A heuristic procedure for the integrated facility layout design and flow assignment problem

We present an efficient iterative heuristic procedure for solving the integrated layout design and product flow assignment problem. The layout design decisions involve planar location of unequal-area machines with duplicates. The product flows are assigned to machines according to the product processing routes. The integrated decision problem is a nonlinear mixed integer model which cannot be efficiently solved using classical methods for large problems. We propose a novel integrated heuristic procedure based on the alternating heuristic, a perturbation algorithm and sequential location heuristic. Since the alternating heuristic between facility layout design and product-machine assignment sub-problems terminates with local optima, we developed a perturbation algorithm based on assignment decisions. The results of an experimental study show that proposed procedure is both efficient and effective in identifying quality solutions for small to very large-sized problems.     

An entropy-based algorithm to solve the facility layout design problem

This paper presents a new design approach used in order to solve the facility layout problem. The layout problem is viewed from the general perspective as a problem of the arrangement of elements within a system. The main attributes and relationships among the elements of the system are analyzed.     

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 mixed-integer approach to Core-Edge design of storage area networks

In this paper we address the problem of optimal network design for a storage area network. We consider the Core-Edge reference topology and present two formulations for the Core-Edge storage area network design problem. One formulation excludes explicit host/device connections to the edge (as is common in currently available heuristics), the other includes these connections to allow the modeling of multiple disjoint paths between hosts and devices. These formulations include generic component types to reduce the number of constraints and variables, with the properties of these components being determined as part of the solution process. The size of the formulation is further reduced by a preprocessing method that removes suboptimal switches and links from consideration.     

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.     

A continuous approach to considering uncertainty in facility design

This paper presents a formulation of the facilities block layout problem which explicitly considers uncertainty in material handling costs on a continuous scale by use of expected values and standard deviations of product forecasts. This formulation is solved using a genetic algorithm meta-heuristic with a flexible bay construct of the departments and total facility area. It is shown that depending on the attitude of the decision-maker towards uncertainty, the optimal design can change significantly. Furthermore, designs can be optimized directly for robustness over a range of uncertainty that is pre-specified by the user. This formulation offers a computationally tractable and intuitively appealing alternative to previous stochastic layout formulations that are based on discrete scenario probabilities.     

Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings,lot splitting and flexible reconfiguration by simulated annealing

This paper presents a novel mixed-integer non-linear programming model for the layout design of a dynamic cellular manufacturing system (DCMS). In a dynamic environment, the product mix and part demands are varying during a multi-period planning horizon. As a result, the best cell configuration for one period may not be efficient for successive periods, and thus it necessitates reconfigurations. Three major and interrelated decisions are involved in the design of a CMS; namely cell formation (CF), group layout (GL) and group scheduling (GS). A novel aspect of this model is concurrently making the CF and GL decisions in a dynamic environment. The proposed model integrating the CF and GL decisions can be used by researchers and practitioners to design GL in practical and dynamic cell formation problems. Another compromising aspect of this model is the utilization of multi-rows layout to locate machines in the cells configured with flexible shapes. Such a DCMS model with an extensive coverage of important manufacturing features has not been proposed before and incorporates several design features including alternate process routings, operation sequence, processing time, production volume of parts, purchasing machine, duplicate machines, machine capacity, lot splitting, intra-cell layout, inter-cell layout, multi-rows layout of equal area facilities and flexible reconfiguration. The objective of the integrated model is to minimize the total costs of intra and inter-cell material handling, machine relocation, purchasing new machines, machine overhead and machine processing. Linearization procedures are used to transform the presented non-linear programming model into a linearized formulation. Two numerical examples taken from the literature are solved by the Lingo software using a branch-and-bound method to illustrate the performance of this model. An efficient simulated annealing (SA) algorithm with elaborately designed solution representation and neighborhood generation is extended to solve the proposed model because of its NP-hardness. It is then tested using several problems with different sizes and settings to verify the computational efficiency of the developed algorithm in comparison with the Lingo software. The obtained results show that the proposed SA is able to find the near-optimal solutions in computational time, approximately 100 times less than Lingo. Also, the computational results show that the proposed model to some extent overcomes common disadvantages in the existing dynamic cell formation models that have not yet considered layout problems.     

Application of the Analytic Network Process to facility layout selection

This paper applies the Analytic Network Process (ANP) method to the selection of the best facility layout plan based on multiple dependent and independent criteria. This is the first time that this method is used in such a context. An ANP model is built taking into account the interdependencies between criteria that are found based on experts’ opinions and fundamental equations. A network structure is built that shows all elements and clusters and their interactions that can be used to find the most effective layout. Limit priorities are computed which identify the most important factors in the selection process. A case study is conducted in a wood factory which represents a real demonstration of the developed model. A comparison is conducted between ANP and Analytic Hierarchy Process (AHP) which shows the differences between the two methods. Finally, sensitivity analysis shows the robustness of the model.     

Mathematical formulation and a new metaheuristic for the constrained double-floor corridor allocation problem

A layout plan for a manufacturing system that is designed without any facility constraints will most likely be infeasible when confronted with reality. Additionally, considering that land available for building industrial plants is limited and its cost is high, it is necessary to investigate the layout planning of two and multi-floor facilities. To address these shortages in the scientific literature, we focus on the double-floor corridor allocation problem (DFCAP) which covers a wide range of complex facility constraints, such as fixed floor constraints, fixed row constraints, fixed positioning constraints, mutual floor constraints, mutual row constraints, sequencing constraints and adjacency constraints. For the model mentioned above, we term it as a constrained DFCAP (cDFCAP). A mixed-integer linear programming model is formulated for the cDFCAP. In order to solve larger realistic problems, a constrained metaheuristic with the memetic algorithm framework customised for solving the cDFCAP is introduced in this work. In our algorithm, four problem-specific heuristic rules to construct a set of initial solutions are developed. In addition, an ideal parameter combination for our constrained memetic algorithm is determined through a Taguchi experimental design. Finally, the results of a set of cDFCAP instances with different sizes (n = 10∼80) report that our provided approach is effective for the considered problem.     

An integrated model and a decomposition-based approach for concurrent layout and material handling system design

This paper presents an integer programming formulation that integrates decisions concerning the layout of the resource groups on the shop floor with the design of the material handling system. The model reflects critical practical concerns, including the capacity of the material flow network and of the handling transporters, as well as the tradeoff between fixed (construction and acquisition) and variable (operational) costs. For realistic industrial cases, the size of the problem prevents the solution using explicit or implicit enumeration methods. Instead, the global model is decomposed into standard optimization problems: quadratic assignment, fixed charge capacitated network design, and non-depot distance-constrained vehicle routing. An integrated solution method, guided by a simulated annealing scheme, solves the global shop design problem. The algorithm takes advantage of the proposed decomposition and converges to a final design which is feasible with respect to all problem constraints. The method is applied to redesign the facility of a large manufacturer of radar antennas. The resulting shop configuration exhibits substantially decreased material handling effort, and requires significantly smaller investment costs compared to the existing facility.     

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.     

A simple filter-and-fan approach to the facility location problem

The design of effective neighborhood search procedures is a primary issue for the performance of local search and advanced metaheuristic algorithms. Several recent studies have focused on the development of variable depth neighborhoods that generate sequences of interrelated elementary moves to create more complex compound moves. These methods are chiefly conceived to produce an adaptive search as the number of elementary moves in a compound move may vary from one iteration to another depending on the state of the search. The objective is to achieve this goal with modest computational effort. Although ejection chain methods are currently the most advanced methods in this domain, they usually require more complex implementations. The filter-and-fan (F&F) method appears as an alternative to ejection chain methods allowing for the creation of compound moves based on an effective tree search design.     

A knowledge-based approach for multi-factory production systems

This paper investigates scheduling of jobs with deadlines across a serial multi-factory supply chain which involves minimizing sum of total tardiness and total transportation costs. Jobs can be transported among factories and can be delivered to the customer in batches which have limited capacity. The aim of this optimization problem is threefold: (1) determining the number of batches, (2) assigning jobs to batches, and (3) scheduling the batches production and delivery in each factory. The proposed problem formulated as a mixed-integer linear program. Then the model's performance is analyzed and evaluated through two examples. Moreover, a knowledge-based imperialistic competitive algorithm (KBICA) is also presented to find an approximate optimum solution for the problem. Computational experiments of the proposed problem investigate the efficiency of the method through different sizes of the test problems.     

A new mixed-integer linear programming model for rescue path planning in uncertain adversarial environment

Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far rely on key assumptions and heuristic procedures to reduce problem complexity. In this paper, a new model is proposed to solve the discrete rescue path planning problem for a single agent navigating in uncertain adversarial environment. It relies on a novel and simplified mathematical mixed-integer linear programming formulation aimed at minimizing traveled distance and threat exposure. Exploiting a user-defined survivability function approximation and survivability threshold, the approximate model allows constructing a solution providing an adjustable optimality gap interval on the optimal solution. Experimental results show the value of the proposed approach in computing near optimal solutions reasonably fast for various problem instances.     

Hybrid optimization and simulation to design a logistics network for distributing perishable products

Dynamics in product quality complicate the design of logistics networks for perishable products, like flowers and other agricultural products. Complications especially arise when multiple products from different origins have to come together for processes like bundling. This paper presents a new MILP model and a hybrid optimization–simulation (HOS) approach to identify a cost-optimal network design (i.e. facility location with flow and process allocation) under product quality requirements. The MILP model includes constraints on approximated product quality. A discrete event simulation checks the feasibility of the design that results from the MILP assuming uncertainties in supply, processing and transport. Feedback on product quality from the simulation is used to iteratively update the product quality constraints in the MILP. The HOS approach combines the strengths of strategic optimization via MILP and operational product quality evaluation via simulation. Results, for various network structures and varying degrees of dynamics and uncertainty, show that if quality decay is not taken into account in the optimization, low quality products are delivered to the final customer, which results in not meeting service levels and excess waste. Furthermore, case results show the effectiveness of the HOS approach, especially when the change from one iteration to the next is in the choice of locations rather than in the number of location. It is shown that the convergence of the HOS approach depends on the gap between the product quality requirements and the quality that can be delivered according to the simulation.     

Using simulation for facility design: A case study

A discrete event simulation model was developed and used to estimate the storage area required for a proposed overseas textile manufacturing facility. It was found that the simulation was able to achieve this because of its ability to both store attribute values and to show queuing levels at an individual product level. It was also found that the process of undertaking the simulation project initiated useful discussions regarding the operation of the facility. Discrete event simulation is shown to be much more than an exercise in quantitative analysis of results and an important task of the simulation project manager is to initiate a debate among decision makers regarding the assumptions of how the system operates.     

Human evolutionary model: A new approach to optimization

The aim of this paper is to propose the Human Evolutionary Model (HEM) as a novel computational method for solving search and optimization problems with single or multiple objectives. HEM is an intelligent evolutionary optimization method that uses consensus knowledge from experts with the aim of inferring the most suitable parameters to achieve the evolution in an intelligent way. HEM is able to handle experts’ knowledge disagreements by the use of a novel concept called Mediative Fuzzy Logic (MFL). The effectiveness of this computational method is demonstrated through several experiments that were performed using classical test functions as well as composite test functions. We are comparing our results against the results obtained with the Genetic Algorithm of the Matlab’s Toolbox, Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), Particle Swarm Optimizer (PSO), Cooperative PSO (CPSO), G3 model with PCX crossover (G3-PCX), Differential Evolution (DE), and Comprehensive Learning PSO (CLPSO). The results obtained using HEM outperforms the results obtained using the abovementioned optimization methods.     

A unified approach to pattern recognition

The paper is an outline of a new approach to pattern recognition developed by the author. A fuller introduction to the approach will appear soon.⁽¹⁾ Within the proposed framework the two principal approaches to pattern recognition—vector and syntactic—are unified.     

A hierarchical/relational approach to modeling

We describe a hierarchical/relational approach to math programming modeling. The approach transforms free-form generic modeling constructs into linear and nonlinear models which are independent of end-user data structures. The underlying relationships with graph-based interfaces and the inherent aggregation/disaggregation capabilities of the approach are also discussed.The modeling approach will be illustrated with several process industry applications including distribution planning, operations planning, and production scheduling.     

三元统计语言模型对基因表达载体设计的优化

不断地将一些合成生物学标准“零件”以一定的标准装配,就可以得到由数十个功能片段组成的复杂表达载体。但是每一类的合成生物学标准“零件”数量众多,随着这些标准“零件”的不断开发,其数量也在进一步增加。在进行表达载体构建的最后阶段,从众多的“零件”中选择合适的以组装成功能性表达载体费时费力,并且容易发生错误。为解决这一问题,采用了自然语言处理的统计语言模型,并以该模型为基础应用动态规划算法优化表达载体设计,从众多的选项中找出最优者。利用这一方法可以减少进行生物学实验的冗余操作,从而减少表达载体构建过程中的花费。