Metaheuristics for fuzzy dynamic facility layout problem with unequal area constraints and closeness ratings

Dynamic facility layout problem deals with the problem of arranging and rearranging the layout plan of a system throughout several periods. In each period, the material handling costs are different from the past period due to the change in the market demand and product mix. In this paper, the uncertainty that exists in transportation values’ forecast is modeled by fuzzy theory. In this paper, departments have unequal areas. This means that in each period, departments can be placed only in certain places due to different spacing requirements. In addition, closeness rating matrix is considered in order to model the goodness of different layout plans with regard to qualitative factors according to decision maker. Accordingly, fuzzy dynamic facility layout problem with unequal areas and closeness rating matrix is considered that aims to minimize the uncertain material handling costs as well as the shifting costs, and maximize closeness rating with regard to space requirements of unequal area departments. A number of fuzzy algorithms are developed in order to deal with the problem. A number of ranking criteria from the literature are implemented in order to compare the performance of the developed algorithms.     

Capacities-based supply chain network design considering demand uncertainty using two-stage stochastic programming

The design of a supply chain (SC) aims to minimize cost so the product can reach the customer at the cheapest cost with flexible demand. The demand of a product is variable with time and environment. Most of the researchers have considered investment cost, processing cost, and transportation cost as variable costs to minimize the cost while considering a constant demand. In actual practice, the demands are flexible. In this paper, a two-stage stochastic programming model has been proposed for a capacities-based network design of a supply chain for flexible demands while considering inventory carrying cost and missed opportunity cost in addition to the above-mentioned costs. It will enhance the logistic planning and seek the location network optimally. Furthermore, in the first stage, decision variables represent different nodes (facility locations of echelons) of the supply chain, with the assumption that they will be considered at the design stage before uncertain parameters are unveiled. On the other hand, decision variables related to the amount of products to be produced and stored in the nodes of the SC, the flows of materials among the entities of the network, and shortfalls and excess at the customer centers are considered as second-stage variables. The methodology has been illustrated by solving an example. It was found that the proposed model yields more feasible and advantageous results.     

A new probabilistic coverage model for ambulances deployment with hypercube queuing approach

Aiming at improving the efficiency and reliability of ambulance service, several location models for ambulance stations have been proposed in the facility location literature. Two well-known approaches to this problem are coverage and median models. Coverage model looks for the location to maximize the (deterministic or probabilistic) covered demand of ambulance calls. Hence, this model can be thought of reliability-oriented model. In median model, the objective is to minimize the total traveling distance of the ambulances from the station to the scene of call and thus is the cost-oriented model. In this paper, a new probabilistic coverage model is introduced. It uses the idea of MEXCLP and mixes it with the hypercube queuing model. The model is applied in a real case for one of Tehran city's zones in Iran. The results were satisfactory and show the applicability of the model. Sensitivity analysis was conducted to determine the overall behavior of system responsiveness against parameters of the model. Discussions on sensitivity analysis are presented.     

Models for first-pass FMS investment analysis

Consider a firm that could choose either an inexpensive product-specific (dedicated) facility or a costly flexible facility, or a combination of the two, in order to satisfy demand for two product groups. Flexible technology offers benefits of scale and scope economies. However, it may have added operational costs due to the need to have excess capacity (to permit changeovers) and to maintain cycle stocks. As a consequence, the economic viability of flexible technology is significantly affected by the choice of the operating doctrine. This article presents first-pass decision models to help the firm choose the optimum sizes of facilities and the degree of flexibility for the flexible facility such that the contingent operational costs are simultaneously optimized. Two variations of the problem are considered. Whereas, the first applies to a situation in which the demand rates are constant and predictable, the second considers random demands. In each case, this study provides a formulation of the problem, structural results, and the sensitivity of the results to cost parameters. These results have modest data and computational requirements, making them suitable for first-cut attempts at narrowing available choices.This research has been supported by the Natural Sciences and Engineering Research Council of Canada through operating grants to the authors.     

The impact of production mix variations and models varieties on the parts-feeding policy selection in a JIT assembly system

One of the significant challenges in operating a mix-model assembly system is the feeding of parts to the productive units. In order to avoid production loss, assembly systems require uninterrupted availability of components to feed workstations. On the other hand, the feeding of assembly components has to be performed in a way that minimises the related costs. In the past, the feeding system most widely used was so-called ‘line storage’ in which the components were stored along the assembly stations in large quantities and were periodically refilled by the central warehouse. Following just-in-time principles, nowadays, assembly system feeding is undertaken by supermarkets, as in decentralised storage areas close to the assembly lines. From such kinds of warehousing, a growing number of manufacturers are adopting two other feeding strategies: the kanban system, which continuously refills the assembly stations through the pull kanban system, or the kitting system, in which kits of components are prepared and delivered following the product through the assembly stations. This paper aims to quantitatively analyse and compare these two recent feeding strategies, considering the production mix variation and the assembled models variety influence. Moreover, kanban-kitting feeding policy and the related optimization issues are considered as hybrid. The findings from an industrial case study and a simulation analysis are also reported. Finally, a decision-making tool that defines a series of ‘convenience areas’ for the different feeding policies is provided.     

A fuzzy DEMATEL-based solution approach for facility layout problem: a case study

Facility layout in production systems is a difficult activity since both qualitative and quantitative factors affect the location decision, and also influence and causal relationship between these factors should be determined for a better location. In this respect, it is demonstrated in this study that fuzzy decision-making trial and evaluation laboratory (fuzzy DEMATEL) method can effectively be used in handling facility layout problems in practice. The qualitative factors that described by linguistic terms can be taken into account through fuzzy structure of the method. Considering this, a fuzzy DEMATEL-based solution approach for facility layout problem is proposed in this study. The proposed approach takes into account both qualitative and quantitative location factors. To address the need in practice, six important location factors are considered in this study. These are material flow, information flow, personnel flow, equipment flow, environmental condition, and supervision of personnel. This study differs from the previous works in that it applies fuzzy DEMATEL method to facility layout problem. To explore the viability of the proposed approach, a real world problem in a machinery industry firm is handled.     

Managing a new multi-objective model for the dynamic facility layout problem

In today’s manufacturing environment, the facility layout needs to be adaptable to changes. This situation requires the solution of the dynamic layout problem. But in previous studies of dynamic facility layout optimization, the main objective is to minimize the sum of the re-arrangement and material handling costs. To be more realistic, each of these cost terms in objective function might be of different importance to decision makers. In this research, the objective function has been considered as two distinct functions. This formulation enables decision makers to apply their own views. On the other hand, in the proposed model the adjacency-based objective aims at maximizing adjacency scores between the facilities in a facility layout has also been used. The proposed multi-objective model was defined as a complex combinatorial optimization problem. It has also been the objective of the present study to evaluate some of the known methods that have been proposed to solve the multi-objective problem. The results for test problems showed that the population based metaheuristic methods are useful tools in solving proposed model.     

A simulation-based approach for risk assessment of facility layout designs under stochastic product demands

In traditional facility layout design method, the facility layout is governed by the flow intensity between departments, which is the product flow quantity between departments. A facility layout design must maintain its efficiency over time while coping with the uncertainty in product demand. An error in the product demand assessment can render the layout inefficient with respect to material handling costs. Analytical models for the determination of the risk involved in a facility layout design are difficult to derive when the problem of size and complexity increases. This paper presents a simulation-based method for predicting the uncertainty associated with the layout. The validation of the simulation approach against analytical procedures is first detailed. The methodology for the simulation based approach is provided. Three scenarios of increasing complexity and size are also detailed. Results from case studies have shown that the procedure results in reduction of risk as high as 80%.     

滑翔靶机飞行控制系统的设计与实现

以DSP和FPGA相结合设计飞行控制计算机。以飞行控制计算机为核心,配合GPS接收机、垂直陀螺仪和舵机等器件,并以数字电台作为靶机与地面站之间的通信设备,构成靶机的新型低成本飞行控制系统。     

基于DSP28335的某型靶机系统设计

针对某大型军用无人靶机的实际需求,根据飞行控制相关原理,进行了该靶机的控制系统设计。描述了靶机飞控系统的硬件组成结构,基于DSPF28335处理器,详细分析设计了各组件与飞控计算机的信号输入输出方式。仿真实验表明,该靶机飞控系统控制策略有效,且各机载子系统与飞控计算机能够协调工作。     

Parallel variable neighborhood search for solving fuzzy multi-objective dynamic facility layout problem

In spite of the classic approaches of solution of dynamic facility layout problem, which only material handling and rearrangement costs are considered as objective function, these problems are the multi-objective problems. In this paper, a mixed integer linear programming formulation is presented for multi-objective dynamic facility layout problem concerning flexible bay structure. In addition, three current objectives in dynamic facility layout problems including minimizing material handling and rearrangement costs, maximizing adjacency rate, and minimizing shape ratio difference have been considered. Also, for solving this problem, two methods including the GAMS software and proposed parallel variable neighborhood search (PVNS) algorithm are used. So, it is worth mentioning that four test problems are solved by them, and the results show that the proposed PVNS algorithm is more efficient than the GAMS software.     

A Simulation Study of Dynamic Order-up-to Policies in a Supply Chain with Non-Stationary Customer Demand and Information Sharing

This study deals with the development of heuristic settings for dynamic order-up-to levels at different installations in a supply chain with non-stationary customer demand, in order to minimize the sum of inventory, shortage and transport costs. Balance equations are developed to capture the dynamics of supply chain. Mathematical expressions are derived to determine the dynamic order-up-to levels as a function of forecasted demand, forecasted replenishment leadtime and safety factor. Towards this attempt, different heuristic settings with respect to the safety factor are first proposed. Next, three levels of information sharing among the members of the supply chain are considered to study the impact of information sharing on the total cost. Finally, the use of a forecasting technique for predicting the replenishment leadtime is considered. A supply chain is simulated to evaluate the proposed dynamic order-up-to policies based on various heuristic settings. It is found that the use of forecast of replenishment leadtime and customer demand information sharing, and the proposed safety-stock heuristic settings in the determination of dynamic order-up-to levels, significantly reduce the sum of discounted inventory, shortage and transport costs of the supply chain.     

Locating service centers optimizing customers’ perspective criteria

A network of several demand nodes is considered. We want to locate a number of multiserver facilities at some nodes assuming at least one server must be located at each node. Accordingly, the number of servers has to be more than the nodes number. We propose a model for locating these facilities from customers’ perspective in such a way as to minimize the total average queue length, when customers choose the facilities they patronize, by the travel distance (or time) to the facility and the number of servers at the facility. This means that each customer can obtain the service or goods from several (i.e., rather than only one) facilities, according to a probabilistic distribution. Genetic and simulated annealing (SA) algorithms based heuristics are proposed to solve this problem, which belongs to nonlinear integer programming problems. We evaluate the proposed algorithms for the solution quality solving a number of numerical problems. The results indicate that SA whose objective function is around 26% less than that of genetic is of higher performance.     

A method to exchange the demand of products for cost improvement

In a multiproduct manufacturing environment, the actual demands of various products are either available, or these are expected. There are situations when demand of a product can be substituted with that of another. In the context of cyclic manufacture, all the items are produced in an optimal cycle time, and the production facility runs at certain cost level. The total cost consists of the facility setup cost, inventory carrying costs, and the manufacturing time cost for the basic case. The total cost is optimized. For the purpose of total cost improvement, a method is presented in which the demand of a product is exchanged with that of another item in the group. The basic model without backorders is analyzed first. Then, it is extended for an inclusion of shortages that are either completely backlogged or partially. In addition to the cost components discussed before, shortage costs are included in the total cost for this case. Finally, after a discussion of idle time costs, these are also included briefly in the formulation of the total cost. The proposed methods are useful for implementation in a variety of industrial or business situations in the context of internal benchmarking or gradual improvement.     

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.     

River velocity measurements using optical flow algorithm and unoccupied aerial vehicles: A case study

Accurate and reliable measurements of river flow are critical for a multitude of hydrologic engineering applications. However, flow rate measurements using in-situ sensors are uncertain in many applications and physical measurements of velocity may not be practical due to inaccessible sites or flood conditions. Recent advances in remote sensing using unoccupied aerial vehicles have overcome these limitations through non-contact measurements of river velocities; however, existing approaches have several shortcomings, including the need for artificial tracers in the absence of debris and prior knowledge of tracer size, shape, and flow direction. This case study seeks to overcome these shortcomings through the development of a system that utilizes drones, video imaging, and state-of-the-art optical flow algorithms to measure velocity in rivers. This system was applied along Menomonee River in Wauwatosa, WI. To remotely sense river flow, a DJI Matrice 210 RTK drone equipped with a Zenmuse X5S camera was used to capture video. The video data from the drone was analyzed using optical flow algorithms to generate velocity estimations. River velocity was measured directly at point locations using a hand-held velocimeter. Results indicate that the optical flow algorithms estimate the magnitude of surface velocity to within 13–27% of hand-held measurements without the use of artificial seeding. These outcomes suggest that this system could be used as a possible method to measure velocities in rivers.     

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.     

A modified particle swarm optimization for disaster relief logistics under uncertain environment

Relief logistics is one of the most important elements of a relief operation. This paper investigates a relief chain design problem where not only demands but also supplies and the cost of procurement and transportation are considered as the uncertain parameters. Furthermore, the model considers uncertainty for the locations where those demands can arise and the possibility that a number of the facility could be partially destroyed by the disaster. The proposed model for this study is formulated as a mixed-integer nonlinear programming to minimize the sum of the expected total cost (which includes costs of location, procurement, transportation, holding, and shortage) and the variance of the total cost. The model simultaneously determines the location of relief distribution centers and the allocation of affected area to relief distribution centers. Furthermore, an efficient solution approach based on particle swarm optimization is developed in order to solve the proposed mathematical model. At last, computational results for several instances of the problem are presented to demonstrate the feasibility and effectiveness of the proposed model and algorithm.     

用于热泵空调的异井回灌系统理论计算与试验研究

利用热泵空调系统夏季运行需水量和回灌井渗流量的数学模型,计算回灌井半径,采用增大渗流面积的方法,获得较好回灌效果;由需水量计算匹配的潜水泵功率,使系统节能效果更佳;试验证明,在天津地区农村住户采用一抽一灌的异井回灌方式,效果良好。     

Correlated storage assignment to minimize zone visits for BOM picking

The objective of this study is to design a correlated storage assignment system by storing items with demand dependences together to minimize zone visits when picking materials/parts in production line. The demand dependences can be obtained from the products’ bill of materials (BOMs) and production rates. Due to the large number of materials in a BOM, picking capacity constraint is explicitly considered. A mathematical model is formulated and heuristic algorithms are proposed, including two direct heuristics and two hybrid genetic algorithms with different crossover mechanisms. Numerical experimentation shows that the hybrid genetic algorithm with 2-point crossover outperforms others and can be practical in warehouse design.