Heuristic Allocation Research Articles

Consumer‐driven retail operations

PurposeThe purpose of this paper is to investigate the direct and interaction effects of shelf space, case pack quantity, and consumer demand on shelf stockouts, i.e. stockouts at the shelf level when inventory is available in the backroom of a retail store.Design/methodology/approachThis study uses discrete‐event simulation based on data collected from the ready‐to‐eat breakfast cereal category with multiple stockkeeping units (SKUs) to model a retail supply chain consisting of a supplier, a retailer, and consumers.FindingsThe results indicate that shelf space and case pack quantity have direct effects on shelf stockouts. Furthermore, evidence is found for interactions among shelf space, case pack quantity and consumer demand. Though many retailers adopt simple heuristics for shelf space allocation, such as a multiple of case pack quantity, this study suggests that such heuristics tend to over‐ or underestimate shelf space requirements when consumer demand is ignored.Originality/valueThis study suggests that managers should allocate shelf space for SKUs on the basis of not only case pack quantity but also consumer demand.

Experience with user-centred requirements engineering

This paper describes the application of human–computer interaction (HCI) principles and methods to requirements engineering in a case study development of a visualisation tool, ADVISES, to support epidemiological research. The development approach consisted of scenario-based design and analysis of the users’ tasks and mental model of the domain. Prototyping and storyboarding techniques were used to explore design options with users as well as specifying functionality for two versions of the software to meet the needs of novice and expert users. Application of HCI functional allocation heuristics to guide system requirements decisions is explained. An evaluation of the prototype was carried out to assess the extent to which the expert model would support public health professionals in their analysis activities. The results of the design exploration requirements analysis study are reported. The implications of scenario-based design exploration, functional allocation and software architecture are discussed.

Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing

Cloud computing offers utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of electrical energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only minimize operational costs but also reduce the environmental impact. In this paper, we define an architectural framework and principles for energy-efficient Cloud computing. Based on this architecture, we present our vision, open research challenges, and resource provisioning and allocation algorithms for energy-efficient management of Cloud computing environments. The proposed energy-aware allocation heuristics provision data center resources to client applications in a way that improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS). In particular, in this paper we conduct a survey of research in energy-efficient computing and propose: (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering QoS expectations and power usage characteristics of the devices; and (c) a number of open research challenges, addressing which can bring substantial benefits to both resource providers and consumers. We have validated our approach by conducting a performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant cost savings and demonstrates high potential for the improvement of energy efficiency under dynamic workload scenarios.

Power allocation for orthogonal frequency division multiplexing-based cognitive radio systems with and without integral bit rate consideration

Cognitive radio (CR) has been recognised as one of the promising technologies to provide efficient utilisation of the limited wireless spectrum. The authors investigate the power allocation problem for orthogonal frequency division multiplexing (OFDM)-based CR systems that coexist with primary networks. Specifically, in two scenarios with and without integral bit rate constraint, the authors study the secondary users (SUs) power allocation under both individual transmit power constraints of their own and interference power constraints of all the primary users. When the bit rate of SUs can be continuous on each OFDM subchannel, an efficient power allocation algorithm that contains two level Lagrangian dual variable iterations is proposed to maximise the CR system sum-rate. Besides, when integral bit requirement exists, a heuristic power allocation and bit loading methodology which based on the iterative power rescaling operation is developed. Simulation results show that the algorithm proposed for the continuous rate case can achieve the same rate performance as the standard interior-point algorithm, but converges much faster. The proposed allocation scheme for the discrete rate case has the same bit rate performance as the greedy-based Max-Min scheme.

Cluster scheduling for real-time systems: utilization bounds and run-time overhead

Cluster scheduling, where processors are grouped into clusters and the tasks that are allocated to one cluster are scheduled by a global scheduler, has attracted attention in multiprocessor real-time systems research recently. In this paper, assuming that an optimal global scheduler is adopted within each cluster, we investigate the worst-case utilization bounds for cluster scheduling with different task allocation/partitioning heuristics. First, we develop a lower limit on the utilization bounds for cluster scheduling with any reasonable task allocation scheme. Then, the lower limit is shown to be the exact utilization bound for cluster scheduling with the worst-fit task allocation scheme. For other task allocation heuristics (such as first-fit, best-fit, first-fit decreasing, best-fit decreasing and worst-fit decreasing), higher utilization bounds are derived for systems with both homogeneous clusters (where each cluster has the same number of processors) and heterogeneous clusters (where clusters have different number of processors). In addition, focusing on an efficient optimal global scheduler, namely the boundary-fair (Bfair) algorithm, we propose a period-aware task allocation heuristic with the goal of reducing the scheduling overhead (e.g., the number of scheduling points, context switches and task migrations). Simulation results indicate that the percentage of task sets that can be scheduled is significantly improved under cluster scheduling even for small-size clusters, compared to that of the partitioned scheduling. Moreover, when comparing to the simple generic task allocation scheme (e.g., first-fit), the proposed period-aware task allocation heuristic markedly reduces the scheduling overhead of cluster scheduling with the Bfair scheduler.

Risk Parity Portfolio vs. Other Asset Allocation Heuristic Portfolios

In this article, the authors conduct a horse race between representative risk parity portfolios and other asset allocation strategies, including equal weighting, minimum variance, mean–variance optimization, and the classic 60/40 equity/ bond portfolio. They find that the traditional risk parity portfolio construction does not consistently outperform (in terms of risk-adjusted return) equal weighting or a model pension fund portfolio anchored to the 60/40 equity/bond portfolio structure. However, it does significantly outperform such optimized allocation strategies as minimum variance and mean–variance efficient portfolios. Over the last 30 years, the Sharpe ratios of the risk parity and the equal-weighting portfolios have been much more stable across decade-long subperiods than either the 60/40 portfolio or the optimized portfolios. Although risk parity performs on par with equal weighting, it does provide better diversification in terms of risk allocation and thus warrants further consideration as an asset allocation strategy. The authors show, however, that the performance of the risk parity strategy can be highly dependent on the investment universe. Thus, to execute risk parity successfully, the careful selection of asset classes is critical, which, for the time being, remains an art rather than a formulaic exercise based on theory. <b>TOPICS:</b>Portfolio management/multi-asset allocation, quantitative methods, volatility measures

Statistical measures for quantifying task and machine heterogeneities

We study heterogeneous computing (HC) systems that consist of a set of different machines that have varying capabilities. These machines are used to execute a set of heterogeneous tasks that vary in their computational complexity. Finding the optimal mapping of tasks to machines in an HC system has been shown to be, in general, an NP-complete problem. Therefore, heuristics have been used to find near-optimal mappings. The performance of allocation heuristics can be affected significantly by factors such as task and machine heterogeneities. In this paper, we identify different statistical measures used to quantify the heterogeneity of HC systems, and show the correlation between the performance of the heuristics and these measures through simple mapping examples and synthetic data analysis. In addition, we illustrate how regression trees can be used to predict the most appropriate heuristic for an HC system based on its heterogeneity.

Joint admission and power control for quality-of-service in the wireless downlink

This paper addresses the problem of joint admission and power control in the downlink of a single-cell code-division-multiple-access (CDMA) wireless network. The objective is to maximize the user-capacity (or base station revenue), while meeting the quality-of-service (QoS) requirements of each admitted user. This paper considers bounded maximum delay and bounded minimum data rate as the QoS measures. For applications with strict QoS requirements, we show that the joint problem can be solved to optimality by performing admission control and power allocation separately. To solve the admission control problem, we give an exact polynomial-time algorithm for user-capacity maximization, and an efficient approximation algorithm with guaranteed performance for base station revenue maximization. To solve the power control problem, we provide two simple power allocation heuristics. For applications with soft QoS requirements, we consider the problem of allocating transmit powers to all users such that the minimum fraction of QoS requirement satisfied for any user is maximized. Regardless of the number of users in the system, we show that the problem always reduces to one-dimensional search. This leads an efficient power control algorithm that provides, in O(1) iterations, solutions arbitrarily close to the optimal one.

Scratchpad allocation for concurrent embedded software

Software-controlled scratchpad memory is increasingly employed in embedded systems as it offers better timing predictability compared to caches. Previous scratchpad allocation algorithms typically consider single-process applications. But embedded applications are mostly multitasking with real-time constraints, where the scratchpad memory space has to be shared among interacting processes that may preempt each other. In this work, we develop a novel dynamic scratchpad allocation technique that takes these process interferences into account to improve the performance and predictability of the memory system. We model the application as a Message Sequence Chart (MSC) to best capture the interprocess interactions. Our goal is to optimize the Worst-Case Response Time (WCRT) of the application through runtime reloading of the scratchpad memory content at appropriate execution points. We propose an iterative allocation algorithm that consists of two critical steps: (1) analyze the MSC along with the existing allocation to determine potential interference patterns, and (2) exploit this interference information to tune the scratchpad reloading points and content so as to best improve the WCRT. We present various alternative scratchpad allocation heuristics and evaluate their effectiveness in reducing the WCRT. The scheme is also extended to work on Message Sequence Graph models. We evaluate our memory allocation scheme on two real-world embedded applications controlling an Unmanned Aerial Vehicle (UAV) and an in-orbit monitoring instrument, respectively.

Speeding-up heuristic allocation, scheduling and binding with SAT-based abstraction/refinement techniques

Hardware synthesis is the process by which system-level, Register Transfer (RT)-level, or behavioral descriptions can be turned into real implementations, in terms of logic gates. Scheduling is one of the most time-consuming steps in the overall design flow, and may become much more complex when performing hardware synthesis from high-level specifications. Exploiting a single scheduling strategy on very large designs is often reductive and potentially inadequate. Furthermore, finding the “best” single candidate among all possible scheduling algorithms is practically infeasible. In this article we introduce a hybrid scheduling approach that is a preliminary step towards a comprehensive solution not yet provided by industrial or by academic solutions. Our method relies on an abstract symbolic representation of data flow nodes (operations) bound to control flow paths: it produces a more realistic lower bound during the prescheduling resource estimation step and speeds up slower but accurate heuristic scheduling techniques, thus achieving a globally improved result.

Lower Bounds and Heuristics for Supply Chain Stock Allocation

Assume that m periods with stochastic demand remain until the next replenishment arrives at a central warehouse. How should the available inventory be allocated among N retailers? This paper presents a new policy and a new lower bound for the expected cost of this problem. The lower bound becomes tight as N → ∞ . The infinite horizon problem then decomposes into N independent m-period problems with optimal retailer ship-up-to levels that decrease over the m periods, and the warehouse is optimally replenished by an order-up-to level that renders zero (local) warehouse safety stock at the end of each replenishment cycle. Based on the lower bound solution, we suggest a heuristic for finite N. In a numerical study it outperforms the heuristic by Jackson [Jackson, P. L. 1988. Stock allocation in a two-echelon distribution system or what to do until your ship comes in. Management Sci. 34(7) 880–895], and the new lower bound improves on Clark and Scarf's [Clark, A. J., H. Scarf. 1960. Optimal policies for a multi-echelon inventory problem. Management Sci. 6(4) 475–490] bound when N is not too small. Moreover, the warehouse zero-safety-stock heuristic is comparable to Clark and Scarf's warehouse policy for lead times that are not too long. The suggested approach is quite general and may be applied to other logistical problems. In the present application it retains some of the risk-pooling benefits of holding central warehouse stock.

Heuristics for Production Allocation and Ordering Policies in Multi-Plants with Capacity

Joint decisions in production allocation and ordering policies for single and multiple products in a production-distribution network system consisting of multiple plants are discussed, production capacity constraints of multi-plants and unit production capacity for producing a product are considered. Based on the average total cost in unit time, the decisive model is established. It tries to determine the production cycle length, delivery frequency in a cycle from the warehouse to the retailer and the economic production allocation. The approach hinges on providing an optimized solution to the joint decision model through the heuristics methods. The heuristic algorithms are proposed to solve the single-product joint decision model and the multi-products decision problem. Simulations on different sizes of problems have shown that the heuristics is effective, and in general more effective than Quasi-Newton method (QNM).

Subquery Allocation Problem and Heuristics for Secret Sharing Distributed Database System

We discuss query optimization in a secure distributed database system, called the Secret Sharing Distributed DataBase System (SSDDBS). We have to consider not only subquery allocations to distributed servers and data transfer on the network but also decoding distributed shared data. At first, we formulated the subquery allocation problem as a constraints satisfaction problem. Since the subquery allocation problem is NP-complete in general, it is not easy to obtain the optimal solution in practical time. Secondly, we proposed aheuristic evaluationfunction for the best-first search. We constructed an optimization model on an available optimization software, and evaluated the proposed method. The results showed that feasible solutions could be obtained by using the proposed method in practical time, and that quality of the obtained solutions was good.

How Should Private Investors Diversify? - An Empirical Evaluation of Alternative Asset Allocation Policies to Construct A World Market Portfolio

This paper evaluates numerous diversification strategies as a possible remedy against widespread costly investment mistakes of individual investors. Our results reveal that a very broad range of simple heuristic allocation schemes offers similar diversification gains, as well-established or recently developed portfolio optimization approaches. This holds true for both international diversification in the stock market and diversification over different asset classes. We thus suggest easy-to-implement allocation guidelines for individual investors.

E-Work based collaborative optimization approach for strategic logistic network design problem

We propose a collaborative e-Work based optimization approach for assisting the strategic logistic network design problem, which considers fleet design and customer clustering decisions. Normally, fleet design and customer clustering decisions are made by mid-level logistics managers while network design decision is made independently by high-level logistics managers. In the proposed approach, strategic distribution network design is modeled as a Facility Location Problem, considering long term inventory control decisions. On the other hand, tactical fleet design and customer clustering decisions are modeled based on a Hub and Spoke cost structure. An e-Work based heuristic approach is proposed to solve collaboratively the network design problem at strategic and tactical levels. The collaborative solution approach results from a particular sequential decomposition of the problem, similarly to traditional location–allocation heuristics, modeling an information sharing strategy between decision makers involved at each organizational level. A numerical application of the approach with real data based instances shows significant benefits, when compared to a non-collaborative independent optimization, where the hierarchical levels share no dynamic information and base their decisions on static and independent optimization models. These results show evidence of the advantage of the e-collaborative approach to deal with logistic decisions at different hierarchical levels, organizational units, or companies, compared to non-integrated non-linear mixed integer programming models.

A Heuristic Method for Channel Allocation and Scheduling in an OFDMA System

In this letter, a heuristic channel allocation and scheduling scheme is proposed. By comparing the size of the alternative-factor assessment, which is obtained by simple calculation, we can easily find the most appropriate channel for each user for overall throughput enhancement. Numerical results show that the downlink throughput of the proposed scheme is higher than that of proportional fairness and is almost the same as that of the maximum C/I scheme, while user fairness remains better than that of the maximum C/I scheme.

Sum-of-squares heuristics for bin packing and memory allocation

The sum-of-squares algorithm (SS) was introduced by Csirik, Johnson, Kenyon, Shor, and Weber for online bin packing of integral-sized items into integral-sized bins. First, we show the results of experiments from two new variants of the SS algorithm. The first variant, which runs in time O ( n √ B log B ), appears to have almost identical expected waste as the sum-of-squares algorithm on all the distributions mentioned in the original papers on this topic. The other variant, which runs in O ( n log B ) time, performs well on most, but not on all of those distributions. We also apply SS to the online memory-allocation problem. Our experimental comparisons between SS and Best Fit indicate that neither algorithm is consistently better than the other. If the amount of randomness in item sizes is low, SS appears to have lower waste than Best Fit, whereas, if the amount of randomness is high Best Fit appears to have lower waste than SS. Our experiments suggest that in both real and synthetic traces, SS does not seem to have an asymptotic advantage over Best Fit, in contrast with the bin-packing problem.

Heuristics for allocation of reconfigurable resources in a serial line with reliability considerations

We consider the allocation of reconfigurable resources in a serial line with machine failures. Each station is equipped with non-idling dedicated servers while the whole system is equipped with a finite number of reconfigurable servers. The reconfigurable servers are available to be assigned to any station and all servers are allowed to collaborate on a single job. We provide conditions for a policy to achieve throughput optimality. We also show in the two-station case that transition monotone optimal policies exist. We discuss heuristics based on the two-server model that reduce average holding costs significantly. These heuristics are compared to several heuristics from the literature via a detailed numerical study.

Static heuristics for robust resource allocation of continuously executing applications

We investigate two distinct issues related to resource allocation heuristics: robustness and failure rate. The target system consists of a number of sensors feeding a set of heterogeneous applications continuously executing on a set of heterogeneous machines connected together by high-speed heterogeneous links. There are two quality of service (QoS) constraints that must be satisfied: the maximum end-to-end latency and minimum throughput. A failure occurs if no allocation is found that allows the system to meet its QoS constraints. The system is expected to operate in an uncertain environment where the workload, i.e., the load presented by the set of sensors, is likely to change unpredictably, possibly resulting in a QoS violation. The focus of this paper is the design of a static heuristic that: (a) determines a robust resource allocation, i.e., a resource allocation that maximizes the allowable increase in workload until a run-time reallocation of resources is required to avoid a QoS violation, and (b) has a very low failure rate (i.e., the percentage of instances a heuristic fails). Two such heuristics proposed in this study are a genetic algorithm and a simulated annealing heuristic. Both were “seeded” by the best solution found by using a set of fast greedy heuristics.

Allocation Models and Heuristics for the Outsourcing of Repairs for a Dynamic Warranty Population

We consider a scenario in which a large equipment manufacturer wishes to outsource the work involved in repairing purchased goods while under warranty. Several external service vendors are available for this work. We develop models and analyses to support decisions concerning how responsibility for the warranty population should be divided between them. These also allow the manufacturer to resolve related questions concerning, for example, whether the service capacities of the contracted vendors are sufficient to deliver an effective post-sales service. Static allocation models yield information concerning the proportions of the warranty population for which the vendors should be responsible overall. Dynamic allocation models enable consideration of how such overall workloads might be delivered to the vendors over time in a way which avoids excessive variability in the repair burden. We apply dynamic programming policy improvement to develop an effective dynamic allocation heuristic. This is evaluated numerically and is also used as a yardstick to assess two simple allocation heuristics suggested by static models. A dynamic greedy allocation heuristic is found to perform well. Dividing the workload equally among vendors with different service capacities can lead to serious losses.