A bicriteria approach to minimize maximal lateness and resource consumption for scheduling a single machine

Yedidsion, Liron; Shabtay, Dvir; Kaspi, Moshe

doi:10.1007/s10951-007-0044-6

Cited by 21 publications

(9 citation statements)

References 18 publications

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“…Although the TSP is known to be strongly NPhard, and Deinako et al (2011) have proved that the well-known sub-tour patching heuristic is asymptotically optimal for large sets of the TSP distance matrices. Moreover, in a series of published works, Kaspi and Shabtay (2006); ; , Yedidsion et al (2007) and Leyvand et al (2010) studied the bi-criteria scheduling problems with resource constraints which have common features with the agent scheduling problems P1-P4 mentioned above. Leyvand et al (2010) have also solved just-in-time scheduling problems that are further extended and exploited in the considered decision support system for failure detection and planning preventive maintenance activities.…”

Section: Methods and Algorithmsmentioning

confidence: 99%

Detection and improvement of deficiencies and failures in public-transportation networks using agent-enhanced distribution data mining

Levner

Ceder

Elalouf

et al. 2011

2011 IEEE International Conference on Industrial Engineering and Engineering Management

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The main goal of this paper is to develop a general methodology for both pinpointing the weak elements of public transportation (PT) systems and finding least-cost solutions for improvements. The methodology is based on network routing, scheduling, and real-time control algorithms. These algorithms detect deficiencies and failures of the PT network and in operations planning. The main practical objective and challenge of this work is to provide a decision-support system for the prognosis and detection of the deficiencies of the PT network and measures required to their remedy. The system is based on off-and online algorithms and methods associated with multi-agent systems.Keywordsfailure detection; decision support system; mobile agents; public transportation; transport network I. INTRODUCTIONToday the public transportation (PT) and logistics network environment is dynamic and can change dramatically in a very short time. Therefore, traditional optimization and simulation techniques based on static off-line training and decision making in stationary situations cannot satisfactorily predict customer and network behavior. Because PT systems are complex, dynamic and have different distributed sources of voluminous data, the intelligent multi-agent (IMA) and data-mining approaches are efficient ways for analyzing and optimizing them. This paper considers PT systems, mostly those associated with large urban transportation networks. Failure detection providing PT service focuses on two components: the inadequacy of the planned or existed network and a dynamic non-fulfillment of the planned scheduling tasks and expected operation.The first component is related to the existing PT network and planned routing, scheduling and expected operations. In regard to these functions, there is a need to plan the preventive maintenance actions and detect the weak segments and bottlenecks for the optimization of corrective/improvement actions. The second component is related to the detection and repair of real-time failures during actual operation.

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Section: Methods and Algorithmsmentioning

confidence: 99%

Detection and improvement of deficiencies and failures in public-transportation networks using agent-enhanced distribution data mining

Levner

Ceder

Elalouf

et al. 2011

2011 IEEE International Conference on Industrial Engineering and Engineering Management

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“…In various real-life systems, however, processing times may be controllable by allocating resources, such as additional money, overtime, energy, fuel, catalysts, subcontracting, or additional manpower, to the job operations (see, e.g., [22,53,29,50]). Due to the large variety of applications, there is extensive literature on the subject of scheduling with controllable processing times (see, e.g., [55,21,20,23,24,2,8,57,19,37,51,46,14,15,49,59,33]). A survey of results up to 1990 is provided by Nowicki and Zdrzalka [38], and more recent surveys are presented by Chudzik et al [10], Janiak et al [26] and Shabtay and Steiner [48].…”

Section: Some Important Scheduling Problems Which Are Equivalent To Rmentioning

confidence: 99%

Complexity analysis of an assignment problem with controllable assignment costs and its applications in scheduling

Yedidsion

Shabtay

Kaspi

2011

Discrete Applied Mathematics

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a b s t r a c tWe extend the classical linear assignment problem to the case where the cost of assigning agent j to task i is a multiplication of task i's cost parameter by a cost function of agent j. The cost function of agent j is a linear function of the amount of resource allocated to the agent. A solution for our assignment problem is defined by the assignment of agents to tasks and by a resource allocation to each agent. The quality of a solution is measured by two criteria. The first criterion is the total assignment cost and the second is the total weighted resource consumption. We address these criteria via four different problem variations. We prove that our assignment problem is N P -hard for three of the four variations, even if all the resource consumption weights are equal. However, and somewhat surprisingly, we find that the fourth variation is solvable in polynomial time. In addition, we find that our assignment problem is equivalent to a large set of important scheduling problems whose complexity has been an open question until now, for three of the four variations.

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“…The optimal resource allocation problem will be solved as a shortest path problem within an acyclic directed graph. The similar method has been used by Shabtay and Kaspi (2004b) and Yedidsion and Shabtay (2007) to minimize maximum lateness and resource consumption. The solving procedure involves following two steps: First, the problem is decomposed into a finite set of relaxed sub-problems.…”

Section: Optimal Resource Allocationmentioning

confidence: 99%

A branch and bound algorithm for scheduling jobs with controllable processing times on a single machine to meet due dates

Feng

2010

Ann Oper Res

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In most deterministic scheduling problems, job-processing times are regarded as constant and known in advance. However, in many realistic environments, job-processing times can be controlled by the allocation of a common resource to jobs. In this paper, we consider the problem of scheduling jobs with arbitrary release dates and due dates on a single machine, where job-processing times are controllable and are modeled by a nonlinear convex resource consumption function. The objective is to determine simultaneously an optimal processing permutation as well as an optimal resource allocation, such that no job is completed later than its due date, and the total resource consumption is minimized. The problem is strongly N P-hard. A branch and bound algorithm is presented to solve the problem. The computational experiments show that the algorithm can provide optimal solution for small-sized problems, and near-optimal solution for medium-sized problems in acceptable computing time.

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A bicriteria approach to minimize maximal lateness and resource consumption for scheduling a single machine

Cited by 21 publications

References 18 publications

Detection and improvement of deficiencies and failures in public-transportation networks using agent-enhanced distribution data mining

Detection and improvement of deficiencies and failures in public-transportation networks using agent-enhanced distribution data mining

Complexity analysis of an assignment problem with controllable assignment costs and its applications in scheduling

A branch and bound algorithm for scheduling jobs with controllable processing times on a single machine to meet due dates

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