基于启发式调度规则的能力需求计划算法

传统的能力需求计划方法不能准确地反映实际生产能力需求与负荷.为此设计了基于启发式调度规则的能力需求计划算法,该算法将基于启发式规则的调度算法作用于物料需求计划进行能力需求计算,并对设备负荷率、关键设备资源和关键任务等关键能力信息的计算方法进行了重新定义,以更加真实地反映计划期内计划的能力需求与设备负荷状态.该算法已应用...     

面向柔性作业调度问题的启发性规则改进遗传算法

对柔性作业调度问题,提出了一种启发性规则的改进遗传求解方法,此方法从启发性规则出发产生初始调度解。通过对初始调度解进行比较而产生初始种群。对初始种群通过启发规则的改进遗传算法进行优化计算,对染色体进行交叉、变异、交换和选择操作,应用启发式规则搜索关键工序并提高关键工序的交换、变异操作概率,在变异操作中利用启发式规则对变异过程加以引导,从而得到优化解。将此方法运用于一系列典型柔性调度问题进行了实验求解,并将求解结果与其他的计算方法进行了比较,表明此方法能提高求解效率,适合复杂的柔性作业调度问题求解。     

飞机移动生产线作业调度问题的启发式算法

飞机移动生产线作业调度问题是一种具有特殊约束的项目调度问题。综合分析了飞机移动生产线的作业顺序关序、资源约束、空间限制的多重特点,建立了以最小装配完工时间为目标的模型。将问题分为确定作业的优先顺序和选择作业人数的两个阶段进行决策,设计了由不同规则组合成27种启发式方法。通过比较启发式方法在250个测试问题集的表现,确定最有效的解决此问题的方法。     

钢管冷区生产调度的一种启发式算法

基于国内大型钢铁公司的实际操作,运用离散化时间的方法把钢管冷区生产调度抽象为可中断Job-Shop问题.同时考虑前置库存限制、生产尽可能连续、部分人工调度等约束条件,以最小化中断次数和尽可能满足人工调度为目标,建立相应的数学模型.再依据约束条件的主次性设计生产调度规则(赋值规则和生产规则),根据实际生产流程设计启发式算法.运用该算法对该问题的大规模算例进行求解,其结果在满足了约束条件情况下达到比较理想的中断次数.     

基于均衡生产的启发式调度方法探讨

均衡生产是合理组织生产过程的基本要求,均衡生产目标的实现要靠合理的生产调度来保证.本文对平行加工时实现均衡生产的启发式调度方法进行了分析,并结合不同的优化目标进行了求解和比较.     

基于优先规则的模糊资源受限多项目调度

在分析国内外相关研究现状的基础上,结合资源受限多项目调度问题的特点,针对多项目中各个工序资源参数的不确定性,通过对工程项目工序资源需求量的模糊表示,建立了模糊资源受限多项目调度模型,并运用基于优先规则的启发式算法对所建立的模型进行调度计算,最后通过算例验证模型的有效性和可行性,以实现多项目资源的优化配置。     

一种改进的蚁群算法在工艺规划与车间调度集成优化中的应用

改进标准蚁群算法的执行策略,可提高工艺规划和调度集成问题的求解质量和效率。通过节点集、有向弧/无向弧集、AND/OR关系,建立了基于AND/OR图的工艺规划和调度集成优化模型。提出一种求解工艺规划与车间调度集成问题的改进蚁群优化算法,采用了信息素动态更新策略避免收敛过慢和局部收敛,利用多目标优化策略提高求解质量。仿真结果证明了该算法的有效性。     

研发型企业多项目人力资源调度研究——基于蚁群优化的超启发式算法

现阶段,研发型企业的项目处于多项目环境下,为了解决多项目并行时人力资源争夺问题,本文针对该类企业多项目管理中人力资源调度进行优化研究,以考虑项目延期惩罚成本的最小总成本为目标函数,将现实问题抽象建模。基于国内外的研究提出了一种超启发式算法进行求解,该算法将人力资源调度问题分为项目活动分配和人员选择项目活动两个部分,采用蚁群优化作为高层启发式策略搜索低层启发式规则,再进一步根据规则解构造出可行解。最后本研究设计多组仿真实验与启发式规则进行对比,结果表明该算法有较好的搜索性能,为人力资源的调度问题提供了新的解决方案。     

质量屋优化的混合整数规划模型

利用QFD(Quality Function Deployment)进行产品规划决策时,根据已建立的质量屋中的信息,帮助设计人员确定满足顾客需求的技术特征目标值,用于指导新产品开发,是质量屋决策的最终目标。已有的规划模型大多不能直接给出技术特征目标值,且在建立模型中没有分开考虑离散型技术特征,而是把离散型技术特征和连续型技术特征等同考虑,从而使得规划结果不能很好反映实际情况。笔者充分利用质量屋中的信息,同时考虑离散型技术特征,提出了一个质量屋优化的混合整数规划模型,并给出了实例。     

一类多资源调度问题的优化算法

若调度系统中的作业或任务需要一个或多个资源共同完成， 则这一类调度问题被称为多资源（或多处理机）调度问题。本文针对Jobshop中的这一调度问题，提出了基于遗传的优化调度算法，并用实例证实了该算法的有效性。     

A mixed integer programming model for a closed-loop supply-chain network

In this paper, a new mixed integer mathematical model for a closed-loop supply-chain network that includes both forward and reverse flows with multi-periods and multi-parts is proposed. The proposed model guarantees the optimal values of transportation amounts of manufactured and disassembled products in a closed-loop supply chain while determining the location of plants and retailers. Finally, computational results are presented for a number of scenarios to show and validate the applicability of the model.     

A mixed integer linear programming model for set-up reduction superstructures

The objective of this research is to introduce the concept of a set-up reduction superstructure and evaluate its utility in set-up reduction projects. For a given set-up project, consisting of several interdependent activities as well as existing ones, a superstructure is defined. For this superstructure, a corresponding mixed integer linear programming model is developed with a view to selecting the optimum set-up project. The resulting model is applied to the set-up reduction problem in a real example of the fabrication in a CNC lathe of a vibration reduction pulley for lorry motors. The model is implemented in the Mathematica platform. It is shown that the set-up reduction superstructure is a useful concept in practice.     

A mixed integer linear programming solution for single hoist multi-degree cyclic scheduling with reentrance

This article considers single hoist multi-degree cyclic scheduling problems with reentrance. Time window constraints are also considered. Firstly, a mixed integer programming model is formulated for multi-degree cyclic hoist scheduling without reentrance, referred to as basic lines in this article. Two valid inequalities corresponding to this problem are also presented. Based on the model for basic lines, an extended mixed integer programming model is proposed for more complicated scheduling problems with reentrance. Phillips and Unger's benchmark instance and randomly generated instances are applied to test the model without reentrance, solved using the commercial software CPLEX. The efficiency of the model is analysed based on computational time. Moreover, an example is given to demonstrate the effectiveness of the model with reentrance.     

A general quantity discount and supplier selection mixed integer programming model

Recently, new models and heuristics for exploiting quantity discounts have been proposed that are applicable in classical purchasing as well as in an e-business environment and can be implemented as part of an advanced planning system. These models can now handle both the single- and multi-item case with fixed cost to be shared among several products ordered at the same point in time from a single supplier. Furthermore, the supplier selection problem is addressed, i.e., how to best exploit quantity discounts over time offered by several suppliers. Last but not least, additional constraints on the buyer’s or on the supplier’s side may be included. While so far only purpose-built heuristics have been proposed for this generalized problem, we present a linear mixed integer programming (MIP) model, which not only represents the all-units discount but also the incremental discount case. Furthermore, the objective function chosen resolves (former) conflicts among proponents of a purely cost oriented and a cash flow oriented modeling approach. Computational tests show that our model yields near optimal solutions within a given CPU time limit by making use of a standard MIP solver.     

Production scheduling with outsourcing scenarios: a mixed integer programming and efficient solution procedure

When market demand exceeds the company's capacity to manufacture, outsourcing is commonly considered as an effective alternative option. In traditional scheduling problems, processing of received orders is just possible via in-house resources, while in practice, outsourcing is frequently found in various manufacturing industries, especially in electronics, motor and printing companies. This paper deals with the scheduling problem, minimising the cost of outsourcing and a scheduling measure represented by weighted mean flow time, in which outsourcing of manufacturing operations is allowed through subcontracts. Each order can be either scheduled for in-house production or outsourced to an outside supplier in order to meet customer due dates. In this problem, not only should the sequence of orders be determined, but also decision on picking the jobs for outsourcing, selecting the appropriate subcontractor, and scheduling of the outsourced orders are considered as new variables. To formulate the given problem, four different outsourcing scenarios are derived and mixed integer programming models are developed for each one separately. Furthermore, to solve the suggested problem, a computationally effective team process algorithm is devised and then a constraint handling technique is embedded into the main algorithm in order to ensure satisfaction of customer due dates. Numerical results show that the suggested approach possesses high global solution rates as well as fast convergence.     

Identifying differentially expressed pathways via a mixed integer linear programming model

The identification of genes and pathways involved in biological processes is a central problem in systems biology. Recent microarray technologies and other high-throughput experiments provide information which sheds light on this problem. In this article, the authors propose a new computational method to detect active pathways, or identify differentially expressed pathways via integration of gene expression and interactomic data in a sophisticated and efficient manner. Specifically, by using signal-to-noise ratio to measure the differentially expressed level of networks, this problem is formulated as a mixed integer linear programming problem (MILP). The results on yeast and human data demonstrate that the proposed method is more accurate and robust than existing approaches.     

A mixed integer goal programming model for discrete values of design requirements in QFD

A main feature of quality function deployment (QFD) planning process is to determine target values for the design requirements (DRs) of a product, with a view to achieving a higher level of overall customer satisfaction. However, in real world applications, values of DRs are often discrete instead of continuous. Therefore, a mixed integer linear programming (MILP) model considering discrete data is suggested. As opposed to the existing literature, the fulfilment levels of DRs are assumed to have a piece-wise linear relationship with cost; because, constraints of technology and resource rarely provides a linear relationship in manufacturing systems. In the proposed MILP model, we considered customer satisfaction as the only goal. But, QFD process may be necessary to optimise cost and technical difficulty goals as well as customer satisfaction. Therefore, by developing the MILP model with multi-objective decision making (MODM) approach, a novel mixed integer goal programming (MIGP) model is proposed to optimise these goals simultaneously. Finally, MILP model solution turns out to be a more realistic approach to real applications because piece-wise linear relationship is taken into account. The solution of MIGP model provided different alternative results to decision makers according to usage of the lexicographic goal programming (LGP) approach. The applicability of the proposed models in practice is demonstrated with a washing machine development problem.     

An integer programming approach to elective surgery scheduling

The scope of this work covers a real case of elective surgery planning in a Lisbon hospital. The aim is to employ more efficiently the resources installed in the surgical suite of the hospital in question besides improving the functioning of its surgical service. Such a planning sets out to schedule elective surgeries from the waiting list on a weekly time horizon with the objective of maximizing the use of the surgical suite. For this purpose, the authors develop an integer linear programming model. The model is tested using real data obtained from the hospital’s record. The non-optimal solutions are further improved by developing a custom-made, simple and efficient improvement heuristic. Application of this heuristic effectively improves almost all non-optimal solutions. The results are analyzed and compared with the actual performance of the surgical suite. This analysis reveals that the solutions obtained using this approach comply with the conditions imposed by the hospital and improve the use of the surgical suite. It also shows that in this case study the plans obtained from the proposed approach may be implemented in real life.     

A generalized interval fuzzy mixed integer programming model for a multimodal transportation problem under uncertainty

A generalized interval fuzzy mixed integer programming model is proposed for the multimodal freight transportation problem under uncertainty, in which the optimal mode of transport and the optimal amount of each type of freight transported through each path need to be decided. For practical purposes, three mathematical methods, i.e. the interval ranking method, fuzzy linear programming method and linear weighted summation method, are applied to obtain equivalents of constraints and parameters, and then a fuzzy expected value model is presented. A heuristic algorithm based on a greedy criterion and the linear relaxation algorithm are designed to solve the model.