A Comparative Study of Two Combinatorial Reverse Auction Models

Online group-buying is one of the most innovative business models employed by many companies. From the perspective of buyers, quantity based discounts provide a huge incentive to form coalitions and take advantage of lower prices without ordering more than their actual demand. Traditional group-buying mechanisms are usually based on a single item and uniform cost sharing. One way to reduce the cost for acquiring the required items is to take into account the complementarities between items provided by the sellers. By holding a combinatorial reverse auction, the total cost to acquire the required items will be significantly reduced due to complementarities between items. However, combinatorial reverse auctions suffer from high computational complexity. If there are multiple buyers, there are two different business models for procurement based on combinatorial reverse auctions: (1) independent combinatorial reverse auctions: each buyer may hold a combinatorial reverse auction independently and (2) combinatorial reverse auctions based on group buying: multiple buyers delegate the auction to a group buyer and the group buyer holds only one combinatorial reverse auction for all the buyers. In developing an effective tool to support the decision of multiple buyers’ procurement, a comparative study on the performance and efficiency of these two different business models is needed. In this paper, we compare the performance as well as the computational efficiency for these two combinatorial reverse auction models. Our analysis indicates that group buying combinatorial reverse auction outperforms multiple separate combinatorial reverse auctions not only in performance but also in efficiency

Vehicle Routing Based on Discrete Particle Swarm Optimization and Google Maps API

Transportation imposes considerable cost on goods and has a significant influence on competitive advantage of a company. How to reduce the costs and improve the profit of a company is an important issue. Vehicle routing is a critical factor in reducing transportation costs. Finding optimal vehicle routes offers great potential to efficiently manage fleets, reduce costs and improve service quality. An effective scheme to manage fleets and determine vehicle routes for delivering goods is important for carriers to survive. In the existing literature, a variety of vehicle routing problems (VRP) have been studied. However, most papers do not integrate with GIS. In this paper, we consider a variant of VRP called Vehicle Routing Problem with Arbitrary Pickup and Delivery points (VRPAPD). The goal of this paper is to develop an algorithm for VRPAPD based on Google Maps API. To achieve this goal, we propose an operation model and formulate an optimization problem. In our problem formulation, we consider a set of goods to be picked up and delivered. Each goods has a source address and a destination address. The vehicles to transport the goods have associated capacities, including the maximal weight a vehicle can be carried and the maximal distance a vehicle can travel. The problem is to minimize the routes for picking up and delivering goods. The emerging Google Maps API provides a convenient package to develop an effective vehicle routing system. In this paper, we develop a vehicle routing algorithm by combining a discrete particle swarm optimization (DPSO) method with Google Maps API. We illustrate the effectiveness of our algorithm by an example

COMBINATORIAL AUCTIONS WITH TRANSPORTATION COST

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Predicting the Severity and Prognosis of Trismus after Intensity-Modulated Radiation Therapy for Oral Cancer Patients by Magnetic Resonance Imaging

To develop magnetic resonance imaging (MRI) indicators to predict trismus outcome for post-operative oral cavity cancer patients who received adjuvant intensity-modulated radiation therapy (IMRT), 22 patients with oral cancer treated with IMRT were studied over a two-year period. Signal abnormality scores (SA scores) were computed from Likert-type ratings of the abnormalities of nine masticator structures and compared with the Mann-Whitney U-test and Kruskal–Wallis one-way ANOVA test between groups. Seventeen patients (77.3%) experienced different degrees of trismus during the two-year follow-up period. The SA score correlated with the trismus grade (r = 0.52, p<0.005). Patients having progressive trismus had higher mean doses of radiation to multiple structures, including the masticator and lateral pterygoid muscles, and the parotid gland (p<0.05). In addition, this group also had higher SA-masticator muscle dose product at 6 months and SA scores at 12 months (p<0.05). At the optimum cut-off points of 0.38 for the propensity score, the sensitivity was 100% and the specificity was 93% for predicting the prognosis of the trismus patients. The SA score, as determined using MRI, can reflect the radiation injury and correlate to trismus severity. Together with the radiation dose, it could serve as a useful biomarker to predict the outcome and guide the management of trismus following radiation therapy

A Theoretical Foundation for Context-Aware Cyber-Physical Production Systems

The complex workflows and interactions between heterogeneous entities in Cyber-Physical Production Systems (CPPS) call for the use of context-aware computing technology to operate effectively and meet the order requirements in a timely manner. In addition to the objective to meet the order due date, due to resource contention between production processes, CPPS may enter undesirable states. In undesirable states, all or part of the production activities are in waiting states or blocked situation due to improper allocation of resources. The capability to meet the order due date and prevent the system from entering an undesirable state poses challenges in the development of context-aware computing applications for CPPS. In this study, we formulate two situation awareness problems, including a Deadline Awareness Problem and a Future States Awareness Problem to address the above issues. In our previous study, we found that Discrete Timed Petri Nets provide an effective tool to model and analyze CPPS. In this paper, we present a relevant theory to support the operation of CPPS by extending the Discrete Timed Petri Nets to lay a foundation for developing context-aware applications of CPPS with deadline awareness and future states awareness capabilities. We illustrate the theory developed in this study by an example and conduct experiments to verify the computational feasibility of the proposed method

An Efficient Method to Assess Resilience and Robustness Properties of a Class of Cyber-Physical Production Systems

Widely available real-time data from the sensors of IoT infrastructure enables and increases the adoption and use of cyber-physical production systems (CPPS) to provide enterprise-wide status information to promptly respond to business opportunities through real-time monitoring, supervision and control of resources and activities in production systems. In CPPS, the failures of resources are uncertainties that are inevitable and unexpected. The failures of resources usually lead to chaos on the shop floor, delayed production activities and overdue orders. This calls for the development of an effective method to deal with failures in CPPS. An effective method to assess the impacts of failures on performance and create an alternative plan to mitigate the impacts is important. Robustness, which refers to the ability to tolerate perturbations, and resilience, which refers to the capability to recover from perturbations, are two concepts to evaluate the influence of resource failures on CPPS. In this study, we developed a method to evaluate the influence of resource failures on CPPS based on the concepts of robustness and resilience. We modeled CPPS by a class of discrete timed Petri nets. A model of CPPS consists of asymmetrically decomposed models of tasks. The dynamics of tasks can be represented by spatial-temporal networks (STN) with a similar but asymmetrical structure. A joint spatial-temporal networks (JSTN) model constructed based on the fusion of the asymmetrical STNs is used to develop an efficient algorithm to optimize performance. We characterized robustness and resilience as properties of CPPS with respect to the failures of resources. We analyzed the complexity of the proposed method and conducted experiments to illustrate the scalability and efficiency of the proposed method

Fault Tolerant Analysis For Holonic Manufacturing Systems Based On Collaborative Petri Nets

Uncertainties are significant characteristics of today's manufacturing systems. Holonic manufacturing systems are new paradigms to handle uncertainties and changes in manufacturing environments. Among many sources of uncertainties, failure prone machines are one of the most important ones. This paper focuses on handling machine failures in holonic manufacturing systems. Machine failure will reduce the number of available resources. Feasibility analysis need to be conducted to check whether the works in process can be completed. To facilitate feasibility analysis, we characterize feasible conditions for systems with failure prone machines. This paper combines the flexibility and robustness of multi-agent theory with the modeling and analytical power of Petri net to adaptively synthesize Petri net agents to control holonic manufacturing systems. The main results include: (1) a collaborative Petri net (CPN) agent model for holonic manufacturing systems, (2) a feasible condition to test whether a certain type of machine failures are allowed based on collaborative Petri net agents and (3) fault tolerant analysis of the proposed method

A Comparative Study of Several Metaheuristic Algorithms to Optimize Monetary Incentive in Ridesharing Systems

The strong demand on human mobility leads to excessive numbers of cars and raises the problems of serious traffic congestion, large amounts of greenhouse gas emissions, air pollution and insufficient parking space in cities. Although ridesharing is a potential transport mode to solve the above problems through car-sharing, it is still not widely adopted. Most studies consider non-monetary incentive performance indices such as travel distance and successful matches in ridesharing systems. These performance indices fail to provide a strong incentive for ridesharing. The goal of this paper is to address this issue by proposing a monetary incentive performance indicator to improve the incentives for ridesharing. The objectives are to improve the incentive for ridesharing through a monetary incentive optimization problem formulation, development of a solution methodology and comparison of different solution algorithms. A non-linear integer programming optimization problem is formulated to optimize monetary incentive in ridesharing systems. Several discrete metaheuristic algorithms are developed to cope with computational complexity for solving the above problem. These include several discrete variants of particle swarm optimization algorithms, differential evolution algorithms and the firefly algorithm. The effectiveness of applying the above algorithms to solve the monetary incentive optimization problem is compared based on experimental results

Temporal Analysis of Influence of Resource Failures on Cyber-Physical Systems Based on Discrete Timed Petri Nets

Advancement of IoT and ICT provide infrastructure to manage, monitor and control Cyber-Physical Systems (CPS) through timely provision of real-time information from the shop floor. Although real-time information in CPS such as resource failures can be detected based on IoT and ICT, improper response to resource failures may cripple CPS and degrade performance. Effective operations of CPS relies on an effective scheme to evaluate the impact of resource failures, support decision making needed and take proper actions to respond to resource failures. This motivates us to develop a methodology to assess the impact of resource failures on operations of CPS and provide the decision support as needed. The goal of this study is to propose solution algorithms to analyze robustness of CPS with respect to resource failures in terms of the impact on temporal properties. Given CPS modeled by a class of discrete timed Petri nets (DTPNs), we develop theory to analyze robustness of CPS by transforming the models to residual spatial-temporal network (RSTN) models in which capacity loss due to resources is reflected. We formulate an optimization problem to determine the influence of resource failures on CPS based on RSTNs and analyze the feasibility to meet the order deadline. To study the feasibility to solve a real problem, we analyze the computational complexity of the proposed algorithms. We illustrate the proposed method by application scenarios. We conduct experiments to study efficiency and verify computational feasibility of the proposed method to solve a real problem