作业时间依赖顺序的拆卸线平衡多目标优化

在废旧产品拆卸时无先后关系约束的任务之间可能存在拆卸干扰，使任务作业时间依赖于拆卸顺序，导致产品总作业时间不确定，从而影响拆卸线的平衡。为此，考虑拆卸过程中任务间的相互干扰，以最小化拆卸成本和环境危害为目标，构建作业时间依赖顺序的拆卸线平衡多目标优化模型，并提出一种Pareto人工蜂群算法进行求解，采用混合生成法保证种群质量与多样性，设计变邻域深度搜索策略以提高雇佣蜂局部开采效率，为降低侦察蜂探测的盲目性采用基于外部存档的单点变异算子进行搜索。最后通过算例与实例验证算法的有效性以及平衡任务在拆卸线上分配的重要性。     

Disassembly to order system under uncertainty

This paper presents a multi-criteria optimization model of a disassembly-to-order (DTO) system under uncertainty. The goal of the proposed model is to determine the best combination of the number of each product type to be taken back from the last user and/or collectors. The EOL products are then disassembled for the retrieval of reusable components and materials and resold in order to meet a certain level of demand under a variety of physical, financial and environmental constraints. The surplus components are recycled, stored for usage in subsequent periods or properly disposed. The problem is modeled as a multi-criteria decision-making problem under uncertainty, where the aspiration levels for various goals are more likely to be in the “approximately more (less) than” and/or “more (less) is better” form. We employ fuzzy goal programming technique to solve the problem. When solved, the model provides the number of EOL products to be taken back as well as the number of items reused, recycled, stored and disposed. The values of a host of other performance measures are also obtained, including total profit, materials and items sales revenues, take back cost, transportation costs as well as costs of preparation of EOL products, destructive disassembly, non-destructive disassembly, recycling, storage and disposal. A case example is presented to illustrate the model's implementation.     

STATE-OF-THE-ART SURVEY: OPEN QUEUEING NETWORKS: OPTIMIZATION AND PERFORMANCE EVALUATION MODELS FOR DISCRETE MANUFACTURING SYSTEMS

In this survey we review methods to analyze open queueing network models for discrete manufacturing systems. We focus on design and planning models for job shops. The survey is divided in two parts: in the first we review exact and approximate decomposition methods for performance evaluation models for single and multiple product class networks. The second part reviews optimization models of three categories of problems: the first minimizes capital investment subject to attaining a performance measure (WIP or lead time), the second seeks to optimize the performance measure subject to resource constraints, and the third explores recent research developments in complexity reduction through shop redesign and products partitioning.     

Exact methods in optimum disassembly sequence search for problems subject to sequence dependent costs

Disassembling complex products is formally approached via network representation and subsequent mathematical modeling, aimed at selecting a good or optimum sequence of disassembly operations. This is done via heuristics, metaheuristics or mathematical programming. In contrast with heuristics and metaheuristics, which select a near-optimum solution, mathematical programming guarantees the selection of the global optimum. This problem is relatively simple if the disassembly costs can be assumed sequence independent. In practice, however, sequence dependent disassembly costs are frequently encountered, which causes NP-completeness of the problem. Although methods, e.g., based on the two-commodity network flow approach, are available to solve this constrained asymmetric Traveling Salesperson problem rigorously, this requires the introduction of integer variables. In this paper, a modification of the two-commodity network flow approach is proposed, which reduces the number of integer variables. This is applied to product structures that can be represented by a disassembly precedence graph. It is demonstrated that use of integer variables is completely avoided by iteratively solving a binary integer linear programming problem. This appears to be more efficient than solving the corresponding integer linear programming problem. It is demonstrated, on the basis of some cases, that this method might provide the exact solution of problems with increased complexity compared to those discussed so far in the literature. This appears particularly useful for evaluating heuristic and metaheuristic approaches.     

A general approach to disassembly planning and control

Closing product and material cycles have emerged as a paradigm for industry in the 21st century, and are directed towards an economical and responsible use of our limited resources. Disassembly plays a key role in recycling. It enables the recovery of functioning components for re-use of pure materials for re-utilization and the separation of harmful substances. Major challenges are presented by the enormous variety of products to be disassembled, by uncertainty as to quantity and by their unknown properties. An appropriate planning and control of disassembly processes is essential for an economic disassembly. Based on an investigation of the suitability of common production planning and control (PPC) methods for disassembly, a specific disassembly planning and control concept (DPC) is outlined.     

Health care in the 21st Century

Our primary agenda for the 21st Century is the reinvention of America. We must reinvent democracy, capitalism, entrepreneurism, and community. Indeed, we must recreate all of our major social institutions. This includes health care. A design for a new society requires a new design for health care. In fact, health care enjoys a special privilege in this regard. It is the gateway to total societal redesign. Health is the common denominator in any society. If you loose your health you cannot work, you cannot play, you cannot study; and, if you lose it sufficiently, you cannot even pray. Health and well-being create the foundation for all other constructive human endeavors. Therefore, the design of healthy communities is the necessary first step in the redesign of total human habitats. This massive redesign effort will take a century. However, it will be launched in the next few years.     

Making sense of loss: a content analysis of end-of-life practitioners' therapeutic approaches

Clinical professionals working in end-of-life (EOL) contexts are frequently relied upon to address questions of meaning with dying and bereaved persons. Similar to the gulf between researchers and practitioners besetting the larger healthcare community, the voices of EOL practitioners are often underrepresented in the empirical literature. This study aimed to further the dialogue in the field of thanatology by surveying and describing the therapeutic approaches that EOL practitioners most commonly report using to facilitate meaning-making. A total of 119 practitioners from a range of EOL disciplines were surveyed to write about their intervention strategies for helping clients/patients make sense of loss. Overall, participants discussed using 23 different therapeutic approaches that comprised three overarching categories: 1) presence of the helping professional; 2) elements of the process; and 3) therapeutic procedures. Importantly, the results also indicated that practitioners from the different EOL occupations are converging on many of the same strategies for promoting meaning-making. Implications for future research on evaluating the effectiveness of meaning-making interventions are also discussed.     

A Periodic Inventory Model for Stocking Modular Components

We study the benefit obtained by exploiting modular product design in fulfilling exogenous demand for both a complete assembly and its components in a service parts inventory system. Our goal is to reduce overall service system costs by allowing assembly and/or disassembly (A/D) to occur at some unit cost per A/D action. In an extensive set of computational experiments, we compare a naïve stocking and operating policy that treats all items independently and ignores the modular product structure and related A/D capability to the optimal base stock policy, and to a policy that allows A/D from the naïve stocking levels. While extensive computational analysis shows that the optimal base stock policy improves the system cost between 3 to 26% over the naïve approach, simply allowing A/D from the naïve stocking levels captures a significant portion (an average of 67%) of the naïve–optimal gap. Our computational results demonstrate that the optimization shifts the component‐assembly mix from the naïve levels and that limiting A/D capacity affects this mix. Limiting A/D capacity can actually increase the expected number of A/D actions (versus the uncapacitated case), since the optimization shifts stocking levels to reduce the probability that “too many” actions will be required.     

Empirical research opportunities in reverse supply chains

This study reviews the literature in reverse supply chains (RSCs) and develops 10 research propositions to be studied using empirical research methods. Businesses increasingly have to cope with product returns, mandated environmental regulations and increasing costs associated with product disposal. Through effective management of the RSC, managers can improve process efficiencies, customer service, supply chain design, product design, after-market product sales and after-sales service. Most research in RSCs has relied on case studies and optimization models. Opportunities exist to use survey-based research methods to explain current practices, predominant and critical issues, and managerial techniques used to manage the RSC.     

MIXED ASSEMBLY AND DISASSEMBLY OPERATIONS FOR REMANUFACTURING

In this paper we consider the problem of designing a mixed assembly‐disassembly line for remanufacturing. That is, parts from the disassembly and repair of used products can be used to build “new” products. This is a problem common to many OEM remanufacturers, such as Xerox or Kodak. We study two main configurations, under the assumption that the disassembly sequence is exactly the reverse of the assembly sequence. Under a parallel configuration, there exist two separate dedicated lines, one for assembly and one for disassembly, which are decoupled by buffers—from both disassembly operations, which have preference, as well as parts from an outside, perfectly reliable supplier. Under a mixed configuration, the same station is used for both disassembly and assembly of a specific part. The problem is studied using GI/G/c networks, as well as simulation. Due to a loss of pooling, we conclude that the parallel configuration outperforms the mixed line only when the variability of both arrivals and processing time are significantly higher for disassembly and remanufacturing than for assembly. Via a simulation, we explore the impact of having advanced yield information for the remanufacturing parts. We find that advanced yield information generally improves flow times; however, there are some instances where it lengthens flow times.     

Supply chain configuration for diffusion of new products: An integrated optimization approach

We develop an integrated/hybrid optimization model for configuring new products’ supply chains while explicitly considering the impact of demand dynamics during new products’ diffusion. The hybrid model simultaneously determines optimal production/sales plan and supply chain configuration. The production and sales plan provides decisions on the optimal timing to launch a new product, as well as the production and sales quantity in each planning period. The supply chain configuration provides optimal selection of options and safety stock level kept at each supply chain function. Extensive computational experiments on randomly generated testbed problems indicate that the hybrid modeling and solution approach significantly outperforms non-hybrid alternative modeling and solution approaches under various diffusion and supply chain topologies. We provide insights on optimal production/sales plan and supply chain configuration for new products during their diffusion process. Also, managerial implications relevant to effectiveness of the hybrid approach are discussed.     

Best practices in business process redesign: an overview and qualitative evaluation of successful redesign heuristics

To implement business process redesign several best practices can be distinguished. This paper gives an overview of heuristic rules that can support practitioners to develop a business process design that is a radical improvement of a current design. The emphasis is on the mechanics of the process, rather than on behavioral or change management aspects. The various best practices are derived from a wide literature survey and supplemented with experiences of the authors. To evaluate the impact of each best practice along the dimensions of cost, flexibility, time and quality, a conceptual framework is presented that synthesizes views from areas such as information systems development, enterprise modeling and workflow management. The best practices are thought to have a wide applicability across various industries and business processes. They can be used as a “check list” for process redesign under the umbrella of diverse management approaches such as Total Cycle Time compression, the Lean Enterprise and Constraints Management.     

A robust approach to the share-of-choice product design problem

A critical issue when solving the share-of-choice product design problem is the reliability of the optimal solution in the presence of partworth uncertainty. Existing approaches use point estimates of an individual's partworth utilities as input to the product optimization stage, ignoring within-person variability in estimates. Post-optimality sensitivity analysis is occasionally performed to assess the degree to which a solution is negatively impacted by partworth uncertainty. We propose a robust optimization model that explicitly captures variation in partworth estimates during the optimization process. Using a large, commercial dataset, we benchmark our model's performance against its deterministic counterpart. We also present inferential theory to guide the selection of model parameters controlled by the analyst. Results reveal that the new approach produces robust solutions in the face of measurement error. Out-of-sample coverage for individuals drawn from the target population is significantly higher than corresponding solutions from published methods.     

The Design of Feature‐Limited Demonstration Software: Choosing the Right Features to Include

Today, software supports many important tasks in a variety of industries. In the specialized nature of these environments, a common problem faced by software vendors is to correctly signal the true value of a software product to the end users. For example, telecommunications equipment manufacturers design complex software for important functions like provisioning new users in the network. These software products automate various functions that would otherwise need to be done manually. In order to enable potential customers—telecommunications providers—to evaluate and recognize the full value of the product, equipment vendors often provide a free, feature‐limited version of the product to the customer. As the specific features included in the feature‐limited version influence whether the full product is purchased or not, it is essential that the features included in the feature‐limited version be selected judiciously. While the importance of identifying the best set of features has been well recognized, there has been little research to date that systematically addresses this fundamental business decision. This study fills this gap in the literature by providing an objective approach to the design of demonstration software. We illustrate the benefits of our approach through a case study involving the design of a feature‐limited demo for a wireless telecommunications equipment manufacturer.     

Redundant configuration of automated flow lines based on “Industry 4.0”-technologies

Over the past decades, robots have been heavily used for flow lines to increase productivity and product quality and to relieve workers of repetitive and dangerous tasks. However, despite continuous improvement of robots, the occurrence of failures remains a significant challenge in the operation of automated flow lines. Due to the connection of the stations in a flow line via a material handling system, failures at one station can quickly lead to throughput losses due to blocking and starving of upstream and downstream stations, respectively. To some extent, these throughput losses can be reduced by installing buffers between the stations. However, the installation of buffers requires considerable investments and scarce factory space. Therefore, the minimization of the total number of buffers is one of the primary objectives in flow line planning. Due to the advances of manufacturing technologies that form the foundation of “Industry 4.0”, new solutions to reduce throughput losses caused by equipment failures open up. One solution is a redundant configuration, in which downstream stations automatically take over the operations of failed stations in the event of failure. The throughput loss in these situations mainly depends on the level of redundancy designed into the system. Based on existing methods for the design of automated flow lines, we present two line balancing formulations for the configuration of automated flow lines under consideration of redundancies. The first formulation aims at maximizing the lines’ level of redundancy. The second formulation aims at a balanced allocation of redundancies along the line. To evaluate the presented formulations, we compare the performance with an existing line balancing approach for automated lines. With respect to this approach, improvements of the throughput rate between 3 % and 7 % are achieved.     

The resource recovery level decision for end-of-life products

Resource recovery from end-of-life (EOL) products is becoming increasingly engaged in by companies as a response to customer, consumer, government and social pressures, the electronics industry cites competitor pressures as being the main driver of EOL product take-back and recovery. Recovery can occur at three levels; i.e. product, part and material. Each of these options has economic and environmental advantages and disadvantages depending on the product type and a host of other influencing factors. The work described in this paper supports the decision as to the most favourable route from a cost and value perspective, a software model that provides such support is also described.     

相关多质量特性的优化设计

针对小样本情形下的相关多质量特性优化设计问题,在运用田口(Taguchi)质量损失函数度量多质量特性的稳健性基础上,本文提出了使用多变量偏最小二乘(Partial Least-Squares,PLS)回归模型处理此类问题的新方法,并结合实际的工业案例进行了分析.结果表明,本文所提出的新方法能够有效地处理相关多质量特性的优化设计问题.     

Dynamics in Groups: Are We There Yet?

We know that groups are dynamic entities, and yet we rarely study them as such. Previously hamstrung by limited theory, a decade of advances in understanding the fundamental nature of groups and change promised a revolution in group research. Our goal here is to review those theoretical developments and then examine their impact on our empirical understanding of group dynamics. Examining work done and not done, we will take stock of this work, identify the obstacles that seem to keep us focused more on group statics than dynamics, and then close by offering suggestions about not only what approaches to take when studying group dynamics, but also how the field can help develop these approaches. We hope that a review of the group dynamic literature in 2021 will celebrate our coming empirical accomplishments rather than lament a lack of them.     

UTILITY MODELS FOR MULTIPLE OBJECTIVE DECISIONS: DO THEY ACCURATELY REPRESENT HUMAN PREFERENCES?*

Recent developments in multiattribute utility (MAU) theory in principle provide a formal basis for applying optimization methods to decision problems involving multiple competing objectives. In practice, MAU models and procedures are rarely applied. This paper is addressed to one major source of reluctance to employ these methods; namely, the belief that they cannot accurately reflect the structure of human preferences. This objection to MAU modeling procedures is inherently empirical. In fact, a large body of psychological research has been addressed to this question. This paper examines the relationship between formal MAU theory and the relevant psychological literature. A critical evaluation of this psychological research suggests that a variety of approaches to MAU modeling will typically yield valid mathematical representations of the preferences of individual decision makers.     

Learner Engagement: A New Perspective for Enhancing Our Understanding of Learner Motivation and Workplace Learning

Abstract

Organizations invest millions of dollars in workplace learning programs to develop human capital for competitive advantage. The effectiveness of workplace learning programs is directly linked to learner motivation. However, we contend that our current understanding of learner motivation and workplace learning is limited by the tendency to conduct research based on the traditional instructional system design model and the limited adoption of organizational behavior theories to guide such research. We propose that studying workforce learning from a psychological engagement perspective is necessary to gain a better understanding of learner motivation and subsequent knowledge and skill acquisition. We review studies of learning methods and contextual factors that likely influence learner engagement. We propose a research agenda focusing on six directions for future research.