Using linear programming to simulate service engineers

This paper describes the use of a network flow model to simulate a proposed spatial allocation of engineers to service requests. The model involves generating a pattern of demand and then determining an allocation of engineers to jobs which maximises the number of jobs immediately serviced. Within the maximum number of jobs the allocation pattern which minimises the amount of travelling is determined. The results obtained were compared with the allocation rules then in use.     

Location and allocation for distribution systems with transshipments and transportion economies of scale

Locating transshipment facilities and allocating origins and destinations to transshipment facilities are important decisions for many distribution and logistic systems. Models that treat demand as a continuous density over the service region often assume certain facility locations or a certain allocation of demand. It may be assumed that facility locations lie on a rectangular grid or that demand is allocated to the nearest facility or allocated such that each facility serves an equal amount of demand. These assumptions result in suboptimal distribution systems. This paper compares the transportation cost for suboptimal location and allocation schemes to the optimal cost to determine if suboptimal location and allocation schemes can produce nearly optimal transportation costs. Analytical results for distribution to a continuous demand show that nearly optimal costs can be achieved with suboptimal locations. An example of distribution to discrete demand points indicates the difficulties in applying these results to discrete demand problems.     

The Placing of Service Points to Minimize Travel

We wish to site a given number of offices or other service points in a district, so that the mean travelling distance between clients and their nearest office is as low as possible. It is shown that the density of the offices should be proportional to the two-thirds power of the population density. This theoretical result does not take account of local conditions, but it may be made the basis for a study on the ground.     

不确定环境下服务资源配置优化

在服务资源配置过程中,按维修状态将服务对象分成三类:完全维修、部分维修及最小维修.由于维修服务的特性,服务成本与服务时间均是一个模糊数,并且维修对象在服务时所处的维修状态也具有一定的不确定性.针对这类情况下的服务资源配置问题,提出了同时考虑模糊服务成本和模糊服务时间及不确定维修状态的最小化服务成本为优化指标的服务资源配置模型.在模型的求解过程中采用多粒子群算法,通过仿真计算表明了该方法的可行性和有效性.     

A note on the allocation of queuing facilities using a minisum criterion

This paper suggests a formulation and a solution procedure for resource allocation problems which consider a central planner, m static queuing facilities providing a homogeneous service at their locations, and a known set of demand points or customers. It is assumed that upon a request for service the customer is routed to a facility by a probabilistic assignment. The objective is to determine how to allocate a limited number of servers to the facilities, and to specify demand rates from customers to facilities in order to minimize a weighted sum of response times. This sum measures the total time lost in the system due to two sources: travel time from customer to facility locations and waiting time for service at the facilities. The setting does not allow for cooperation between the facilities.     

Approximate dynamic programming for capacity allocation in the service industry

We consider a problem where different classes of customers can book different types of service in advance and the service company has to respond immediately to the booking request confirming or rejecting it. The objective of the service company is to maximize profit made of class-type specific revenues, refunds for cancellations or no-shows as well as cost of overtime. For the calculation of the latter, information on the underlying appointment schedule is required. In contrast to most models in the literature we assume that the service time of clients is stochastic and that clients might be unpunctual. Throughout the paper we will relate the problem to capacity allocation in radiology services. The problem is modeled as a continuous-time Markov decision process and solved using simulation-based approximate dynamic programming (ADP) combined with a discrete event simulation of the service period. We employ an adapted heuristic ADP algorithm from the literature and investigate on the benefits of applying ADP to this type of problem. First, we study a simplified problem with deterministic service times and punctual arrival of clients and compare the solution from the ADP algorithm to the optimal solution. We find that the heuristic ADP algorithm performs very well in terms of objective function value, solution time, and memory requirements. Second, we study the problem with stochastic service times and unpunctuality. It is then shown that the resulting policy constitutes a large improvement over an “optimal” policy that is deduced using restrictive, simplifying assumptions.     

Dynamic scheduling of a single-server two-class queue with constant retrial policy

We analyze the non-preemptive assignment of a single server to two infinite-capacity retrial queues. Customers arrive at both queues according to Poisson processes. They are served on first-come-first-served basis following a cost-optimal routing policy. The customer at the head of a queue generates a Poisson stream of repeated requests for service, that is, we have a constant retrial policy. All service times are exponential, with rates depending on the queues. The costs to be minimized consist of costs per unit time that a customer spends in the system. In case of a scheduling problem that arise when no new customers arrive an explicit condition for server allocation to the first or the second queue is given. The condition presented covers all possible parameter choices. For scheduling that also considers new arrivals, we present the conditions under which server assignment to either queue 1 or queue 2 is cost-optimal.     

Online simulation for a real-time route dispatching problem

In this article we present an online simulation application for a decision problem that operates in real time, where products have to be dispatched from two depots to clients that are geographically distributed throughout the city. The system's behaviour is highly stochastic, due to the random behaviour of the client's demand (in time and space), and the random times of order preparation, travelling times of dispatchers (these are motorcycle drivers) and absence rate of drivers each day. A decision scheme is proposed that combines elements of vehicle routing with time windows, real-time dispatching of drivers and online simulation, through which information on future events is considered in the decision-making process. Two major conclusions are obtained when this scheme is applied to real data. First, we show that the proposed algorithm for order consolidation and route dispatching can be very advantageous from the point of view of logistics costs and quality of service. Second, we show that online simulation and, specifically, the Simulation-based Real-time Decision Making methodology (SRDM) can further improve the quality of the results. New ideas for further work are also proposed.     

The Balance of Care Project: Modelling the Allocation of Health and Personal Social Services

The Balance of Care model was designed to predict from aggregate health and personal social service plans the care which will be received by different types of potential client. Planners can use the model to assess the implications of various competing strategies and hence choose the one which will yield a pattern of care closest to that desired. In order to predict the pattern of care inherent in a service strategy it is necessary to establish the priorities governing the allocation of services to clients. This paper concentrates on the structure of the model and the techniques developed to deduce priorities from the past behaviour of the health and personal social service system. Applications of the model are described more fully elsewhere.     

Single facility location and relocation problem with time dependent weights and discrete planning horizon

In this paper a single facility location problem with multiple relocation opportunities is investigated. The weight associated with each demand point is a known function of time. We consider either rectilinear, or squared Euclidean, or Euclidean distances. Relocations can take place at pre-determined times. The objective function is to minimize the total location and relocation costs. An algorithm which finds the optimal locations, relocation times and the total cost, for all three types of distance measurements and various weight functions, is developed. Locations are found using constant weights, and relocations times are the solution to a Dynamic Programming or Binary Integer Programming (BIP) model. The time horizon can be finite or infinite.     

The optimal allocation of server time slots over different classes of patients

We present a model for assigning server time slots to different classes of patients. The objective is to minimize the total expected weighted waiting time of a patient (where different patient classes may be assigned different weights). A bulk service queueing model is used to obtain the expected waiting time of a patient of a particular class, given a feasible allocation of service time slots. Using the output of the bulk service queueing models as the input of an optimization procedure, the optimal allocation scheme may be identified. For problems with a large number of patient classes and/or a large number of feasible allocation schemes, a step-wise heuristic is developed. A common example of such a system is the allocation of operating room time slots over different medical disciplines in a hospital.     

Allocation strategies in hub networks

In this paper, we study allocation strategies and their effects on total routing costs in hub networks. Given a set of nodes with pairwise traffic demands, the p-hub median problem is the problem of choosing p nodes as hub locations and routing traffic through these hubs at minimum cost. This problem has two versions; in single allocation problems, each node can send and receive traffic through a single hub, whereas in multiple allocation problems, there is no such restriction and a node may send and receive its traffic through all p hubs. This results in high fixed costs and complicated networks. In this study, we introduce the r-allocation p-hub median problem, where each node can be connected to at most r hubs. This new problem generalizes the two versions of the p-hub median problem. We derive mixed-integer programming formulations for this problem and perform a computational study using well-known datasets. For these datasets, we conclude that single allocation solutions are considerably more expensive than multiple allocation solutions, but significant savings can be achieved by allowing nodes to be allocated to two or three hubs rather than one. We also present models for variations of this problem with service quality considerations, flow thresholds, and non-stop service.     

A Markov Sensitivity Model for Examining the Impact of Cost Allocations in Hospitals

The cost allocation process in hospitals typically entails an accounting step-down procedure whereby costs are allocated from non-revenue producing service centres to revenue centres. The resulting revenue centre costs are then compared with the third party (Blue Cross, Medicare, Medicaid) allowable costs. Any costs in excess of the allowable costs are not reimbursable. This procedure has been conceptualized using a Markov chain in a recent journal article. The purpose of this paper is to demonstrate how the Markov model may be used to assess the impact of various changes in the original data without having to recalculate the entire step-down process via a Markov model or any other procedure. The changes include an alternate step-down model, a different cost allocation basis for one or more service centres, and the expansion or contraction of one or more service centres.     

The uncapacitated facility location problem with demand-dependent setup and service costs and customer-choice allocation

We consider a generalization of the uncapacitated facility location problem, where the setup cost for a facility and the price charged for service may depend on the number of customers patronizing the facility. Customers are represented by the nodes of the transportation network, and facilities can be located only at nodes; a customer selects a facility to patronize so as to minimize his (her) expenses (price for service + the part of transportation costs paid by the customer). We assume that transportation costs are paid partially by the service company and partially by customers. The objective is to choose locations for facilities and balanced prices so as to either minimize the expenses of the service company (the sum of the total setup cost and the total part of transportation costs paid by the company), or to maximize the total profit. A polynomial-time dynamic programming algorithm for the problem on a tree network is developed.     

Algorithms for a stochastic selective travelling salesperson problem

In this paper, the selective travelling salesperson problem with stochastic service times, travel times, and travel costs (SSTSP) is addressed. In the SSTSP, service times, travel times and travel costs are known a priori only probabilistically. A non-negative value of reward for providing service is associated with each customer and there is a pre-specified limit on the duration of the solution tour. It is assumed that not all potential customers can be visited within this tour duration limit, even under the best circumstances. And, thus, a subset of customers must be selected. The objective of the SSTSP is to design an a priori tour that visits each chosen customer once such that the total profit (total reward collected by servicing customers minus travel costs) is maximized and the probability that the total actual tour duration exceeds a given threshold is no larger than a chosen probability value. We formulate the SSTSP as a chance-constrained stochastic program and propose both exact and heuristic approaches for solving it. Computational experiments indicate that the exact algorithm is able to solve small- and moderate-size problems to optimality and the heuristic can provide near-optimal solutions in significantly reduced computing time.     

The planar hub location problem: a probabilistic clustering approach

Given the demand between each origin-destination pair on a network, the planar hub location problem is to locate the multiple hubs anywhere on the plane and to assign the traffic to them so as to minimize the total travelling cost. The trips between any two points can be nonstop (no hubs used) or started by visiting any of the hubs. The travel cost between hubs is discounted with a factor. It is assumed that each point can be served by multiple hubs. We propose a probabilistic clustering method for the planar hub-location problem which is analogous to the method of Iyigun and Ben-Israel (in Operations Research Letters 38, 207–214, 2010; Computational Optmization and Applications, 2013) for the solution of the multi-facility location problem. The proposed method is an iterative probabilistic approach assuming that all trips can be taken with probabilities that depend on the travel costs based on the hub locations. Each hub location is the convex combination of all data points and other hubs. The probabilities are updated at each iteration together with the hub locations. Computations stop when the hub locations stop moving. Fermat-Weber problem and multi-facility location problem are the special cases of the proposed approach.     

Server allocation for zero buffer tandem queues

In this paper we consider the problem of allocating servers to maximize throughput for tandem queues with no buffers. We propose an allocation method that assigns servers to stations based on the mean service times and the current number of servers assigned to each station. A number of simulations are run on different configurations to refine and verify the algorithm. The algorithm is proposed for stations with exponentially distributed service times, but where the service rate at each station may be different. We also provide some initial thoughts on the impact on the proposed allocation method of including service time distributions with different coefficients of variation.     

Consolidating or non-consolidating queues: A game theoretic queueing model with holding costs

We consider a two-server queueing system in which the servers choose their service rate based on the demand and holding cost allocation scheme offered by the demand generating entity. We provide an optimal holding cost allocation scheme that leads to the maximum possible service rate for each of a pooled and a split system. Our results suggest that careful allocation of holding costs can create incentives that enable minimum turnaround times using a common queue.     

Geographical simulation modelling for the regional planning of oral and maxillofacial surgery across London

Oral and maxillofacial surgery (OMFS) is a recognized surgical specialty, with its foundations in dentistry. The current configuration of OMFS services across London has evolved over time and reflects historical rather than contemporary patterns of care. The creation of a London Health Region in 1998 provided the opportunity for rational planning of hospital services to serve the resident population of London (7.2 million) and beyond, with recent change focusing on London's five sectors that are represented within this planning model. A detailed geographical simulation model has been developed and has enabled planners to consider a number of OMFS service configurations and evaluate their impact on providers, variations in caseload, travelling distances and times for patients, and thus inform consultation over change. The research confirms that any in-patient service rationalization which concentrates care in one designated hub (main centre) per sector, involves a significant increase in caseload for the designated hub. Average travelling distances and times for in-patient admissions also increase significantly. However, it does suggest that current commissioned provision of day surgery patterns may not be well aligned to the geographical distribution of need for services, resulting in many patients travelling further than necessary for day surgery treatment. These may be overcome by sending patients to their local centre, which may be out with their sector of residence.