Showing papers on "Service level published in 2018"
TL;DR: In this article, a two-echelon supply chain is considered, where a manufacturer sells products through both her own online channel and a traditional retailer, and the retailer provides customers with some pre-sales services, which have positive impact on the market demand.
185 citations
TL;DR: In this article, the authors study a hybrid strategy that uses both process flexibility and finished goods inventory for supply chain risk mitigation, which is modeled as a two-stage robust optimization problem.
Abstract: We study a hybrid strategy that uses both process flexibility and finished goods inventory for supply chain risk mitigation. The interplay between process flexibility and inventory is modeled as a two‐stage robust optimization problem. In the first stage, the firm allocates inventory before disruption happens; in the second stage, after a disruption happens, the firm determines production quantities at each plant to minimize demand loss. Our robust optimization model can be solved efficiently using constraint generation, and under some stylized assumptions, can be solved in closed form. For a canonical family of flexibility designs known as the K‐chains, we provide an analytical expression for the optimal inventory solution, which allows us to study the effectiveness of different degrees of flexibilities. Moreover, we find that firms should allocate more inventory to high variability products when its level of flexibility is low, but as flexibility increases, the inventory allocation pattern “flips” and firms should allocate more inventory to low variability products. These observations are further verified through a numerical case study of an automobile supply chain. Finally, we extend our robust optimization model to the time‐to‐survive metric, a metric that computes the longest time a supply chain can guarantee a predetermined service level under disruption.
103 citations
TL;DR: In this article, a multi-objective collaborative decision-making model is proposed to coordinate the pharmaceutical supply chain with service level consideration through a multiobjective decision making model, and the augmented econstraint method is applied to obtain the Pareto optimal solutions and the trade-offs between the objectives are examined.
98 citations
TL;DR: A two-stage quick response supply chain with cleaner technology, where the manufacturer determines the cleaner technology investment and afterwards the retailer decides the ordering quantity, and two supply chain contracts, minimum ordering quantity and MOQ with buyback to achieve supply chain coordination are proposed.
Abstract: Quick response strategy (QRS) has been widely adopted in a supply chain where members collect timely market information for better forecasting, and then respond promptly to the market changes by adjusting initial inventory decision. After adopting the QRS, sustainability issues such as greenhouse gas emission and energy waste may be more serious as production lead time is shorter. In this study, due to this dilemma, we develop a two-stage quick response supply chain with cleaner technology, where the manufacturer determines the cleaner technology investment and afterwards the retailer decides the ordering quantity. Based on Bayesian theory, we depict an information updating process for the QRS with cleaner technology. First, we find that the inventory service level significantly affects both manufacturer’s and retailer’s performance under the QRS with cleaner technology. Moreover, our analytical results indicate that the performance of centralised supply chain system is always better than the decentralise...
92 citations
TL;DR: FA2ST and its architecture are proposed to underpin a multi-level system of fog computing services for end-to-end support of IoT applications and a use case in a vertical industry, and a performance study.
Abstract: Fog computing has emerged as a promising solution for the IoT and next generation mobile networks. As an extension to cloud computing, it enables service provisioning along the continuum from the cloud to things to reduce latency and bandwidth demands, and empower end users in their vicinity. Such a cloud-to-thing service continuum requires full technology support in infrastructure, platform, software and service levels. This article proposes FA2ST and its architecture to underpin a multi-level system of fog computing services for end-to-end support of IoT applications. It presents the concept of FA2ST and describes its architecture, main features, a use case in a vertical industry, and a performance study.
78 citations
TL;DR: In this article, the effect of potential market demand disruptions on price and service level for competing retailers is examined, and it is shown that decentralized decision makers tend to lower wholesale and retail prices under potential demand disruptions, whereas a proactive retailer needs to increase service level with an increased level of possible disruptions.
76 citations
TL;DR: The results of this study show that both perceptions and expectations of the passengers’ are important to estimate the service quality of the city bus service.
Abstract: This study aims to find out the service quality of the city bus service based on users’ perceptions and expectations of the service. The results of this study show that both perceptions and expectations of the passengers’ are important to estimate the service quality. To collect the passengers’ perceptions and expectations data, a questionnaire survey has been conducted and the respondents are asked to rate some qualitative attributes of the city bus service as per their perceptions and their minimum expectations from the service. Data has been analyzed by a combination of statistical tools comprising of factor analysis, linear regression analysis, and structural equation modeling to find out the latent factors which affect users’ perception and expectation. From these analysis four latent factors namely safety, comfort, accessibility, and timely performance have been extracted along with their perceived and expected values. Using the percentage differences of the perceived and expected values, a level of service (LOS) scale has been established to find out the service level of the city bus service. The range of this LOS scale varies from LOS 1 to LOS 5 depicting best to worst service quality. It is found that, safety, comfort, and timely performance fall under LOS 3 group while accessibility falls under LOS 2 group. Based on the results of the study, some recommendations have been made to improve the service quality of the bus service.
57 citations
TL;DR: An optimization framework which relies on the accuracy of the G/D/1 queue in characterizing the workload distribution, and taps on the merits of the workload’s decomposition into green and brown workloads served by green andbrown energy resources, respectively is formulated.
Abstract: This paper aims at maximizing the profit associated with running geographically dispersed green data centers, which offer multiple classes of service. To this end, we formulate an optimization framework which relies on the accuracy of the G/D/1 queue in characterizing the workload distribution, and taps on the merits of the workload’s decomposition into green and brown workloads served by green and brown energy resources, respectively. Moreover, we take into account not only the service level agreements between the data centers and clients but also different deregulated electricity markets of data centers located at different regions. We prove the convexity of our optimization problem, and the performance of the proposed workload distribution strategy is evaluated via extensive simulations.
53 citations
TL;DR: The primary goal of this research is to obtain optimal or near-optimal joint production/maintenance control policies, by means of a novel reinforcement learning-based approach, which is found to clearly outperform the parametric policies in all cases.
Abstract: The model of a stochastic production/inventory system that is subject to deterioration failures is developed and examined in this paper. Customer interarrival times are assumed to be random and backorders are allowed. The system experiences a number of deterioration stages before it ultimately fails and is rendered inoperable. Repair and maintenance activities restore the system to its initial and previous deterioration state, respectively. The duration of both repair and maintenance is assumed to be stochastic. We address the problem of minimizing the expected sum of two conflicting objective functions: the average inventory level and the average number of backorders. The solution to this problem consists of finding the optimal tradeoff between maintaining a high service level and carrying as low inventory as possible. The primary goal of this research is to obtain optimal or near-optimal joint production/maintenance control policies, by means of a novel reinforcement learning-based approach. Furthermore, we examine parametric production and maintenance policies that are often used in practical situations, namely, Kanban, ( s, S ), threshold-type condition based maintenance and periodic maintenance. The proposed approach is compared with the parametric policies in an extensive series of simulation experiments and it is found to clearly outperform them in all cases. Based on the numerical results obtained by the experiments, the behavior of the parametric policies as well as the structure of the control policies derived by the Reinforcement Learning-based approach is investigated.
53 citations
TL;DR: In this paper, travel data from intra-city travellers can advise discrete policy recommendations based on a residential area or development's public transport demand, which contributes greatly to the wider goal of a sustainable built environment, provided the critical transit system attributes are measured and addressed to improve commuter uptake of public systems by residents living and working in local communities.
Abstract: Public transport can discourage individual car usage as a life-cycle asset management strategy towards carbon neutrality. An effective public transport system contributes greatly to the wider goal of a sustainable built environment, provided the critical transit system attributes are measured and addressed to (continue to) improve commuter uptake of public systems by residents living and working in local communities. Travel data from intra-city travellers can advise discrete policy recommendations based on a residential area or development’s public transport demand. Commuter segments related to travelling frequency, satisfaction from service level, and its value for money are evaluated to extract econometric models/association rules. A data mining algorithm with minimum confidence, support, interest, syntactic constraints and meaningfulness measure as inputs is designed to exploit a large set of 31 variables collected for 1,520 respondents, generating 72 models. This methodology presents an alternative to multivariate analyses to find correlations in bigger databases of categorical variables. Results here augment literature by highlighting traveller perceptions related to frequency of buses, journey time, and capacity, as a net positive effect of frequent buses operating on rapid transit routes. Policymakers can address public transport uptake through service frequency variation during peak-hours with resultant reduced car dependence apt to reduce induced life-cycle environmental burdens of buildings by altering residents’ mode choices, and a potential design change of buildings towards a public transit-based, compact, and shared space urban built environment.
51 citations
25 Jun 2018
TL;DR: This work presents a novel approach based on Network Function Virtualization (NFV) and Software-Defined Networking (SDN) driven queueing strategies that is central to realizing the potential of 5G networks.
Abstract: Increasing automation in systems such as Smart Grids (SGs), Intelligent Transportation, the Internet of Things (IoT) and Industry 40, involves the need for robust, highly capable Information and Communication Technology (ICT) Traditionally, to meet diverging use case requirements regarding network data rate, delay, security, reliability and flexibility, dedicated communication infrastructures are employed Yet, this is associated with high costs and lengthy roll-out times Therefore it is desirable for multiple tenants to share one Physical Network (PN) However, this may compromise service guarantees, potentially violating Service Level Agreements (SLAs) Network slicing aims to address this challenge by transparently dividing one common infrastructure into multiple, logically independent networks Thereby tenants are isolated from one another, ensuring the fulfillment of hard performance guarantees As slicing is central to realizing the potential of 5G networks, this work presents a novel approach based on Network Function Virtualization (NFV) and Software-Defined Networking (SDN) driven queueing strategies The developed solution is comprehensively evaluated with realistic traffic in a physical testing environment Highly demanding critical infrastructure use cases, with multiple service levels per slice, are used to validate performance and demonstrate functionalities such as dynamic data rate allocation
TL;DR: The computational study highlights that in certain situations although a simple ordering policy can achieve very good performance, statistically and economically significant improvements are achieved when using more advanced solution methods.
Abstract: Different solution methods are developed to solve an inventory routing problem for a perishable product with stochastic demands. The solution methods are empirically compared in terms of average profit, service level, and actual freshness. The benefits of explicitly considering demand uncertainty are quantified. The computational study highlights that in certain situations although a simple ordering policy can achieve very good performance, statistically and economically significant improvements are achieved when using more advanced solution methods. Managerial insights concerning the impact of shelf life and store capacity on profit are also obtained.
TL;DR: Wang et al. as discussed by the authors investigated the relationship between the satisfaction degree and some latent variables such as safety and comfort and found that flexibility is the most significant variable affecting passenger's satisfaction degree followed by safety, convenience, comfort and economy.
Abstract: To improve the mode share of public transport and reduce the transition to private transport of passengers waiting at bus station, the mechanism of passengers’ decision-making procedure and influence factors of the travel mode choice were analyzed. Some latent variables such as safety, comfort, convenience, flexibility and economy were selected to reflect the satisfaction degree of passengers on the service level of public transport. Taking Jinan City as an example, the questionnaire of passengers’ travel choice behavior at bus station was designed and carried out. Based on the structure equation model (SEM), the relationship between the satisfaction degree and some latent variables such as safety and comfort was discussed. The SEM method analysis shows that, of the influence level of the latent variables to the service level of public transport, flexibility is the most significant variable affecting passenger’s satisfaction degree followed by safety, convenience, comfort and economy. Travel mode choice model of passengers waiting at bus station was established with an integration approach of SEM and nested logit (NL) model. The SEM-NL integration model results reveal that gender, monthly income, purpose of the trip, travel distance, safety and convenience service level have a significant effect on the choice of the upper model (public transport or private transport). Passenger’s age, vehicle ownership and bus ride frequency have great influence on the choice of the lower mode (ORB: original route bus; ARB: alternative route bus; Taxi; and Shared bike). Sensitivity analysis reveals that the transition probabilities from private transport to public transport can reach the highest point (respectively, 69.85%, 68.84% and 35.51%) when safety service reaches level 4, convenience service reaches level 3, or comfort service reaches level 2, indicating that the safety level equal to 4, convenience level equal to 3 and comfort level equal to 2 are the key threshold to increase the public transport mode share. Some proposals such as ensuring good accessibility of public transport, shortening the transfer distance of different routes, creating a comfortable travel environment and integrating bus ticket system have been put forward for the sustainable development of public transport system.
TL;DR: In this paper, the authors focus on the operational feasibility of utilizing the spare transportation capacity of a service-driven company as a potential solution to supply small independent retailers, or nanostores.
Abstract: Last mile deliveries in urban areas cause a disproportionate unsustainable impact, while it is also the most expensive part of the supply chain. This is particularly true for freight flows that are characterized by fragmentation. Logistically, this becomes apparent in vehicles that are driving around with a low vehicle fill rate, leading to the unnecessary presence of freight vehicles in our cities. This study focuses on the operational feasibility of utilizing the spare transportation capacity of a service-driven company as a potential solution to supply small independent retailers, or nanostores. The aim is to reduce inefficient vehicle movement. Based on a real-life implementation, we use SYnchronization Model for Belgian Inland Transport (SYMBIT), an agent-based model, to simulate various bundling scenarios. Results show the total vehicle kilometers and lead times to supply nanostores for the service-driven company to serve its customers. There is a potential to utilize spare capacity to supply nanostores while maintaining a decent service level. The number of vehicle kilometers driven highly depends on the location of the distribution center where the service-driven company operates. Based on these results, the conditions that have to be met to replicate this solution in other urban areas are discussed.
TL;DR: Wang et al. as mentioned in this paper evaluated the performance of a public transport priority implementation in the city of Wuhan from 2006 to 2015 by applying the structural entropy-TOPSIS model.
TL;DR: Results of the implementation of a BSS design for Istanbul Technical Universityâ;;s Ayazaga Campus show that the approach provides a balanced BSS network by equalizing the mean demand and return rates, which will decrease the need for relocation efforts once the system is put to use.
Abstract: This study presents an integrated approach for the design of a Bicycle Sharing System (BSS) by jointly considering location decisions and capacity allocation. An important distinction of this approach is the definition of service levels, measured by the amount of unsatisfied demand both for bicycle pick-ups and returns. The method combines a set-covering model to assign location demands to stations with a queuing model to measure the related service levels. The key quality of this approach is its capacity in addressing the issues related to uncertainties in bicycle pick-up and return demand in BSS network design decisions. Results of the implementation of a BSS design for Istanbul Technical Universityâ;;s Ayazaga Campus show that our approach provides a balanced BSS network by equalizing the mean demand and return rates, which will decrease the need for relocation efforts once the system is put to use.
TL;DR: A new metric based on the effect on service level of the collapse of active transportation links is proposed, which shows that flow complexity is the most influential factor affecting supply network and its robustness, as well as the service level that can be maintained after disruptions.
Abstract: Supply chain networks need to respond efficiently to operation disruptions, as one of their aims is to guarantee the on time delivery of products. Hence, robustness has become one of the important ...
TL;DR: It is shown that service levels are overestimated when ignoring unobservable, censored demand effects and an adjusted incentive and reposition policy could increase the booking number of free-standing bikes and thus customer satisfaction as well as the system's profitability.
Abstract: We investigate a hybrid bike-sharing system. We carry out a usage pattern and demand analysis on the booking data of the system and include the effects of censored demand in a service level analysis. Service levels are used as meaningful measures for evaluating the customer-oriented performance of bike-sharing systems. Our results show that service levels are overestimated when ignoring unobservable, censored demand effects. Furthermore, there are significant differences between free-standing and station-based bikes. Based on these results, an adjusted incentive and reposition policy could increase the booking number of free-standing bikes and thus customer satisfaction as well as the system’s profitability.
TL;DR: A compact mathematical formulation, a branch-and-price algorithm, and a hybrid genetic algorithm with population management are proposed, which relies on problem-tailored solution representation, crossover and local search operators, as well as an adaptive penalization mechanism establishing a good balance between service levels and costs.
TL;DR: This paper studies a new discrete network design problem for metropolitan areas, in which some concepts, including the accessible flow, travel time budget function and principles of user equilibrium and system optimization with travel time budgets are proposed.
Abstract: One of the significant aims of transportation network design and management is to improve the service level of the network and the accessibility of individual trips in a certain period. By adopting a well-defined accessibility measure, this paper studies a new discrete network design problem for metropolitan areas, in which some concepts, including the accessible flow, travel time budget function and principles of user equilibrium and system optimization with travel time budgets, are proposed. Then, two deterministic bi-level programming models are firstly formulated to maximize the network accessible flow. The upper level focuses on choosing the potential links in the pre-specified candidate set, and the lower level assigns all the flows to the super network with principles of user equilibrium or system optimization with travel time budgets. Moreover, to handle uncertain potential demands in reality, the problem of interest is further formulated as two-stage stochastic programming models. To solve these proposed models, efficient heuristic algorithms are designed on the basis of probability search algorithm, Frank–Wolfe algorithm and Monte Carlo simulation method. Finally, two sets of numerical experiments in the Sioux Falls network and San Diego freeway network, are executed to test and analyze the rationality and efficiency of the proposed approaches.
TL;DR: It is shown that a collaborative approach between companies offers important benefits and is proposed to develop partnerships between shipping companies and to synchronize their shipments.
Abstract: Less than truckload is an important type of road-based transportation. Based on real data and on a collaboration with industry, we show that a collaborative approach between companies offers important benefits. We propose to develop partnerships between shipping companies and to synchronize their shipments. Four operational collaborative schemes with different objectives are developed. The first one focuses on minimizing shipping costs for shippers. The second and third ones minimize the carrier’s costs and the environmental cost, respectively. The fourth one is a combination of all three. The results of our computational experiments demonstrate that collaboration lead to significant cost reductions.
TL;DR: This analysis helps the cloud service provider to choose an appropriate prediction approach (whether time series or machine learning based) and to utilize the best method depending on input data patterns to obtain an accurate prediction result and better manage their SLAs to avoid violation penalties.
TL;DR: The experimental results show that the proposed model adapts to the behavioural changes of the application and learns the new behavioural patterns rapidly in comparison to the other state-of-the-art methods such as moving average, linear regression, neural networks and hybrid prediction approaches.
01 Nov 2018
TL;DR: The customized bus (CB) has been introduced and popularized in China to improve the attraction and service level of public transportation and is a key point of the CB system.
Abstract: In recent years, the customized bus (CB) has been introduced and popularized in China to improve the attraction and service level of public transportation. A key point of the CB system, the...
TL;DR: The problem is solved by means of a Multi-Population Memetic Algorithm (MPMA) and is tested on modified instances initially proposed for the deterministic problem, demonstrating its efficiency and flexibility.
TL;DR: It is demonstrated that the success factors have significant positive impacts on the overall performance, innovation performance, customer satisfaction, and long-term partner retention of service networks in the context of servitization.
TL;DR: The proposed supply chain network for this paper is formulated as a mixed-integer nonlinear programming, and three numbers of genetic algorithm called Non-dominated Sorting Genetic Algorithm (NSGA-II), non-dominated Ranking Genetic Al algorithm (NRGA), and Pareto Envelope-based Selection Algorithm are applied and compared to validate the obtained results.
Abstract: The competitive market and declined economy have increased the relevant importance of making supply chain network efficient. Up to now, this has resulted in great motivations to reduce the cost of services, and simultaneously, to improve their quality. A mere network model, as a tri-echelon, consists of Suppliers, Warehouses or Distribution Centers (DCs), and Retailer nodes. To bring it closer to reality, the majority of parameters in this network involve retailer demands, lead-time, warehouses holding and shipment costs, and also suppliers procuring and stocking costs which are all assumed to be stochastic. The aim is to determine the optimum service level so that total cost is minimized. Obtaining such conditions requires determining which supplier nodes, and which DC nodes in network should be active to satisfy the retailers’ needs, an issue which is a network optimization problem per se. The proposed supply chain network for this paper is formulated as a mixed-integer nonlinear programming, and to solve this complicated problem, since the literature for the related benchmark is poor, three numbers of genetic algorithm called Non-dominated Sorting Genetic Algorithm (NSGA-II), Non-dominated Ranking Genetic Algorithm (NRGA), and Pareto Envelope-based Selection Algorithm (PESA-II) are applied and compared to validate the obtained results. The Taguchi method is also utilized for calibrating and controlling the parameters of the applied triple algorithms.
TL;DR: Investigation of an integrated P-D planning problem within a four-echelon supply chain with two main objective functions: minimizing total chain cost and maximizing service level showed that although competition improves chain performance in terms of quality of the delivered products, it might also raise the cost of the chain.
TL;DR: In this article, the graph theory is presented as a means to identify the most critical elements in a network with respect to malfunctioning of the system as a whole, and the proposed method does not rely on iterative hydraulic calculations; instead, the structure of the network is taken as a starting point.
Abstract: Underground water infrastructure is essential for life in cities. The aging of these infrastructures requires maintenance strategies to maintain a minimum service level. Not all elements are equally important for the functioning of the infrastructure as a whole. Identifying the most critical elements in a network is crucial for formulating asset management strategies. The graph theory is presented as a means to identify the most critical elements in a network with respect to malfunctioning of the system as a whole. As opposed to conventional methods, the proposed method does not rely on iterative hydraulic calculations; instead, the structure of the network is taken as a starting point. In contrast to methods applied in practise, the results are independent on the chosen test-load. Because of the limited calculation effort, the method allows the analysis of large networks that are now, for practical reasons, beyond the scope of methods applied so-far.
TL;DR: In this article, a customer satisfaction survey was conducted from January to March 2013 in Hong Kong, with the respondents invited to give specific satisfaction ratings for ten service aspects individually and a global satisfaction rating for the overall taxi service quality, as well as rank the important aspects that influence the given global rating.