Assembly Line Balancing with Resource Dependent Task Times*

Paced assembly lines are widely used in repetitive manufacturing applications. Most previous research on the design of paced lines has assumed that each task along the line can be performed by only one worker (or a fixed number of workers). In many cases, however, task duration times may be reduced by increasing the number of workers or changing the equipment assigned to work stations. Thus, the problem becomes one of assigning resource alternatives (e.g., workers and/or equipment) and tasks to work stations to minimize total cost for a desired production rate. For this problem, we present three procedures. The first formulates the problem as a shortest path problem and guarantees optimality. The second and third are heuristic adaptations of the shortest path procedure that are capable of solving large problems. The procedures are compared in terms of solution quality and computation time on a set of 128 randomly generated problems for which optimal solutions could be found. Our simulation results indicate that the choice of procedure depends on problem complexity and resource costs.     

Balancing assembly line with skilled and unskilled workers

In this paper, we present the process of rebalancing the line at motorcycle-assembly plant. The company found it necessary to rebalance its line, since it needs to increase production in the spring and summer months. The main characteristics of the problem are as follows: (i) the company hires temporary staff, who need more time to carry out their tasks than permanent workers; (ii) there must always be at least one skilled employee working alongside an unskilled one; and (iii) different task groups are incompatible with each other (clean-hands tasks and dirty-hands tasks). The goal is to minimise the number of temporary workers required, given a cycle time and the team of workers on staff. The problem is modelled as a binary linear program (BLP) and solved optimally by means of the ILOG CPLEX 9.0 optimiser. The solution provided, namely 12 permanent workers (skilled) and two temporary workers (unskilled), is an improvement on the solution implemented by the business, which involved 12 permanent workers and four temporary workers.     

遗传算法在库存模糊逻辑控制中的应用

本文结合库存模糊逻辑控制模型,提出了一种用遗传算法获取并优化模糊规则的新方法——二阶段学习算法,本方法可进化生成模糊规则,并结合实例,说明该算法解决的实际问题,指出其应用前景。     

A Multiperiod Audit Staff Planning Model Using Multiple Objectives: Development and Evaluation*

The audit staff planning problem, a specific type of manpower planning problem, has been modeled using goal programming and, more recently, multiple objective linear programming. Prior studies developed single-period models and did not go beyond the model building stage. This study develops a multiperiod audit staff planning model and evaluates the model using a test application involving actual decision makers (partners in public accounting firms). The multiperiod model includes seven objectives to be optimized: profit (to be maximized), late completion of work, work declined, staff augmentation, staff reduction, underutilization of the work force, and shortfall in meeting professional development targets (all to be minimized). Over a four-quarter planning horizon with one “busy season,” the model is subject to constraints with respect to the projected audit work load, ability to substitute personnel and to perform interim audit work, available staff hours (including overtime limitations), supervisory requirements, and professional development targets. Results of the test application showed that the model was capable of producing a range of values for each objective. The participants were exposed to much of that range when making their decisions. The results also showed that all objectives were important and that participants were consistent in choosing their preferred level of each objective over several runs of the model. These results and the reactions of the participants demonstrate that the model is usable by actual decision makers and has potential for a number of specific applications.     

Strategic Assessment Model (SAM): A Multiple Criteria Decision Support System for Evaluation of Strategic Alternatives*

The evaluation of strategic alternatives is a particularly difficult task. This difficulty is due to the complexities inherent in the evaluation process and the lack of structured information. The evaluation process must consider a multitude of relevant information from both the internal and external environments of the organization. Various analytical and normative models have helped decision makers utilize large volumes of information in strategic evaluation; however, most of these models have some limitations. We present a multiple criteria decision support system, called strategic assessment model (SAM), that addresses some of the limitations inherent in the existing models. SAM captures the decision maker's beliefs through a series of sequential, rational, and analytical processes. The environmental forces—decomposed into internal, task, general opportunities, and threats—are used along with the analytic hierarchy process (AHP), subjective probabilities, the entropy concept, and utility theory to enhance the decision maker's intuition in evaluating a set of strategic alternatives.     

Multiple Criteria Hierarchy Process with ELECTRE and PROMETHEE

Robust Ordinal Regression (ROR) supports Multiple Criteria Decision Process by considering all sets of parameters of an assumed preference model, that are compatible with preference information elicited by a Decision Maker (DM). As a result of ROR, one gets necessary and possible preference relations in the set of alternatives, which hold for all compatible sets of parameters, or for at least one compatible set of parameters, respectively. In this paper, we propose an extension of ELECTRE and PROMETHEE methods to the case of the hierarchy of criteria, which was never considered before. Then, we adapt ROR to the hierarchical versions of ELECTRE and PROMETHEE methods.     

Scenario Discovery with Multiple Criteria: An Evaluation of the Robust Decision‐Making Framework for Climate Change Adaptation

There is increasing concern over deep uncertainty in the risk analysis field as probabilistic models of uncertainty cannot always be confidently determined or agreed upon for many of our most pressing contemporary risk challenges. This is particularly true in the climate change adaptation field, and has prompted the development of a number of frameworks aiming to characterize system vulnerabilities and identify robust alternatives. One such methodology is robust decision making (RDM), which uses simulation models to assess how strategies perform over many plausible conditions and then identifies and characterizes those where the strategy fails in a process termed scenario discovery. While many of the problems to which RDM has been applied are characterized by multiple objectives, research to date has provided little insight into how treatment of multiple criteria impacts the failure scenarios identified. In this research, we compare different methods for incorporating multiple objectives into the scenario discovery process to evaluate how they impact the resulting failure scenarios. We use the Lake Tana basin in Ethiopia as a case study, where climatic and environmental uncertainties could impact multiple planned water infrastructure projects, and find that failure scenarios may vary depending on the method used to aggregate multiple criteria. Common methods used to convert multiple attributes into a single utility score can obscure connections between failure scenarios and system performance, limiting the information provided to support decision making. Applying scenario discovery over each performance metric separately provides more nuanced information regarding the relative sensitivity of the objectives to different uncertain parameters, leading to clearer insights on measures that could be taken to improve system robustness and areas where additional research might prove useful.     

基于多子样的贝叶斯动态过程能力估计与评价方法研究

针对参数随机化情况下生产过程能力的评价问题,提出了新的过程能力指数估计与评价方法。通过质量控制模型的统计结构分析,研究了扩散先验分布下参数后验分布,据此构造了过程能力指数的贝叶斯点估计和区间估计;在此基础上,将前一阶段模型参数后验分布作为下一阶段的参数先验分布,充分利用历史数据信息,建立了过程能力指数及其下限的贝叶斯动态评价模型。研究结果表明:与现有的贝叶斯过程能力指数估计方法比较,贝叶斯动态过程能力指数的预测精度优于前者,更能反映实际生产过程能力水平。     

基于遗传算法的具有主次仓库的企业资源供应链管理模型研究

本文研究如何在有容量限制的主次仓库间分配客户需求的企业资源供应链管理问题,当主仓库不能满足客户需求时,通过一个次仓库弥补主仓库的不足。首先把该问题构造成一个整数规划模型,其次,运用遗传算法加以解决,实际应用和数值分析说明了该遗传算法在解决这类问题中的有效性和实用生。