Generalised design for optimal product configuration

Design of configurable product family or design for configuration has emerged as an efficient tool to deal with the new challenges of a continuous dynamic and volatile market. Design for configuration provides an efficient and effective mean to realise the product variety. This paper tackles the optimal product configuration. The introduction of fuzziness to capture the subjective nature of the design for configuration process, the building of multiple fuzzy models, which correspond to the multiple views of a configurable product, permits to propose the possible physical solutions to configure a product. It corresponds to the consensus problem of design for configuration. The extended problem formulation is the proposition of an optimal product configuration. The optimal solution is searched using p median problem formulation. The proposed approach is validated by the design case study of a configurable product.     

A new two-stage Stackelberg fuzzy data envelopment analysis model

Data Envelopment Analysis (DEA) is a widely used mathematical programming approach for evaluating the relative efficiency of Decision Making Units (DMUs). Conventional DEA methods treat DMUs as “black boxes”, focusing entirely on their relative efficiencies. We propose an efficient two-stage fuzzy DEA model to calculate the efficiency scores for a DMU and its sub-DMUs. We use the Stackelberg (leader–follower) game theory approach to prioritize and sequentially decompose the efficiency score of the DMU into a set of efficiency scores for its sub-DMUs. The proposed models are linear and independent of the α-cut variables. The linear feature allows for a quick identification of the global optimum solution and the α-cut independency feature allows for a significant reduction in the computational efforts. Monte Carlo simulation is used to discriminately rank the efficiencies in the proposed method. We also present a case study to exhibit the efficacy of the procedures and to demonstrate the applicability of the proposed method to a two-stage performance evaluation problem in the banking industry.     

基于免疫算法的并行机间歇过程模糊生产调度

研究了一类具有顺序无关模糊产品切换时间和成本以及模糊单位加工时间和成本的并行机间歇过程调度问题,目的是确定每种产品在每个设备上处理的批次数目、批量以及批次顺序,优化目标为最小化总完成时间和最小化总生产成本。根据任意设备上同种产品的所有批次均顺序处理的性质,建立了问题的模糊运输模型。利用加权和方法将多目标函数转化为单目标函数,并使用基于积分值的方法对模糊数进行排序。提出了基于排列边集编码的免疫算法,通过求解不同规模的问题实例证明,免疫算法不仅能获得比遗传算法和免疫遗传算法更好的解,而且比免疫遗传算法更高效,同时具有良好的动态性能。     

A two-phase linear programming methodology for fuzzy multi-objective mixed-model assembly line problem

We develop a fuzzy multi-objective linear programming (FMOLP) model for solving multi-objective mixed-model assembly line problem. In practice, vagueness and imprecision of the goals in this problem make the fuzzy decision-making complicated. The proposed model considers minimizing total utility work, total production rate variation, and total setup cost, using a two-phase linear programming approach. In the first phase, the problem is solved using a max–min approach. The max–min solution not being efficient, in general, we propose a new model in the second phase to maximize a composite satisfaction degree at least as good as the degrees obtained by phase one. To show the effectiveness of the proposed approach, a numerical example is solved and the results are compared with the ones obtained by the fuzzy mixed integer goal programming and weighted additive methods. The computational results show that the proposed FMOLP model achieves lower objective functions as well as higher satisfaction degrees.     

Supplier selection problem in global supply chains by AHP and ANP approaches under fuzzy environment

In this paper, we proposed a model for selecting of the global supplier by analytical hierarchy process (AHP) and analytical network process (ANP) based on linguistic variable weight. The fuzzy AHP and fuzzy ANP methods are suggested to be a good tool for solving our proposed multiple-criteria decision-making problem. Fuzzy AHP-based methodology evaluated different decision criteria such as service quality, cost, risk factors, and supplier’s characteristics involved in the selection of the best supplier in a global supply chain and ANP represented an effective tool for providing a suitable solution for managers. The linguistic levels of comparisons produced by the experts with triangular fuzzy numbers for constructing fuzzy pairwise comparison matrices. The implementation of the system was demonstrated by a problem having four stages of hierarchy which contains four criteria and 28 attributes. In conclusion, the fuzzy AHP and fuzzy ANP results were compared.     

Design and optimization of an RFID-enabled automated warehousing system under uncertainties: a multi-criterion fuzzy programming approach

In this paper, we investigated the design and optimization of a proposed radio-frequency identification (RFID)-enabled automated warehousing system in terms of the optimal number of storage racks and collection points that should be established in an efficient and cost-effective approach. To this aim, a fuzzy tri-criterion programming model was developed and used for obtaining trade-off decisions by measuring three conflicting objectives. These are minimization of the warehouse total cost, maximization of the warehouse capacity utilization, and minimization of the travel time of products from storage racks to collection points. To reveal the alternative Pareto optimal solutions using the developed model, a new approach was developed and compared with a recently developed fuzzy approach so-called Selim and Ozkarahan (SO). A decision-making algorithm was used to select the best Pareto optimal solution, and the applicability of the developed model was examined using a case study. Research findings demonstrate that the developed model is capable of generating an optimal solution as an aid for the design of the proposed RFID-enabled automated warehousing system.     

Design of a neuro-fuzzy–regression expert system to estimate cost in a flexible jobshop automated manufacturing system

We propose a cost estimation model based on a fuzzy rule backpropagation network, configuring the rules to estimate the cost under uncertainty. A multiple linear regression analysis is applied to analyze the rules and identify the effective rules for cost estimation. Then, using a dynamic programming approach, we determine the optimal path of the manufacturing network. Finally, an application of this model is illustrated through a numerical example showing the effectiveness of the proposed model for solving the cost estimation problem under uncertainty.     

Non-identical parallel machine scheduling with fuzzy processing times using genetic algorithm and simulation

This paper addresses non-identical parallel machine scheduling problem with fuzzy processing times (FPMSP). A genetic algorithm (GA) approach embedded in a simulation model to minimize maximum completion time (makespan) is proposed. The results are compared with those obtained by using longest processing time rule, known as the most appropriate dispatching rule for such problems. This application illustrates the need for efficient and effective heuristics to solve FPMSPs. The proposed GA approach yields good results and reaches them fast and several times in one run. Moreover, due to its advantage of being a search algorithm, it can explore alternative schedules providing the same results.     

Genetic and differential evolution algorithms for the allocation of customers to potential distribution centers in a fuzzy environment

In order to serve the customers' demands in a supply chain, one of the important decisions is to select some candidate places as distribution centers (DCs) in the network. For opening a potential DC and also shipping from the DC to the customers, there are two types of costs named fixed and variable costs, respectively. Contrary to previous work, we consider fuzzy costs and utilize differential evolution (DE) algorithm for the first time for the given problem. In addition, some new crossover and mutation operators are proposed in DE. We also address the problem with genetic algorithm (GA) and compare the results with the presented DE algorithm. In the both presented algorithms, Prüfer number representation is employed. Besides, the Taguchi experimental design method is employed to study the behavior of the parameters dealing with the problem. To evaluate the performance of proposed algorithms, various problem sizes are considered and the computational results are analyzed. Finally, the impact of the rise in the problem size on the performance of the algorithms is investigated. The DE depicts a superior performance over GA in all problem sizes.     

Solving a multi-product distribution planning problem in cross docking networks: An imperialist competitive algorithm

Cross docking is a distribution strategy to efficiently control the physical flow of inventory. In this approach, the concept of warehouse shifts from a place for holding the inventory to a site for receiving, classifying, and shipping to the product destination in a very short time. Shipments are typically kept for less than 24 h in the cross dock. The major advantage of this strategy is to reduce the cycle time and inventory level while fulfilling customer’s demands. In general, relevant studies are based on one-product type delivery and pickup assumption whereas in reality, customers are likely to order different types of products in various amounts. This paper focuses on considering the scheduling problem of cross docking multi-product type deliveries and pickups, so that the objective, minimizing the summation of transportation cost and holding cost, can be satisfied. The efficiency and capability of the mathematical model is evaluated by presenting a case study and sensitivity analysis of the parameters influencing the problem. In order to solve the model, as the problem is NP-hard, an efficient heuristic procedure to provide effective initial solution within reasonable computational times is applied and then improved by an imperialist competitive algorithm. Computational results which consist of comparing the proposed heuristic and meta-heuristic with optimal solution obtained by Generalized Algebraic Modeling System show that the suitability of the proposed solving approach is quite sensible for solving such complicated problems.     

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.     

Mixed H∞ and passive filtering for switched Takagi-Sugeno fuzzy systems with average dwell time

This paper investigates the mixed H_∞ and passive filtering problem for switched Takagi-Sugeno (T-S) fuzzy systems with average dwell time (ADT) in both continuous-time and discrete-time contexts. To deal with this problem, a new performance index is proposed for switched systems. This new performance index can be viewed as the mixed weighted H_∞ and passivity performance index. Based on this new performance index, the weighted H_∞ filtering problem and the passive filtering problem for switched T-S fuzzy systems can be solved in a unified framework. Combining the multiple Lyapunov functions approach with a matrix decoupling technique, new sufficient conditions for the existence of mixed weighted H_∞ and passive filters are obtained for switched T-S fuzzy systems. All these conditions are expressed in terms of linear matrix inequalities (LMIs). The desired filters can be constructed by solving these LMIs. Finally, numerical examples and practical examples are provided.     

具有混合数据的维修策略决策方法

设备维修策略决策问题的研究,不但可以降低维修成本,还可以提高设备的可用性、可靠性、安全性及产品质量。目前虽取得了一定的进展,但还从未解决混合数据下的维修策略决策问题。应用随机优势构造局部和谐指数和非和谐指数以解决维修策略决策中随机性评价下的比较问题;将模糊评价转化为模糊数,并利用模糊数质心距建立局部和谐指数和非和谐指数以解决模糊性评价下的择优问题。通过以上处理,在ELECTRE III方法中可以把维修策略决策中的随机性评价和模糊性评价视为确定性评价来对待,进而解决了设备维修策略决策中的混合数据问题。将其应用到火力发电系统的汽轮机中,试验结果表明,此方法是合理的和有效的。     

An efficient bi-objective heuristic for scheduling of hybrid flow shops

This paper considers the problem of scheduling n independent jobs in hybrid flow shop environment with sequence-dependent setup times to minimize the makespan and total tardiness. For the optimization problem, an algorithm namely; bi-objective heuristic (BOH) is proposed for searching Pareto-optimal frontier. The aim of the proposed algorithm is to generate a good approximation of the set of efficient solutions. The BOH procedure initiates by generating a seed sequence. Since the output results are strongly dependent on the initial solution and in order to increase the quality of output results algorithm, we have considered how the generation of seed sequence with random way and particular sequencing rules. Two methods named Euclidean distance and percent error have been proposed to compare non-dominated solution sets obtain of each seed sequence. It is perceived from these methods that the generation of seed sequence using earliest due date rule is more effective. Then, the performance of the proposed BOH is compared with a simulated annealing proposed in the literature and a VNS heuristic on a set of test problems. The data envelopment analysis is used to evaluate the performance of approximation methods. From the results obtained, it can be seen that the proposed algorithm is efficient and effective.     

Solving a bi-objective cell formation problem with stochastic production quantities by a two-phase fuzzy linear programming approach

We propose a bi-objective cell formation problem with demand of products expressed in a number of probabilistic scenarios. To deal with the uncertain demand of products, a framework of two-stage stochastic programming model is presented. The proposed model considers minimizing the sum of the miscellaneous costs (machine constant cost, expected machine variable cost, cell fixed-charge cost, and expected intercell movement cost) and expected total cell loading variation. Because of conflicting objectives, we develop a two-phase fuzzy linear programming approach for solving bi-objective cell formation problem. To show the effectiveness of the proposed approach, numerical examples are solved and the results are compared with the two existing approaches in the literature. The computational results show that the proposed fuzzy method achieves lower objective functions as well as higher satisfaction degrees.     

Multi-Input Single Output SSSC based damping controller design by a hybrid Improved Differential Evolution-Pattern Search approach

In this paper, a hybrid Improved Differential Evolution and Pattern Search (hIDEPS) approach is proposed for the design of a PI-Type Multi-Input Single Output (MISO) Static Synchronous Series Compensator (SSSC) based damping controller. The improvement in Differential Evolution (DE) algorithm is introduced by a simple but effective scheme of changing two of its most important control parameters i.e. step size and crossover probability with an objective of achieving improved performance. Pattern Search (PS) is subsequently employed to fine tune the best solution provided by modified DE algorithm. The superiority of a proposed hIDEPS technique over DE and improved DE has also been demonstrated. At the outset, this concept is applied to a SSSC connected in a Single Machine Infinite Bus (SMIB) power system and then extended to a multi-machine power system. To show the effectiveness and robustness of the proposed design approach, simulation results are presented and compared with DE and Particle Swarm Optimization (PSO) optimized Single Input Single Output (SISO) SSSC based damping controllers. It is observed that the proposed approach yield superior damping performance compared to some approaches available in the literature.     

基于遗传算法的铣削加工参数模糊优化系统研究

优化铣削参数对于降低铣削加工成本、提高生产率有重要的作用。传统的铣削参数优化模型中,铣削参数和条件约束的匹配取值往往是通过实际加工的经验获得,这种结果具有不确定性和模糊性。文章分析了模糊参数优化的数学模型,根据模糊集合原理将模糊模型转化为一个传统的单目标模糊优化问题,借用IDEF1x方法建立了铣削参数模糊数据库模型,并运用遗传算法（GA）为优化引擎开发实现了模糊优化系统。给出的运行实例表明该优化系统对铣削参数优化具有更好的效果,同时,系统为CAPP优选铣削参数提供了支持平台。     

Solving a multi-objective mixed-model assembly line sequencing problem by a fuzzy goal programming approach

This paper presents a fuzzy goal programming approach to solve a multi-objective mixed-model assembly line sequencing problem in a just-in-time production system. A mixed-model assembly line is a type of production line capable of diversified small-lot production and is able to promptly respond to sudden demand changes for a variety of models. Determining the sequence of introducing models to such an assembly line is of particular importance for the efficient implementation of just-in-time (JIT) systems. In this paper, we consider three objectives, simultaneously: minimizing total utility work, total production rate variation, and total setup cost. Because of conflicting objectives, we propose a fuzzy goal programming-based approach to solve the model. This approach is constructed based on the desirability of decision maker (DM) and tolerances considered on goal values. To illustrate the behavior of the proposed model, some of instances are solved optimally and computational results reported.     

具有模糊约束的广义预测控制

本文研究了预测控制中的模糊决策问题。与传统的误差加权性能指标相比,在预测控制中利用模糊目标和模糊约束具有更大的灵活性,等式约束和不等式约束可以统一为模糊软约束,约束预测控制可以转化为标准的模糊优化问题,用一组不精确的满意优化解取代了传统的唯一最优解,这样,设计者对特定的被控对象有更多的灵活性。仿真结果表明了对控制量或系统输出加入约束时该方法的有效性。     

Pareto lexicographic α-robust approach and its application in robust multi objective assembly line balancing problem

Robustness in most of the literature is associated with min-max or min-max regret criteria. However, these criteria of robustness are conservative and therefore recently new criteria called, lexicographic α-robust method has been introduced in literature which defines the robust solution as a set of solutions whose quality or jth largest cost is not worse than the best possible jth largest cost in all scenarios. These criteria might be significant for robust optimization of single objective optimization problems. However, in real optimization problems, two or more than two conflicting objectives are desired to optimize concurrently and solution of multi objective optimization problems exists in the form of a set of solutions called Pareto solutions and from these solutions it might be difficult to decide which Pareto solution can satisfy min-max, min-max regret or lexicographic α-robust criteria by considering multiple objectives simultaneously. Therefore, lexicographic α-robust method which is a recently introduced method in literature is extended in the current research for Pareto solutions. The proposed method called Pareto lexicographic α-robust approach can define Pareto lexicographic α-robust solutions from different scenarios by considering multiple objectives simultaneously. A simple example and an application of the proposed method on a simple problem of multi objective optimization of simple assembly line balancing problem with task time uncertainty is presented to get their robust solutions. The presented method can be significant to implement on different multi objective robust optimization problems containing uncertainty.