Future Arrivals Research Articles

Experience with PV‐diesel hybrid village power systems in Southern Morocco

AbstractIn October 2002, under the auspices of Spanish Cooperation, a pilot electrification project put into operation two centralised PV‐diesel hybrid systems in two different Moroccan villages. These systems currently provide a full‐time energy service and supply electricity to more than a hundred of families, six community buildings, street lighting and one running water system. The appearance of the electricity service is very similar to an urban one: one phase AC supply (230 V/50 Hz) distributed up to each dwelling using a low‐voltage mini‐grid, which has been designed to be fully compatible with a future arrival of the utility grid. The management of this electricity service is based on a ‘fee‐for‐service’ scheme agreed between a local NGO, partner of the project, and electricity associations created in each village, which are in charge of, among other tasks, recording the daily energy production of systems and the monthly energy consumption of each house. This register of data allows a systematic evaluation of both the system performance and the energy consumption of users. Now, after 4 years of operation, this paper presents the experience of this pilot electrification project and draws lessons that can be useful for designing, managing and sizing this type of small village PV‐hybrid system. Copyright © 2007 John Wiley & Sons, Ltd.

Development of an Optimization Methodology for Adaptive Traffic Signal Control at Diamond Interchanges

This research develops a methodology and a corresponding implementation algorithm to provide optimal signal control of diamond interchanges in response to real-time traffic fluctuations. The problem is formulated as to find a phase sequencing decision with a phase duration that makes a prespecified performance measure minimized over a finite horizon that rolls forward. The problem is solved by a forward dynamic programming (DP) method. The optimal signal switches over each 2.5 s interval are found for each horizon of 10 s. The optimization process is based on the advanced vehicle information obtained from loop detectors set back a certain distance from the stop line. Vehicle trajectories from detections till future arrivals and departures is modeled at the microscopic level to estimate the traffic flows at the stop-line for each horizon. The DP algorithm is coded in C++ language and dynamically linked to AIMSUN, a stochastic microsimulation package, for evaluation. The simulation results have exhibited that the DP algorithm is superior to PASSER III and TRANSYT-7F in handling demand fluctuations for medium to high flow scenarios when the field demand is increased from the one used in off-line optimization. The performance of the three algorithms is almost identical if the simulation demand is similar to off-line demand situation and does not vary much.

Route Choice on Transit Networks with Online Information at Stops

Passengers on a transit network with common lines are often faced with the problem of choosing between either to board the arriving bus or to wait for a faster one. Many assignment models are based on the classical assumption that at a given stop passengers board the first arriving carrier of a certain subset of the available lines, often referred to as the attractive set. In this case, it has been shown that, if the headway distributions are exponential, then an optimal subset of lines minimizing the passenger travel time can be easily determined. However, when online information on future arrivals of buses are posted at the stop, it is unlikely that the above classical assumption holds. In this case, passengers may choose to board a line that offers the best combination of displayed waiting time and expected travel time to their destination once boarded. In this paper, we propose a general framework for determining the probability of boarding each line available at a stop when online information on bus waiting times is provided to passengers. We will also show that the classical model without online information may be interpreted as a particular instance of the proposed framework, this way achieving an extension to general headway distributions. The impact of the availability of information regarding bus arrivals and that of the regularity of transit lines on the network loads, as well as on the passenger travel times, will be illustrated with small numerical examples.

Dynamic scheduling of batch servers with compatible product families

Dynamic scheduling is considered for batch processing machines. Research is motivated by the burn-in ovens found in semiconductor manufacturing. So far, research in this field has concentrated on control strategies that assume batches to be homogeneous, i.e. products should all belong to the same family. However, burn-in ovens may allow simultaneous processing of alternative families of products. Each family may set different requirements to processing times. The processing time of a batch is equal to the longest processing time required among all products in the batch. A new scheduling approach is proposed that addresses these situations. The objective is to minimize average flow time per product for the batch operation. The so-called look-ahead strategy adapts its scheduling decision to shop status, which includes information on a limited number of near future arrivals. The potential of the new strategy is demonstrated by an extensive simulation study.

Modeling Tourism Demand

SUMMARY Most of the existing studies on tourism demand forecasting apply economic models that use mathematical functions, which require many statistical assumptions and limitations. This paper presents a new approach that applies the rough sets theory to form a forecasting model for tourism demand. The objective of this research is to create patterns which are able to distinguish between the classes of arrivals in terms of volume, based upon differences in the characteristics in each arrival. The information about the arrivals was organized in an Information Table where the number of arrivals corresponds to condition attributes, and the classification was defined by a decision attribute that indicated the forecast categorical value of future arrivals. Utilizing Japanese arrivals data in Hong Kong, empirical results showed the induced decision rules could accurately forecast (86.5%) of the test data.

A study of robust active queue management schemes for correlated traffic

Random Early Detection detects the presence of congestion by measuring the exponentially weighted moving average (EWMA) of the queue length against two thresholds. Recently, rate-based packet marking schemes, like Adaptive Virtual Queue, have shown that they are more flexible in detecting and dealing with congestion as compared to schemes based on EWMA of queue length. In this paper we shed light on the operation of distributed local Active Queue Management (AQM) schemes by decomposing them based on their logical functions and by studying them as a combination of a measurement module and a control module. We also exploit the presence of strong correlation in network traffic in order to formulate a generalized Predictive AQM (PAQM) scheme. For this, we extend the calculation of the EWMA by including a term that represents the future traffic intensity. We compare the performance of our generalized predictive AQM scheme with a pure rate-based packet marking scheme. We study the performance of PAQM for various combinations of predictors and controllers with respect to robustness, end-to-end delay jitter, goodput and linkutilization. We present the goodput results purely with the intention that increased robustness is not at the expense of decreased goodput. Based on the simulation results, we have the following observations: (i) for proper selection of the weight of future observations, PAQM schemes achieve better delay bounds than the traditional (non-PAQM) ones. (ii) AQM schemes with stochastic based traffic prediction are usually more robust when the control is based on EWMA of queue length. Similarly, AQM schemes with traffic prediction based on finite impulse response filters are more robust when the control is based on fixed queue occupancy.(iii) More robust AQM schemes usually have higher goodput for comparable average queueing delay values, (iv) excessive dependence on predicted future arrivals always results in deterioration of performance. This sets a bound on the optimistic performance of AQM schemes even when using prediction.

Routing restorable bandwidth guaranteed connections using maximum 2-route flows

Routing with service restorability is of much importance in Multi-Protocol Label Switched (MPLS) networks, and is a necessity in optical networks. For restoration, each connection has an active path and a link-disjoint backup path. The backup path enables service restoration upon active path failure. For bandwidth efficiency, backups may be shared. This requires that at least the aggregate backup bandwidth used on each link be distributed to nodes performing route computations. If this information is not available, sharing is not possible. Also, one scheme in use for restorability in optical networks is for the sender to transmit simultaneously on the two disjoint paths and for the receiver to choose data from the path with stronger signal. This has the advantage of fast receiver-initiated recovery upon failure but it does not allow backup sharing. In this paper, we consider the problem of efficient dynamic routing of restorable connections when backup sharing is not allowed. Our objective is to be able to route as many connections as possible for one-at-a-time arrivals and no knowledge of future arrivals. Since sharing cannot be used for achieving efficiency, the goal is to achieve efficiency by improved path selection. We show that by using the minimum-interference ideas used for nonrestorable routing, we can develop efficient algorithms that outperform previously proposed algorithms for restorable routing such as routing with the min-hop like objective of finding two disjoint paths with minimum total hop-count. We present two new and efficient algorithms for restorable routing without sharing, and one of them requires only shortest path computations. We demonstrate that both algorithms perform very well in comparison to previously proposed algorithms.

Dynamic routing of restorable bandwidth-guaranteed tunnels using aggregated network resource usage information

The paper presents new algorithms for dynamic routing of restorable bandwidth-guaranteed paths. We assume that connections are requested one-by-one and there is no prior knowledge of future arrivals. In order to guarantee restorability an alternate link (node) disjoint backup (restoration) path has to be determined, as well as an active path, when the connection is initiated. This joint on-line routing problem is particularly important in optical networks and in MPLS networks for dynamic provisioning of bandwidth-guaranteed or wavelength paths. A simple solution is to find two disjoint paths, but this results in excessive resource usage. Backup path bandwidth usage can be reduced by judicious sharing of backup paths amongst certain active paths while still maintaining restorability. The best sharing performance is achieved if the routing of every path in progress in the network is known to the routing algorithm at the time of a new path setup. We give a new integer programming formulation for this problem. Complete path routing knowledge is a reasonable assumption for a centralized routing algorithm, but is not often desirable, particularly when distributed routing is preferred. We show that a suitably developed algorithm which uses only aggregated information, and not per-path information, is able to perform almost as well as one using complete information. Disseminating this aggregate information is feasible using proposed traffic engineering extensions to routing protocols. We formulate the dynamic restorable bandwidth routing problem in this aggregate information scenario and develop efficient routing algorithms. The performance of our algorithm is close to the complete information bound.

THE INCLUSION OF FUTURE ARRIVALS AND DOWNSTREAM SETUPS INTO WAFER FABRICATION BATCH PROCESSING DECISIONS

This paper presents a new batch machine dispatching policy that incorporates knowledge about future arrivals and the status of critical machines in subsequent (downstream) processing into the batch processing decision process. The intent is to create a methodology that balances the time lots spend waiting at a batch machine with the time spent in setup, thus improving the overall cycle time. Using discrete-event simulation, this heuristic is compared to existing heuristics that do not consider downstream operations to evaluate their impact on the cycle time both for a small three-machine system and a semiconductor manufacturing facility model. The results showed considerable improvement in cycle times for the small three-machine system. Results for the semiconductor-manufacturing model with downstream batching indicate that the new heuristic is robust but produces results consistent with the current standard heuristic; however, with further modifications, this heuristic may be capable of producing significantly better results.

On-line scheduling of multi-server batch operations

The batching of jobs in a manufacturing system is a very common policy in many industries. The main reasons for batching are the avoidance of setups and/or facilitation of material handling. Good examples of batch-wise production systems are the ovens that are found in the aircraft industry and in the manufacture of semiconductors. These systems often consist of multiple machines of different types for the range and volumes of products that have to be handled. Building on earlier research in the aircraft industry, where the process of hardening synthetic aircraft parts was studied, we propose a new heuristic for the dynamic scheduling of these types of systems. Our so-called look-ahead strategy bases its decision to schedule a job on a certain machine on the availability of information on a limited number of near future arrivals. The new control strategy distinguishes itself from existing heuristics by an integrated approach that involves all machines in the scheduling decision, instead of only considering idle machines. It is shown by an extensive series of simulation experiments that the new heuristic outperforms existing heuristics for most system configurations. Especially in the case of complex systems, where multiple products have to be handled by non-identical machines, the new heuristic proves its value as a practical scheduling tool. Important insight is obtained with regard to the relation between the system is configuration and its performance.

Transit Vehicle Arrival Prediction: Algorithm and Large-Scale Implementation

An algorithm is presented to predict transit vehicle arrival times up to 1 h in advance. It uses the time series of data from an automated vehicle location system, consisting of time and location pairs. These data are used with historical statistics in an optimal filtering framework to predict future arrivals. The algorithm is implemented for a large transit fleet in Seattle, Washington, and the prediction results for hundreds of locations are made widely available on the Web. An evaluation of the second busiest but most complex prediction site is presented to demonstrate the value of prediction over the use of schedules alone.

Dynamic single-machine scheduling under distributed decision-making

This paper presents a deterministic dynamic model for the single-machine scheduling problem. The model uses forecasts of future job arrivals with the current data to extract job interactions over time, updating the information on rolling basis. The model is implemented in a distributed structure with both the machine and the jobs involved in decision-making to create a schedule. The decision-making is modelled similar to an auction with a theoretical basis for problem decomposition, bid construction and bid evaluation. Numerical results indicate that the model outperforms other distributed implementations in both static and dynamic implementations for a wide range of single-machine scheduling problems.

Stochastic approximation and user adaptation in a competitive resource sharing system

This paper is motivated by the work of Altman and Shimkin (1998). Customers arrive at a service center and must choose between two types of service: a channel that is shared by all currently in it and a dedicated line. The mean service cost (or time) for any customer entering the shared resource depends on the decisions of all future arrivals up to the time of departure of that customer, and so has a competitive aspect. The decision rule of each arriving customer is based on its own immediate self-interest, given the available data on the past performance. If the current estimate of the cost for the shared resource equals that of the dedicated line, any decision is possible. The procedure is a type of learning algorithm. The convergence problem is one in asynchronous stochastic approximation, where the ODE may be a differential inclusion. It is shown that, asymptotically, the performance of the learning system is that for the symmetric Nash strategy, despite the allowed arbitrariness and lack of coordination.

Control of multiproduct bulk server diffusion/oxidation processes. Part 2: multiple servers

We investigate how knowledge of future arrivals can be used to control bulk server diffusion and oxidation processes in semiconductor manufacturing to reduce the average waiting time of lots. While past research has dealt with the control of bulk server queueing systems, only a few studies have addressed the use of knowledge of future arrivals, and those studies were limited to a single server system. We extend prior strategies for the single product-single server case to a multiple product-multiple server case, and devise a control strategy that is tested through the use of simulation. The performance of the new policy is compared to that of the optimal control strategy ignoring future arrivals (i.e., a Minimum Batch Size strategy). Results indicate that the new strategy performs well under a wide variety of circumstances. To demonstrate the control strategy performance in a realistic setting, a detailed simulation model of the diffusion area of an existing wafer fab was developed. The model was run with several start rates and the results compared to those from a Minimum Batch Size strategy. Results indicate that the new strategy performs well over a wide range of start rates.

Limitations of artificial neural networks for traffic prediction in broadband networks

B-ISDN is expected to support a variety of services, each with its own traffic characteristics and quality-of-service requirements. Such diversity, however, has created new congestion control problems, some of which could be alleviated by a traffic-prediction scheme. The paper investigates the applicability of artificial neural networks for traffic prediction in broadband networks. Recent work has indicated that such prediction is possible, as the neural networks are able to learn a complex mapping between past and future arrivals. Such work, however, has been based on the use of artificially generated traffic, and by definition the past and future arrivals are related. Real traffic is considered and it is shown that prediction is possible for certain traffic types but not for others. It is demonstrated that simple linear regression prediction techniques perform equally as well as do neural networks.

Control of multiproduct bulk server diffusion/oxidation processes. Part 2: multiple servers

We investigate how knowledge of future arrivals can be used to control bulk server diffusion and oxidation processes in semiconductor manufacturing to reduce the average waiting time of lots. While past research has dealt with the control of bulk server queueing systems, only a few studies have addressed the use of knowledge of future arrivals, and those studies were limited to a single server system. We extend prior strategies for the single product-single server case to a multiple product-multiple server case, and devise a control strategy that is tested through the use of simulation. The performance of the new policy is compared to that of the optimal control strategy ignoring future arrivals (i.e., a Minimum Batch Size strategy). Results indicate that the new strategy performs well under a wide variety of circumstances. To demonstrate the control strategy performance in a realistic setting, a detailed simulation model of the diffusion area of an existing wafer fab was developed. The model was run with several start rates and the results compared to those from a Minimum Batch Size strategy. Results indicate that the new strategy performs well over a wide range of start rates.

Stock Rationing in an M/Ek/1 Make-to-Stock Queue

This paper considers the stock rationing problem of a single-item, make-to-stock production system with several demand classes and lost sales. When demand is Poisson and processing time has an Erlang distribution, we show that a single-state variable called work storage level can be employed to completely capture the information regarding inventory level and the status of current production. The optimal rationing policy can be characterized by a sequence of monotone critical work storage levels. For each demand class, there exists a work storage level at or below which it is optimal to start rejecting the demand of this class in anticipation of future arrival of higher-priority demands. The optimal production policy can also be characterized by a critical work storage level. Our numerical examples indicate that a critical stock level policy, which ignores information on the status of current production, performs very well.

Computing with Time-Varying Data: Sequential Complexity and Parallel Speed-Up

We discuss the design of sequential and parallel algorithms working on a time-increasing data set, within two paradigms of computation. In Paradigm 1 the process terminates when all the data currently arrived have been treated, independently of future arrivals. In Paradigm 2 an endless process is divided in stages, and in each stage the computation is carried out on the data set updated up to the previous stage. A problem may be unsolvable because no algorithm is fast enough to cope with the increasing data set. The computational cost of succeeding algorithms is studied in a new perspective, in the sequential RAM and parallel PRAM models, with the running time possibly tending to infinity for proper values of the parameters. It is shown that the traditional time bounds of parallel versus sequential computation (i.e., speed-up and slow-down under the so-called Brent's principle) do not hold, and new bounds are provided. Several problems are examined in the new paradigms, and the new algorithms are compared with the known ones designed for time-invariant data. Optimal sequential and parallel algorithms are also defined, and given whenever possible. In particular it is shown that some problems do not gain anything from a parallel solution, while others can be practically solved only in parallel. Paradigm 1 is the most innovative, and the relative results on parallel speed-up and scaling are probably unexpected. Paradigm 2 opens a new perspective in dynamic algorithms, because processing batches of data may be more efficient than processing single incoming data on-line.

Dynamic job assignment heuristics for multi-server batch operations- A cost based approach

In many industries production facilities are used which process products in a batch-wise manner. Guided by research in the aircraft industry, where the process of hardening synthetic aircraft parts was studied, we propose a new control strategy for these types of systems. Given the availability of information on a few near future arrivals the strategy decides on when to schedule a job in order to minimize logistical costs. The fact that different cost structures can be incorporated, makes it a valuable tool for use in practical situations in business. The potential of the new strategy is demonstrated by an extensive series of simulation experiments, in which its response for various system configurations was tested in comparison with existing heuristics.

Inventory Rationing in a Make-to-Stock Production System with Several Demand Classes and Lost Sales

This paper considers the stock rationing problem of a single-item, make-to-stock production system with several demand classes and lost sales. For the case of Poisson demands and exponential production times, we show that the optimal policy can be characterized by a sequence of monotone stock rationing levels. For each demand class, there exists a stock rationing level at or below which it is optimal to start rejecting the demand of this class in anticipation of future arrival of higher priority demands. A simple queueing model is analyzed to compute the operating cost of a rationing policy. In a numerical study, we compare the optimal rationing policy with a first-come first-served policy to investigate the benefit of stock rationing under different operating conditions of the system.