考虑货架存放期的经济批量排产问题的改良算法

经济批量排产问题是关于在单一设备上协调地、周期性地生产多种产品的问题.其解要求在生产准备与库存总成本最小的条件下,决定 I 种产品的生产序列.本文研究的经济批量排产问题考虑了产品货架存放期因素.指出了Dobson算法的不足,并提出了求解该问题的新算法(改进的装箱算法),新算法不仅以生产次数最大的产品为基础进行装箱,而且进一步以生产次数略低的产品为基础进行装箱.排产时,先按生产次数降序进行装箱,再按单次生产时间与生产准备时间之和降序装箱.计算结果显示,本算法结果更优.     

ERW钢管考虑动态库存和交货期的多阶段生产计划优化方法研究

多阶段生产计划在钢管制造企业中起着重要的作用,在学术研究中也具有一定的复杂性.由于企业库存与生产及计划密切相关、订单交货期对生产和库存又具有很强的关联性,在研究中将库存的动态性和交货期纳入多阶段生产计划中,试图寻求找到优化的生产计划的方法.根据ERW直缝焊管企业的生产特点,对其生产计划的制订方式进行了优化.针对多阶段生产计划建立了考虑库存和交货期的提前/拖期惩罚数学模型,通过遗传算法对其进行求解及优化,从而得到生产计划的较优解.     

ERW钢管考虑动态库存和交货期的多阶段生产计划优化方法研究北大核心

多阶段生产计划在钢管制造企业中起着重要的作用,在学术研究中也具有一定的复杂性.由于企业库存与生产及计划密切相关、订单交货期对生产和库存又具有很强的关联性,在研究中将库存的动态性和交货期纳入多阶段生产计划中,试图寻求找到优化的生产计划的方法.根据ERW直缝焊管企业的生产特点,对其生产计划的制订方式进行了优化.针对多阶段生产计划建立了考虑库存和交货期的提前/拖期惩罚数学模型,通过遗传算法对其进行求解及优化,从而得到生产计划的较优解.     

金属矿山企业生产计划优化方法

针对金属矿山企业的单位开采与运输成本大、优化求解结果偏差大问题, 首先, 依据金属矿山企业编制开采计划的基本原则, 以矿石开采与运输成本最小化为优化目标, 利用整数规划方法, 构建了金属矿山企业生产计划数学模型, 其次, 为了精准快速求解金属矿山企业生产计划模型, 提出了改进的量子粒子群优化算法, 采用进化速度和聚集度因子对算法中的惯性权重进行动态调整, 并设计了双层可行域搜索策略, 提高了算法的局部和全局搜索能力。最后, 以某大型金属矿山企业采运生产作业为案例, 通过与矿山实际生产指标、非线性规划结果以及粒子群优化结果进行比较分析。结果表明:在相同经济指标和参数环境下, 本文算法优于其它两种优化方法, 且每吨矿石的开采和运输成本减少了0.05元左右, 降低了金属矿山企业的开采运输成本, 提高了企业的整体经济效益。     

单件小批生产系统下模糊优化的动态随机投入产出模型

本文针对单件小批量生产系统 ,建立了模糊优化的动态随机投入产出模型 ,同时给出了该模型的递推解法 ,并用此模型对某单件小批企业在生产计划期的商品量进行了规划     

装配型企业订货量分配与订单排产联合决策研究

针对供应商交货数量不确定环境下,多品种小批量装配型制造企业因生产物料不配套造成生产计划不可行甚至客户订单拖期的问题,从企业运作整体出发,考虑订货量分配决策对订单生产和交货的影响,以最小化采购成本和最小化订单排产相关成本为优化目标,在允许零部件拖期交货且供应商提供拖期价格折扣条件下,建立订货量分配与订单排产联合优化模型。针对可行解空间巨大、传统数学规划方法难以求解的问题,从增强搜索性能角度出发,设计基于自定义邻域搜索算子的局部搜索机制和基于随机与种群重构变异机制的改进粒子群算法的模型求解策略。通过应用实例对本文模型和算法进行了有效性验证和灵敏度分析,结果表明,相比于传统的分散决策方案,本文模型能够有效降低整体成本水平,引入的改进机制能够显著提升算法搜索性能,为企业供应风险下的运营决策制定提供理论参考。     

经济批量问题的数学模型与算法新进展

经济批量问题一般是讨论稳定的外部需求和有限的生产能力条件下实际生产过程的优化计划排产，具有重要应用价值。本文在综合大量国内外有关文献的基础上，对经济批量问题的数学模型和算法新进展作了比较系统，全面的介绍。     

经济批量排产问题的改进算法:设定最高允许高度

经济批量排产问题是指在生产准备费用与库存费用最低的情况下,协调地、周期性地生产多种产品的问题.由于此问题是NP-hard的,人们一种致力于寻找快速地求解高质量的近似最优解的方法.在将生产次数舍入为2的幂次后,误差小,获得可行解的速度快.研究的经济批量排产问题考虑了产品货架存放期因素.指出了Dobson算法的不足,并提出了基于2的幂次条件的改进算法.改进算法设定了最高允许高度,首先给部分箱进行装箱.由于能获得高质量的生产排产,因此,算法能获得2的幂次条件下的高质量解.给出一个算例,计算结果显示,算法结果更优.     

约束理论在H公司生产系统的应用

以制造企业H公司作为研究对象,以约束理论(简称TOC)为基础,运用TOC五步骤法、"鼓缓冲绳"生产计划与控制系统(简称DBR)等方法,对H公司生产系统系进行改进.通过改变排产方式、提升瓶颈工序产能等活动,使H公司在生产效率有了明显的改善.各项数据结果表明,H公司借助TOC实施的生产系统改善是有效而且成功的,验证了TOC约束理论在制造企业的有效性.     

化纤厂生产和作业计划总体优化的模型

本文建立了化纤厂的生产和作业计划总体优化的混合型非线性规划模型,并给出了求近似最优解的方法.生产计划和作业是企业计划管理中两个层次的工作,它们彼此联系,密切相关,对于企业的生产和经营的效果,起着重大的影响作用.然而,由于生产作业计划的优化问题往往是一个复杂的与组合优化、网络优化、排序、动态规划等相关的问题,所以,迄今为止还很少利用生产作业的优化模型求解来安排作业计划.有趣的是,对于象化学纤维等类型的工厂,不仅可以建立生产作业优化的数学模型,而且可以将生产计划和作业计划一起考虑,建立总体优化的数学模型,实现全面的优化.     

Pricing,relaxing and fixing under lot sizing and scheduling

We present a novel mathematical model and a mathematical programming based approach to deliver superior quality solutions for the single machine capacitated lot sizing and scheduling problem with sequence-dependent setup times and costs. The formulation explores the idea of scheduling products based on the selection of known production sequences. The model is the basis of a matheuristic, which embeds pricing principles within construction and improvement MIP-based heuristics. A partial exploration of distinct neighborhood structures avoids local entrapment and is conducted on a rule-based neighbor selection principle. We compare the performance of this approach to other heuristics proposed in the literature. The computational study carried out on different sets of benchmark instances shows the ability of the matheuristic to cope with several model extensions while maintaining a very effective search. Although the techniques described were developed in the context of the problem studied, the method is applicable to other lot sizing problems or even to problems outside this domain.     

Hybrid heuristics for the capacitated lot sizing and loading problem with setup times and overtime decisions

The capacitated lot sizing and loading problem (CLSLP) deals with the issue of determining the lot sizes of product families/end items and loading them on parallel facilities to satisfy dynamic demand over a given planning horizon. The capacity restrictions in the CLSLP are imposed by constraints specific to the production environment considered. When a lot size is positive in a specific period, it is loaded on a facility without exceeding the sum of the regular and overtime capacity limits. Each family may have a different process time on each facility and furthermore, it may be technologically feasible to load a family only on a subset of existing facilities. So, in the most general case, the loading problem may involve unrelated parallel facilities of different classes. Once loaded on a facility, a family may consume capacity during setup time. Inventory holding and overtime costs are minimized in the objective function. Setup costs can be included if setups incur costs other than lost production capacity. The CLSLP is relevant in many industrial applications and may be generalized to multi-stage production planning and loading models. The CLSLP is a synthesis of three different planning and loading problems, i.e., the capacitated lot sizing problem (CLSP) with overtime decisions and setup times, minimizing total tardiness on unrelated parallel processors, and, the class scheduling problem, each of which is NP in the feasibility and optimality problems. Consequently, we develop hybrid heuristics involving powerful search techniques such as simulated annealing (SA), tabu search (TS) and genetic algorithms (GA) to deal with the CLSLP. Results are compared with optimal solutions for 108 randomly generated small test problems. The procedures developed here are also compared against each other in 36 larger size problems.     

A linear programming embedded genetic algorithm for an integrated cell formation and lot sizing considering product quality

Production lot sizing models are often used to decide the best lot size to minimize operation cost, inventory cost, and setup cost. Cellular manufacturing analyses mainly address how machines should be grouped and parts be produced. In this paper, a mathematical programming model is developed following an integrated approach for cell configuration and lot sizing in a dynamic manufacturing environment. The model development also considers the impact of lot sizes on product quality. Solution of the mathematical model is to minimize both production and quality related costs. The proposed model, with nonlinear terms and integer variables, cannot be solved for real size problems efficiently due to its NP-complexity. To solve the model for practical purposes, a linear programming embedded genetic algorithm was developed. The algorithm searches over the integer variables and for each integer solution visited the corresponding values of the continuous variables are determined by solving a linear programming subproblem using the simplex algorithm. Numerical examples showed that the proposed method is efficient and effective in searching for near optimal solutions.     

Joint rolling-horizon scheduling of materials processing and lot-sizing with sequence-dependent setups

A lot sizing and scheduling problem from a foundry is considered in which key materials are produced and then transformed into many products on a single machine. A mixed integer programming (MIP) model is developed, taking into account sequence-dependent setup costs and times, and then adapted for rolling horizon use. A relax-and-fix (RF) solution heuristic is proposed and computationally tested against a high-performance MIP solver. Three variants of local search are also developed to improve the RF method and tested. Finally the solutions are compared with those currently practiced at the foundry.     

Solution approaches for the soft drink integrated production lot sizing and scheduling problem

In this paper we present a mixed integer programming model that integrates production lot sizing and scheduling decisions of beverage plants with sequence-dependent setup costs and times. The model considers that the industrial process produces soft drink bottles in different flavours and sizes, and it is carried out in two production stages: liquid preparation (stage I) and bottling (stage II). The model also takes into account that the production bottleneck may alternate between stages I and II, and a synchronisation of the production between these stages is required. A relaxation approach and several strategies of the relax-and-fix heuristic are proposed to solve the model. Computational tests with instances generated based on real data from a Brazilian soft drink plant are also presented. The results show that the solution approaches are capable of producing better solutions than those used by the company.     

Hybrid simulated annealing and MIP-based heuristics for stochastic lot-sizing and scheduling problem in capacitated multi-stage production system

This paper addresses lot sizing and scheduling problem of a flow shop system with capacity constraints, sequence-dependent setups, uncertain processing times and uncertain multi-product and multi-period demand. The evolution of the uncertain parameters is modeled by means of probability distributions and chance-constrained programming (CCP) theory. A new mixed-integer programming (MIP) model with big bucket time approach is proposed to formulate the problem. Due to the complexity of problem, two MIP-based heuristics with rolling horizon framework named non-permutation heuristic (NPH) and permutation heuristic (PH) have been performed to solve this model. Also, a hybrid meta-heuristic based on a combination of simulated annealing, firefly algorithm and proposed heuristic for scheduling is developed to solve the problem. Additionally, Taguchi method is conducted to calibrate the parameters of the meta-heuristic and select the optimal levels of the algorithm’s performance influential factors. Computational results on a set of randomly generated instances show the efficiency of the hybrid meta-heuristic against exact solution algorithm and heuristics.     

Carryover sequence-dependent group scheduling with the integration of internal and external setup times

This paper addresses a group scheduling problem in a two-machine flow shop with a bicriteria objective and carryover sequence-dependent setup times. This special type of group scheduling problem typically arises in the assembly of printed circuit boards (PCBs). The objective is to sequence all board types in a board group as well as board groups themselves in a way that the objective function is minimized. We introduce the carryover sequence-dependent setup on machines, and call it internal setup. As an opportunity for manufacturers to decrease the costs, the focus is to completely eliminate the role of the kitting staff. Thus, we introduce the external setup (kitting) time for the next board group and require it to be performed by the machine operator during the time he is idle. Consequently, the internal and external setup times are integrated in this research, and to the best of our knowledge it is for the first time a research on PCB group scheduling is performed by integrating both setups. In order to solve this problem, first a mathematical model is developed. Then a heuristic together with two other meta-heuristic algorithms (one based on tabu search and the other based on genetic algorithm) are proposed and their efficiency and effectiveness on several problems are tested. Also a statistical experimental design is performed in order to evaluate the impact of different factors on the performance of the algorithms.     

Capacitated lot sizing and scheduling with parallel machines and shared buffers: A case study in a packaging company

The aim of this work is to propose a solution approach for a capacitated lot sizing and scheduling real problem with parallel machines and shared buffers, arising in a packaging company producing yoghurt. The problem has been formulated as a hybrid Continuous Set-up and Capacitated Lot Sizing Problem (CSLP–CLSP). A new effective two stage optimisation heuristic based on the decomposition of the problem into a lot sizing problem and a scheduling problem has been developed. An assignment of mixture to buffers is made in the first stage, and therefore the corresponding orders are scheduled on the production lines by performing a local search. Computational tests have been performed on the real data provided by the company. The heuristic exhibits near-optimal solutions, all obtained in a very short computational time.     

Heuristic Approaches for a Scheduling Problem in the Plastic Molding Department of an Audio Company

A production scheduling problem for making plastic molds of hi-fi models is considered. The objective is to minimize the total machine makespan in the presence of due dates, variable lot size, multiple machine types, sequence dependent, machine dependent setup times, and inventory limits. Goal programming and load balancing are applied to select the set of machine types and assign mold types to machines, resulting in a set of single-machine scheduling problems. A mixed-integer program (MIP) is formulated for the general problem but could solve only small instances. A single-machine scheduling heuristic is designed to adopt a production sequence from a travelling salesman solution. The start time of every cycle is determined by a simplified MIP. Production cycles are defined to equalize the stockout times of mold types. A post-processing step reduces the number of setups in the last cycle. Results using real-life data are promising. Characteristics giving rise to high machine utilization are discussed.     

A multi-class multi-level capacitated lot sizing model

When demand loading is higher than available capacity, it takes a great deal of effort for a traditional MRP system to obtain a capacity-feasible production plan. Also, the separation of lot sizing decisions and capacity requirement planning makes the setup decisions more difficult. In a practical application, a production planning system should prioritize demands when allocating manufacturing resources. This study proposes a planning model that integrates all MRP computation modules. The model not only includes multi-level capacitated lot sizing problems but also considers multiple demand classes. Each demand class corresponds to a mixed integer programming (MIP) problem. By sequentially solving the MIP problems according to their demand class priorities, this proposed approach allocates finite manufacturing resources and generates feasible production plans. In this paper we experiment with three heuristic search algorithms: (1) tabu search; (2) simulated annealing, and (3) genetic algorithm, to solve the MIP problems. Experimental designs and statistical methods are used to evaluate and analyse the performance of these three algorithms. The results show that tabu search and simulated annealing perform best in the confirmed order demand class and forecast demand class, respectively.