基于混合蚁群算法的生产系统设施规划问题研究

设施规划问题主要研究生产设备的布局规划,从而减小厂区内的物料搬运成本。一个有效的设施规划有利于生产过程中整体运作效率的提高。随着市场竞争的日趋激烈,市场环境处于不断的变化之中,制造企业需不断对设施布局进行重新规划来适应不断变化的市场环境对产品需求量的影响,并达到降低成本的目的。这一问题便需要用多阶段设施规划（MFLP）的方法来解决。本文提出了一种改进的混和蚁群算法（HACO）来解决带有财务预算约束的多阶段设施规划问题,并将此方法与其他一些典型的启发式算法进行了对比分析。结果表明,本文提出的HACO算法是求解带有财务预算约束的MFLP问题的一种有效的方法。     

The simulation of industrial robot systems

This paper describes the GRASP computer aided design system for modelling and evaluating industrial robot workplaces. GRASP satisfies a range of simulation needs within the context of designing, implementing and operating industrial robotic systems. The GRASP software may be used to investigate robots operating by themselves or, more likely, as part of an integrated cell. Facilities within GRASP assist workplace layout, position and velocity evaluations, clash detection and co-ordination between items. A robot library exists and is being extended, and an embryo off-line programming facility has been used under restricted conditions. GRASP has been used to help solve a wide range of practical industrial robot problems and has proved itself technically, and also as an educational tool, by showing how a proposed system would operate. The paper describes the facilities within GRASP.     

On multi-criteria chance-constrained capacitated single-source discrete facility location problems

This work aims at investigating multi-criteria modeling frameworks for discrete stochastic facility location problems with single sourcing. We assume that demand is stochastic and also that a service level is imposed. This situation is modeled using a set of probabilistic constraints. We also consider a minimum throughput at the facilities to justify opening them. We investigate two paradigms in terms of multi-criteria optimization: vectorial optimization and goal programming. Additionally, we discuss the joint use of objective functions that are relevant in the context of some humanitarian logistics problems. We apply the general modeling frameworks proposed to the so-called stochastic shelter site location problem. This is a problem emerging in the context of preventive disaster management. We test the models proposed using two real benchmark data sets. The results show that considering uncertainty and multiple objectives in the type of facility location problems investigated leads to solutions that may better support decision making.     

An approachto the machine layout problem in a cellular manufacturing environment

The model of the machine layout problem MLP in a cellular manufacturing environment attains additional dimensions as it should satisfy the qualitative interconnections between the machines and the location restrictions of an existing factory environment. A new MLP model based on merging pre-emptive goal programming and simulated annealing has been developed for machine layout in cells. This model seeks to find feasible solutions by addressing practical issues of implementation as well as reducing the total travel distances for parts between machines. The new model can also be applied to facility layout problems FLP . The computational work is demonstrated by applying the model to problems of both quantitative and qualitative types, and has produced encouraging results. This model is particularly attractive for layout problems with realistic goals and constraints. To show the performance of the model in handling real-world problems, a practical example has been introduced and solved using the proposed model.     

An ant colony algorithm for solving budget constrained and unconstrained dynamic facility layout problems

The main characteristic of today's manufacturing environments is volatility. Under a volatile environment, demand is not stable. It changes from one production period to another. To operate efficiently under such environments, the facilities must be adaptive to changing production requirements. From a layout point of view, this situation requires the solution of the dynamic layout problem (DLP). DLP is a computationally complex combinatorial optimization problem for which optimal solutions can only be found for small size problems. It is known that classical optimization procedures are not adequate for this problem. Therefore, several heuristics including taboo search, simulated annealing and genetic algorithm are applied to this problem to find a good solution. This work makes use of the ant colony optimization (ACO) algorithm to solve the DLP by considering the budget constraints. The paper makes the first attempt to show how the ACO can be applied to DLP with the budget constraints. In the paper, example applications are presented and computational experiments are performed to present suitability of the ACO to solve the DLP problems. Promising results are obtained from the solution of several test problems.     

Simulation investigation of the stochastic behaviour of selected group technology similarity coefficients

Abstract

Abstract. The problem of restructuring manufacturing facilities into a cellular format has been addressed using a variety of approaches, including block-diagonalization of the machine-component matrix, mathematical programming formulations and cluster analyses. This paper addresses the problem of assessing whether or not the machine-component mix of a particular facility is amenable to cellular restructuring. The focus is cluster analytic; specifically a number of recently developed similarity coefficients are applied to problems generated via Monte Carlo simulation. The results show that, in a number of cases, the quality of the original block diagonal structure had a significant effect on the shape (in particular, skewncss and kurtosis) of the distribution of similarity coefficient values.     

Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services

Simulation is a powerful tool for modeling complex systems with intricate relationships between various entities and resources. Simulation optimization refers to methods that search the design space (i.e., the set of all feasible system configurations) to find a system configuration (also called a design point) that gives the best performance. Since simulation is often time consuming, sampling as few design points from the design space as possible is desired. However, in the case of multiple objectives, traditional simulation optimization methods are ineffective to uncover the efficient frontier. We propose a framework for multi-objective simulation optimization that combines the power of genetic algorithm (GA), which can effectively search very large design spaces, with data envelopment analysis (DEA) used to evaluate the simulation results and guide the search process. In our framework, we use a design point's relative efficiency score from DEA as its fitness value in the selection operation of GA. We apply our algorithm to determine optimal resource levels in surgical services. Our numerical experiments show that our algorithm effectively furthers the frontier and identifies efficient design points.     

Social Optimal Location of Facilities with Fixed Servers,Stochastic Demand,and Congestion

We consider two capacity choice scenarios for the optimal location of facilities with fixed servers, stochastic demand, and congestion. Motivating applications include virtual call centers, consisting of geographically dispersed centers, walk‐in health clinics, motor vehicle inspection stations, automobile emissions testing stations, and internal service systems. The choice of locations for such facilities influences both the travel cost and waiting times of users. In contrast to most previous research, we explicitly embed both customer travel/connection and delay costs in the objective function and solve the location–allocation problem and choose facility capacities simultaneously. The choice of capacity for a facility that is viewed as a queueing system with Poisson arrivals and exponential service times could mean choosing a service rate for the servers (Scenario 1) or choosing the number of servers (Scenario 2). We express the optimal service rate in closed form in Scenario 1 and the (asymptotically) optimal number of servers in closed form in Scenario 2. This allows us to eliminate both the number of servers and the service rates from the optimization problems, leading to tractable mixed‐integer nonlinear programs. Our computational results show that both problems can be solved efficiently using a Lagrangian relaxation optimization procedure.     

Two-level network design with intermediate facilities: An application to electrical distribution systems

We consider the two-level network design problem with intermediate facilities. This problem consists of designing a minimum cost network respecting some requirements, usually described in terms of the network topology or in terms of a desired flow of commodities between source and destination vertices. Each selected link must receive one of two types of edge facilities and the connection of different edge facilities requires a costly and capacitated vertex facility. We propose a hybrid decomposition approach which heuristically obtains tentative solutions for the vertex facilities number and location and use these solutions to limit the computational burden of a branch-and-cut algorithm. We test our method on instances of the power system secondary distribution network design problem. The results show that the method is efficient both in terms of solution quality and computational times.     

An efficient heuristic procedure for solving the layout design problem

The limited ability of schematic procedures, constraints of linear programming techniques, inflexibility of construction methods and inadequacy of dynamic programming approaches to provide acceptable solutions to realistic size layout design problems has led to an ever increasing interest in heuristic procedures. Comparative studies have shown that heuristic procedures can satisfy more desirable qualities in an ‘ideal algorithm’ to a greater extent than competitive techniques. Excessive computational effort, which has been one of the main criticisms levelled against heuristic approaches in the past, proves to be less of a problem in relation to layout design. By utilizing unique attributes associated with real-life problems and using small incisive modifications it is demonstrated how a heuristic procedure can be significantly improved.     

具有遗憾值约束的鲁棒供应链网络设计模型研究

考虑不确定性环境,研究战略层次的供应链网络鲁棒设计问题,目标是设计参数发生摄动时,供应链性能能够保持稳健性。基于鲁棒解的定义,建立从上游供应商选择到下游设施选址-需求分配的供应链网络设计鲁棒优化模型;提出确定遗憾值限定系数上限和下限的方法,允许决策者调节鲁棒水平,选择多种供应链网络结构;通过模型分解与协调,设计了供应链节点配置的禁忌搜索算法。算例的计算结果表明了禁忌搜索算法具有良好的收敛特性,以及在处理大规模问题上的优越性;同时也反映了利用鲁棒优化模型进行供应链网络设计,可以有效规避投资风险。     

废弃物回收的多层逆向物流网络优化设计问题研究

废弃物逆向物流网络设计问题是一个关系到经济效益和社会效益的问题.本文研究了废弃物逆向物流网络设计问题,包括选择中转站和处理站的地址,确定将产生点的废弃物分配给中转站以及从中转站运送到处理站的最佳策略,确定处理站的容量.考虑了在满足公众的意愿的情况下,建立了一个多目标的纯整数规划模型,最小化总的建设费用和设施对公众产生的负效用.设计了基于启发式的两阶段分解算法求解,从而构建一个废弃物回收的多层逆向物流网络.最后的算例仿真表明了算法的有效性和可行性.     

A simulated annealing algorithm for designing cellular manufacturing systems with productivity consideration

In this paper, we propose a productivity model for solving the machine-part grouping problem in cellular manufacturing (CM) systems. First, a non-linear 0-1 integer programming model is developed to identify machine groups and part families simultaneously. This model aims to maximize the system productivity defined as the ratio of total output to the total material handling cost. Second, an efficient simulated annealing (SA) algorithm is developed to solve large-scale problems. This algorithm provides several advantages over the existing algorithms. It forms part families and machine cells simultaneously. It also considers production volume, sales price, and maximum number of machines in each cell and total material handling cost. The proposed SA also has the ability to determine the optimum number of manufacturing cells. The performance of the developed models is tested on eight problems of different size and complexity selected from the literature. The results show the superiority of the SA algorithm over the mathematical programming model in both productivity and computational time.     

The min-p robust optimization approach for facility location problem under uncertainty

Improper value of the parameter p in robust constraints will result in no feasible solutions while applying stochastic p-robustness optimization approach (p-SRO) to solving facility location problems under uncertainty. Aiming at finding the lowest critical p-value of parameter p and corresponding robust optimal solution, we developed a novel robust optimization approach named as min-p robust optimization approach (min-pRO) for P-median problem (PMP) and fixed cost P-median problem (FPMP). Combined with the nearest allocation strategy, the vertex substitution heuristic algorithm is improved and the influencing factors of the lowest critical p-value are analyzed. The effectiveness and performance of the proposed approach are verified by numerical examples. The results show that the fluctuation range of data is positively correlated with the lowest critical p-value with given number of new facilities. However, the number of new facilities has a different impact on lowest critical p-value with the given fluctuation range of data. As the number of new facilities increases, the lowest critical p-value for PMP and FPMP increases and decreases, respectively.

     

考虑顾客选择行为的逆向物流网络设计问题研究

逆向物流网络设计是企业获得经济利益和提高竞争力的战略决策之一.本文在进行逆向物流网络设计时,考虑了企业和顾客有不同的利益,通过双层规划建模来描述物流网络设计问题.上层模型为双目标模型,描述企业同时优化物流总成本和服务水平,下层模型描述顾客选择回收点的行为.设计了求解算法,得到一组Pareto解构成的效率边界,通过分析效率边界,确定企业的物流网络结构.最后用算例验证了模型和算法的有效性.     

基于持续运营机会约束的竞争设施点选址研究——一种有效的实数编码遗传求解算法

竞争设施点选址是空间经济、区域发展、组合优化和系统工程的重要课题之一。本文以市场份额最大化为目标,研究了基于持续运营机会约束的竞争设施点选址问题,并给出了一种有效的实数编码遗传求解算法。在求解模型方面,首先假定运营成本是竞争设施点规模大小的函数,并对设施点持续运营概率进行机会约束,借鉴引力模型建立竞争设施点选址-设计问题的非线性混合整数规划模型。其次,考虑到选址变量和规模变量的数值类型,以及编码变换问题,设计了一种实数编码遗传求解算法。通过数值实验表明,对不同规模问题的实际计算结果,该算法可以在较短时间内获得最优解,可行解和精确解之间误差小于0.5%,相关比较分析也讨论了该算法的优越性和实用性,为竞争设施点选址问题的研究提供了不同的视角和实用求解算法。     

A two-phase robot selection procedure

In this paper, a two-phase methodology is proposed for robot selection. In phase 1, data envelopment analysis is used as a means to determine the technically efficient robot alternatives, considering cost and technical performance parameters. Using data envelopment analysis permits us to consider the fact that the performance parameters specified by the vendors are generally unattainable in practice. In the second phase, a fuzzy robot selection algorithm is utilized to rank the technically efficient robots according to both predetermined objective criteria and additional vendor-related subjective criteria. The algorithm is based on calculating fuzzy suitability indices for the technically efficient robot alternatives, and then, ranking the fuzzy indices to select the best robot alternative. A comprehensive example is provided to illustrate the decision procedure. The algorithm proposed in here is also applicable to a broader area of decision problems, e.g. facility site selection, determination of the best CNC machine or flexible manufacturing system among a set of mutually exclusive alternatives.     

Locations and vehicle routing designs of physical distribution systems

The design of a physical distribution system (PDS) involves the determination of the number and locations of distribution centres, estimation of required vehicle numbers and design of vehicle routings. Due to the enormous numbers of possible combinatorial designs of the system, it is difficult to obtain an optimal design in acceptable computation effort on many occasions. In this paper, a new solution framework for the design of PDS by implementing the genetic algorithm (GA) is presented. With the characteristic of simultaneous optimization of a large population of configuration, the proposed methodology has been proved to be an extremely efficient optimizer. In the experimental simulation conducted in this paper, it also indicates this approach can provide a near-optimal solution to the design of PDS. To analyse the growth and decay of many schemas contained in the population, the effects of the operation of reproduction, crossover and mutation on the schema are studied. The simulation evaluation about the system performance and genetic parameters is presented along with the discussions at the end of this paper.     

A general space-time model for combinatorial optimization problems (and not only)

We consider the problem of defining a strategy consisting of a set of facilities taking into account also the location where they have to be assigned and the time in which they have to be activated. The facilities are evaluated with respect to a set of criteria. The plan has to be devised respecting some constraints related to different aspects of the problem such as precedence restrictions due to the nature of the facilities. Among the constraints, there are some related to the available budget. We consider also the uncertainty related to the performances of the facilities with respect to considered criteria and plurality of stakeholders participating to the decision. The considered problem can be seen as the combination of some prototypical operations research problems: knapsack problem, location problem and project scheduling. Indeed, the basic brick of our model is a variable x_ilt which takes value 1 if facility i is activated in location l at time t, and 0 otherwise. Due to the conjoint consideration of a location and a time in the decision variables, what we propose can be seen as a general space-time model for operations research problems. We discuss how such a model permits to handle complex problems using several methodologies including multiple attribute value theory and multiobjective optimization. With respect to the latter point, without any loss of the generality, we consider the compromise programming and an interactive methodology based on the Dominance-based Rough Set Approach. We illustrate the application of our model with a simple didactic example.     

基于双层规划的反恐应急设施选址模型及算法

恐怖袭击常以人流密集地区的平民作为袭击目标，并存在突发性和随机性等特点，极易造成严重的袭击后果。通过反恐应急设施的合理布局可以缩短救援人员和物资的到达时间，从而减轻袭击后果。首先，对反恐应急设施选址问题进行描述，并将其构造为一类离散双层规划模型。其中，上层规划是关于政府选址的0-1规划问题，下层规划则是关于恐怖分子袭击目标选择的0-1规划问题。其次，结合模型和问题的特征设计算法，利用分支定界算法实现上层选址变量的隐枚举，同时通过下层问题的求解来确定上下界并判断是否满足分枝或剪枝的条件。最后，结合南疆地区的交通拓扑网络进行算例分析，结果证明有效的选址方案可以大大降低袭击损失。