Qingjin Peng

Challenges and Solutions for Location of Healthcare Facilities

Healthcare infrastructure is essential for effective operations of healthcare systems. An efficient facility location can save cost and improve the facility utilization. It is important to update the knowledge of methods and applications to locate healthcare facilities for different purposes. This paper provides an overview of methods and challenges for decision-making of the healthcare facility location to ensure an optimal solution. This research suggests answers for defined questions. Challenges are discussed in detail for modeling and applications of healthcare facility location problems. It is noticed that many existing location models were developed to cover the need of special cases. Cost and efficiency are two important criteria for healthcare services to minimize total traveling distance between patients and the healthcare facility. Uncertainty is recognized as an inevitable part of healthcare location problems. Reliability studies and sustainability should be considered in the location modeling. It is necessary to extend dynamic models to meet the trend of parameter changes over the time for decision-making. Considering the level of complexity and trend of needs for the healthcare facility, more research is required on the operation efficiency, patient safety and cost-effective solutions for the better resource utilization in the human-centered healthcare environment. The contribution of this paper is an overview of challenges and methods in the area to provide guidance for performance measures and improvements of healthcare facility location problems. This paper can be used as a guide of methods for the location or relocation of healthcare facilities.

Modeling and quantifying uncertainty in the product design phase for effects of user preference changes

Purpose – The purpose of this paper is to quantify external and internal uncertainties in product design process. The research addresses the measure of product future changes. Design/methodology/approach – Two methods are proposed to model and quantify uncertainty in the product life cycle. Changes of user preferences are considered as the external uncertainty. Changes stemming from dependencies between components are addressed as the internal uncertainty. Both methods use developed mechanisms to capture and treat changes of user preferences. An agent-based model is developed to simulate sociotechnical events in the product life cycle for the external uncertainty. An innovative application of Big Data Analytics (BDA) is proposed to evaluate the external and internal uncertainties in product design. The methods can identify the most affected product components under uncertainty. Findings – The results show that the proposed method could identify product changes during its life cycle, particularly using the...

Using Big Data to Minimize Uncertainty Effects in Adaptable Product Design

One of the major concerns for adaptable products is to ensure the products to meet customer preferences. As customers may update their preferences over the product lifetime, designers need methods to measure those preferences. Lack of knowledge (uncertainty) in customer preferences could endanger the product success. If designers can update their views for customer requirements, a product can be designed to follow the user requirements. Huge data are generated continuously in product user behavior, product usage, manufacturing cost etc., now called as Big Data. Collecting, managing and applying such huge set of data in an innovative method can reduce uncertainties. In this paper, a method is discussed to minimize uncertainty effects on products to improve the product adaptability. Uncertainty is considered as changes of the customer preference. The proposed method uses Big Data (BD) in the analysis of uncertainty. The effect of quantified uncertainties on product adaptability is investigated. The method is concluded with the most affected parts and functional requirements to be updated to meet changing requirements. The proposed method is compared to a developed agent-based modeling (ABM) method in a case study. Although there are some differences between both methods in the uncertainty effect evaluation, The BD method provides more confidence for the design solution. The paper also proposes some future research directions for design of adaptable products using Big Data.Copyright

Modeling Evolution of Uncertainty in Sustainable Product Design

Although a large number of research activities have been conducted for sustainable product development, it is not easy to find a practical method applied in sustainable product design as there are many uncertain factors existed in particular problems faced in different phases of product development. Based on reviewed literature, it is found that it is necessary to have an optimization metric accompanied with uncertainty effects in product development. A model is proposed in this research to evaluate the effects of uncertainty in product life cycle. The goal is to quantify various types of uncertainty from internal and external sources to assess the design efficiency for mitigating undesirable effects of uncertainty. The design phase is aimed in the research to look at product parameters that are subject to change in the design process. Inaccuracy, indecision and imprecision are selected as information uncertainty levels to quantify the evolution of uncertainty. Using the proposed concept, the discrete-event simulation (DES) is used to model and evaluate scenarios to minimize design phase duration of a sustainable wheelchair. Suggested improvements are compared to search the optimal solution. The analysis and comparison of scenarios show ways to reduce design time by (1) revision of the design process, (2) breaking down the product into design details, and (3) providing a clear and technical definition of uncertainty to be mitigated. The model is also used to locate areas for sustainability improvement in the studied case.Copyright

An Agent-Based Method to Investigate Customers’ Preference in Product Lifecycle

Consumer requirements for products vary dynamically based on the change of technologies, social influence, individual taste, etc. A sustainable product should meet customer requirements in its lifecycle. Different methods and techniques have been proposed to find possible changes of product needs or customers’ preferences. This paper introduces an agent-based technique to address the change of product requirements. Major contribution of the proposed method is to embed customers’ preference in the analysis of product performance using agent interactions. Using the combination of Quality Function Deployment (QFD), agent-based modeling and data mining methods, customers’ preference trends related to elements and functions of product are simulated. The prediction period is flexible based on estimated product lifecycle. The proposed method is compared with other techniques in a case study.© 2013 ASME

Reducing effects of design uncertainties on product sustainability

Abstract Product designers need to consider users’ requirement changes in the product life cycle. Existing practices of product design lack an effective method to quantify uncertainty effects on products. This research proposes a method to evaluate product sustainability under the generational variety uncertainty. An integrated method of agent-based modeling, quality function deployment (QFD), and axiomatic design theory is developed to simulate users’ preference changes. The contribution in this paper is to address all sustainability pillars including the social, environmental, and economy in the proposed solution. The quantified uncertainty is then used to evaluate impacts on product sustainability. The proposed method is validated using an example of the wheelchair design.

Multi-objective optimisation of facility location decisions within integrated forward/reverse logistics under uncertainty

Increasing interest to the environmental, social and economic aspects of the supply chains has motivated supply chain managers to optimise location-allocation decisions within closed-loop logistics networks. This paper presents a multi-objective model to optimise facility location decisions in integrated forward/reverse streams under uncertainty. The objectives of the model are to minimise total costs and simultaneously maximise customer satisfaction considering uncertainties in demand and return rate. The proposed model is solved by integrating genetic algorithm with sampling average method. The application of the model is examined in a real case study of car after sales network. The result of the model is compared to a deterministic model to identify how uncertainties affect the optimal configurations. The other experiment is carried out to study the effect of integrating forward and reverse logistics operations on the stakeholders objectives. Finally, a post-analysis is applied to help in choosing one solution among many different solutions.

Stochastic Optimization of the Repair Shops Location Problem Using Particle Swarm Optimization Algorithm

The optimization of facility location decisions is critical for the success of a supply chain in a market since it can contribute to long-term performance of the supply chain. In the last two decades, the number of research in this field has been growing to address more realistic problems such as incorporating uncertainties in repair time and demand. In this paper, a particle swarm optimization algorithm (PSO) is employed to locate repair shops in a stochastic environment. The problem aim is to decide about the location and the capacity of local repair shops as well as identifying the capacity of central repair shop to minimize total expected cost. It is assumed that customers select the closest local repair shop. In the local repair shops, services are available to repair customer’s broken items and a number of spare parts are stored to supply customers’ needs. Additionally, each repair shop is allowed to open some servers, depending on the number of customers, to serve its customers. If a stock-out happens, a customer should wait until the part is repaired in that shop. When a local repair shop is unable to repair a part, the part is sent to the central repair shop to be repaired. The central repair shop follows similar strategy for spare part inventory. The contribution of this paper is to employ a meta-heuristic solution approach based on particle swarm optimization for locating repair shops problem. In order to evaluate the performance of the employed solutions approach, its result is compared to other methods and differences are highlighted.Copyright