Setting expediting repair policy in a multi-echelon repairable-item inventory system with limited repair capacity

Inventory systems with limited repair capacity are affected by congestion externalities, caused by use of a shared service. There is incompatibility between individual and system optimisation in considering congestion externalities. Three models are described that investigate the congestion effect in a multi-echelon inventory system which has two modes of repair, each with a limited repair capacity. An expanding repair policy employed by the bases in order to choose which repair mode to use is described and compared with different expediting policies related to congestion externalities. The expanding repair policy that considers congestion externalities was found to lead to better system performance measurement than an expanding policy with no congestion. The results of the numerical experiment indicate that the model that ignores congestion externalities—that is, the model that measures each base as an individual—leads to poorer performance measurement for every expediting repair policy, and particularly for the optimal expediting repair policy.     

Optimising a general repair kit problem with a service constraint

Field services are a particular type of after-sales service performed at the customer’s location where technicians repair malfunctioning machines. The inventory decisions about which spare part types to take to the repair site and in what quantities is called the repair kit problem. This problem is characterized by an order-based performance measure since a customer is only satisfied when all required spare parts are available to fix the machine. As a result, the service level in the decision making process is defined as a job fill rate. In this paper we derive a closed-form expression for the expected service level and total costs for the repair kit problem in a general setting, where multiple units of each part type can be used in a multi-period problem. Such an all-or-nothing strategy is a new characteristic to investigate, but commonly used in practice. Namely, items are only taken from the inventory when all items to perform the repair are available in the right quantity. We develop a new algorithm to determine the contents of the repair kit both for a service and cost model while incorporating this new expression for the job fill rate. We show that the algorithm finds solutions which differ on average 0.2% from optimal costs. We perform a case study to test the performance of the algorithm in practice. Our approach results in service level improvements of more than 30% against similar holding costs.     

Models for multi-echelon repairable item inventory systems with limited repair capacity

The dominant models for inventory control of repairable items, both in the literature and in practical applications, are based on the assumption of ample repair capacity. This assumption can introduce a serious underestimation of the spare parts requirements in systems with high repair facility utilization, as is typical in industry. In this paper, we introduce approximations that can deal with limited repair facilities, under the scenarios of single-class exponentially distributed repair distributions, single-class general repair distribution, and multi-class general repair distributions. We provide numerical experiments that demonstrate how these models significantly outperform traditional models in the case of high repair facility utilization. Their ease of implementation is illustrated in a case study of the spare parts requirements at the Caracas subway system     

Economic production quantity model with repair failure and limited capacity

We develop an economic production quantity (EPQ) model with random defective items and failure in repair. The existence of only one machine results with limited production capacity and shortages. The aim of this research is to derive the optimal cycle length, the optimal production quantity and the optimal back ordered quantity for each product so as to minimize the total expected cost (holding, shortage, production, setup, defective items and repair costs). The convexity of the model is derived and the objective function is proved convex. Two numerical examples illustrate the practical usage of the proposed method.     

Optimal internal pricing and capacity planning for service facility with finite buffer

An economic model is developed for exploring optimal internal pricing and capacity planning for service facility with finite buffer capacity. Because of the limited buffer capacity, jobs finding the system full upon their arrival would be rejected. Such rejections create a gap between the value collectively perceived by users and the actual achievement of the organizational value. This gap, called a loss externality, has never been studied before and plays an important role for designing optimal pricing scheme. In general, the underlying economic structure may involve multiple equilibria and it is unclear whether or not the system can be controlled through internal pricing. In this regard, a sufficient condition is given under which the system administrator can find two separate prices for accepted and rejected users at any demand level to be desired so that the desired demand level becomes the unique equilibrium of the system. For a short-run problem, it is shown that the optimal pricing scheme can be expressed as the sum of the congestion and the loss externalities. For a long-run problem, the optimal pricing scheme is expressed in a unified manner so that a structural relationship between the short-run problem and the long-run problem at optimality can be readily observed. A necessary and sufficient condition is also given for the marginal capacity pricing to be optimal, i.e., the optimal long-run pricing consists of the marginal cost for processing capacity and the marginal cost for buffer capacity without involving any externality at all.     

Analysis of capacity management of the intensive care unit in a hospital

A hospital's intensive care unit (ICU) is a limited and critical resource. The efficient utilization of ICU capacity impacts on both the welfare of patients and the hospital's cost effectiveness. Decisions made in the ICU affect the operations of other departments. Yet, decision making in an ICU tends to be mainly subjective and lacking in clear criteria upon which to base any given decision. This study analyzes the admission-and-discharge processes of one particular ICU, that of a public hospital in Hong Kong, by using queuing and computer simulation models built with actual data from the ICU. The results provide insights into the operations management issues of an ICU facility to help improve both the unit's capacity utilization and the quality of care provided to its patients.     

Sensitivity analysis of machine repair problems in manufacturing systems with service interruptions

This paper models a manufacturing system consisting of M operating machines and S spare machines under the supervision of a group of technicians in a repair facility. Machines fail according to a Poisson process, and the repair (service) process of a failed machine may require more than one phase. In each phase, service times are assumed to be exponentially distributed but may be interrupted when the repair facility encounters unpredictable breakdowns. Two models of manufacturing systems are considered. In the first model, technicians repair failed machines at different rates in each phase. In the second model, a two-phase service system with differing numbers of technicians is considered. Profit functions are developed for both models and optimized by a suitable allocation of the number of machines, spares, and technicians in the system. Finally, a sensitivity analysis (see Cao [X.R. Cao, Realization Probabilities: The Dynamics of Queuing Systems, Springer-Verlag: London, 1994; X.R. Cao, The relations among potentials, perturbation analysis, and Markov decision processes, Discrete Event Dynam. Syst.: Theory Applicat. 8 (1998) 71–87]) is performed to provide an approach that quantifies the impact of changes in the parameters on the profit models.     

双边市场异业合作网络平台的定价策略

本文以网络平台异业合作为背景,在Hotelling模型框架下,基于双边用户都是部分多归属的假定,结合平台采取异业合作可以提高用户固有收益这一前提,分析了产品和服务的差异化程度、拥挤效应及组间网络外部性强度对网络平台定价策略的影响。结果显示,双边用户的组间网络外部性强度不同时,随着拥挤效应的增强或产品和服务的差异化程度的提高,无论是在位平台还是采取异业合作的新进入平台,都会对一边用户提高,而对另一边用户降低收取的交易费;组间网络外部性强度相同时,平台对双边用户收取的交易费相同,且只与用户固有收益及交易量有关。研究表明,新进入市场的网络平台不应采取传统双边市场中常用的价格战策略来争夺市场份额,而应积极采取异业合作,通过提供优质优价的产品和服务来同在位平台竞争。     

Stochastic models of space priority mechanisms with Markovian arrival processes

In this paper, we study a single-server queue with finite capacity in which several space priority mechanisms are implemented. The arrival process is the general Markovian arrival process (MAP) which has been used to model the bursty arrival processes commonly arising in communication applications. The service times are generally distributed. These buffer mechanisms enable the Asynchronous Transfer Mode (ATM) layer to adapt the quality of the cell transfer to the quality of service requirements of the specific broadband ISDN services and to improve the utilization of the network resources. This is done by a selective discarding of cells according to the class they belong to. Computable expressions for various performance parameters are obtained. Numerical results are given for the case of a two-state Markov-modulated Poisson process (MMPP) and deterministic service times. The values derived can be used to evaluate the benefits of using priorities in an ATM network when the traffic is bursty and to make a comparative study of the buffer mechanisms. These results extend the models previously developed, which were limited to Poisson arrivals.     

Multi-stage stochastic programming models for provisioning cloud computing resources

We focus on the resource provisioning problem of a cloud consumer from an Infrastructure-as-a-Service type of cloud. The cloud provider offers two deployment options, which can be mixed and matched as appropriate. Cloud instances may be reserved for a fixed time period in advance at a smaller usage cost per hour but require a full commitment and payment for the entire contract duration. In contrast, on-demand instances reflect a pay-as-you-go policy at a premium. The trade-off between these two options is rooted in the inherent uncertainty in demand and price and makes it attractive to complement a base reserved capacity with on-demand capacity to hedge against the spikes in demand. This paper provides several novel multi-stage stochastic programming formulations to enable a cloud consumer to handle the cloud resource provisioning problem at a tactical level. We first formulate the cloud resource provisioning problem as a risk-neutral multi-stage stochastic program, which serves as the base model for further modeling variants. In our second set of models, we also incorporate a certain concept of system reliability. In particular, chance constraints integrated into the base formulation require a minimum service level met from reserved capacity, provide more visibility into the future available capacity, and smooth out expensive on-demand usage by hedging against possible demand fluctuations. An extensive computational study demonstrates the value of the proposed models by discussing computational performance, gleaning practical managerial insights from the analysis of the solutions of the proposed models, and quantifying the value of the stochastic solutions.     

Wireless network capacity management: A real options approach

This paper applies financial option valuation methods to new wireless network capacity investment decision timing. In particular, we consider the case of network capacity for cellular telephone service. Given a cluster of base stations (with a certain traffic capacity per base station), we determine when it is optimal to increase capacity for each of the base stations contained in the cluster. We express this in terms of the fraction of total cluster capacity in use, i.e. we calculate the optimal time to upgrade in terms of the ratio of observed usage to existing capacity. We study the optimal decision problem of adding new capacity in the presence of stochastic wireless demand for services. A four factor algorithm is developed, based on a real options formulation. Numerical examples are provided to illustrate various aspects of the model.     

Product line pricing for services with capacity constraints and dynamic substitution

In this paper, we address a service provider’s product line pricing problem for substitutable products in services, such as concerts, sporting events, or online advertisements. For each product, a static price is selected from a pre-defined set such that the total revenue is maximised. The products are differentiated by some of their attributes, and their availability is restricted due to individual capacity constraints. Furthermore, they are simultaneously sold during a common selling period at the end of which the service is delivered. Consumers differ from one another with respect to their willingness to pay, and, hence, their reservation prices vary depending on the product. In the event of a purchase, they choose the product that maximises their consumer surplus.     

GAME-THEORETIC RESOURCE MODELING

Games—mathematical models of strategic interaction—embrace a multitude of disciplines, in each of which they form a largely separate field of study with its own traditions and priorities. Game-theoretic resource modeling concerns itself primarily with strategic aspects of externalities arising from joint use of the commons, although it also deals with externalities of privately owned resources and with questions of fairness in allocating shared resource costs. This essay surveys resource games—in fisheries, forestry, water resources and environmental regulation—within a novel unifying framework, assesses their contribution to current understanding of resource issues, and indicates their promise for the future.     

Designing multi-echelon service parts networks with finite repair capacity

We propose an approach to model and solve the joint problem of facility location, inventory allocation and capacity investment in a two echelon, single-item, service parts supply chain with stochastic demand. The objective of the decision problem is to minimize the total expected costs associated with (1) opening repair facilities, (2) assigning each field service location to an opened facility, (3) determining capacity levels of the opened repair facilities, and (4) optimizing inventory allocation among the locations. Due to the size of the problem, computational efficiency is essential. The accuracy of the approximations and effectiveness of the approach are analyzed with two numerical studies. The approach provides optimal results in 90% of scenarios tested and was within 2% of optimal when it did not.We explore the impact of capacity utilization, inventory availability, and lead times on the performance of the approach. We show that including tactical considerations jointly with strategic network design resulted in additional cost savings from 3% to 12%. Our contribution is the development of a practical model and approach to support the decision making process of joint facility location and multi-echelon inventory optimization.     

考虑维修时间的总质保需求预测模型

激烈的市场竞争迫使制造商们逐渐向以顾客需求为中心的公司转变。在近 20 年内,作为影响顾客满意度的主要因素,产品的质保服务管理的相关研究开始成为学术界的焦点。良好的质保服务会给企业节省较多的运营成本,故对于刚投入市场的新产品而言,准确地预测质保需求对制造商合理分配资金等具有重要意义。以往对质保需求的预测模型都局限于分析长期意义上一个产品的总质保成本,忽略了产品的维修时间和动态销售过程对准确预测产品的总质保需求及成本的影响。为此,以销售期内的产品所产生的维修需求为主要的研究对象,深入探讨维修时间对预测质保需求的影响。模型中,利用非齐次泊松过程模拟产品的动态销售过程,并利用复合随机过程中的交错更新理论来刻画维修时间对总质保需求的影响。最后的参数分析,为企业更好地管理质保服务提供了重要的现实依据。     

Service capacity procurement of logistics service supply chain with demand updating and loss-averse preference

This paper studies the impacts of loss-averse preference on the service capacity procurement decisions with demand updating in a logistics service supply chain, which consists of one logistics service integrator and one functional logistics service provider. It starts from a basic two-stage Stackelberg game model, then, extends to three scenarios where either the integrator or the provider or neither of them has loss-averse preference. The impact of loss-averse preference on the decisions of supply chain members is discussed by comparing the four models. Our results reveal, first, the loss-averse preferences do not always affect the decisions of supply chain members. If certain conditions are satisfied, the logistics service integrator can benefit from its loss-averse preference. Second, the increased service level can affect the logistics service integrator's procurement strategy and the functional logistics service provider's pricing strategy. This effect is only related to the loss-averse preference of the functional logistics service provider. Last, under certain conditions, the total service capacity decreases with the increased service level, regardless of whether or not the supply chain members have loss-averse preferences.     

Queueing Systems with a Removable Service Station

A single-server queueing system with constant Poisson input is considered and the partial elimination of the station's idle fraction is envisaged by intermittent close-down and set-up. The rule pertaining to the dismantling and re-establishing of the service station—the management doctrine—is based on the instantaneous size of the queue, but these processes are assumed to consume time. Operating characteristics of such systems—in particular, average queue length and queueing time—are evaluated. A cost structure is superimposed on the system and optimization procedures are outlined. The close relationship with (a) priority queueing and (b) storage models is pointed out.     

The risk and information sharing of application services supply chain

We study an application services supply chain consisting of one application service provider (ASP) and one application infrastructure provider (AIP). The AIP supplies the computer capacity to the ASP that in turn sells the value-added application services to the market. The market is characterized by a price-sensitive random demand. The ASP’s objective is to determine the optimal price of its service to the market and the optimal capacity to purchase from the AIP. The AIP’s goal on the other hand is to maximize its profit from selling the capacity to the ASP.     

Lot‐sizing policies for deterioration items under two‐level trade credit with partial trade credit to credit‐risk retailer and limited storage capacity

The main purpose of this article is to investigate the optimal wholesaler's replenishment decisions for deterioration items under two levels of the trade credit policy and two storage facilities in order to reflect the supply chain management situation within the economic order quantity framework. In this study, each of the following assumptions have been made: (1) The own warehouse with limited capacity always is not sufficient to store the order quantity, so that a rented warehouse is needed to store the excess units over the capacity of the own warehouse; (2) The wholesaler always obtains the partial trade credit, which is independent of the order quantity offered by the supplier, but the wholesaler offers the full trade credit to the retailer; (3) The wholesaler must take a loan to pay his or her supplier the partial payment immediately when the order is received and then pay off the loan with the entire revenue. Under these three conditions, the wholesaler can obtain the least costs. Furthermore, this study models the wholesaler's optimal replenishment decisions under the aforementioned conditions in the supply chain management. Two theorems are developed to efficiently determine the optimal replenishment decisions for the wholesaler. Finally, numerical examples are given to illustrate the theorems that are proven in this study, and the sensitivity analysis with respect to the major parameters in this study is performed. Copyright © 2016 John Wiley & Sons, Ltd.