A robust counterpart approach to the bi-objective emergency medical service design problem

The paper presents a bi-objective robust program to design a cost-responsiveness efficient emergency medical services (EMS) system under uncertainty. The proposed model simultaneously determines the location of EMS stations, the assignment of demand areas to EMS stations, and the number of EMS vehicles at each station to balance cost and responsiveness. We develop a robust counterpart approach to cope with the uncertain parameters in the EMS system. Extensive numerical studies are performed to demonstrate the benefits of our robust optimization approach.     

Cost/risk balanced management of scarce resources using stochastic programming

We consider the situation when a scarce renewable resource should be periodically distributed between different users by a Resource Management Authority (RMA). The replenishment of this resource as well as users demand is subject to considerable uncertainty. We develop cost optimization and risk management models that can assist the RMA in its decision about striking the balance between the level of target delivery to the users and the level of risk that this delivery will not be met. These models are based on utilization and further development of the general methodology of stochastic programming for scenario optimization, taking into account appropriate risk management approaches. By a scenario optimization model we obtain a target barycentric value with respect to selected decision variables. A successive reoptimization of deterministic model for the worst case scenarios allows the reduction of the risk of negative consequences derived from unmet resources demand. Our reference case study is the distribution of scarce water resources. We show results of some numerical experiments in real physical systems.     

First-and second-order ridge regression models of the rate of demand for emergency medical services

This paper presents first- and second-order statistical models of the total emergency medical service (EMS) demand rate using socio-economic, demographic and other characteristics of an area as the exogenous variables. Due to the multicollinear or non-orthogonal nature of the exogenous data, parameter estimates result are compared with those derived from the ordinary least squares method and a related Bayesian approach. These models are shown to provide highly significant fits to the empirical data. It is suggested that such models possess considerable utility as quantitative tool for evaluating and planning EMS systems.     

Alternative metrics to measure EMS system performance

In the development of strategy for the response to emergent incidents, emergency medical services (EMS) organizations must properly manage their resources while also adhering to response time mandates established by contractual agreements. Performance of an EMS system is typically measured by focusing on the response time of its first responders. However, given that some incidents require the response of multiple emergency vehicles, investigating only the initial response to incidents is inadequate. In this research, we propose two new metrics, in addition to the first response metric, to evaluate the performance of EMS operations: total response time and last responder response time. We develop three mixed integer programming formulations, each one focused on minimizing one of the three metrics, to model the assignment of emergency vehicles to incidents. We also propose a fourth model that combines the metrics via a weighted objective function. This model allows for the simultaneous consideration of the response metrics when evaluating the effectiveness of an emergency response dispatch policy. Experimental results, from comparisons of the models against a greedy dispatch policy, suggest the consideration of multiple response metrics leads to a more robust and effective dispatch policy. Finally, analysis using the models has potential to shape improved strategic and operational policies of EMS organizations. Journal of the Operational Research Society advance online publication, 29 June 2016; doi:10.1057/jors.2016.39     

Robust production planning in a manufacturing environment with random yield: A case in sawmill production planning

This paper addresses a multi-period, multi-product sawmill production planning problem where the yields of processes are random variables due to non-homogeneous quality of raw materials (logs). In order to determine the production plans with robust customer service level, robust optimization approach is applied. Two robust optimization models with different variability measures are proposed, which can be selected based on the tradeoff between the expected backorder/inventory cost and the decision maker risk aversion level about the variability of customer service level. The implementation results of the proposed approach for a realistic-scale sawmill example highlights the significance of using robust optimization in generating more robust production plans in the uncertain environments compared with stochastic programming.     

Multistage optimization of option portfolio using higher order coherent risk measures

Choosing a suitable risk measure to optimize an option portfolio’s performance represents a significant challenge. This paper is concerned with illustrating the advantages of Higher order coherent risk measures to evaluate option risk’s evolution. It discusses the detailed implementation of the resulting dynamic risk optimization problem using stochastic programming. We propose an algorithmic procedure to optimize an option portfolio based on minimization of conditional higher order coherent risk measures. Illustrative examples demonstrate some advantages in the performance of the portfolio’s levels when higher order coherent risk measures are used in the risk optimization criterion.     

Probabilistic approach to the strong Feller property

A new probabilistic method, based on the Girsanov theorem, for establishing the strong Feller property of diffusion processes in both finite and infinite dimensional spaces is proposed. Applications to second order stochastic differential equations, stochastic delay equations and stochastic partial differential equations of parabolic type are discussed, with a twofold aim: both to extend some older results, usually by weakening the assumptions on the drift term, and to obtain simpler proofs of them. Received: 13 December 1999 / Published online: 5 September 2000     

Elliptic equations for measures on infinite dimensional spaces and applications

We introduce and study a new concept of a weak elliptic equation for measures on infinite dimensional spaces. This concept allows one to consider equations whose coefficients are not globally integrable. By using a suitably extended Lyapunov function technique, we derive a priori estimates for the solutions of such equations and prove new existence results. As an application, we consider stochastic Burgers, reaction-diffusion, and Navier-Stokes equations and investigate the elliptic equations for the corresponding invariant measures. Our general theorems yield a priori estimates and existence results for such elliptic equations. We also obtain moment estimates for Gibbs distributions and prove an existence result applicable to a wide class of models. Received: 23 January 2000 / Revised version: 4 October 2000 / Published online: 5 June 2001     

Joint ground and air emergency medical services coverage models: A greedy heuristic solution approach

Aeromedical and ground ambulance services often team up in responding to trauma crashes, especially when the emergency helicopter is unable to land at the crash scene. We propose location-coverage models and a greedy heuristic for their solution to simultaneously locate ground and air ambulances, and landing zones (transfer points). We provide a coverage definition based on both response time and total service time, and consider three coverage options; only ground emergency medical services (EMS) coverage, only air EMS coverage, or joint coverage of ground and air EMS in which the patient is transferred from an ambulance into an emergency helicopter at a transfer point. To analyze this complex coverage situation we develop two sets of models, which are variations of the Location Set Covering Problem (LSCP) and the Maximal Covering Location Problem (MCLP). These models address uncertainty in spatial distribution of motor vehicle crash locations by providing coverage to a given set of both crash nodes and paths. The models also consider unavailability of ground ambulances by drawing upon concepts from backup coverage models. We illustrate our results on a case study that uses crash data from the state of New Mexico. The case study shows that crash node and path coverage percentage values decrease when ground ambulances are utilized only within their own jurisdiction.     

随机需求的车辆路线问题的新模型

本主要研究随机需求的VRP问题,其中服务需求量满足二项式分布,根据期望值的大小我们提出了在一条路线上理想最大服务点数的新概念,并在此基础上建立了三种VRP问题的新模型,由于允许服务失败两次和部分服务使得模糊能适应多种实际问题,以模拟退火思想为基础的两阶段方法经修正后用于解新模型并取得较好的数值结果,理论分析和数值结果表明,新模型较好地描述随机需求的VRP问题,并且容易求解。     

Stochastic optimization models for a single-sink transportation problem

In this paper, we study a single-sink transportation problem in which the production capacity of the suppliers and the demand of the single customer are stochastic. Shipments are performed by capacitated vehicles, which have to be booked in advance, before the realization of the production capacity and the demand. Once the production capacity and the demand are revealed, there is an option to cancel some of the booked vehicles against a cancellation fee; if the quantity shipped from the suppliers using the booked vehicles is not enough to satisfy the demand, the residual quantity is purchased from an external company. The problem is to determine the number of vehicles to book in order to minimize the total cost. We formulate a two-stage and a multistage stochastic mixed integer linear programming models to solve this problem and test them on a real case provided by Italcementi, the primary Italian cement producer and the fifth largest cement producer in the world. We test the influence of different scenario-tree structures on the solutions of the problem, as well as sensitivity of the results with respect to the cancellation fee.     

Joint spare parts inventory and reliability decisions under a service constraint

Reliability and inventory levels of spare parts are major factors that determine the service level for the maintenance of machines provided by original equipment manufacturers (OEMs). In general, decisions on reliability and stock levels are made separately in practice, and academic literature offers little guidance on how to jointly make these two decisions. In order to fill in the gap in the literature and provide guidance to OEMs, we jointly model reliability and inventory problems. We consider three different service measures: aggregate fill rate, average downtime per system per year and expected total number of long downs in a year. Our models minimize the sum of holding and emergency shipment costs subject to a limited reliability improvement budget and a target service level. We develop an algorithm that considers reliability and inventory decisions simultaneously, test our solution approach on real-life and randomly generated data sets and compare the results with an approach that considers reliability and inventory decisions sequentially. Numerical results show substantial benefits of integrating reliability and inventory decisions.     

Valid inequalities and restrictions for stochastic programming problems with first order stochastic dominance constraints

Stochastic dominance relations are well studied in statistics, decision theory and economics. Recently, there has been significant interest in introducing dominance relations into stochastic optimization problems as constraints. In the discrete case, stochastic optimization models involving second order stochastic dominance constraints can be solved by linear programming. However, problems involving first order stochastic dominance constraints are potentially hard due to the non-convexity of the associated feasible regions. In this paper we consider a mixed 0–1 linear programming formulation of a discrete first order constrained optimization model and present a relaxation based on second order constraints. We derive some valid inequalities and restrictions by employing the probabilistic structure of the problem. We also generate cuts that are valid inequalities for the disjunctive relaxations arising from the underlying combinatorial structure of the problem by applying the lift-and-project procedure. We describe three heuristic algorithms to construct feasible solutions, based on conditional second order constraints, variable fixing, and conditional value at risk. Finally, we present numerical results for several instances of a real world portfolio optimization problem. This research was supported by the NSF awards DMS-0603728 and DMI-0354678.     

Single and multi-period optimal inventory control models with risk-averse constraints

This paper presents some convex stochastic programming models for single and multi-period inventory control problems where the market demand is random and order quantities need to be decided before demand is realized. Both models minimize the expected losses subject to risk aversion constraints expressed through Value at Risk (VaR) and Conditional Value at Risk (CVaR) as risk measures. A sample average approximation method is proposed for solving the models and convergence analysis of optimal solutions of the sample average approximation problem is presented. Finally, some numerical examples are given to illustrate the convergence of the algorithm.     

Managing inventory and service levels in a safety stock‐based inventory routing system with stochastic retailer demands

The inherent uncertainty in supply chain systems compels managers to be more perceptive to the stochastic nature of the systems' major parameters, such as suppliers' reliability, retailers' demands, and facility production capacities. To deal with the uncertainty inherent to the parameters of the stochastic supply chain optimization problems and to determine optimal or close to optimal policies, many approximate deterministic equivalent models are proposed. In this paper, we consider the stochastic periodic inventory routing problem modeled as chance‐constrained optimization problem. We then propose a safety stock‐based deterministic optimization model to determine near‐optimal solutions to this chance‐constrained optimization problem. We investigate the issue of adequately setting safety stocks at the supplier's warehouse and at the retailers so that the promised service levels to the retailers are guaranteed, while distribution costs as well as inventory throughout the system are optimized. The proposed deterministic models strive to optimize the safety stock levels in line with the planned service levels at the retailers. Different safety stock models are investigated and analyzed, and the results are illustrated on two comprehensively worked out cases. We conclude this analysis with some insights on how safety stocks are to be determined, allocated, and coordinated in stochastic periodic inventory routing problem. Copyright © 2017 John Wiley & Sons, Ltd.     

Models for planning capacity expansion of convenience stores under uncertain demand and the value of information

Several stochastic optimization models for planning capacity expansion for convenience store chains (or other similar businesses) are developed that incorporate uncertainty in future demand. All of these models generate schedules for capacity expansion, specifying the size, location, and timing of these expansions in order to maximize the expected profit to the company and to remain within a budget constraint on available resources. The models differ in how uncertainty is incorporated, specifically they differ in the point in the decision-making process that the uncertainty in the demand is resolved. Several measures of the value of information are defined by comparing the results from the different models. Two sample problems are given and their solutions for the various approaches compared.     

Diffusion approximation for nonparametric autoregression

A nonparametric statistical model of small diffusion type is compared with its discretization by a stochastic Euler difference scheme. It is shown that the discrete and continuous models are asymptotically equivalent in the sense of Le Cam's deficiency distance for statistical experiments, when the discretization step decreases with the noise intensity ε. Received: 12 April 1996 / Revised version: 29 October 1997     

Retail service for mixed retail and E-tail channels

Together with regular retail channel, a firm can distribute products directly through Internet (referred to as an “e-tail” distribution channel). The competitive edge of the retail channel lies in more value-added services, some of which are unavailable through the e-tail channel. We consider a model mixed with retailing and e-tailing distribution channels where the service level and price decision are made, respectively, ex ante and ex post demand realizations. From the firm’s perspective of managing the two channels, we acquire the optimal decisions and characterize the effects of the demand uncertainty on the firm’s optimal retail service and expected profit. Applying stochastic comparison method, we show the firm’s retail service and profit both increase in the demand mean, and the firm profits from the increase of the convex order of the demand by decreasing his service level. Further, if the coefficient of the demand increases, we characterize that the firm benefits from it. Finally, several numerical studies are presented to gain insights.     

Invariant manifolds for weak solutions to stochastic equations

Viability and invariance problems related to a stochastic equation in a Hilbert space H are studied. Finite dimensional invariant C ² submanifolds of H are characterized. We derive Nagumo type conditions and prove a regularity result: any weak solution, which is viable in a finite dimensional C ² submanifold, is a strong solution. These results are related to finding finite dimensional realizations for stochastic equations. There has recently been increased interest in connection with a model for the stochastic evolution of forward rate curves. Received: 15 April 1999 / Revised version: 4 February 2000 / Published online: 18 September 2000     

Real-time dispatch of trams in storage yards

Real-time dispatch problems arise when preparing and executing the daily schedule of local transport companies. We consider the daily dispatch of transport vehicles like trams in storage yards. Immediately on arrival, each tram has to be assigned to a location in the depot and to an appropriate round trip of the next schedule period. In order to achieve a departure order satisfying the scheduled demand, shunting of vehicles may be unavoidable. Since shunting takes time and causes operational cost, the number of shunting movements should be minimized without violation of operational constraints. As an alternative, we may serve some round trips with trams of type differing from the requested type. In practice, the actual arrival order of trams may differ substantially from the scheduled arrival order. Then, dispatch decisions are due within a short time interval and have to be based on incomplete information. For such real-time dispatch problems, we develop combinatorial optimization models and exact as well as heuristic algorithms. Computational experience for real-world and random data shows that the derived methods yield good (often optimal) solutions within the required tight time bounds. This revised version was published online in June 2006 with corrections to the Cover Date.