Linear Programming Based Genetic Algorithm for the Unequal Area Facility Layout Problem

The facility layout problem (FLP) is generally defined as locating a set of departments in a facility with a given dimension. In this paper, a hybrid genetic algorithm (GA)/linear programming (LP) approach is proposed to solve the FLP on the continuous plane with unequal area departments. This version of the FLP is very difficult to solve optimally due to the large number of binary decision variables in mixed integer programming (MIP) models as well as the lack of tight lower bounds. In this paper, a new encoding scheme, called the location/shape representation, is developed to represent layouts in a GA. This encoding scheme represents relative department positions in the facility based on the centroids and orientations of departments. Once relative department positions are set by the GA, actual department locations and shapes are determined by solving an LP problem. Finally, the output of the LP solution is incorporated into the encoding scheme of the GA. Numerical results are provided for test problems with varying sizes and department shape constraints. The proposed approach is able to either improve on or find the previously best known solutions of several test problems.     

Unequal-area stochastic facility layout problems: solutions using improved covariance matrix adaptation evolution strategy,particle swarm optimisation,and genetic algorithm

Determining the locations of departments or machines in a shop floor is classified as a facility layout problem. This article studies unequal-area stochastic facility layout problems where the shapes of departments are fixed during the iteration of an algorithm and the product demands are stochastic with a known variance and expected value. These problems are non-deterministic polynomial-time hard and very complex, thus meta-heuristic algorithms and evolution strategies are needed to solve them. In this paper, an improved covariance matrix adaptation evolution strategy (CMA ES) was developed and its results were compared with those of two improved meta-heuristic algorithms (i.e. improved particle swarm optimisation [PSO] and genetic algorithm [GA]). In the three proposed algorithms, the swapping method and two local search techniques which altered the positions of departments were used to avoid local optima and to improve the quality of solutions for the problems. A real case and two problem instances were introduced to test the proposed algorithms. The results showed that the proposed CMA ES has found better layouts in contrast to the proposed PSO and GA.     

Unequal-area,fixed-shape facility layout problems using the firefly algorithm

In manufacturing industries, the facility layout design is a very important task, as it is concerned with the overall manufacturing cost and profit of the industry. The facility layout problem (FLP) is solved by arranging the departments or facilities of known dimensions on the available floor space. The objective of this article is to implement the firefly algorithm (FA) for solving unequal-area, fixed-shape FLPs and optimizing the costs of total material handling and transportation between the facilities. The FA is a nature-inspired algorithm and can be used for combinatorial optimization problems. Benchmark problems from the previous literature are solved using the FA. To check its effectiveness, it is implemented to solve large-sized FLPs. Computational results obtained using the FA show that the algorithm is less time consuming and the total layout costs for FLPs are better than the best results achieved so far.     

A hybrid genetic algorithm for the single row layout problem

The arrangement of machines or departments along a straight line is known as single row layout and it is a widely employed configuration in flexible manufacturing systems. In this paper, a hybrid genetic algorithm (HGA) is proposed to solve the single row layout design problem with unequal sized machines and unequal clearances. The algorithm is developed by hybridisation of a genetic algorithm with a local search operator. The proposed HGA is tested on 51 well known data sets from the literature with equal and unequal clearances, and the results are compared with the best known solutions. Finally, algorithm's effectiveness in reaching previously known best solutions is revealed and improvements up to 7% in problems with unequal clearance are obtained.     

A sequence-pair representation and MIP-model-based heuristic for the facility layout problem with rectangular departments

In Facility Layout Problem (FLP) research, the continuous-representation-based FLP can consider all feasible all-rectangular-department solutions. Given this flexibility, this representation has become the representation of choice in FLP research. Much of this research is based on a methodology of Mixed-Integer Programming (MIP) models. However, these MIP-FLP models can only solve problems with a limited number of departments to optimality due to the large number of combinations of the binary variables used in the models to maintain feasibility with respect to departments overlapping. Our research centers around the sequence-pair representation, a concept that originated in the Very Large Scale Integration (VLSI) design literature. We show that an exhaustive search of the sequence-pair solution space will result in finding the optimal layout of the MIP-FLP and that every sequence-pair solution is position consistent (although possibly not layout feasible) in the MIP-FLP. We propose a genetic-algorithm-based heuristic that combines the sequence-pair representation with the MIP-FLP model. Numerical experiments based on different sized test problems from both the literature and industrial applications are provided and the solutions are compared with both the optimal solutions and the solutions from other heuristics to show the effectiveness and efficiency of our heuristic. For 11 data sets from the literature we provide solutions better than those previously found. For two large industrial application data sets we perform a sensitivity analysis with respect to the department aspect ratio constraint.     

A genetic algorithm for optimal unequal-area block layout design

The use of a genetic algorithm (GA) to optimise the binary variables in a mixed-integer linear programming model for the block layout design problem with unequal areas that satisfies area requirements is analysed. The performance of a GA is improved using a local search through the possible binary variables assignment; results encourage the use of this technique to find a set of feasible solutions for the block layout design with more than nine departments.     

Hybridization of ABC and PSO algorithms for improved solutions of RCPSP

A resource-constrained project scheduling problem (RCPSP) is one of the most famous intractable NP-hard problems in the operational research area in terms of its practical value and research significance. To effectively solve the RCPSP, we propose a hybrid approach by integrating artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms. Moreover, a novel structure of ABC-PSO is devised based on embedded ABC-PSO (EABC-PSO) and sequential ABC-PSO (SABC-PSO) strategies. The EABC-PSO strategy mainly applies the PSO algorithm to update the process of the ABC algorithm while the SABC-PSO strategy demonstrates an approach in which computational results obtained from the ABC algorithm are further improved based on the PSO algorithm. In both strategies, bees in the ABC process are entitled to learning capacity from the best local and global solutions in terms of the PSO concept. Subsequently, the updates of solutions are premeditated with crossover and insert operators together with double justification methods. Computational results obtained from the tests on benchmark sets show that the proposed ABC-PSO algorithm is efficient in solving RCPSP problems, demonstrating clear advantages over the pure ABC algorithm, the PSO algorithm, and a number of listed heuristics.     

An exact penalty function method for optimising QAP formulation in facility layout problem

A quadratic assignment problem (QAP), which is a combinatorial optimisation problem, is developed to model the problem of locating facilities with material flows between them. The aim of solving the QAP formulation for a facility layout problem (FLP) is to increase a system’s operating efficiency by reducing material handling costs, which can be measured by interdepartmental distances and flows. The QAP-formulated FLP can be viewed as a discrete optimisation problem, where the quadratic objective function is optimised with respect to discrete decision variables subject to linear equality constraints. The conventional approach for solving this discrete optimisation problem is to use the linearisation of the quadratic objective function whereby additional discrete variables and constraints are introduced. The adoption of the linearisation process can result in a significantly increased number of variables and constraints; solving the resulting problem can therefore be challenging. In this paper, a new approach is introduced to solve this discrete optimisation problem. First, the discrete optimisation problem is transformed into an equivalent nonlinear optimisation problem involving only continuous decision variables by introducing quadratic inequality constraints. The number of variables, however, remains the same as the original problem. Then, an exact penalty function method is applied to convert this transformed continuous optimisation problem into an unconstrained continuous optimisation problem. An improved backtracking search algorithm is then developed to solve the unconstrained optimisation problem. Numerical computation results demonstrate the effectiveness of the proposed new approach.     

Multi-objective optimization of engineering systems using game theory and particle swarm optimization

This study proposes particle swarm optimization (PSO) based algorithms to solve multi-objective engineering optimization problems involving continuous, discrete and/or mixed design variables. The original PSO algorithm is modified to include dynamic maximum velocity function and bounce method to enhance the computational efficiency and solution accuracy. The algorithm uses a closest discrete approach (CDA) to solve optimization problems with discrete design variables. A modified game theory (MGT) approach, coupled with the modified PSO, is used to solve multi-objective optimization problems. A dynamic penalty function is used to handle constraints in the optimization problem. The methodologies proposed are illustrated by several engineering applications and the results obtained are compared with those reported in the literature.     

Mitigation of risk in facility layout design for single and multi-period problems

The most desirable characteristic of a facility layout is its ability to maintain its efficiency over time while coping with the uncertainty in product demand. In the traditional facility layout design method, the facility layout is governed by the flow intensity between departments, which is the product flow quantity between departments. Hence, an error in the product demand assessment can render the layout inefficient with respect to material handling costs. Most of the research integrates uncertainty in the form of probability of occurrence of different from-to charts. In an environment where the variability of each product demand is independent, the derivation of ‘probabilistic from-to chart’ based scenario cannot be used to address uncertainty of individual demands. This paper presents an FLP (facility layout problem) approach to deal with the uncertainty of each product demand in the design of a facility layout. Two procedures are presented: the first procedure is utilised to assess the risk associated with the layout, while the second procedure is used to develop the layout that minimises the risk. Results from case studies have shown that the procedure produces a reduction of risk as high as 68%.     

Simulated annealing based parallel genetic algorithm for facility layout problem

The facility layout problem (FLP), a typical combinational optimisation problem, is addressed in this paper by implementing parallel simulated annealing (SA) and genetic algorithms (GAs) based on a coarse-grained model to derive solutions for solving the static FLP with rectangle shape areas. Based on the consideration of minimising the material flow factor cost (MFFC), shape ratio factor (SRF) and area utilisation factor (AUF), a total layout cost (TLC) function is derived by conducting a weighted summation of MFFC, SRF and AUF. The evolution operations (including crossover, mutation, and selection) of GA provide a population-based global search in the space of possible solutions, and the SA algorithm can lead to an efficient local search near the optimal solution. By combing the characteristics of GA and SA, better solutions will be obtained. Moreover, the parallel implementation of simulated annealing based genetic algorithm (SAGA) enables a quick search for the optimal solution. The proposed method is tested by performing a case study simulation and the results confirm its feasibility and superiority to other approaches for solving FLP.     

Solving the container pre-marshalling problem using variable length genetic algorithms

In container terminals, the yard area consists of a set of blocks, which consists of a set of bays. Each bay consists of a set of stacks, which consists of a set of tiers. In the container pre-marshalling problem, an initial layout of a bay is converted to a final desired layout. The final layout follows the given loading schedule of this bay. This has a direct impact on the most important container terminal performance measure: the vessel loading time. The deviation between the current layout and the desired layout is expressed by the value of the mis-overlays. The objective of the pre-marshalling problem is to eliminate the mis-overlays with the minimum number of container movements. In this article, a variable chromosome length genetic algorithm was applied to solve the problem. The results of the new solution approach were compared against benchmark instances and the results were remarkably better.     

Preprocessing for static and dynamic facility layout problems

Most facility layout problems have departments with unequal areas and have significant rearrangement costs. This paper describes a model and improved algorithm which simultaneously handles these parameters. An existing algorithm solves the dynamic facilities layout problem while permitting the departments to have unequal areas. One part of the algorithm solves a mixed integer programming problem to find the desired block diagram layout. This large, complex problem could only be solved optimally for small problems. Therefore a preprocessing method was developed to prespecify certain obvious department pair orientations, which had previously required binary variables. The method uses estimated location, department sizes, and flow costs to determine the probable variable values. Then, a revised branch and bound strategy solves for the less obvious department pair orientations. Test results show a significant cost reduction on a variety of previously published problems, and feasible solutions to previously unsolved problems. The algorithm found a layout solution to the standard . CRAFT problem which has 10 5% lower costs than the previously best published layout.     

混流装配线调度问题的离散粒子群优化解

混流装配线调度问题是JIT生产中的一个重要问题。借鉴二进制遗传算法中的交叉操作过程，对传统的连续型粒子群算法进行改进，使其适用于离散问题的优化处理。然后以丰田公司的汽车组装调度函数作为目标函数，利用改进的离散粒子群算法进行求解。对比分析表明：新算法所得结果优于常用的目标追随法、遗传算法、模拟退火等方法。     

A methodology for solving the unequal area facility layout problem using distance and shape-based measures

This paper presents a methodology for solving the unequal area facility layout problem commonly encountered in industry practice. A mixed-binary nonlinear-programming model is formulated to capture the operational issues encountered on the shop floor. In particular, in addition to the distance measure that is typically used to quantify the material handling costs, the impact of geometry or the shape of the departments is quantified in the formulation of the model. A higher-level heuristic solution algorithm, based on a concept known as ‘tabu search’, is proposed to efficiently solve industry-relevant problems. The methodology not only considers the impact of both distance and shape-based measures simultaneously in the proposed initial solution finding mechanism, but also in the evaluation of the objective function during the entire search procedure, in the hope that it will lead to identifying a better final solution. Taking into consideration fixed and variable tabu list sizes, along with long-term memory with maximum and minimum frequencies, has led to developing six different heuristics for the solution algorithm. A single factor experiment based on randomized block design has been used to compare the performances of the six different heuristics on three different problem structures—small, medium, and large—using the total cost as the criterion. Based on this experiment, the characterizations of search procedures have been recommended to facilitate identifying the best solution for each problem structure. The proposed method is also compared with those in the published literature by solving fairly well known unequal area facility layout problems. When an improvement is observed, the comparison has led to identifying a percentage improvement in total cost of approximately 2.8% to 11.8%, thus demonstrating the effectiveness of the model and the algorithm.     

An optimal approach for maximizing the number of adjacencies in multi floor layout problem

Multi-floor facility layout problem concerns the arrangement of departments on the different floors. In this paper, a new mathematical model is proposed for multi-floor layout with unequal department area. Maximising the number of useful adjacencies among departments is considered as the objective function. The adjacencies are divided into two major categories: horizontal and vertical adjacencies. The horizontal adjacency may be occurred between the departments assigned to same floors while the vertical can be happened between departments assigned to any consecutive floors. A minimum common boundary length (surface area) between any two horizontal (vertical) adjacent departments is specified. The efficiency of the model is demonstrated by six illustrative examples. The proposed model is practical in multi-floor plant where the existence of adjacencies between departments is useful or essential due to possible establishment of conveyor, transferring pipes, lift truck route, etc.     

A multi-objective particle swarm optimisation algorithm for unequal sized dynamic facility layout problem with pickup/drop-off locations

This paper deals with a multi-objective unequal sized dynamic facility layout problem (DFLP) with pickup/drop-off locations. First, a mathematical model to obtain optimal solutions for small size instances of the problem is developed. Then, a multi-objective particle swarm optimisation (MOPSO) algorithm is implemented to find near optimal solutions. Two new heuristics to prevent overlapping of the departments and to reduce ‘unused gaps’ between the departments are introduced. The performance of the MOPSO is examined using some sets of available test problems in the literature and various random test problems in small, medium, and large sizes. The percentage of improvements on the initial solutions is calculated for small, medium and large size instances. Also, the generation metric and the space metric for non-dominated solutions are examined. These experiments show the good performance of the developed MOPSO and sensitivity analysis show the robustness of the obtained solutions.     

A large-scale hybrid simulated annealing algorithm for cyclic facility layout problems

The cyclic facility layout problem (CFLP) is a special case of the dynamic facility layout problem (DFLP) in which there are several production periods and the production cycle repeats itself by going to the first period after the last one because of the seasonal nature of products. In this article, a mixed integer programming formulation is developed for the CFLP. In the DFLP literature, department shapes are assumed to be given or fixed. However, this assumption does not hold in the case of the CFLP because the facility size is limited and the area requirements of the departments change significantly throughout the planning horizon. Therefore, department dimensions and sizes are considered as decision variables in the CFLP. A large-scale hybrid simulated annealing algorithm (LS-HSA) is proposed to solve the formulated problem and shown to be effective and versatile as it can be applied to various facility layout problems.     

Bi-objective facility expansion and relayout considering monuments

In this paper, the unequal area facility expansion and relayout problem is studied. The facility relayout problem is important since both manufacturing and service entities must modify their layouts over time when their operational characteristics change. A bi-objective approach is proposed to solve the relayout problem for cases of both a fixed facility area and an expanded facility area. Material handling costs and relayout costs are minimized using a tabu search meta-heuristic optimizer. This heuristic randomly alternates the objective function between the two objectives of the problem in each step and, by doing so, eliminates the difficulty of weighting and scaling the two objectives. The approach is flexible in handling various aspects of the problem such as stationary portions of departments (i.e., monuments), addition of new departments, and changes in existing department and facility areas. Computational experiments show that the bi-objective tabu search approach is effective and tractable. The use of the Pareto front of designs is demonstrated by showing a few approaches to analyzing the trade-offs between initial costs (relayout cost) and ongoing expenses (material handling costs).     

Coupling ant colony optimization and the extended great deluge algorithm for the discrete facility layout problem

This article uses a hybrid optimization approach to solve the discrete facility layout problem (FLP), modelled as a quadratic assignment problem (QAP). The idea of this approach design is inspired by the ant colony meta-heuristic optimization method, combined with the extended great deluge (EGD) local search technique. Comparative computational experiments are carried out on benchmarks taken from the QAP-library and from real life problems. The performance of the proposed algorithm is compared to construction and improvement heuristics such as H63, HC63-66, CRAFT and Bubble Search, as well as other existing meta-heuristics developed in the literature based on simulated annealing (SA), tabu search and genetic algorithms (GAs). This algorithm is compared also to other ant colony implementations for QAP. The experimental results show that the proposed ant colony optimization/extended great deluge (ACO/EGD) performs significantly better than the existing construction and improvement algorithms. The experimental results indicate also that the ACO/EGD heuristic methodology offers advantages over other algorithms based on meta-heuristics in terms of solution quality.