A new iterated fast local search heuristic for solving QAP formulation in facility layout design

In facility layout design, the problem of locating facilities with material flow between them was formulated as a quadratic assignment problem (QAP), so that the total cost to move the required material between the facilities is minimized, where the cost is defined by a quadratic function. In this paper, we propose a modification to iterated fast local search algorithm (IFLS) with a new recombination crossover operator and the modified IFLS is addressed as NIFLS. The ideas we incorporate in the NIFLS are iterated self-improvement with evolutionary based perturbation tool, which includes (i) recombination crossover as perturbation tool and (ii) self-improvement in mutation operation followed by a local search. Three schemes of NIFLS are proposed and the obtained solution qualities by the three schemes are compared. We test our algorithm on all the benchmark instances of QAPLIB, a well-known library of QAP instances. The performance of proposed recombination crossover with sliding mutation (RCSM) scheme of NIFLS is well superior to the other two schemes of NIFLS.     

A hybrid fuzzy-GA algorithm for the integrated machine allocation problem with fuzzy demands

The integrated machine allocation and facility layout problem (IMALP) is a branch of the general facility layout problem in which, besides selecting machine locations, the processing route of each product is determined. Most research in this area suppose that the flow of material is certain and exact, which is an unrealistic assumption in today's dynamic and uncertain business environment. Therefore, in this paper the demand volume has been assumed as fuzzy numbers with different membership functions. To solve this problem, the deterministic model is first integrated with a fuzzy implication via the expected value model, and thereafter an intelligent hybrid algorithm, including a genetic algorithm and a fuzzy simulation approach has been applied. Finally, the efficiency of the proposed algorithm is evaluated with a set of numerical examples. The results show the effectiveness of the hybrid algorithm in finding the IMALP solutions.     

基于遗传算法的大规模矩形件优化排样

大规模矩形件优化排样是一个典型的组合优化问题，属于NP-hard问题．实际工程中对一个排样方案一般有满足“一刀切”的工艺要求，“一刀切”要求增加了对排样的约束．提出的优化算法，将矩形匹配分割算法作为遗传算法染色体的解码器实现一个排样方案，用遗传算法进行排样方案的全局搜索．算例比较表明，该算法可以求得满足“一刀切”约束的最优解．     

基于Wang-Landau算法的动态设施布局方法

动态设施布局问题是设施在车间内多个阶段的布局规划问题。目前,针对动态设施布局问题,国内外学者对离散模型研究较多,而对连续模型的研究却较少。根据连续动态设施布局的特性与需求,构建了不等面积设施的动态设施布局连续模型。求解该模型的难点在于缺乏一种高效的布局优化方法。Wang-Landau算法是一种改进的蒙特卡罗算法。通过将Wang-Landau算法与空位点放置策略、外推移动策略、内压移动策略三种启发式策略相结合,提出一种基于Wang-Landau抽样的启发式算法,并以此求解该模型。使用文献中已有的测试算例对提出的算法进行测试,计算结果表明,所提出的算法在求解连续动态设施布局问题上是有效的。     

A fast two-stage hybrid meta-heuristic algorithm for robust corridor allocation problem

The optimisation of the corridor allocation problem (CAP) belongs to the optimisation of the efficiency of the automated production line. The goal is to reduce the material handling cost (MHC) in the production process through a reasonable layout of the facilities, so as to save expenses for the enterprise. In recent years, with the acceleration of market changes, product design and production process adjustments have become more frequent, and more attention has been paid to the research on the layout of facilities under the condition of changes in the flow of materials between production facilities over time. On the basis of the CAP model, this paper considers the optimisation problem of row layout when the flow of materials between facilities fluctuates in a certain range. The new model can be utilised to obtain the overall optimisation solution under the condition of the floating material flow matrix, so as to achieve the goal of optimising the total MHC in the entire production process. As the new model introduces more variables and intermediate parameters, a two-stage solution method is previously required, which greatly increases the time to solve the problem. This paper proposes a targeted meta-heuristic algorithm optimisation method combining the advantages of tabu search algorithm and harmony search algorithm, which simplifies the solution phase of calling the precise solver in the two-stage algorithm of row facility layout problem, improves the problem solving efficiency, and makes the solution of large-scale problems become possible. The proposed model is verified through Lingo software, and then the model and the hybrid algorithm in the MATLAB environment are verified with each other. Finally, the proposed simplified algorithm is utilised to solve the large-scale problems that could not be solved by the two-stage algorithm before.     

A multi objective genetic algorithm for the facility layout problem based upon slicing structure encoding

This paper proposes a new multi objective genetic algorithm (MOGA) for solving unequal area facility layout problems (UA-FLPs). The genetic algorithm suggested is based upon the slicing structure where the relative locations of the facilities on the floor are represented by a location matrix encoded in two chromosomes. A block layout is constructed by partitioning the floor into a set of rectangular blocks using guillotine cuts satisfying the areas requirements of the departments. The procedure takes into account four objective functions (material handling costs, aspect ratio, closeness and distance requests) by means of a Pareto based evolutionary approach. The main advantage of the proposed formulation, with respect to existing referenced approaches (e.g. bay structure), is that the search space is considerably wide and the practicability of the layout designs is preserved, thus improving the quality of the solutions obtained.     

The facility layout problem approached using a fuzzy model and a genetic search

The problem of facility layout design is discussed, taking into account the uncertainty of production scenarios and the finite production capacity of the departments. The uncertain production demand is modelled by a fuzzy number, and constrained arithmetic operators are used in order to calculate the fuzzy material handling costs. By using a ranking criterion, the layout that represents the minimum fuzzy cost is selected. A flexible bay structure is adopted as a physical model of the system while an effective genetic algorithm is implemented to search for a near optimal solution in a fuzzy contest. Constraints on the aspect ratio of the departments are taken into account using a penalty function introduced into the fitness function of the genetic algorithm. The efficiency of the genetic algorithm proposed is tested in a deterministic context and the possibility of applying the fuzzy approach to a medium-large layout problem is explored.This revised version was published in June 2005 with corrected page numbers.     

Solving the design of distributed layout problem using forecast windows: A hybrid algorithm approach

In today’s competitive environment, manufacturing facilities have to be more responsive to the frequent changes in product mix and demand by realigning their organizational structure for minimizing material handling cost. However, manufacturing firms are reluctant to modify the layout as it leads to operation disruption and excess rearrangement cost. In this paper, we present an alternative approach for designing a multi-period layout (i.e., distributed layout) that maintains a tradeoff between re-layout cost and cost of excess material handling. Obtaining an optimal solution to distributed layout problem is generally a difficult task, owing to larger size of quadratic assignment problem. In order to overcome the aforementioned drawback, a meta-heuristic, named ‘CSO-DLP’ (Clonal Symbiotic Operated-Distributed Layout Planning) is developed for designing a distributed layout that jointly determines the arrangement of department and flow allocation among them. It inherits its trait from Symbiotic algorithm and Clonal algorithm. In addition to these; the concept of ‘forecast window’ is used, which evaluates the layout for varying number of periods at a given time. The proposed meta-heuristic is applied on a benchmark dataset and the effect of system parameters, such as rearrangement cost, department disintegration, and duplication are investigated and benchmarked in this paper.     

Solving an extended multi-row facility layout problem with fuzzy clearances using GA

Multi-row facility layout problem (MRFLP) is a class of facility layout problems, which decides upon the arrangement of facilities in some fixed numbers of rows in order to minimize material handling cost. Nowadays, according to the new layout requirements, the facility layout problems (FLPs) have many applications such as hospital layout, construction site layout planning and layout of logistics facilities. Therefore, we study an extended MRFLP, as a novel layout problem, with the following main assumptions: 1) the facilities are arranged in a two-dimensional area and without splitter rows, 2) multiple products are available, 3) distance between each pair of facilities, due to inaccurate and flexible manufacturing processes and other limitations (such as WIPs, industrial instruments, transportation lines and etc.), is considered as fuzzy number, and 4) the objective function is considered as minimizing the material handling and lost opportunity costs. To model these assumptions, a nonlinear mixed-integer programming model with fuzzy constraints is presented and then converted to a linear mixed-integer programming model. Since the developed model is an NP-hard problem, a genetic algorithm approach is suggested to find the best solutions with a minimum cost function. Additionally, three different crossover methods are compared in the proposed genetic algorithm and finally, a sensitivity analysis is performed to discuss important parameters.     

Vector Based Genetic Algorithm to optimize predictive analysis in network security

A new Intrusion Detection System (IDS) for network security is proposed making use of a Vector-Based Genetic Algorithm (VBGA) inspired by evolutionary approaches. The novelty in the algorithm is to represent chromosomes as vectors and training data as matrices. This approach allows multiple pathways to calculate fitness function out of which one particular methodology is used and tested. The proposed method uses the overlap of the matrices with vector chromosomes for model building. The fitness of the chromosomes is calculated from the comparison of true and false positives in test data. The algorithm is flexible to train the chromosomes for one particular attack type or to detect the maximum number of attacks. The VBGA has been tested on two datasets (KDD Cup-99 and CTU-13). The proposed algorithm gives high detection rate and low false positives as compared to traditional Genetic Algorithm. A detailed comparative analysis is given of proposed VBGA with the traditional string-based genetic algorithm on the basis of accuracy and false positive rates. The results show that vector based genetic algorithm provides a significant improvement in detection rates keeping false positives at minimum.     

带有一刀切约束的二维非规则装箱算法

针对切割下料领域的二维非规则一刀切装箱问题,首先给出了最小移动距离的定义,然后给出了一种基于最大移动距离的启发式算法。该算法通过计算一个凸多边形滑动至另一个凸多边形内部所允许的最大移动距离,对待排件的摆放位置进行一次性定位,避免使用传统的NFP(Not-Fit-Polygon)预判交方法,极大地缩短了排样的整体时间,最后使用模拟退火算法对下料流程进行了优化,改善了排样结果。     

Capacitated lot sizing by using multi-chromosome crossover strategy

The capacitated lot sizing problem (CLSP) arises when, under capacity constraints, the decision maker has to determine the production schedule and lot sizes that will minimize the total costs involved. The costs considered in this article are order, inventory carrying, and labor costs. The fitness function for the chromosome is computed using these cost elements. Next, the chromosomes are partitioned into good and poor segments based on the individual product chromosomes. This information is later used during crossover operation and results in crossover among multiple chromosomes. Product chromosomes are grouped into three groups, group 1 (top X%), group 2 (next Y%), and group 3 (last Z%). Product chromosomes from Groups 1, 2 and 3 can only form pairs with chromosomes from group 1. Besides, different crossover and mutation probabilities are applied for each group. The results of the experimentation showed that the different strategies of the proposed approach produced much better results than the classical genetic algorithm. This article has been submitted to the Journal of Intelligent Manufacturing for publication.     

Heuristic algorithm to solve the multi-floor layout problem with the consideration of elevator utilization

This paper presents an algorithm to get a solution of the facility layout problem for multiple-floors. In a number of previous research, these problems are formulated as a combinatorial optimization problem to minimize only the cost owing to material flow between departments ignoring the utilization of the elevators, which is a typical transportation equipment for vertical transportation. In our proposed algorithm, the capacity of elevators is taken into consideration. Then the proposed algorithm optimizes the number and location of elevator with the consideration of the assignment of each carriage to the elevators. Numeric results are given to demonstrate the effectiveness of the proposed algorithm.     

Evolutionary algorithms for scheduling m-machine flow shop with lot streaming

This paper addresses the problem of making sequencing and scheduling decisions for n jobs–m-machines flow shops under lot sizing environment. Lot streaming (Lot sizing) is the process of creating sub lots to move the completed portion of a production sub lots to down stream machines. There is a scope for efficient algorithms for scheduling problems in m-machine flow shop with lot streaming. In recent years, much attention is given to heuristics and search techniques. Evolutionary algorithms that belong to search heuristics find more applications in recent research. Genetic algorithm (GA) and hybrid genetic algorithm (HEA) also known as hybrid evolutionary algorithm fall under evolutionary heuristics. On this concern this paper proposes two evolutionary algorithms namely, GA and HEA to evolve best sequence for makespan/total flow time criterion for m-machine flow shop involved with lot streaming and set-up time. The following two algorithms are used to evaluate the performance of the proposed GA and HEA: (i) Baker's algorithm (BA), an optimal solution procedure for two-machine flow shop problem with lot streaming and makespan objective criterion and (ii) simulated annealing algorithm (SA) for m-machine flow shop problem with lot streaming and makespan and total flow time criteria.     

Genetic algorithms for the optimal common due date assignment and the optimal scheduling policy in parallel machine earliness/tardiness scheduling problems

Earliness/tardiness scheduling problems with undetermined common due date which have wide application background in textile industry, mechanical industry, electronic industry and so on, are very important in the research fields such as industry engineering and CIMS. In this paper, a kind of genetic algorithm based on sectional code for minimizing the total cost of assignment of due date, earliness and tardiness in this kind of scheduling problem is proposed to determine the optimal common due date and the optimal scheduling policy for determining the job number and their processing order on each machine. Also, simulated annealing mechanism and the iterative heuristic fine-tuning operator are introduced into the genetic algorithm so as to construct three kinds of hybrid genetic algorithms with good performance. Numerical computational results focusing on the identical parallel machine scheduling problem and the general parallel machine scheduling problem shows that these algorithms outperform heuristic procedures, and fit for larger scale parallel machine earliness/tardiness scheduling problem. Moreover, with practical application data from one of the largest cotton colored weaving enterprises in China, numerical computational results show that these genetic algorithms are effective and robust, and that especially the performance of the hybrid genetic algorithm based on simulated annealing and the iterative heuristic fine-tuning operator is the best among them.     

Hybrid imperialist competitive algorithm,variable neighborhood search,and simulated annealing for dynamic facility layout problem

Today’s manufacturing plants tend to be more flexible due to rapid changes in product mix and market demand. Therefore, this paper investigates the problem of location and relocation (when there are changes incurred to the material flows between departments) manufacturing facilities such that the total cost of material flows and relocation costs are minimized. This problem is known as the dynamic facility layout problem (DFLP), which is a general case of static facility layout problem. This paper proposes a robust and simply structured hybrid technique based on integrating three meta-heuristics: imperialist competitive algorithms, variable neighborhood search, and simulated annealing, to efficiently solve the DFLP. The novel aspect of the proposed algorithm is taking advantage of features of all above three algorithms together. To test the efficiency of our algorithm, a data set from the literature is used for the experimental purpose. The results obtained are quite promising in terms of solution quality for most of the test problems.     

Optimization of earth recycling and dump truck dispatching

Earth is one of the major materials used in the construction industry. Recycling earth reduces the cost of waste disposal and generates revenue for contractors. More importantly, utilizing reusable materials minimizes construction waste and reduces the environmental impact. One of the key requirements to ensure the effectiveness of earth recycling is a well-designed schedule for the movement of the dump trucks that transport earth materials to their destinations in a timely fashion. Therefore, in this study a mathematical model is developed to plan earth recycling and dump truck dispatching simultaneously from a system optimization perspective. The time-space network technique is adopted to formulate the dump truck and earth material flows in the dimensions of time and space. The model is formulated as a special multiple commodity network flow problem, which is characterized as NP-hard. To solve the real-world large-scale problems in a reasonable time, a heuristic algorithm using problem decomposition and relaxation is proposed. To suitably evaluate the model and algorithm, a case study is performed using actual parameters obtained from a large construction project in Taiwan and statistical data gleaned from government reports. The test results show that the problem can be efficiently solved and the error gap is within 0.5%. The scenario analysis indicates that the proposed framework could also be used for different problem scales. The model, coupled with the solution algorithm, has potential to serve as a planning tool for construction contractors to design optimal schedules for recycling earth materials and dispatching trucks.     

Solving a multi-floor layout design model of a dynamic cellular manufacturing system by an efficient genetic algorithm

This paper presents a mixed-integer programming model for a multi-floor layout design of cellular manufacturing systems (CMSs) in a dynamic environment. A novel aspect of this model is to concurrently determine the cell formation (CF) and group layout (GL) as the interrelated decisions involved in the design of a CMS in order to achieve an optimal (or near-optimal) design solution for a multi-floor factory in a multi-period planning horizon. Other design aspects are to design a multi-floor layout to form cells in different floors, a multi-rows layout of equal area facilities in each cell, flexible reconfigurations of cells during successive periods, distance-based material handling cost, and machine depot keeping idle machines. This model incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. The objective is to minimize the total costs of intra-cell, inter-cell, and inter-floor material handling, purchasing machines, machine processing, machine overhead, and machine relocation. Two numerical examples are solved by the CPLEX software to verify the performance of the presented model and illustrate the model features. Since this model belongs to NP-hard class, an efficient genetic algorithm (GA) with a matrix-based chromosome structure is proposed to derive near-optimal solutions. To verify its computational efficiency in comparison to the CPLEX software, several test problems with different sizes and settings are implemented. The efficiency of the proposed GA in terms of the objective function value and computational time is proved by the obtained results.     

求解第二类广义旅行商问题的虚顶点遗传算法

按照费用函数满足约束条件的不同,可以把广义旅行商问题(GeneralizedTravelingSalesmanProblem,简称GTSP)分为两类。目前,对GTSP解法的研究主要是面向费用函数满足三角不等式的第一类问题,而对于费用函数不满足三角不等式的第二类问题,则研究的比较少。文章针对第二类GTSP问题,提出了在广义染色体中加入虚顶点的新遗传算法。经过14个TSP问题库内的基准问题的测试表明,新算法是有效的。     

Proposed genetic algorithms for construction site layout

The positioning of temporary facilities on a construction site is an area of research which has been recognised as important but which has received relatively little attention. In this paper, a genetic algorithm is proposed to solve the problem in which m facilities are to be positioned to n available sites such that the total cost of construction and interactive cost due to facility layout constraints are minimised. A sequence-based genetic formulation of the problem is presented. Genetic crossover and mutation operators are developed for the problem and their performance evaluated and compared on an example project. The results obtained suggest that the different operators perform very differently but that one could be relied on to find the optimum. Overall, experiments suggest that the technique will prove useful when tackling real problems.