模糊环境下含缺陷率且允许缺货的经济生产批量模型

鉴于实际生产库存管理中的产品缺陷问题,研究了允许缺货情况下含确定缺陷率和模糊缺陷率的经济生产批量问题.根据产品检验速率的不同,建立了两种含LR型模糊缺陷率、模糊需求、模糊缺货费、模糊检验费、模糊存储费和模糊订货费的经济生产批量模型.运用符号距离,将模糊模型转化为确定模型,进而确定了其最优生产策略.通过理论分析,揭示了模糊与经典经济生产批量模型的关系.结合算例,分析了产品缺陷率的模糊性和检验速率对最优生产量和最小成本的影响.     

A multi-product multi-period inventory control problem under inflation and discount: a parameter-tuned particle swarm optimization algorithm

In this paper, a seasonal multi-product multi-period inventory control problem is modeled in which the inventory costs are obtained under inflation and all-unit discount policy. Furthermore, the products are delivered in boxes of known number of items, and in case of shortage, a fraction of demand is considered backorder and a fraction lost sale. Besides, the total storage space and total available budget are limited. The objective is to find the optimal number of boxes of the products in different periods to minimize the total inventory cost (including ordering, holding, shortage, and purchasing costs). Since the integer nonlinear model of the problem is hard to solve using exact methods, a particle swarm optimization (PSO) algorithm is proposed to find a near-optimal solution. Since there is no bench mark available in the literature to justify and validate the results, a genetic algorithm is presented as well. In order to compare the performances of the two algorithms in terms of the fitness function and the required CPU time, they are first tuned using the Taguchi approach, in which a metric called “smaller is better” is used to model the response variable. Then, some numerical examples are provided to demonstrate the application and to validate the results obtained. The results show that, while both algorithms have statistically similar performances, PSO tends to be the better algorithm in almost all problems.     

运用带有记忆库的遗传算法求解作业车间调度问题

在遗传算法的基础上,提出了一种带有记忆库的遗传算法,用于求解生产调度问题。该算法通过轮换的方法,分析了记忆库充满后如何更新和识别相同个体的问题,从而达到将加工任务分配到不同的并行机器上去执行,以利于机器的负载平衡。仿真结果表明,运用带有记忆库的遗传算法不但使整个加工过程的执行时间得到优化,而且各类机器完成的操作数相同、使用的时间也较为平均,达到了设计目标。同时,该算法的计算速度较快,话用干较大规模作业车间调度问题的求解。     

A new algorithm for determining production schedules when solving the multi-product economic lot sizing problem in flow shops

In this paper, we study the generation of production schedules in the multi-product economic lot sizing problem in flow shops (i.e., the FS-ELSP) under the power-of-two policy. To investigate this problem, we first review the mathematical model for the FS-ELSP and Ouenniche and Boctor’s (O&B’s) heuristic, and we comment that there exist several problems in O&B’s heuristic if we try to use it to generate feasible production schedules for the FS-ELSP. Therefore, we propose two new heuristics, namely, the modified O&B’s heuristic and Huang and Yao’s (H&Y’s) heuristic to improve O&B’s heuristic. To compare the performance of these three heuristics, we randomly generate 500 instances for each of the seven levels of utilization rate from 0.4 to 0.75. Based on our numerical experiments, we conclude that H&Y’s heuristic significantly outperforms O&B’s heuristic and the modified O&B’s heuristic.     

A novel hybrid genetic algorithm to solve the sequence-dependent permutation flow-shop scheduling problem

Flow-shop scheduling problem (FSP) deals with the scheduling of a set of jobs that visit a set of machines in the same order. The FSP is NP-hard, which means that there is no efficient algorithm to reach the optimal solution of the problem. To minimize the make-span of large permutation flow-shop scheduling problems in which there are sequence-dependent setup times on each machine, this paper develops one novel hybrid genetic algorithms (HGA). Proposed HGA apply a modified approach to generate the population of initial chromosomes and also use an improved heuristic called the iterated swap procedure to improve them. Also the author uses three genetic operators to make good new offspring. The results are compared to some recently developed heuristics and computational experimental results show that the proposed HGA performs very competitively with respect to accuracy and efficiency of the solutions.     

The economic lot scheduling problem with deteriorating items and shortage: an imperialist competitive algorithm

This paper addresses an economic lot scheduling problem (ELSP) for manufacturing environments regarding slack costs and deteriorating items using the extended basic period approach under Power-of-Two (PoT) policy. The purpose of this research is to determine an optimal batch size for a product and minimizing total related costs to such a problem. The cost function consists of three components, namely, setup cost, holding cost includes deteriorating factor, and slack cost. The ELSP is concerned with the scheduling decision of n items and lot sizing. Avoiding schedule interference is the main problem in ELSP. The used PoT policy ensures that the replenishment cycle of each item to be integer and this task reduces potential schedule interferences. Since the ELSP is shown as an NP-hard problem, an imperialist competitive algorithm is employed to provide good solutions within reasonable computational times. Computational results show that the proposed approach can efficiently solve such complicated problems.     

基于改进遗传算法的空间管道无网格布局研究

针对空间管道布局这一复杂问题,创建了包括障碍物在内的布局空间的无网格模型,将整体布局空间依据障碍物尺寸划分为若干个规则的长方体,得到较少数量的布局节点,通过改进遗传算法从布局节点中搜索全局性最优路径.无网格模型有效地减少了均匀网格模型中的节点数量,改进遗传算法通过自适应调整变异率提高了寻优效果和收敛速度.算法利用VB6.0实现,操作简单.最后通过实例,并与均匀网格布局模型进行比较,验证了所提方法的快速性、正确性和有效性.     

Hybrid evolutionary algorithm with marriage of genetic algorithm and extremal optimization for production scheduling

This paper presents a hybrid evolutionary algorithm with marriage of genetic algorithm (GA) and extremal optimization (EO) for solving a class of production scheduling problems in manufacturing. The scheduling problem, which is derived from hot rolling production in steel industry, is characterized by two major requirements: (i) selecting a subset of orders from manufacturing orders to be processed; (ii) determining the optimal production sequence under multiple constraints, such as sequence-dependant transition costs, non-execution penalties, earliness/tardiness (E/T) penalties, etc. A combinatorial optimization model is proposed to formulate it mathematically. For its NP-hard complexity, an effective hybrid evolutionary algorithm is developed to solve the scheduling problem through combining the population-based search capacity of GA and the fine-grained local search efficacy of EO. The experimental results with production scale data demonstrate that the proposed hybrid evolutionary algorithm can provide superior performances in scheduling quality and computation efficiency.     

A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model

This paper aims to develop a fuzzy-based solution approach to address a machine-loading problem of a flexible manufacturing system (FMS). The proposed solution methodology effectively deals with all the three main constituents of a machine loading problem, viz. job sequence determination, operation machine allocation and the reallocation of jobs. The main objectives of the FMS loading problem considered here are minimisation of system imbalance and maximisation of throughput; the constraints to be satisfied are the available machining time and tool slots. An analytical argument has been provided to support the membership function related to the operation machine allocation vector. Computational results revealed the superiority of the proposed algorithm over other heuristics when it is tested on a standard data set adopted from literature. A new class of petri net model called the Extended neuro fuzzy petri net is constructed to capture clearly the various details of the machine loading problem which can be further extended to learn from experience and perform inferences so that truly intelligent system characteristics can be realised.     

Integrated model for production planning and scheduling in a supply chain using benchmarked genetic algorithm

Production planning and scheduling is one of the core functions in manufacturing systems. Furthermore, this task is drawing even more attention in supply chain environments as problems become harder and more complicated. Most of the traditional approaches to production planning and scheduling have adopted a multi-phased, hierarchical and decompositional approach. This traditional approach does not guarantee a feasible production schedule. And even when capacity constraints are satisfied, it may generate an expensive schedule. In order to overcome the limitations of the traditional approach, several previous studies tried to integrate the production planning and scheduling problems. However, these studies also have some limitations, due to their intrinsic characteristics and the method for incorporating the hierarchical product structure into the scheduling model. In this paper we present a new integrated model for production planning and scheduling for multi-item and multi-level production. Unlike previous lot sizing approaches, detailed scheduling constraints and practical planning criteria are incorporated into our model. We present a mathematical formulation, propose a heuristic solution procedure, and demonstrate the performance of our model by comparing the experimental results with those of a traditional approach and optimal solution.     

A new algorithm and multi-response Taguchi method to solve line balancing problem in an automotive industry

One of the most important issues in an assembly line balancing problem is to control a flow of production and manufacturing to provide continuous flow to balance the production line. For this purpose, a line balancing problem was considered for a special assembly line in an automotive factory. A new algorithm was required to deal with balancing an assembly line which consisted of the same job which must be performed by more than one worker at the same time. In this way, the new algorithm was expected to be effective in such a case that jobs were simultaneously completed in a parallel way. In order to measure effectiveness of the proposed algorithm, performance criteria were identified as total number of assembly station, total number of workers, and line productivity. As a result of the application of the proposed algorithm with taking into consideration factors such as cycle time, an allowable number of workers in an assembly station, and an allowable idle time of a worker, alternative solutions were determined in order to measure these criteria. However, these alternative solutions do not give any information about which of the solutions provide not only minimization of the number of assembly station and number of workers on the line but also maximize the line productivity at the same time. Hereby, a multi-response Taguchi method was applied in order to investigate levels of factors which directly affected system performance criteria.     

Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm

This paper presents a multiobjective formulation of the buffer allocation problem in unreliable production lines. Majority of the solution methods for buffer allocation problems assume that the process times, time between failures, and repair times are deterministic or exponentially distributed. This paper relaxes these restrictions by proposing a simulation-based methodology which can consider general function distributions for all parameters of production lines. Factorial design has been used to build a meta-model for estimating production rate based on a detailed, discrete event simulation model. We use genetic algorithm combined to line search method to solve the multiobjective model and determining the optimal (or near optimal) size of each buffer storage.     

A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm

A novel intelligent diagnosis model based on wavelet support vector machine (WSVM) and immune genetic algorithm (IGA) for gearbox fault diagnosis is proposed. Wavelet support vector machine is a powerful novel tool for solving the diagnosis problem with small sampling, nonlinearity and high dimension. Immune genetic algorithm is developed in this study to determine the optimal parameters for WSVM with the highest accuracy and generalization ability. Moreover, the feature vectors for fault diagnosis are obtained from vibration signal that preprocessed by empirical mode decomposition (EMD). The experimental results indicate that this proposed approach is an effective method for gearbox fault diagnosis, which has more strong generalization ability and can achieve higher diagnostic accuracy than that of the artificial neural network and the SVM which has randomly extracted parameters.     

A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making

Model Predictive Control is a valuable tool for the process control engineer in a wide variety of applications. Because of this the structure of an MPC can vary dramatically from application to application. There have been a number of works dedicated to MPC tuning for specific cases. Since MPCs can differ significantly, this means that these tuning methods become inapplicable and a trial and error tuning approach must be used. This can be quite time consuming and can result in non-optimum tuning. In an attempt to resolve this, a generalized automated tuning algorithm for MPCs was developed. This approach is numerically based and combines a genetic algorithm with multi-objective fuzzy decision-making. The key advantages to this approach are that genetic algorithms are not problem specific and only need to be adapted to account for the number and ranges of tuning parameters for a given MPC. As well, multi-objective fuzzy decision-making can handle qualitative statements of what optimum control is, in addition to being able to use multiple inputs to determine tuning parameters that best match the desired results. This is particularly useful for multi-input, multi-output (MIMO) cases where the definition of "optimum" control is subject to the opinion of the control engineer tuning the system. A case study will be presented in order to illustrate the use of the tuning algorithm. This will include how different definitions of "optimum" control can arise, and how they are accounted for in the multi-objective decision making algorithm. The resulting tuning parameters from each of the definition sets will be compared, and in doing so show that the tuning parameters vary in order to meet each definition of optimum control, thus showing the generalized automated tuning algorithm approach for tuning MPCs is feasible.     

A genetic algorithm to optimize multiproduct multiconstraint inventory control systems with stochastic replenishment intervals and discount

There are two main assumptions in multiperiodic inventory control problems. The first is the continuous review, where, depending on the inventory level, orders can happen at any time, and the other is the periodic review, where orders can only happen at the beginning of each period. In this paper, these assumptions are relaxed, and the periods between two replenishments are assumed independent and identically distributed random variables. Furthermore, the decision variables are assumed integer-type and that there are two kinds of space and budget constraints. The incremental discounts to purchase products are considered, and a combination of backorder and lost sales are taken into account for the shortages. The model of this problem is shown to be a mixed integer-nonlinear programming type, and in order to solve it, both genetic algorithm and simulated annealing approaches are employed. At the end, two numerical examples are given to demonstrate the applicability of the proposed methodologies in which genetic algorithm method performs better than simulated annealing in terms of objective function values.     

A staged algorithm to determine production lot size and delivery period for long-term production plan

This study submits a mathematical model called the optimal production lot size (OPLS) model to reach the optimal design of a network-type production system for long-term production undergoing the limitation of finite machines of each machine type. This study not only discusses the production rate, the production lot size, the production period, the machine cost, and the number of machines for each workstation into the objective, but develops an efficient step-by-step algorithm, staged algorithm, with the combinatorial applications of the maximal flow problem and the shortest route method to optimize the system profit. Additionally, the detailed procedure of searching the optimal solution is presented by a numerical example. This paper indeed contributes the applicable scheme to the design of a network-type production system for the long-term manufacturing, and provides the optimal production lot size and period for the production planners in today's manufacturing industry with profound insight     

A hybrid model using supporting vector machine and multi-objective genetic algorithm for processing parameters optimization in micro-EDM

In micro-electrical discharge machining (EDM), processing parameters greatly affect processing efficiency and stability. However, the complexity of micro-EDM makes it difficult to determine optimal parameters for good processing performance. The important output objectives are processing time (PT) and electrode wear (EW). Since these parameters influence the output objectives in quite an opposite way, it is not easy to find an optimized combination of these processing parameters which make both PT and EW minimum. To solve this problem, supporting vector machine is adopted to establish a micro-EDM process model based on the orthogonal test. A new multi-objective optimization genetic algorithm (GA) based on the idea of non-dominated sorting is proposed to optimize the processing parameters. Experimental results demonstrate that the proposed multi-objective GA method is precise and effective in obtaining Pareto-optimal solutions of parameter settings. The optimized parameter combinations can greatly reduce PT while making EW relatively small. Therefore, the proposed method is suitable for parameter optimization of micro-EDM and can also enhance the efficiency and stability of the process.     

A mixed-integer linear programming model along with an electromagnetism-like algorithm for scheduling job shop production system with sequence-dependent set-up times

In this paper, we consider the job shop scheduling problem (JSS) with non-anticipatory, per-machine, sequence-dependent setup times (SDST). The contributions of this paper are twofold. First, we propose a formulation in the form of a mixed-integer linear programming (MILP) model to modelize the aforementioned problem. Second, we play a pioneering effort for the effective adaptation of a novel metaheuristic known as electromagnetism-like algorithm (EMA) to solve the foregoing problem under the minimization of makespan. Afterwards, we evaluate the performance of the proposed MILP model, the EMA, and other effective metaheuristic algorithms from the literature on two different sets of benchmarks: small-sized and large-sized instances. The rationale behind applying the MILP model and the other algorithms at the small-sized instances is to compare the solutions obtained by the metaheuristic algorithms and the optimal solutions obtained by the MILP model (optimality gap analysis). Subsequently, to demonstrate the competitiveness of the EMA against some effective algorithms in the literature, we conduct an experimental design based on Taillard's benchmark, which is considered as large-sized instances. The purpose of conducting this very experiment is to show whether the acceptable performance of the EMA is transferrable to large-sized instances. The computational evaluations simply manifest the superiority of our proposed algorithm vs the other high-performing algorithms over both small and large instances.