw material received at the end of the t-th day
  • ⁇ k the productivity loss coefficient of the k-th pellet produced from raw materials
  • PGT km single-pot processing time of the k-th granule. For granules that cannot be processed by the granulator m, the processing time is 0;
  • MG km the single-pot output (kg) produced by the granulator k on the granulator m;
  • PT jn the processing speed of product j on the n-th tablet press (ten thousand tablets/hour). For products that cannot be processed by tablet press n, the processing speed of the product is 0 for processing;
  • MP j The single batch output of product j (10,000 pieces). If there are two machines in a room that cannot produce different products at the same time, the output of one machine per pot will be doubled;
  • B kj the amount of particles k required to produce a unit of product j
  • DMT jt the demand for the j-th product on the t-th day (calculated based on the due date of the order);
  • Cor j the color attribute of product j, 1 represents dark color, 0 represents light color, and -1 represents mortar color;
  • SATT n The longest time used for a single clearing place when the product is switched on the tablet press n;
  • SAVTT n Extra time for product mold switching on tablet press n, which is also the cleaning time of the Philippines;
  • SC n The frequency of small cleaning of the tablet press n, that is, a small cleaning is required after every batch of the same product is produced;
  • CLT n The extra time required for the color switching of the tablet press n;
  • v jt Judge whether the order of product j is delayed on day t, if so, it will be 1, otherwise it will be 0;
  • xg kmt the number of batches of granules k processed on the granulator m on the t day;
  • xt jnt the number of batches of product j processed on the tablet press n on the t day;
  • IF jt the inventory of product j at the beginning of day t (can be negative, indicating product delay);
  • IG kt the inventory of the k-th particle at the beginning of the t day
  • IR kt the inventory of the k-th raw material at the beginning of the t-th day
  • sg mt idle time of the m-th granulator on the t-th day (hours);
  • st nt idle time (hours) of the nth tablet press on the tth day;
  • sav nt used to judge whether the same mold is used on the same machine for two consecutive days, which is a continuous variable from 0 to 1;
  • sav2 nt used to judge whether the same product is produced on the same machine on two consecutive days, which is a continuous variable from 0 to 1;
  • Copr nt Used to judge whether the color of the product produced on the same machine two days next to each other is changed from dark to light, if it is, it is 1, otherwise it is 0.
  • the present invention proposes the following 4 assumptions: 1) The tablet pressing process is finished with a large clearing every day: During the actual tablet pressing process, the production line is cleared in accordance with the regulations of the Quality Department It is not necessary to clear the field every day. The main purpose of defining this hypothetical condition: The actual production site often has various emergencies, such as shortage of materials, abnormal equipment requiring maintenance, etc. Therefore, the time for the clearing of the site is added to the model every day to reserve for these emergencies Processing time. In addition, when more than one product is produced every day, the production sequence is also involved. When producing across multiple days, the production sequence may have a significant impact on the large cleaning time between products.
  • Raw materials that arrive before 24:00 on the same day need to be stored in the warehouse and included in the raw material inventory at the beginning of the next day: All raw materials must be stored in the warehouse before being released before they can be used. Therefore, it is assumed that all raw materials that arrive on the same day are included in the next day.
  • the inventory data is in line with the actual production situation.
  • the pellets produced before 24:00 on the same day need to be stored in the warehouse and included in the pellet inventory at the beginning of the next day: the same as the raw material inventory data assumption, the amount of self-made pellets stored in the warehouse on the same day will be included in the pellet inventory data of the next day .
  • the present invention establishes the following optimization model for the production process of tablet health products:
  • Constraint (2) The daily capacity constraint of the granulator during working days
  • Constraints (3)-(6) The daily capacity constraints of relevant model tablet presses during working days;
  • Constraint (8) Restriction of rest periods, no production on rest days such as holidays;
  • Constraints (9)-(10) The inventory of product j at the beginning of day t is equal to the inventory at the beginning of the previous day + the production volume of the previous day's product-the demand for the product the previous day;
  • Constraints (14)-(15) The raw material inventory at the beginning of each day is equal to the beginning inventory of the previous day + the raw materials purchased outside the warehouse the previous day-the raw material consumption of the previous day;
  • Constraints (17)-(18) The particle inventory at the beginning of each day is equal to the particle inventory of the previous day + the particles produced the previous day-the particle consumption of the previous day;
  • Constraints (21)-(22) Whether the product is produced and production batch constraints;
  • Constraints (25)-(26) The types of granules and products processed by the granulator and tablet press on the same machine on the same day shall not exceed one;
  • Constraints (29)-(31) Whether the same tablet press produces the same product two days next to each other;
  • Constraint (32) The color changes of products produced on the same tablet press two days next to each other;
  • Constraints (33)-(42) regular non-negative constraints, natural number constraints, and 0-1 variable definitions.
  • the present invention will run the developed optimization algorithm through simulation experiments and obtain related experimental results, and analyze and verify the effectiveness of the algorithm through comparison between the experimental results and actual data.
  • the specific instructions are as follows:
  • the present invention collects relevant basic data based on the actual production situation of tablet nutritional products, mainly including the main equipment list of the granule, the main equipment list of the tablet, the order information, the corresponding table of the tablet product and the granule, and the corresponding tabletting of the tablet product.
  • the present invention uses MATLAB to simulate the results of the CPLEX model and the actual production data after sorting, compare the delays, and analyze three main indicators: the number of delays (cumulative Ten thousand pieces), delayed batches (total of delayed batches), average delay time.
  • the comparison result is shown in Fig. 4, it can be seen that through the model scheduling, the delay situation is obviously improved.
  • the goal of the present invention is to minimize the delayed batch. Through the model scheduling, the improvement of the delayed batch reaches 52.7%.
  • the method for optimizing the tablet nutrition supply chain based on the mathematical optimization model of the present invention can significantly improve the level of customer order delivery and improve the phenomenon of order delays without investing new labor or adding new equipment.
  • the advantages of model scheduling are as follows:
  • Real-time operation Use the mathematical model to schedule production. You only need to sort out and clearly define the limiting factors of the factory when the model is established. In the later stage of the model operation, the computer can automatically consider all the defined constraints. The production scheduling conclusion is given; for the constraint conditions, the iteration can also be updated at any time to achieve real-time production scheduling.
  • this model can also be used to measure various factory operation optimization plans, provide sufficient theoretical data support, clarify the optimization direction and priority level, and help analyze the investment of each optimization plan output.
  • the invention solves the scheduling problem of tablet nutritional products, and at the same time has important reference value for improving the production efficiency of other products, the order delivery level, and customer satisfaction.
  • the improvement of delivery level can bring positive impacts to various aspects, such as: refined management of raw material suppliers, improvement of inventory turnover efficiency, customer satisfaction, decline of finished products, etc., which can enhance the competitiveness of the entire supply chain .

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • Development Economics (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Educational Administration (AREA)
  • Game Theory and Decision Science (AREA)
  • Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • General Health & Medical Sciences (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Manufacturing & Machinery (AREA)
  • Epidemiology (AREA)
  • Biomedical Technology (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • General Factory Administration (AREA)

Abstract

一种基于数学优化模型的片剂营养品生产现场计划与调度方法，从核心的片剂生产环节入手，用数学优化模型实现片剂营养品生产现场计划与调度的优化。该数学优化模型将多级生产、库存、原材料、半成品统一考虑，兼顾客户优先级，实现制粒和压片之间的协调互动、现场优化以及片剂订单的快速响应，大幅降低库存，加强生产环节之间的互动连接，从而在不投入新人工、不增加新设备的情况下，通过现场排产优化，大幅提升客户订单响应能力，显著降低拖期现象，提升设备利用率，并能非常灵活地处理现场中常见的插单、维修、原材料不能齐套等复杂问题。

Description

基于数学优化模型的片剂营养品生产现场计划与调度方法 技术领域
本发明涉及片剂营养品生产计划与调度优化技术领域，尤其是一种基于数学优化模型的片剂营养品生产现场计划与调度方法。
背景技术
对于片剂营养品的规模生产，由于生产现场常涉及上千种产品，数百种模具，上百台不同产能机器，且多个生产环节联动，还涉及到库存的制约问题和大量法规合规性要求，传统人工方式进行排产以及供应链管理的方式已经越来越不能适应企业发展的需要，耗时长、效率低、成效差、成本高的问题极其突出。反映在企业外部，体现为客户订单交付能力差、经常拖期、供应商管理混乱、接单能力减弱以及无法满足供应链下游的响应速度等问题。因此对于片剂营养品的生产而言，急需解决大规模复杂生产系统与有限人力之间的矛盾、订单严重拖期与现阶段产能负荷之间的矛盾、客户需求多变与研发集成一体化之间的矛盾等技术瓶颈问题。
发明内容
本发明要解决的技术问题是：克服上述技术瓶颈问题，采用运筹优化理论和仿真实验等方法，设计高效的优化算法和仿真模型，对片剂营养品的生产现场计划及调度进行一体化建模、求解和优化，对可能的订单预期和现场变化带来的影响进行定量评估，进而进一步平衡产线，合理布局产能，加强敏捷生产能力，提升供应链响应速度，并为企业的智能制造升级提供理论及数据支持。
本发明解决其技术问题所采用的技术方案是：针对复杂且快速变化的片剂营养品生产现场，借助于片剂营养品生产的相关数据，运用运筹学理论和仿真优化理论，从核心生产环节入手，开发具有自适应能力的高级计划排产系统，囊括并综合考虑所有生产性参数以及各类约束条件(换模、清洗、产品加工、包装等)，对不同优先级的客户订单进行优化排产，在降低整体订单拖期的基础上，满足各生产环节流程环节在时间、中间品库存等方面无缝衔接的要求，以期达到全生产环节综合优化的目标。具体实施步骤包括如下：
步骤1、对片剂营养品生产运营过程进行梳理；
针对颗粒准备和片剂营养品的生产过程进行归纳与提炼。
步骤2、对片剂营养品生产调度情况进行分析；
基于对片剂营养品目前调度情况的分析，厘清相关调度方案的制订过程和实施方式，以便在后续的优化过程中做到取长补短，有机结合。
步骤3、对片剂营养品生产交付情况进行诊断；
基于当前的业务情况，获取并分析其片剂营养品的交付情况，探究影响有效交付的相关原因，为进一步优化做铺垫。
步骤4、对片剂营养品优化模型及方案进行构建；
把片剂营养品生产过程中的原材料库存、颗粒和产成品生产、库存和交付有机地结合起来，通过运筹与规划等手段在统一的数学模型框架中进行协同优化，设计生产调度优化模型，实现片剂营养品的高效生产与及时交付。主要包括以下几部分：
1)模型目标及约束条件，对片剂营养品生产的优化目标及相关的约束条件进行梳理，以便将现实问题进行合理的理论抽象，便于优化过程的实施。
2)模型变量及定义，对以上理论问题中的相关核心要素和变量进行提炼，并通过恰当的数学语言进行准确的定义，从而实现对问题的科学、精准表述。
3)优化模型设计，基于以上对于目标及约束的梳理，运用所定义的变量来设计片剂营养品调度的优化模型。鉴于现实的复杂性，研究将首先基于市场现场实际操作的要求凝练相关假设条件，在此基础上构建优化问题的数学规划模型，以便在满足约束的前提下更好地实现优化目标。
步骤5、对所构建的调度优化方案进行仿真实验验证；
通过仿真实验的方式来获取实施本优化方案后的系统表现，并通过与企业实际运营数据的对比，在累计延误数量(总延误片数)、延误批量(延误批次的合计)、平均延误时间等指标方面对方案的质量进行评估。
本发明的基于数学优化模型的片剂营养品生产现场计划与调度方法，其有益效果是：第一，本发明对片剂营养品的生产过程进行数学优化建模，综合利用大数据与优化手段，实现企业供应链整体协同，结果可量化、可实施、可重复，大幅提升客户订单响应能力；第二，本发明以生产优化带动全产业链的智能优化，且未来可以通过企业工业互联网平台推广应用至相关行业，全面提升企业商业智能水平，综合提升运营效率，降低订单履行成本，提升企业盈利能力。
附图说明
下面结合附图和实施例对本发明进一步说明。
图1是本发明的流程图；
图2是片剂生产主流程图；
图3是调度优化模型排产输出样表；
图4是手动排产与模型排产结论汇总表。
具体实施方式
现在结合附图对本发明作进一步详细的说明。这些附图均为简化的示意图，仅以示意方式说明本发明的基本结构，因此其仅显示与本发明有关的构成。
参照图1，本发明的具体实施步骤如下：
步骤1、片剂营养品生产运营过程的梳理
基于生产现场及相关数据的分析，本发明将首先对片剂营养品的生产运营过程进行梳理。通过图2可以看出，片剂的生产过程主要为物理过程，生产过程中会涉及到自加工的颗粒和采购于其他供应商的原辅料。其中所用的颗粒可以自制也可以外采，结合成本、管理复杂度等因素，会针对自制颗粒的种类进行控制。除颗粒之外，还需要添加其他的原辅料，比如低含量的功效成分、压片过程中的一些赋形剂、香精香料、色素等等。所有原辅料、颗粒等中间体准备好之后，根据工艺将全配方的物料进行总混，保证均一性。片剂的生产是按照批次进行的，压片结束后，某些产品因稳定性问题或客户要求等因素，需要在片剂表面包一层薄膜衣。片剂生产结束后，根据销售渠道，进行最终的包装。
步骤2、片剂营养品生产调度情况的分析
业务端提供产品需求给到生产计划部门，需求的形式多样，主要包括两种模式：一、给出品种、数量和交期，计划部门同事据此直接排产，如确实无法满足交期，则与相应业务部门反馈，确认是否延期还是取消订单；二、 给出客户信息、品种、数量(主要针对外部客户)，计划部门根据客户的优先级别和产能情况，排出预计交期给到业务部门，作为给客户承诺的交期。
每个产品有相应的配方BOM，据此可以分解出所需的颗粒中间体的数量和原辅料的数量。颗粒和原辅料都有相应的检测放行时间，压片的排产必须在中间体和原料放行之后。现场排产通过列出所有压片机台的产能，秉承订单“先到先得”的原则，完成订单“粗排产”。所谓“粗排产”是指，生产计划员根据订单交付情况，列出每个车间每条生产线每天需要完成的产品种类及批次数。粗排产到车间生产现场后，现场管理人员还会根据现场实际情况，针对在一条生产线上的产品确定生产的先后顺序，在生产车间形成最终可执行的“细排产”。粗排产周期一般固定，但因为客户的订单时有调整，尤其针对优先级别高的客户，会有临时插单和调单的情况，因此生产部门的粗排产实际每天都在进行。
步骤3、片剂营养品生产交付情况的诊断
片剂营养品的整体的交付水平一般，主要原因有：1)产能因素：片剂产能通常非常紧张，且人工排产这种生产调度方式离“最优解”有很大的差距。2)原料因素：每个片剂产品的配方中会用到10～30种的原辅料，不同的产品又会存在共用某一种原辅料的情况，因此，一个原料未准时到货会影响到多个品种的正常生产。而营养品行业的透明度越来越高，客户对越来越多的原料会要求指定供应商，有的甚至是客户直接提供的原料，这种情况不但对于采购端是极大的挑战，对生产安排的灵活性也大大降低。3)物流因素：多样的交付形式以及分布广泛的业务范围，对片剂营养品的物流造成了较大的挑战，极有可能导致货物积压在港口，清关时间长，为了准时交付，导致对之 前的生产环节的加工交付时间形成更大压力。针对上述的影响因素，因为产能因素占了绝对高的比例，影响巨大，也是生产运营的长期瓶颈环节，因此本发明期望通过研究生产调度模型，优化产能利用，以达到改善订单交付水平的目的。
步骤4、片剂营养品优化模型及方案的构建
4.1模型目标及约束条件
对于片剂营养品的调度优化，本发明的目标是根据客户对片剂类产品的需求，满足所有约束条件，优化产品的生产排程，设计交付片剂生产的全自动调度模型，输出可供生产车间使用的排产信息，使得订单累计延误的天数最小。
考虑实际生产要求以及模型的可操作性，满足以下约束条件：1)物料齐套性：要求颗粒生产前，制造该颗粒所需原材料均已备齐；片剂生产前，制造该产品所需的颗粒及其他原材料的量都已经准备好，并检验合格。2)清场规则：清场是指岗位操作人员对生产环境、设备、容器、器具、文件等进行清洁和整理，确保无上次生产的遗留物，达到生产前的清洁状态。片剂生产切换产品种类或连续生产相同产品超过若干批次或若干数量时需要大清场，清场时间与机器型号、是否涉及换模、片剂颜色是否由深到浅变化等因素有关；颗粒生产切换颗粒种类时需要大清场，清场时间与诸如是否为钙、是否有色等颗粒属性有关。3)片剂与颗粒对应关系：颗粒作为片剂生产的中间体，一个片剂产品可能会用到多种颗粒作为中间体，一种颗粒也可能被多种片剂共用。颗粒先于片剂制备，颗粒的齐套性不满足时，相应的片剂无法进入生产。4)工作日历：假期调休期间不安排生产。5)产品与设备对应关系：根据 产品特性及设备性能，颗粒与制粒机之间以及片剂与压片机之间存在匹配关系，即某种产品、某种颗粒只能在某几台机器上加工生产，如：某些需要酒精制粒的颗粒必须要在防爆制粒线生产。6)制粒线生产限制：制粒线中相同机型需要同时生产相同产品(因相同型号的制粒产线布局在一个房间内，需要避免产品之间的交叉污染)。7)片剂与模具、压片机对应关系：某种产品在某台机器上需要用到某种特定的模具。客户对片剂的形状要求决定了模具的选择；模具与压片机的型号也存在对应关系：即使相同的产品，在不同型号的压片机上生产，模具不同。8)模具数量限制：同一天所有机器上用到的某一种模具的数量总和不能超过该模具拥有的数量。9)检测放行：产品订单的交付时间应减去7天检测放行时间，即在交付时间7天前完成片剂产品生产。
4.2模型变量及定义
片剂生产调度模型的变量主要涵盖物料种类(原料、中间体、成品)、设备类型(数量、对应的清场时间)、产品对应关系(片剂与颗粒)、清场规则、优先级别、生产效率等。上述变量信息均可以在模型中实时调整，如设备台数有增减、生产效率有提升之后，可以立即更新模型，具体定义的模型变量如下：
1)编号类
j：产品编号，j＝1，...，J；
k：颗粒编号，k＝1，...，K；
t：时间编号，以天为单位，生产从第一天开始安排，t＝1，...，T；T：订单排产周期；
n：压片机编号，n＝1，...，N；
m：制粒机编号，m＝1，...，M；
s：模具种类编号，s＝1，...，S；
2)参数类
M 1-3：常数；
WD t：调休日期，t＝1，...，T；
FX j：产品原料放行日期，1，...，J；
DM j：排产周期内，产品j的总需求量；
DMG k：排产周期内，颗粒k的总需求量；
γ kj：用第k种颗粒生产产品j时的产能损失系数；
p j：产品j的生产优先级(一般和订单优先级一致，如果两个及以上订单包含同种产品，则所有该产品优先值均取优先值最大的)；
IGA k：颗粒k的初始库存量；
IRA k：原材料k的初始库存量；
RR kt：在第t天结束时收到的第k种原材料的数量；
β k：用原材料生产第k种颗粒的产能损失系数；
PGT km：第k种颗粒单锅加工时间，对于制粒机m不能加工的颗粒，按加工时间为0进行处理；
MG km：颗粒k在制粒机m上生产的单锅产量(公斤)；
PT jn：产品j在第n台压片机上的加工速度(万片/小时)，对于压片机n不能加工的产品，按该产品加工速度为0进行处理；
MP j：产品j的单批产量(万片)，对于一个屋子里有两台机器，不能同时生产不同产品的，按照一台机器单锅产量翻倍处理；
B kj：生产一单位的产品j需要颗粒k的量；
DMT jt：第j种产品在第t天的需求量(根据订单的due date计算得到)；
MODT js：产品与模具关系，″MODT js＝1″代表生产产品j需要使用模具s，″MODT js＝0″则代表不需要使用；
MODN ns：机器与磨具关系，″MODN ns＝1″代表压片机n可以使用模具s，″MODN ns＝0″则代表不可以使用；
MOD s：模具s的数量；
Cor j：产品j的颜色属性，1代表深色，0代表浅色，-1代表臼色；
SATG m：制粒机m上颗粒切换时单次大清场所用的时间；
SATT n：压片机n上产品切换时单次大清场所用最长时间；
SAVTT n：压片机n上产品模具切换额外时间，同时也是菲础大清洗时间；
SIT n：压片机n上单次小清洗所用的时间；
SC n：压片机n小清洗的频率，即每生产同种产品多少批后需要一次小清洗；
CLT n：压片机n产品颜色切换需要的额外时间；
u jt：产品j在第t天的延误量；
3)决策类
v jt：判断产品j的订单在第t天是否延误，若是则为1，否则为0；
xg kmt：第t天在制粒机m上加工颗粒k的批次数；
xt jnt：第t天在压片机n上加工产品j的批次数；
xta jnt：在第t天，如果产品j在第n台压片机上生产，此值为1，否则为0；
xga kma：在第t天，如果颗粒k在第m台制粒机上生产，此值为1，否则为0；
IF jt：产品j在第t天开始时的库存(可为负，表示产品延误)；
IG kt：第k种颗粒在第t天开始时的库存；
IR kt：第k种原材料在第t天开始时的库存；
sg mt：第m台制粒机在第t天空闲时间(小时)；
st nt：第n台压片机在第t天空闲时间(小时)；
sav nt：用于判断同台机器上相邻两天是否用到相同模具，为0到1的连续变量；
sav2 nt：用于判断同台机器上相邻两天是否生产相同产品，为0到1的连续变量；
Copr nt：用于判断同台机器上相邻两天生产的产品颜色是否为由深变浅，若是则为1，否则为0。
4.3优化模型设计
根据片剂和颗粒生产的特点，本发明提出以下4个假设条件：1)片剂的压片工序每天以大清场收尾：实际片剂在压片的过程中，生产线是按照质量部规定的清场规则进行清场，并非必须每天的都要进行大清场。定义此假设条件的主要目的：实际的生产现场往往会有各种突发情况，如：缺料、设备异常需要维修等，因此在模型中每天加入大清场的时间，为这些突发情况预留处理的时间。此外，当每天生产的产品多于一种的时候，还涉及到生产顺序问题，在跨天生产的时候，生产顺序可能会对产品间的大清洗时间有显著影响。然而，引入精细的生产顺序会让整个模型体量变为原来的2～3倍，对 于工业级别应用来说，会显著影响求解速度。当加入大清场结尾的假设后，天与天之间的生产形成了自然的隔离，能够大幅降低模型复杂度。2)当天24时前排程剩余时间计入第二天：车间每天安排三个班次，每班8小时，一天24小时。当一台机器完成当天安排的所有订单后，另有剩余时间可以计入第二天，只要后续订单的原材料全部齐套、模具可用，则可以继续生产后续的订单。做此假设，更加贴合现场实际情况。3)当天24时前到达的原材料需入库，计入第二天开始时的原材料库存：所有原材料，必须先入库经放行后才能使用，因此假设当天到达的所有原材料，计入第二天的库存数据，符合生产实际情况。4)当天24时前所生产的颗粒需入库，计入第二天开始时的颗粒库存：同原材料的库存数据假设，自制的颗粒当天入库的量，计入第二天的颗粒库存数据。
基于以上对于目标、约束、变量及假设的分析与设定，本发明对片剂保健品的生产过程建立以下的优化模型：
Figure PCTCN2021078637-appb-000001
Figure PCTCN2021078637-appb-000002
Figure PCTCN2021078637-appb-000003
Figure PCTCN2021078637-appb-000004
Figure PCTCN2021078637-appb-000005
Figure PCTCN2021078637-appb-000006
Figure PCTCN2021078637-appb-000007
Figure PCTCN2021078637-appb-000008
Figure PCTCN2021078637-appb-000009
Figure PCTCN2021078637-appb-000010
Figure PCTCN2021078637-appb-000011
Figure PCTCN2021078637-appb-000012
Figure PCTCN2021078637-appb-000013
Figure PCTCN2021078637-appb-000014
Figure PCTCN2021078637-appb-000015
Figure PCTCN2021078637-appb-000016
Figure PCTCN2021078637-appb-000017
Figure PCTCN2021078637-appb-000018
Figure PCTCN2021078637-appb-000019
Figure PCTCN2021078637-appb-000020
Figure PCTCN2021078637-appb-000021
Figure PCTCN2021078637-appb-000022
Figure PCTCN2021078637-appb-000023
Figure PCTCN2021078637-appb-000024
Figure PCTCN2021078637-appb-000025
Figure PCTCN2021078637-appb-000026
Figure PCTCN2021078637-appb-000027
Figure PCTCN2021078637-appb-000028
Figure PCTCN2021078637-appb-000029
Figure PCTCN2021078637-appb-000030
Figure PCTCN2021078637-appb-000031
Figure PCTCN2021078637-appb-000032
Figure PCTCN2021078637-appb-000033
Figure PCTCN2021078637-appb-000034
Figure PCTCN2021078637-appb-000035
Figure PCTCN2021078637-appb-000036
Figure PCTCN2021078637-appb-000037
Figure PCTCN2021078637-appb-000038
Figure PCTCN2021078637-appb-000039
Figure PCTCN2021078637-appb-000040
Figure PCTCN2021078637-appb-000041
Figure PCTCN2021078637-appb-000042
该模型的相关解释如下：
目标方程(1)：最小化累计加权的产品延迟交付订单量；
约束(2)：工作日制粒机每天的产能约束；
约束(3)-(6)：工作日相关型号压片机每天的产能约束；
约束(7)：原材料放行约束，在原料放行日期前该产品不进行生产；
约束(8)：调休约束，在假期等休息日不进行生产；
约束(9)-(10)：产品j在第t天开始时的库存量等于前一天开始时的库存量+前一天产品的生产量-前一天产品的需求量；
约束(11)-(12)：当产品j在第t天库存数为负时，延误量为其相反数；当库存量为非负数时，延误量为0；
目标方程(13)：当产品j在第t天出现延误时，即视为一个订单延误；
约束(14)-(15)：每天开始时的原材料库存，等于前一天的期初库存+前一天到库外采购的原材料-前一天的原材料消耗；
约束(16)：每天生产颗粒所需的原材料加上产能损耗量，不能超过当天开始时的原材料库存(保证原材料有库存时间，预防临时出现问题)；
约束(17)-(18)：每天开始时的颗粒库存，等于前一天颗粒库存+前一天生产的颗粒-前一天的颗粒消耗；
约束(19)：每天生产最终产品所需的颗粒加上产能损耗量，不能超过当天开始时颗粒的库存(保证颗粒有库存时间，预防临时出现问题)；
约束(20)：每天压片所用到的模具数量不超过拥有的该模具数量；
约束(21)-(22)：产品是否生产和生产批次约束；
约束(23)-(24)：颗粒是否生产和生产批次约束；
约束(25)-(26)：制粒机和压片机同一天在同台机器上加工的颗粒、产品 种类不超过1种；
约束(27)-(28)：同台压片机相邻两天生产的产品是否用到同一模具；
约束(29)-(31)：同台压片机相邻两天是否生产相同产品；
约束(32)：同台压片机相邻两天生产的产品颜色变化；
约束(33)-(42)：常规非负约束，自然数约束以及0-1变量定义。
步骤5、调度优化方案的仿真实验验证
本发明将通过仿真实验来运行所开发的优化算法并获取相关的实验结果，通过实验结果与实际数据之间的比较，来分析、验证算法的有效性。具体说明如下：
5.1企业运营基础数据
本发明将基于片剂营养品生产的实际情况，收集相关的基础数据，主要包括颗粒主设备清单、片剂主设备清单、订单信息、片剂产品与颗粒对应表、片剂产品对应的压片机、生产速度、模具型号、相应模具的套数、颗粒对应的生产线及生产效率、颗粒中间体的有效期、颗粒库存情况、手动排产数据等。
5.2调度优化模型仿真
以延期交货批次量最少为目标方程，依据上文实际约束条件、定义变量，建立混合整数规划数学模型，在具有3.60GHz CPU和16G内存的台式计算机上，运用IBM ILOGCPLEXOptimization Studio V12.8.0对数学模型进行求解，而后数值模拟在MATLAB R2017a中进行编码并运行，得到片剂的排产结果(见图3，表格中字母表示片剂产品代码)。结果表明，模型输出的排产与手动排产在结构上是一致的，生产现场管理人员无需经过另外的培训，即可执行。
5.3结果对比分析
为了确认所构建调度优化模型与手动排产结论的差异，本发明对CPLEX模型的求解结果与整理后的实际生产数据用MATLAB进行仿真实验，对比延误情况，分析三个主要指标：延误数量(累计万片数)、延误批量(延误批次的合计)、平均延误时间。对比结果如图4所示，可知，通过模型排产，延误的情况明显好转，本发明的目标是延误批量最小，通过模型排产，延误批量的改进达到了52.7％。
本发明的基于数学优化模型的片剂营养品供应链的优化方法，在不投入新人工、不增加新设备的情况下，可以显著提升客户订单交付水平，改善订单延误现象。与手动排产对比，模型排产体现出的优势如下：
1.实时运行：利用数学模型进行排产，只需要在模型建立时针对工厂的限制因素进行梳理分析和清晰定义，后期的在模型运行过程中，则可以由计算机自动考虑所有定义的约束条件，给出排产结论；对于约束条件，也可以随时更新迭代，做到实时排产。
2.效率提升：本发明的数学模型，计算一次只需要几小时就可以得到排产结果，而手动排产，有经验的计划人员至少需要花一天的时间才能迭代更新。
3.协同优化：在片剂生产的过程中，需要原材料、检测、库存、颗粒生产多个环节的协同配合。因此，在生产调度中必须考虑协同效应，手动排产需要考虑的问题太过众多，无法全面考虑，而调度模型可全盘考虑多方面情况，多个环节联动，互相制约，协同优化。
4.结果最优：排产结果在假设条件下是理论最优的，使得片剂订单延迟 交货的总批量最小，大幅提高生产效率。手动排产很难考虑全局情况，只能保证结果可行，不能实现最优性。
5.降低工作难度：模型输出的排产结论，格式简单，容易理解，相对于手动排产，在现有模型的基础上进行简单调整后，无需再做细排产，排产可直接执行，与工业现场运行差异小。
6.利用模型进行排产，因为将繁琐的计算工作交由计算机执行，不但计算准、效率高，而且可拓展性强，可以实时调整和增加约束参数，针对生产运营过程中遇到的问题或者更新，进行快速分析和定量回答。
7.除了运用于订单的生产调度以外，还可以用此模型对工厂各类运营优化方案进行测算，提供充分的理论数据支持，明确优化方向和优先级别，并有助于分析各优化方案的投入产出。
本发明解决了片剂营养品的排程问题，同时对提升其他产品的生产效率、订单交付水平、客户满意度都有重要的参考价值。交付水平的提高，带来的积极影响可以拓展至各个方面，如：原料供应商的精细化管理、库存周转效率的提升客户满意度、产品成品的下降等等，可以提升整个供应链的竞争力。
以上述依据本发明的理想实施例为启示，通过上述的说明内容，相关工作人员完全可以在不偏离本项发明技术思想的范围内，进行多样的变更以及修改。本项发明的技术性范围并不局限于说明书上的内容，必须要根据权利要求范围来确定其技术性范。

Claims (8)

  1. 一种基于数学优化模型的片剂营养品生产现场计划与调度方法，其特征是：包括如下实施步骤：
    步骤1、对片剂营养品生产运营过程进行梳理；
    步骤2、对片剂营养品生产调度情况进行分析；
    步骤3、对片剂营养品生产交付情况进行诊断；
    步骤4、对片剂营养品优化模型及方案进行构建；
    步骤5、对所构建的调度优化方案进行仿真实验验证。
  2. 根据权利要求1所述的基于数学优化模型的片剂营养品生产现场计划与调度方法，其特征是：步骤1针对颗粒准备和片剂营养品的生产过程进行归纳与提炼。
  3. 根据权利要求1所述的基于数学优化模型的片剂营养品生产现场计划与调度方法，其特征是：步骤2基于对片剂保健品目前调度情况的分析，厘清相关调度方案的制订过程和实施方式。
  4. 根据权利要求1所述的基于数学优化模型的片剂营养品生产现场计划与调度方法，其特征是：步骤3基于当前的业务情况，获取并分析其片剂保健品的交付情况，探究影响有效交付的相关原因。
  5. 根据权利要求1所述的基于数学优化模型的片剂营养品生产现场计划与调度方法，其特征是：把片剂营养品生产过程中的原材料库存、颗粒和产成品生产、库存和交付结合起来，在统一的数学模型框架中进行协同优化，设计生产调度优化模型。
  6. 根据权利要求1所述的基于数学优化模型的片剂营养品生产现场计划与调度方法，其特征是：步骤4包括以下几个部分：
    1)、模型目标及约束条件，对片剂营养品生产的优化目标及相关的约束 条件进行梳理，将现实问题进行理论抽象；
    2)、模型变量及定义，对以上理论问题中的核心要素和变量进行提炼，通过数学语言进行定义；
    3)、优化模型设计，基于以上对于目标及约束的梳理，运用所定义的变量来设计片剂保健品调度的优化模型。
  7. 根据权利要求6所述的基于数学优化模型的片剂营养品生产现场计划与调度方法，其特征是：所述的优化模型设计基于市场现场实际操作的要求凝练相关假设条件。
  8. 根据权利要求1所述的基于数学优化模型的片剂营养品生产现场计划与调度方法，其特征是：步骤5通过仿真实验的方式获取实施优化方案后的系统表现，并通过与企业实际运营数据的对比，对方案的质量进行评估。
PCT/CN2021/078637 2020-05-19 2021-03-02 基于数学优化模型的片剂营养品生产现场计划与调度方法 WO2021232882A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US17/459,109 US20210390480A1 (en) 2020-05-19 2021-08-27 Method and device for production scheduling of nutritional tablet, electronic equipment and storage medium

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202010422666.1 2020-05-19
CN202010422666.1A CN111754022A (zh) 2020-05-19 2020-05-19 基于数学优化模型的片剂营养品生产现场计划与调度方法

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US17/459,109 Continuation US20210390480A1 (en) 2020-05-19 2021-08-27 Method and device for production scheduling of nutritional tablet, electronic equipment and storage medium

Publications (1)

Publication Number Publication Date
WO2021232882A1 true WO2021232882A1 (zh) 2021-11-25

Family

ID=72673382

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/078637 WO2021232882A1 (zh) 2020-05-19 2021-03-02 基于数学优化模型的片剂营养品生产现场计划与调度方法

Country Status (3)

Country Link
US (1) US20210390480A1 (zh)
CN (1) CN111754022A (zh)
WO (1) WO2021232882A1 (zh)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117455194A (zh) * 2023-11-27 2024-01-26 无锡雪浪数制科技有限公司 一种基于离散事件仿真的生产调度方法

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111754022A (zh) * 2020-05-19 2020-10-09 江苏艾兰得营养品有限公司 基于数学优化模型的片剂营养品生产现场计划与调度方法
CN113934906A (zh) * 2021-10-29 2022-01-14 广东三扬网络科技有限公司 一种模具与设备的管理方法、管理系统和存储介质
CN114548660A (zh) * 2022-01-06 2022-05-27 青岛海尔科技有限公司 家电设备的生产排程方法、装置、设备及存储介质
CN117933684B (zh) * 2024-01-17 2024-09-03 深圳市链宇技术有限公司 考虑原材料齐套约束和多机器并行加工的车间调度方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1760902A (zh) * 2005-11-11 2006-04-19 杭州电子科技大学 一种流程工业拟实优化调度系统的方法
US20170185943A1 (en) * 2015-12-28 2017-06-29 Sap Se Data analysis for predictive scheduling optimization for product production
CN109102161A (zh) * 2018-07-19 2018-12-28 青岛萨纳斯智能科技股份有限公司 一种智慧生产制造管理方法
CN110501978A (zh) * 2018-05-18 2019-11-26 中国科学院沈阳自动化研究所 一种机器人产品生产车间排产调度方法
CN111754022A (zh) * 2020-05-19 2020-10-09 江苏艾兰得营养品有限公司 基于数学优化模型的片剂营养品生产现场计划与调度方法

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150046363A1 (en) * 2013-08-07 2015-02-12 Flextronics Ap, Llc Method and Apparatus for Managing, Displaying, Analyzing, Coordinating, and Optimizing Innovation, Engineering, Manufacturing, and Logistics Infrastructures
CN110580026A (zh) * 2019-09-18 2019-12-17 工业云制造(四川)创新中心有限公司 一种智能制造mes系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1760902A (zh) * 2005-11-11 2006-04-19 杭州电子科技大学 一种流程工业拟实优化调度系统的方法
US20170185943A1 (en) * 2015-12-28 2017-06-29 Sap Se Data analysis for predictive scheduling optimization for product production
CN110501978A (zh) * 2018-05-18 2019-11-26 中国科学院沈阳自动化研究所 一种机器人产品生产车间排产调度方法
CN109102161A (zh) * 2018-07-19 2018-12-28 青岛萨纳斯智能科技股份有限公司 一种智慧生产制造管理方法
CN111754022A (zh) * 2020-05-19 2020-10-09 江苏艾兰得营养品有限公司 基于数学优化模型的片剂营养品生产现场计划与调度方法

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117455194A (zh) * 2023-11-27 2024-01-26 无锡雪浪数制科技有限公司 一种基于离散事件仿真的生产调度方法
CN117455194B (zh) * 2023-11-27 2024-06-11 无锡雪浪数制科技有限公司 一种基于离散事件仿真的生产调度方法

Also Published As

Publication number Publication date
US20210390480A1 (en) 2021-12-16
CN111754022A (zh) 2020-10-09

Similar Documents

Publication Publication Date Title
WO2021232882A1 (zh) 基于数学优化模型的片剂营养品生产现场计划与调度方法
Sullivan et al. Equipment replacement decisions and lean manufacturing
Chan Effect of kanban size on just-in-time manufacturing systems
Kumar et al. Production and operations management
Weng Strategies for integrating lead time and customer-order decisions
CN113506081A (zh) 一种生产计划管理系统及其排产方法
KR20150033847A (ko) 실시간 공장상황을 반영한 디지털 팩토리 생산능력 관리 시스템
Stapelbroek et al. Eliminating production losses in changeover operations: a case study on a major European food manufacturer
Bo et al. Manufacturing rescheduling after crisis or disaster-caused supply chain disruption
Štefanić et al. Applying the lean system in the process industry
Pritsker et al. Production scheduling using FACTOR
Kleutghen et al. Development and implementation of an integrated inventory management program at Pfizer Pharmaceuticals
Le et al. Production Scheduling Application Software for Small Medium Enterprises
Turnbull et al. Recent developments in the UK automotive industry: JIT/TQC and information systems
Barker Development of demand pull type control methods: the design of block action production control methods to reduce throughput time and improve supply chain synchronization
Kulvatunyou et al. Computer-aided manufacturing simulation (CAMS) generation for interactive analysis-concepts, techniques, and issues
Bhangale et al. Parametric studies in automobile manufacturing industry using cell focused plant layout simulation approach
CN116307083A (zh) 营养保健品订单延误最小化的优化方法
Chistyakova et al. Software Package for Mathematical Modeling of Industrial Scheduling Process
Adelfar et al. Balancing inventory levels and setup times-An analysis and outline of a decision-making model
Mahadevan et al. Lean Management Principles
BG4812U1 (bg) Интелигентна система за управление на производство
Tsang et al. Modelling and planning optimization of a complex flu vaccine facility
CN118627849A (zh) 药品精益生产管理流程自动化机器人系统及方法
White A HIERARCHICAL FRAMEWORK AND TECHNOLOGY STRUCTURE

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21809763

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21809763

Country of ref document: EP

Kind code of ref document: A1