Constraints-driven scheduling and resource assignment

The Skilled Workforce Project Scheduling Problem (SWPSP) is a complex problem of task scheduling and resource assignment that comes up in the daily management of many company Service Centers. In this paper we present a constraint programming (CP) approach for the SWPSP. We extend the project scheduling literature by developing a project scheduling model that accounts for differing skills among workers. The computational results for the SWPSP show that the constraint programming approach increases the performance of the model solving processes. The results for the SWPSP is effective in solving the proposed problem.

Skilled workforce scheduling in Service Centres

Natural and triggered-disasters, have devastating and profound negative effects on human lives that require a speedy declaration of an emergency in order to minimize their severe consequences. Hence, a prompt disaster response, in addition to effective measures such as informed decision making, organized evacuation plan, right hospital selection, proper rescue vehicles, efficient resources assignment and timely vehicle scheduling are critical actions needed to organize successful secured operations that could, if well prepared, save many injured bodies and lessen the human distress. To reach this ultimate goal, a complicated procedure should be in place and any failure can potentially increase the number of causalities, thus a complete alertness and full caution should be exercised. In this paper, we treat the Integrated Problem of Ambulance Scheduling and Resource Assignment (IPASRA) in the case of a sudden disaster. The main resources to be assigned are the ambulances and the hospitals. While, the hospitals serving capacities might be considered or not according to the extent of disaster and particularly to the wounded bodies’ total number. We formulate the (IPASRA) as a linear model, furthermore a novel hybrid algorithm based on Tabu Search (TS) and Greedy Randomized Adaptive Search Procedure (GRASP) is offered to tackle this complex problem. Simulation tests are also presented to prove the efficiency of our modelling and resolution approaches.

In this study, a new and simple alternate routing heuristics, which would be superior to the conventional routing strategies in terms of various system performance measures and easier to apply practically, was presented. Because of the high investment costs of FMSs, it is also definitely worth choosing the best operating policy and system configuration by analyzing the system model, and adapting to changes over time. In future research, the heuristic rule base will be extended to include other supplementary constraints such as dynamic tool allocation and sequence dependent setup times. For practical implementation of the proposed decision support system, additional research in the area of human interfaces could be useful to develop more user friendly system which automatically constructs simulation model of the system from the knowledge base of a production system.

The paper studies a practical problem of resource assignment and optimal human resource allocation scheduling with the objective of minimizing employees' useless working time and retire some workers. We analyze simultaneous employment staff with different competencies (skills) that needed to complete jobs. Each job and worker have a set of individual parameters. The mathematical model of assignment workers to tasks is considered. Three heuristic algorithms are performed. In addition, we show limits used for algorithms in our special case. We study NP-hardness of the problem. We propose numerical results on real data (120 competencies, 250 workers, 1000 tasks) proved potential efficiency and practical value supposed model and algorithms solving the problem. The number of required staff can be reduced by 41,7% and the total salary can be reduced by 16,5%.

In this work we address the problem of joint link scheduling and power assignment in WSNs. It focuses on finding a feasible schedule and a power assignment scheme such that the schedule length is minimized and the concurrent transmissions have a fair quality in terms of SINR (Signal-to-Interference-and-Noise Ratio). As the problem is shown to be NP-hard we propose a greedy heuristic for the scheduling problem which seeks to minimize the number of time slots for link scheduling. Our goal is to design an algorithm that performs transmission power control in order to guarantee fair SINR link transmissions. This insures that every node that is actively transmitting in the network chooses a transmission power which will minimally affect the other concurrent transmissions. Hence, we opt for a cooperative strategy which intends to maximize the minimum value of SINR. This problem is modeled and solved using an iterative algorithm based on linear programming which provides an optimal solution. The solution of this problem may be crucial for the performance of WSNs as it impacts different network requirements such as lifetime (energy savings), delay (schedule length) and throughput (number of concurrent links in a time slot).

Resource assignment problems occur in a vast variety of applications, from scheduling problems over image recognition to communication networks. Often these problems can be modeled by a maximum weight matching problem in (bipartite) graphs or generalizations thereof, and efficient and practical algorithms are known for these problems. Although in some of the applications an assignment of the resources may be needed only once, in many of these applications, the assignment has to be computed more often for different scenarios. In that case it is often essential that the assignments can be computed very fast. Moreover, implementing different assignments in different scenarios may come with a certain cost for the reconfiguration of the system. In this paper, we consider the problem of determining optimal assignments sequentially over a given time horizon, where consecutive assignments are coupled by constraints that control the cost of reconfiguration. We develop fast approximation and online algorithms for this problem with provable approximation guarantees and competitive ratios. Moreover, we present an extensive computational study about the applicability of our model and our algorithms in the context of orthogonal frequency division multiple access (OFDMA) wireless networks, finding a significant performance improvement for the total bandwidth of the system using our algorithms. For this application (the downlink of an OFDMA wireless cell) , the run time of matching algorithms is extremely important, having an acceptable range of a few milliseconds only. For the considered realistic instances, our algorithms perform extremely well: the solution quality is, on average, within a factor of 0.8–0.9 of optimal off-line solutions, and the running times are at most 5 ms per phase even in the worst case. Thus, our algorithms are well suited to be applied in the context of OFDMA systems. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2015.2221 . This paper was accepted by Teck-Hua Ho, optimization.

Article Free Access Share on A new method for modeling and solving the protein fold recognition problem (extended abstract) Authors: Ying Xu Computational Biosciences Section, Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN Computational Biosciences Section, Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TNView Profile , Dong Xu View Profile , Edward C. Uberbacher Computational Biosciences Section, Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN Computational Biosciences Section, Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TNView Profile Authors Info & Claims RECOMB '98: Proceedings of the second annual international conference on Computational molecular biologyMarch 1998 Pages 285–292https://doi.org/10.1145/279069.279134Online:01 March 1998Publication History 2citation309DownloadsMetricsTotal Citations2Total Downloads309Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Identical parallel machine scheduling problem for minimizing the makespan is a very important production scheduling problem which has been proven to be NP-hard. The problem further compounds with additional constraints. Genetic algorithms (GA) have shown great advantages in solving the combinatorial optimization problem in view of its characteristic that has high efficiency and that is fit for practical application. In this chapter we present a spreadsheet based GA approach for minimizing the makespan for scheduling of a set of tasks for identical parallel machines and worker assignment to machines. The results obtained from the proposed approach are compared with two sets of benchmark problems consisting of 100 problems each. It has been demonstrated that the performance of proposed approach is superior to the results that have been obtained earlier. The proposed approach produces optimal solution for almost 95% of the problems demonstrating the effectiveness of the proposed approach. An empirical analysis of GA parameters has also been carried out to see the effect on the performance of the proposed algorithm.

In order to cope with the degradation of communication quality due to fading of wireless signals when transmitted over long distances, cooperative communication techniques are often applied to long-distance wireless communications due to their relay cooperative forwarding characteristics. A reasonable relay assignment scheme can significantly improve the network throughput. In order to solve the problem of irrational relay resource assignment in the existing relay assignment under the multi-node cooperative communication network, this paper proposes a relay assignment method that balances the communication quality (BQRA). Firstly, a multi-node cooperative communication system model is established, and a node pre-assignment set is established based on node distance. Then, with the goal of maximizing the overall network capacity, the proposed relay assignment method is used to allocate relay nodes for each source node. The effective performance of the proposed method is evaluated through simulation experiments. Simulation results show that the system channel capacity index is slightly better than several other methods. 15% increase in system channel capacity compared to the classical relay assignment method, LIRA.

A main component of the Multi-Layer Multi-domain (MLMD) orchestration is the end-to-end path computation over the packet and optical layers. Routing in MLMD networks is complex and requires special computational elements and cooperation between different layers and domains. The goal is to optimize the utilization of Wide Area Networks (WANs) leveraging on FlexE - the new Flexible Ethernet technology which couldn’t be fully achieved from local resource allocation in a single domain. We present MLMD-PCE, a path computation engine for MLMD networks that achieves optimal routes through a hierarchical path computation. We formulate an optimization problem of traffic routing and resource assignment for FlexE-Aware and FlexE-Unaware modes and propose an approximation algorithm that runs in polynomial time. Another issue of multi-domain routing is the lack of visibility over the intra-domain typologies in the parent-PCE. To solve this problem, we use a novel mechanism to gather the intra-domain information from the child-PCEs while keeping the domain privacy. Simulation results show that MLMD-PCE carries 77% more traffic than the current Hierarchical-PCE.

This study investigates patient appointment scheduling and examination room assignment problems involving patients who undergo ultrasound examination with considerations of multiple examination rooms, multiple types of patients, multiple body parts to be examined, and special restrictions. Following are the recommended time intervals based on the findings of three scenarios in this study: In Scenario 1, the time interval recommended for patients’ arrival at the radiology department on the day of the examination is 18 min. In Scenario 2, it is best to assign patients to examination rooms based on weighted cumulative examination points. In Scenario 3, we recommend that three outpatients come to the radiology department every 18 min to undergo ultrasound examinations; the number of inpatients and emergency patients arriving for ultrasound examination is consistent with the original time interval distribution. Simulation optimization may provide solutions to the problems of appointment scheduling and examination room assignment problems to balance the workload of radiological technologists, maintain high equipment utilization rates, and reduce waiting times for patients undergoing ultrasound examination.

The presented paper shows a new method for modeling of a physical system in a rational expression. The method is based on a hybrid combination of the conventional least square method (LSM) and a robust recursive procedure. In the first step of the method, a sampling function is employed to establish a system of linear equations. Then, the QR decomposition method and a very fast iterative technique are simultaneously used to determine the unknown parameters. Also, it is shown that by regarding a scalar gain parameter, the stability of the final model can be controlled. To verify the performance of the presented method, several theoretical and practical examples are examined and the obtained results are compared with the simulation and measurement data.

The aim of this paper is to present a branch and bound approach to solve a k-stage hybrid flowshop scheduling problem in order to minimize the total completion time. This scheduling problem encloses three subproblems: lot sizing, scheduling and assignment problems. In this paper only scheduling and assignment are taken into account because we suppose that the lot sizing problem has already been solved. The method we propose is an optimal method, based on a special architecture of search tree which allows us to solve the two subproblems considered. Some lower bounds are proposed. Then computational results are given and proved that results are acceptable. Finally, two theorems demonstrate some dominance relations.

We address the problem of minimizing power consumption in behavioral synthesis of data-dominated circuits. The complex nature of power as a cost function implies that the effects of several behavioral synthesis tasks like module selection, clock selection, scheduling, and resource sharing on supply voltage and switched capacitance need to be considered simultaneously to fully derive the benefits of design space exploration at the behavior level. Recent work has established the importance of behavioral synthesis in low power VLSI design. However, most of the algorithms that have been proposed separate these tasks and perform them sequentially, and are hence not able to explore the tradeoffs possible due to their interaction. We present an efficient algorithm for performing scheduling, clock selection, module selection, and resource allocation and assignment simultaneously with an aim of reducing the power consumption in the synthesized data path. The algorithm, which is based on an iterative improvement strategy, is capable of escaping local minima in its search for a low power solution. The algorithm considers diverse module libraries and complex scheduling constructs such as multicycling, chaining, and structural pipelining. We describe supply voltage and clock pruning strategies that significantly improve the efficiency of our algorithm by cutting down on the computational effort involved in exploring candidate supply voltages and clock periods that are unlikely to lead to the best solution. Experimental results are reported to demonstrate the effectiveness of the algorithm. Our techniques can be combined with other known methods of behavioral power optimization like data path replication and transformations, to result in a complete data path synthesis system for low power applications.