多品种小批量订单型企业生产调度优化

目的研究多品种小批量订单型企业的生产调度优化问题,方法针对S公司的生产现状,应用遗传算法思想设计调度优化方案,采用不等长矩阵的编码方式实现订单的批量生产及车间排产的方案。结果通过仿真分析和S公司生产调度的实际应用,验证了该算法的可行性及有效性。结论基于遗传算法的调度优化算法实现了多品种小批量流程型生产企业生产调度优化,达到了缩短生产周期、有效利用生产资源的目的。     

基于B/S模式的EMS系统在企业节能工作中的应用

本文以燕京啤酒集团顺义生产厂为例,详细叙述了企业能源管理信息系统的结构设计、功能和运行方式等,该系统基于B/S模型和实时数据库技术构建,管理人员通过浏览器就可看到生产现场的实时数据,解决了啤酒生产企业管理层过去无法实时了解生产现场状况,及时对能源生产和计划作出合理安排与调整的问题。同时,该企业能源管理信息系统(EMS)所建立的企业信息平台,能满足未来MES(制造执行系统)、ERP(企业资源计划)等信息管理系统对生产现场信息数据的要求,为企业实现生产管理信息化起到示范     

基于均衡排产规则的钢管生产计划调度

针对钢管企业中管加工工厂生产计划调度的实际,以均衡排产规则为前提,在考虑生产连续、产线定修和前置库存的情况下,构建两层数学模型,分步骤地解决带有大规模柔性路径选择要求的多目标计划调度问题,并利用ILOG公司CPLEX软件包对其大规模的数据实例进行求解,该模型及求解思路可指导管加工工厂的实际生产计划调度,运用到软件开发中已取得良好的效果.     

面向订单的生产计划与调度的综合优化

研究了一个多订单环境下的生产计划与调度集成优化问题,以实现准时生产为目标,综合考虑产品装配结构约束的订单任务计划与订单产品零部件的加工调度,采用直接面向客户订单的工序调度模式建立了计划和调度的综合优化整数规划模型.设计了带精英策略的蚁群算法作为该数学模型的求解方法,并通过对比试验为该算法选取最佳的搜索参数.实例仿真结果表明,所建模型的正确性以及蚁群算法求解该问题的可行性和有效性.     

混流无等待总装分装流水线任务排序研究

为解决混流产品在无等待多条流水线生产条件下,由于产品生产节拍不一致导致总装分装系统中生产连续性较差的问题,研究总装分装任务排序优化方法,实现在保证批量生产、部件齐套供应前提下,使订单能够按期交货.以最小化总加工时间、最小化总提前/拖期和产品转换惩罚为优化目标,建立了优化数学模型,并设计了改进多种群蚁群算法求解该优化模型.以某机床厂某月生产任务为例进行仿真实验,与多种群蚁群算法、传统蚁群算法对比,验证了该算法性能较好.并与现行的调度方法进行对比,验证了该任务排序方法在混流节拍不一致的多条装配线生产上,能够有效地缩短产品生产周期、降低生产成本,提高订单的准时交付率.     

约束理论在印刷包装企业生产排产中的应用研究

为了使印刷包装企业能够发挥自身生产能力,制定出合理的生产计划,满足客户不同需求,提出了以约束理论及生产排产理论为指导思想,建立企业间网络互动服务,实现资源共享与优势互补的解决方案;同时,以苏北地区小型印刷包装企业生产排产为例,运用约束理论5步骤法,进行了瓶颈资源的识别,建立了网络互动服务,给出了基于约束理论思想的详细解决方案。     

缺货风险下复杂装备生产与配套采购联合决策

面向多品种小批量、按单生产的复杂装备制造企业,将装配节点齐套约束下的生产计划制定问题与供需能力匹配约束下的采购计划生成问题关联起来,综合考虑定向采购方式下配套件的缺货风险因素,兼顾采购管理指标和生产交期指标,建立多物料双源采购与多类型产品订单排产的联合决策整数规划模型。针对问题规模较大,传统精确算法难以求解的问题,设计基于自适应局部搜索机制和种群多样化策略的改进粒子群算法。算例实验结果表明,该算法求解质量良好,能有效提升大规模问题下的求解效率。相比于独立决策,联合决策方案显著提高了总采购价值和准时交货水平,同时降低了订货成本和订单拖期惩罚。最后,对模型参数进行敏感性分析,结果可为企业供应风险下的生产管理决策提供参考。     

基于ACO的瓦楞纸高浓磨浆过程多目标优化

目的在确保磨浆质量的前提下,提高磨浆产量、降低磨浆能耗,进而提高瓦楞纸的产量,降低生产能耗。方法在分析并建立高浓磨浆过程数学模型的基础上,针对该数学模型多目标、非线性的特点,提出一种采用编程简单、鲁棒性强的ACO(蚁群算法)对该多目标优化问题进行求解的新方法。结果 Matlab仿真结果表明,ACO在求解高浓磨浆过程多目标优化问题时,能够快速地找到符合生产工艺要求的最优解。结论基于ACO的多目标优化不仅提高了瓦楞纸制浆产量,而且降低了生产能耗。同罚函数相比,更好地实现了优质、高产、低能耗的生产目标。     

基于规则的钢铁订单归并及炉料结构优化方法

针对订单生产型钢铁企业的组炉计划和原料配方优化问题,综合考虑钢铁订单的产品结构属性、加工过程特征和客户交货要求等多约束条件,提出了基于规则的订单归并及炉料结构优化方法.首先利用订单组批合炉冶炼规则,建立以炼钢余材量最小和拖期/提前惩罚最小为目标的订单归并优化模型;然后利用精炼阶段合金投料配方规则,建立以投入原料成本最小和合金元素目标成分偏差最小为目标的炉料结构优化模型;并分别设计了求解上述两模型的启发式微粒群算法.案例企业的实际数据验证结果表明,基于该方法形成的组炉计划和原料配方方案,能够在满足订单交货期的前提下有效地减少炼钢余材量,合理地降低铁合金等原材料的投入成本.     

基于ISO 10012的计量管理信息系统设计

本文探讨的是一种基于ISO 10012:2003的计量管理信息系统的设计,该信息系统分为B/S和C/S两部分,采用B/S和C/S的混合模型结构,使用SQL Server 2000作为中心数据库.该信息系统能够提高企业的计量管理水平,为企业的规模发展提供一个好的平台.     

考虑库存信息的钢管批量计划优化算法

在考虑库存信息的基础上对钢管批量计划优化问题进行研究,建立了批量计划轧制位置与垛位钢坯多对多关系的批量计划模型,实现对倒垛次数的优化。结合问题特征,设计了一种基于钢坯连续倒垛的两阶段批量计划优化算法：第1阶段,确定当前最优钢坯,进而搜索同垛位上的下层钢坯,匹配最佳的连续轧制钢坯;第2阶段,针对每个轧制位置搜索垛位上的连续轧制钢坯,改进第1阶段的解。通过基于实际生产数据的实验验证,相对经典启发式算法,倒垛次数显著降低,算法和模型是可行且有效的。     

An integrated model for inventory and production planning in a two-stage hybrid production system

A two-stage hybrid flow-shop production system is considered. The first stage is a process production system and the second stage is a job-shop production system. The two stages are separated by an intermediate warehouse to introduce flexibility (some independence) in the planning of production at both stages. The inventory level at the warehouse should be optimized to provide a trade-off between the cost of carrying the inventory of the semi-finished products, the minimum batch size requirement in the first stage, and the required service level at the second stage. An integrated model for planning the production in these hybrid flow-shop production systems types is developed. The objectives of optimizing the production and inventory costs at the two stages of the system, including the warehouse, while satisfying customer demands, are considered. An algorithm to solve the suggested model is described in detail, and a solution is provided for a real world case, which has inspired the study. A computational study to measure the performance of the approach was also carried out and the results are reported.     

Genetic algorithm optimisation of an integrated aggregate production–distribution plan in supply chains

A production plan concerns the allocation of resources of the company to meet the demand forecast over a certain planning horizon and a distribution plan involves the management of warehouse storage assignments, transport routings and inventory management issues. A production–distribution plan integrates the decisions in production, transport and warehousing as well as inventory management. The overall performance of a supply-chain is influenced significantly by the decisions taken in its production–distribution plan and hence one key issue in the performance evaluation of a supply chain is the modelling and optimisation of the production–distribution plan considering its actual complexity. Based on the integration of Aggregate Production Plan and Distribution Plan, this article develops a mixed integer non-linear formulation for a two-echelon supply network (i.e. a production-distribution network) considering the real-world variables and constraints. Genetic Algorithm (GA), known as a robust technique for solving complex problems, is employed for the optimisation of the developed mathematical model due to its ability to effectively deal with a large number of parameters. To demonstrate the applicability of the methodology, a real-life case study will be finally studied incorporating the production of different types of products in several manufacturing plants and the distribution of finished products from plants to a number of end-users via multiple direct/indirect transport routes.     

Designing a collaborative supply-chain plan using the analytic hierarchy process and genetic algorithm with cycle-time estimation

Collaboration management has become a key issue for supply-chain management and can improve the overall production/manufacturing performance and value. This paper aims to conduct collaborative supply-chain planning and establish supplier selection, as well as production and distribution planning. This study uses an Analytic Hierarchy Process with Rough Sets Theory to establish a supplier purchasing value rating system. Also, a cycle-time estimation procedure is developed to effectively estimate the operating time of a collaborative supply chain. Assembly-line balancing technology is also introduced to guarantee smooth production and distribution in a supply chain. A multi-objective optimisation mathematical model, including purchasing value, cost, cycle time, and smoothness index, is constructed to complete the supplier selection and production-distribution planning. To efficiently solve this mathematical model, this paper proposes a genetic algorithm (E_ctGA) combined with a cycle-time estimation procedure. Finally, it presents a case study for validation. The results show that a better collaborative supply chain plan could be achieved by combining the proposed cycle-time estimation procedure.     

A three-stage simulation based approach to inventory management with discrete demand

This paper proposes a three-stage simulation-based approach to determine target inventory level for a plastic moulding company. The classical formula of a periodic review inventory system requires continuous demand with a normal distribution. This is often not the case in real world companies. For this reason, a three-stage approach based on a SLAM II simulation model is proposed. Our model allows for a statistically based determination of target inventory level for both normal and non-normal distributed demand. This model includes inventory management, production planning and control, finished goods shipment, and functional area of manufacturing. Analysis of simulation results indicates an inventory reduction plan and the feasibility of a layout design to relocate equipment for the plant.     

A bilinear programming model and a modified branch-and-bound algorithm for production planning in steel rolling mills with substitutable demand

In this paper, we address an instance of the dynamic capacitated multi-item lot-sizing problem (CMILSP) typically encountered in steel rolling mills. Production planning is carried out at the master production schedule level, where the various end items lot sizes are determined such that the total cost is minimised. Through incorporating the various technological constraints associated with the manufacturing process, the integrated production–inventory problem is formulated as a mixed integer bilinear program (MIBLP). Typically, such class of mathematical models is solved via linearisation techniques which transform the model to an equivalent MILP (mixed integer linear program) at the expense of increased model dimensionality. This paper presents an alternative branch-and-bound based algorithm that exploits the special structure of the mathematical model to minimise the number of branches and obtain the bound at each node. The performance of our algorithm is benchmarked against that of a classical linearisation technique for several problem instances and the obtained results are reported.     

Dynamic planned safety stocks in supply networks

Safety stocks are commonly used in inventory management for tactically planning against uncertainty in demand and/or supply. The usual approach is to plan a single safety stock value for the entire planning horizon. More advanced methods allow for dynamically updating this value. We introduce a new line of research in inventory management: the notion of planning time-phased safety stocks. We assert that planning a time-phased set of safety stocks over a planning horizon makes sense because larger safety stocks are appropriate in times of greater uncertainty while lower safety stocks are more appropriate when demand and/or supply are more predictable. Projecting a vector of safety stock values is necessary to assure upstream members in the supply network have advanced warning of changes. We perform an empirical study of U.S. industry, which demonstrates that significant savings can be achieved by employing dynamic planned safety stocks, confirming recent case study reports. We provide a simple optimisation model for the problem of minimising inventory given a vector of safety stock targets. We propose a computationally efficient solution procedure and demonstrate its implementation in an MRP/ERP system. We then illustrate an MRP/ERP planning system feature, which employs a dynamic planned safety stock module that supports a production planner by showing the inventory implications of safety stock plans.     

Two-stage stochastic master production scheduling under demand uncertainty in a rolling planning environment

This paper proposes a scenario-based two-stage stochastic programming model with recourse for master production scheduling under demand uncertainty. We integrate the model into a hierarchical production planning and control system that is common in industrial practice. To reduce the problem of the disaggregation of the master production schedule, we use a relatively low aggregation level (compared to other work on stochastic programming for production planning). Consequently, we must consider many more scenarios to model demand uncertainty. Additionally, we modify standard modelling approaches for stochastic programming because they lead to the occurrence of many infeasible problems due to rolling planning horizons and interdependencies between master production scheduling and successive planning levels. To evaluate the performance of the proposed models, we generate a customer order arrival process, execute production planning in a rolling horizon environment and simulate the realisation of the planning results. In our experiments, the tardiness of customer orders can be nearly eliminated by the use of the proposed stochastic programming model at the cost of increasing inventory levels and using additional capacity.     

Operator allocation planning for reconfigurable production line in one-of-a-kind production

In a one-of-a-kind production (OKP) company, the operation routing and processing time of an order are usually different from the others due to high customisation. As a result, an OKP company needs to dynamically adjust the production resources to keep the production lines reconfigurable. Through a proper assignment of operators in different sections of a production line, bottlenecks and operator re-allocation during production can be reduced effectively. In this paper, a mathematical model is introduced for optimal operator allocation planning on a reconfigurable production line in OKP. The optimisation objectives are to minimise the total number of the operators, total job earliness and tardiness, and the average work-in-process storage. A branch-and-bound algorithm with efficient pruning strategies is developed to solve this problem. The proposed model and the algorithm are empirically validated by using the data of a windows and doors manufacturing company. A software system based on the proposed approach has been implemented in the company.     

Optimal maintenance plan for two-level assembly system and risk study of machine failure

This paper deals with the optimisation of two-levels assembly system planning. This system is composed of a single machine, inventories at levels 1 and 2 for stock keeping components to assembly and final assembled product. It assumed that the machine processes all assembly operations and is subject to random failure. A mathematical model is developed to incorporate a supply planning for two-level assembly systems under stochastic lead times and breakdowns machine. A preventive maintenance plan is carried out to reduce the frequency of the corrective maintenance actions. This work has double goals. The first one is to find the optimal order release dates for the different components at level 2 and the optimal preventive maintenance plan. The second one is to quantify the risk due to machine failures which have an impact on the lead-time of the finished product. To consider the maintenance actions, preventive maintenance actions are perfectly performed to restore the machine to state “as good as new”, minimal repair is considered at failure. The model minimises the total cost, which is the sum of inventory holding cost for components at levels 1 and 2, backlogging and inventory holding cost for the finished products and maintenance costs. To illustrate the effectiveness of the proposed model, different meta-heuristics are applied; the genetic algorithm shows the most suited to our analytical model, the optimal release date founded by this algorithm allows finding the optimal preventive maintenance plan. The obtained optimal maintenance planning is used in the risk assessment in order to find the threshold repair period that avoids lost profit.