总图运输设计剖析

企业必须充分的认识到总图运输设计的现实意义,并给予高度的重视,不断的优化总图运输的设计,提升总图运输设计的质量。本文就总图运输设计进行简单的概述,并对总图运输设计的特点进行相关的分析,意在不断的促进企业总图运输设计水平有效的提高。     

生产型企业总图运输设计分析

本文对企业总图运输设计中的节约用地、企业总图运输设计中的厂址选择以及企业总图运输设计中的运输方式选择三个方面的内容进行了详细的分析和探讨,从而详细的论述了生产型企业的总图运输设计优化分析的工作。     

浅谈石油企业总图运输设计

本研究根据石油企业总图运输设计的特点,结合其设计总原则,对石油企业总图运输设计的具体内容进行了相关探讨,希望能够促进石油企业的发展。     

浅谈矿山总图运输设计与节约用地

本文首先介绍了总图运输设计,重点论述了矿山总图运输设计与节约用地,主要包括建筑的布置紧凑合理,建筑方位的确定,以及道路的科学规划。     

浅析总图的设计理论及应用研究

总图的设计,作为企业设计中核心的存在,其是在实际情况的基础上,用以满足应用的功能以及经济还有环保高效为目标的存在,其综合的规划了与布置场地中的所有建筑物以及交通的运输线路还有工程管线等等工程设施的设计。总图的设计包含了平面的布置,竖向的布置,管线综合的布置还有方案优选这四个方面。经过大量的实践证明,合理的选择总图的设计方案,可以有效的降低工程的投资,提高企业运营的效果。     

浅析五彩湾矿区四号露天煤矿工业场地的选择

总图运输设计在工程项目中已得到越来越广泛的运用，本文根据五彩湾矿区四号露天煤矿实际，采用方案比较的方法，最终确定了选址方案，有效地降低了投资成本，符合城乡规划、空间管制、生态环境等各方面的要求。     

天津至承德城际铁路引入宝坻地区方案研究

通过分析宝坻地区既有铁路现状及车流特点,结合在建铁路及宝坻地区总图规划,按照充分利用既有设施、运输组织顺畅、客货运设施布置合理以及符合承德市发展规划的原则,研究了两种接入方案,通过对方案综合经济技术比较,推荐宝坻南站西侧引入方案。     

浅析石油化工总图运输设计的节约用地措施

总图运输设计工作是一项非常复杂的工作,而在我国进行总图设计工作时,应充分的认识到节约用地问题的重要性,将节约用地的理念贯穿于设计工作中的每一个环节中,更加科学合理的使用每一寸土地,从而尽可能的提升土地的利用率。下面笔者根据多年的设计实践经验,总结出了石油化工企业总图运输设计过程中的几项有效的节约用地措施。     

石油企业总图优化问题分析

总图优化是石油企业发展所必须要完成的一项工作内容,总图优化出现问题会对石油企业发展造成非常严重的影响。由于石油自身一些性质导致其在运输及储存过程中的不安全因素非常多,相关人员必须要做好总图优化工作。但是现如今我国很多石油企业的总图优化依然存在一些问题,为此本文针对当前石油企业总图优化存在的问题进行了简单的分析,并且提出了相应的解决办法。     

基于可拓工程理论的工业企业总图设计方案评价

通过梳理总图设计方案评价思路的演变过程,提出以处于可行方案经典集合外的“理想解”为方案评价标尺的拓展量化分析思路;并据此将可拓工程理论引入总图设计方案评价,设计了方案评价的物元分析模型。该模型通过特征指标标准化构作可行方案物元,并依据特征指标的取值范围获取理想参照物物元,进而在考虑指标权重的基础上借助测算并对比各可行方案物元与正、负理想参照物物元之间的综合贴近度,实现方案的评价与优选。结合某机械厂的总图设计方案评价实例,验证了基于可拓工程观点的总图设计方案评价具有可操作性与简洁有效性。     

Integrating occupational health and safety in facility layout planning,part I: methodology

An influential factor affecting the efficiency of a manufacturing facility is its layout. In a production facility, measure for efficiency can be based on the total cost of transporting the items between different departments and throughout the facility. However, other factors may influence efficiency of the manufacturing facility too. As such are: supporting the organisation's vision through improved material handling, material flow and control; effectively assigning people, equipment, space and energy; minimising capital investment; adaptability and ease of maintenance; as well as providing for employee safety and job satisfaction. By incorporating health and safety measures in the initial design of a facility layout, the organisation may avoid money and manpower loss resulting from industrial accidents. This paper proposes a facility layout planning methodology which integrates the occupational health and safety (OHS) features in the early design of a facility layout. The model considers transportation cost in the facility as well as safety concerns. By this means, the OHS issues are reflected prior to the construction of a facility.     

Cellular machine layout based on the Segmented Flow Topology

This paper introduces a facility layout design procedure for converting an existing manufacturing system with a predefined aisle structure to a cellular manufacturing system based on the 'segmented flow topology' (SFT) developed by Sinriech and Tanchoco. The proposed procedure is aimed at finding the best machine grouping, along with the locations of pick-up and delivery stations and machine layout for each cell based on an existing facility. The objective is to minimize the total material handling cost. In contrast to previous work in this area, the proposed design procedure takes into account both distance and material flow in forming machine clusters. In addition, a revised cost model for material handling system, which accounts for different aspects of capital and operating cost, is presented.     

An integrated approach to the facilities and material handling system design

The facility layout problem involves the optimal location of manufacturing facilities into a workshop. The classical approach to the layout design is carried out in two separate steps: the first step is the construction of the block layout, i.e. the location of the departments into the workshop, and the second step is the design of the material handling system. The separate optimization of these two aspects of the problem leads to solutions that can be far from the total optimum. In this paper, an integrated approach to the facilities and material handling system design is proposed. Referring to a physical model, named the bay structure , and to a unidirectional AGV system, a genetic approach is proposed to individuate the locations of the departments, the positions of the pickup/delivery stations and the direction of the flow-path. The minimization of material handling cost is adopted as optimality criterion.     

EXCITE: Expert consultant for in-plant transportation equipment

The cost of handling material is a vital factor in the facilities design process, whether it is for a new facility or for the redesign of an existing facility. Handling activities generally account for 30 to 40% of production costs, but in some industries they can be as high as 70%. Well designed handling systems are thus crucial for reduced costs and increased profits.

A key task in the material handling system design process is the selection and configuration of equipment for transport and storage in a facility. Material handling equipment selection is a complex, tedious task, and there is usually more than one good answer for any particular situation. A number of good quantitative techniques are available to aid the industrial engineer in determining layout design with the aim of reducing material handling cost. Unfortunately, there are few tools other than checklists to aid the engineer in the selection of appropriate, cost-effective material handling equipment. Analytical models are not often applied in industry because they generally consider only quantifiable factors such as cost and utilization and are difficult to implement.

This paper describes a knowledge-based approach for addressing the major factors that influence equipment selection. The research effort involved two major activities: compilation of a knowledge base from an in-depth review and modification of traditional checklists and published literature on equipment selection; and development of a prototype expert system for material handling equipment selection.     

MULTI-HOPE: A tool for multiple floor layout problems

The objective of most facility layout problems is to minimize material handling cost, which is directly proportional to both the distance between the machines and the mix, as well as the volume of products handled. The mix and volume of products are dependent on the demand patterns, and the distance is dependent on the layout plan used for the facility. Because it is relatively difficult to change the demand patterns, and hence the mix and volume of products, the primary focus of most designers has been to deal with the distance attribute of the material handling costs. The limitations of available horizontal space create a need to explore vertical expansion of facilities. This brings up new aspects of vertical material handling and flow that need to be considered in the facility design problem. In this paper, we present a genetic algorithm-based heuristic for generating block layouts for multiple-floor layout problems. This approach produces better solutions than existing simulated annealing-based heuristics for all but one of five multiplefloor test problems available in the literature.     

Facility layout optimization using simulation and genetic algorithms

Traditionally, the objective of a facility layout problem has been to minimize the material handling cost of the manufacturing system. While it is important to reduce the amount of material handling, the traditional methods do not address the actual time at which the material is transported. In today's short cycle time production environments, the timing of material movement may have a bigger impact on the productivity of the system than its cost. In this paper, a facility layout optimization technique is presented that takes into consideration the dynamic characteristics and operational constraints of the system as a whole, and is able to solve the facility layout design problem based on a system's performance measures, such as the cycle time and productivity. Each layout solution is presented in the form of a string that is suitable for analysis by a genetic algorithm technique. These solutions are then translated into simulation models by a specially designed automated simulation model generator. Genetic algorithms are used to optimize the layout for manufacturing effectiveness while simulation serves as a system performance evaluation tool. Combined with a statistical comparison technique to reduce the simulation burden, the test results demonstrate that the proposed approach overcomes the limitations of traditional layout optimization methods and is capable of finding optimal or near optimal solutions.     

基于损失函数的随机设施布局优化分析

现有的随机设施布局问题的优化方法或是优化总物料处理成本的均值以获得最鲁棒的布局,或是优化其方差以获得最稳定的布局,但两者均只优化了总物料处理成本随机特征中的一个方面.通过引入田口损失函数的概念,建立了新的随机设施布局优化模型,对总物料处理成本的均值和方差同时进行优化求解,相比于现有的优化方法,新方法得到的布局形式能够使系统同时获得较好的鲁棒性和稳定性.     

Incorporating heterogeneous distance metrics within block layout design

This paper focuses on optimal design of block layouts when using more than one distance metric within a single facility. Previous work in block layout has assumed a single distance metric, usually the shortest rectilinear distance between department centroids, during the design step. However, most facilities have more than one method of material handling and alternative material handling systems can imply alternative distance metrics and cost structures. Specifically, up to three distance metrics within a single facility are considered--the shortest rectilinear distance between centroids (appropriate for automated guided vehicles and forklift trucks), the Tchebychev (maximum) distance (appropriate for overhead cranes) and the shortest Euclidean distance between centroids (appropriate for conveyor lines). Optimal block layouts using each of these distance metrics individually and then collectively are compared and contrasted. This approach can also be used to compare layouts when the choice of material handling system is not clear. It is argued that incorporating the distance metric that best reflects the planned material handling device is more realistic than previous formulations, avoids block layouts that are sub-optimal for the material handling systems installed, and is quite workable within a heuristic optimization framework.     

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 Monte Carlo simulation based heuristic procedure for solving dynamic line layout problems for facilities using conventional material handling devices

A simple procedure is proposed to identify line layout solutions when a production facility with work centres of unequal size uses conventional material handling devices and operates under stochastic demand scenarios. The procedure uses Monte Carlo simulation (MCS) to empirically search for robust solutions defined as those that simultaneously meet minimum material handling cost performance levels across all demand scenarios. The results reported in this study suggest that ‘robust’ line layout solutions can be identified using a modest volume of random sampling. The procedure and results are demonstrated through a series of sample problems.