Maintenance planning and resource allocation in a urea fertilizer plant

This paper is devoted to the analysis and computation of overall as well as various subsystem availabilities of a urea fertilizer plant. Based on the analysis, appropriate maintenance strategies for different situations have been developed. Using Lagrange's multiplier technique the cost of maintenance and manpower is analysed. By minimizing the maintenance cost allocation to various subsystems, the optimum repair policy is determined.     

Modelling of quality function deployment planning with resource allocation

Quality Function Deployment (QFD) is a well-known customer-oriented methodology, which is widely used to assist decision making in product design and development in various types of production. Determining how and to what extent certain characteristics or technical attributes (TAs) of products are to be met, with a view to gaining a higher level of overall customer satisfaction, is a key success factor in product design and development. An operational QFD planning problem with resource allocation is considered in this paper. The aim is to plan the attainment of TAs by allocating resources among the TAs with a view to achieving maximized overall customer satisfaction. Taking into account the technical and resource constraints, and the impact of the correlation among TAs, the operational QFD planning with resource allocation is formulated as a linear program and solved by a heuristics-combined Simplex Method. An overall procedure is presented to help a design team to implement this QFD design planning with resource allocation in practice. This model can bridge the gap and conflicts between the design targets at the strategic level, and resource allocations in the part deployment and operational process planning level.     

A matrix-based Bayesian approach for manufacturing resource allocation planning in supply chain management

Nowadays, the supply chain of manufacturing resources is typically a large complex network, whose management requires network-based resource allocation planning. This paper presents a novel matrix-based Bayesian approach for recommending the optimal resource allocation plan that has the largest probability as the optimal selection within the context specified by the user. A proposed matrix-based representation of the resource allocation plan provides supply chain modelling with a good basis to understand problem complexity, support computer reasoning, facilitate resource re-allocation, and add quantitative information. The proposed Bayesian approach produces the optimal, robust manufacturing resource allocation plan by solving a multi-criteria decision-making problem that addresses not only the ontology-based static manufacturing resource capabilities, but also the statistical nature of the manufacturing supply chain, i.e. probabilities of resource execution and resource interaction execution. A genetic algorithm is employed to solve the multi-criteria decision-making problem efficiently. We use a case study from manufacturing domain to demonstrate the applicability of the proposed approach to optimal manufacturing resource allocation planning.     

The integration of resource allocation and time buffering for bi-objective robust project scheduling

In the recent decades, the recognition that uncertainty lies at the heart of modern project management has induced considerable research efforts on robust project scheduling for dealing with uncertainty in a scheduling environment. The literature generally provides two main strategies for the development of a robust predictive project schedule, namely robust resource allocation and time buffering. Yet, the previous studies seem to have neglected the potential benefits of an integration between the two. Besides, few efforts have been made to protect simultaneously the project due date and the activity start times against disruptions during execution, which is desperately demanded in practice. In this paper, we aim at constructing a proactive schedule that is not only short in time but also less vulnerable to disruptions. Firstly, a bi-objective optimisation model with a proper normalisation of the two components is proposed in the presence of activity duration variability. Then a two-stage heuristic algorithm is developed which deals with a robust resource allocation problem in the first stage and optimally determines the position and the size of time buffers using a simulated annealing algorithm in the second stage. Finally, an extensive computational experiment on the PSPLIB network instances demonstrates the superiority of the combination between resource allocation and time buffering as well as the effectiveness of the proposed two-stage algorithm for generating proactive project schedules with composite robustness.     

Sequential resource allocation utilizing agents

Many manufacturing systems allocate resources, such as machines, sequentially. Sequential allocation of resources can be viewed as a digraph where each vertex represents resources forming nodes in a distributed system and the arcs represent the allocation. The allocation of such resources can be considered to be a distributed problem. Agents are a distributed artificial intelligence paradigm applicable to distributed problems and, therefore, have a potential to be applicable to sequential resource allocation. This paper presents a method of sequential resource allocation utilizing agents, and an AGV system is presented as an example application area. This system was utilized in experiments to test the agent application. Results and an analysis are also presented.     

Incentive compatible resource allocation in concurrent design

This article presents an approach that a manager can use to allocate resources needed to design a system among the members of a concurrent design team. The system being designed is assumed to be composed of a number of subsystems, each designed by a different engineer. These engineers possess private information about the performance of their subsystem as a function of the design resources that they are allocated. This article shows how the manager of such a concurrent design project can induce rational self-interested engineers to reveal truthfully their private subsystem performance functions. This is accomplished through an incentive contract that ties each engineer’s pay to the contribution of their subsystem to the performance of the overall system. The approach builds from a Vickrey–Clarke–Groves mechanism to achieve, as an ex-post Nash equilibrium, truthful reporting of private subsystem performance functions by risk-neutral agents.     

Resource allocation with project management software

We present results of a benchmark test evaluating the resource allocation capabilities of the project management software packages Acos Plus.1 8.2, CA SuperProject 5.0a, CS Project Professional 3.0, MS Project 2000, and Scitor Project Scheduler 8.0.1. The tests are based on 1560 instances of precedence– and resource–constrained project scheduling problems. For different complexity scenarios, we analyze the deviation of the makespan obtained by the software packages from the best feasible makespan known. Among the tested software packages, Acos Plus.1 and Project Scheduler show the best resource allocation performance. Moreover, our numerical analysis reveals a considerable performance gap between the implemented methods and state–of–the–art project scheduling algorithms, especially for large–sized problems. Thus, there is still a significant potential for improving solutions to resource allocation problems in practice.      

Combining cost-based and rule-based knowledge in complex resource allocation problems

A major challenge in the formulation of optimization models for large-scale, complex operational problems is that some data are impossible or uneconomical to collect, producing a cost model that suffers from incomplete information. As a result, even an optimal solution may be “wrong” in the sense that it is solving the wrong problem. In many operational settings, knowledgeable experts will already know, at least approximately, how a model should behave, and can express this knowledge in the form of low dimensional patterns: “high powered locomotives should pull intermodal trains” (because they need to move quickly) or “loaded C-141s should not be flown into Saudi Arabia” (for maintenance reasons). Unlike the literature on inverse optimization which uses observed actions to train the parameters of a cost model, we used exogenous patterns to guide the behavior of a model using a proximal point term that penalizes deviations from these patterns. Under the assumption that the patterns are derived from rational behaviors, we establish the conditions under which incorporating patterns will reduce actual costs rather than just the engineered costs. The effectiveness of the approach is demonstrated in a controlled, laboratory setting using data from a major railroad.     

Comparison of distributed methods for resource allocation

Resource allocation problems aim to achieve certain goals or objectives by allocating resources. Traditionally, centralized methods are applied to such problems. However, the inherent, decentralized nature of many corporates/companies/factories drives the investigation on decentralized or distributed methods. This paper provides an overview of research activities in the field of distributed methods for resource allocation problems. We concentrate on classifying the resource allocation problems, comparing different methods and discussing how these different methods can be applied to the problems. Finally, a range of open issues and future challenges are highlighted.     

网格环境下应用-系统平衡型资源分配优化策略

针对网格环境中资源分配如何同时优化应用执行性能和提高系统资源效率这个难点问题,提出了基于序贯博弈的执行时间最优目标下的Makespan-效率平衡型优化策略以及费用最优目标下的Cost-效率平衡型扩展策略.优化策略利用平衡的思想将资源分配过程分为活动分发和处理器分配两个阶段,通过有限次博弈后获得优化解.仿真实验以实际网格...     

Managing academic performance by optimal resource allocation

Scientometrics - In this paper, we develop and study a complex data-driven framework for human resource management enabling (i) academic talent recognition, (ii) researcher performance measurement,...     

A constraint-based perspective in resource constrained project scheduling

In this study, a new heuristic approach to the resource constrained project scheduling problem is introduced. This approach, which is called local constraint based analysis (LCBA), is more robust than the dispatching rules found in the literature, since it does not depend on an a priori insight as do the dispatching rules. LCBA consists of the application of local essential conditions which respect the current temporal and resource constraints to generate a necessary sequence of activities at a scheduling decision time point in a single-pass parallel scheduling algorithm. LCBA is a time efficient procedure due to the localized aspect with which the activities are handled. Only the activities which are schedulable at the current scheduling time are considered for the application of the essential conditions. LCBA is tested against well-known rules from the literature and some recently developed rules. This testing is done using a set of problems of a special design and also a set of optimally solved problems from a recent benchmark in the literature. It is observed that near optimal time efficient solutions are obtained by LCBA and the procedure's performance is considerably better than that of the dispatching rules.     

一个面向异构多核处理器Cell的资源分配模型

为了充分利用多核处理器提供的多级并行和解决多核资源分配问题,提出了一种将多任务并行程序映射到多核处理器平台上的模型驱动的方法。该方法首先创建一个三维优化空间来表示资源分配配置,而后通过搜索该空间为应用生成多种并行机制,最后对各种并行机制进行静态评估从而找出最优的机制。该方法同时考虑了任务并行、数据并行以及通讯开销。在异构多核处理器Cell上利用一个图像处理应用对此方法进行了测试。实验表明,这种模型驱动的方法能够很好地评估性能并为应用确定有效的并行机制。     

Simultaneous berth allocation and yard planning at tactical level

We present a simultaneous berth allocation and yard planning problem at tactical level, since the berth allocation has a great impact on the yard planning and vice versa. This problem is solved by means of an alternating berth and yard planning heuristic approach. The alternating heuristic quickly converges to a local minimum which heavily depends on the starting point. Therefore, we formulate another optimization problem for generating a suitable starting point. A real size case study provided by PSA Antwerp shows that our approach to simultaneously solve both problems might reduce the total straddle carrier travel distance considerably as compared with a representative allocation.     

A network flow algorithm for optimal resource allocation

This paper introduces a network flow method for manpower scheduling and resource allocation. The network formulation of the problem permits the development of an algorithm that determines the minimal flow of resources through the network. The method presented here corresponds to the Simplex method, for in both methods the value of the objective function is optimized while the capacity limitations are preserved. The procedure proposed here, however, uses a more economical problem representation and is computationally more efficient. The efficacy of the minimal flow algorithm and its use is illustrated for a production system but the applicability of the procedure is sufficiently broad for it to work in other problem situations just as well.     

Cost optimal segregated targeting for resource allocation networks

In this paper, a generalised methodology is proposed to target cost optimal allocation of resources in segregated targeting problems. Cost optimal segregated targeting problems are characterised by the existence of multiple zones; each consisting of a set of demands and using a unique resource with given cost and a single quality index (e.g., emissions factor, contaminant concentrations, etc.). All these zones share a common set of internal sources. This paper presents a rigorous mathematical proof of the decomposition principle that decomposes the problem into a sequence of sub-problems. Decomposition of the original problem is performed based on the prioritised costs for each external resource, attached to a particular zone. Prioritised cost of a resource depends on the pinch quality, quality of the resource and its cost. Applicability of the proposed methodology is illustrated by examples from carbon-constrained energy planning and water allocation networks.     

Optimal resource allocation for multicast sessions in multi-hop wireless networks

In this paper, we extend recent results on fair and stable resource allocation in wireless networks to include multicast sessions, in particular multi-rate multicast. The solution for multi-rate multicast is based on scheduling virtual (shadow) 'traffic' that 'moves' in reverse direction from destinations to sources. This shadow scheduling algorithm can also be used to control delays in wireless networks.     

Customer segmentation,allocation planning and order promising in make-to-stock production

Modern advanced planning systems offer the technical prerequisites for an allocation of “available-to-promise” (ATP) quantities—i.e. not yet reserved stock and planned production quantities—to different customer segments and for a real time promising of incoming customer orders (ATP consumption) respecting allocated quota. The basic idea of ATP allocation is to increase revenues by means of customer segmentation, as it has successfully been practiced in the airline industry. However, as far as manufacturing industries and make-to-stock production are concerned, it is unclear, whether, when, why and how much benefits actually arise. Using practical data of the lighting industry as an example, this paper reveals such potential benefits. Furthermore, it shows how the current practice of rule-based allocation and consumption can be improved by means of up-to-date demand information and changed customer segmentation. Deterministic linear programming models for ATP allocation and ATP consumption are proposed. Their application is tested in simulation runs using the lighting data. The results are compared with conventional real time order promising with(out) customer segmentation and with batch assignment of customer orders. This research shows that—also in make-to-stock manufacturing industries—customer segmentation can indeed improve profits substantially if customer heterogeneity is high enough and reliable information about ATP supply and customer demand is available. Surprisingly, the choice of an appropriate number of priority classes appears more important than the selection of the ATP consumption policy or the clustering method to be applied.     

Using mobile telephones: cognitive workload and attention resource allocation

Driver distraction is recognized as being one of the central causes of road traffic incidents and mobile telephones are tangible devices (among many other electronic devices) that can distract the driver through changes in workload. Forty participants completed a motorway route characterized by a low level of road complexity in the form of vehicle handling and information processing. A peripheral detection task (PDT) was employed to gauge mental workload. We compared effects of conversation type (simple versus complex) and telephone mode (hands-free versus handheld) to baseline conditions. The participants' reaction times increased significantly when conversing but no benefit of hands-free units over handheld units on rural roads/motorways were found. Thus, in regard to mobile telephones, the content of the conversation was far more important for driving and driver distraction than the type of telephone when driving on a motorway or similar type of road. The more difficult and complex the conversation, the greater the possible negative effect on driver distraction.     

Linear-programming-based heuristics for project capacity planning

Many multi-project organizations are capacity driven, which means that their operations are constrained by various scarce resources. An important planning aspect in a capacity driven multi-project organization is capacity planning. By capacity planning, we mean the problem of matching demand for resources and availability of resources for the medium term. Capacity planning is a very useful method to support important tactical decisions such as due date quotation and price quotation for new projects, and to gain an insight into capacity requirements for the medium term. We present a capacity planning model in which aspects such as capacity flexibility, precedence relations between work packages, and maximum work content per period can be taken into account. For this model, we discuss several linear-programming-based heuristics. Using a large set of test instances, we compare these heuristics with some results from the literature. It turns out that some of these heuristics are very powerful for solving capacity planning problems.