Distributed Scheduling Problem Research Articles

Communication infrastructure in distributed scheduling

The emergence of distributed artificial intelligent (DAI) introduced a new approach to solve scheduling problems by a set of scheduling systems that interact with each other in the problem-solving process. In this paper, we describe a communication infrastructure to handle connection and communication between distributed Internet scheduling systems for distributed applications. First, we present an agent model of distributed scheduling systems where agents can communicate and coordinate activities with each other via an agent communication language. Then, we define the syntax and semantics for the agent communication languages, and negotiation mechanism. Following that, we discuss the design and development of the prototype for the multi-agent scheduling systems. We conclude with a discussion of communication issues for heterogeneous agent-based scheduling systems to solve distributed scheduling problems.

GA applications to physical distribution scheduling problem

A physical distribution system has a number of optimization problems. Most of them belong to a combinatorial problem, to which conventional mathematical programming methods may hardly be applied. This paper reports on two applications of the genetic algorithm (GA) to physical distribution scheduling problems, which arise at real physical distribution centers. The developed GA schedulers took the place of conventional schedulers, which were coded by rule-based technologies. Advantages of the introduction of GA schedulers into the physical distribution system are as follows: (1) the GA becomes a general problem-solver engine. Once we develop this engine, we only have to develop interfaces for the applications; and (2) fitness functions necessary for the GA force the physical distribution schedulers to have approximate performance estimation. This was not taken into consideration when the rule-based scheduler was used. Two applications of the discussed schedulers were implemented with real distribution centers, and they brought much efficiency to their management.

Planning logistics operations in the oil industry

In this paper we apply stochastic programming modelling and solution techniques to planning problems for a consortium of oil companies. A multiperiod supply, transformation and distribution scheduling problem—the Depot and Refinery Optimization Problem (DROP)—is formulated for strategic or tactical level planning of the consortium's activities. This deterministic model is used as a basis for implementing a stochastic programming formulation with uncertainty in the product demands and spot supply costs (DROPS), whose solution process utilizes the deterministic equivalent linear programming problem. We employ our STOCHGEN general purpose stochastic problem generator to ‘recreate’ the decision (scenario) tree for the unfolding future as this deterministic equivalent. To project random demands for oil products at different spatial locations into the future and to generate random fluctuations in their future prices/costs a stochastic input data simulator is developed and calibrated to historical industry data. The models are written in the modelling language XPRESS-MP and solved by the XPRESS suite of linear programming solvers. From the viewpoint of implementation of large-scale stochastic programming models this study involves decisions in both space and time and careful revision of the original deterministic formulation. The first part of the paper treats the specification, generation and solution of the deterministic DROP model. The stochastic version of the model (DROPS) and its implementation are studied in detail in the second part and a number of related research questions and implications discussed.

Lower bounds on the competitive ratio for mobile user tracking and distributed job scheduling

We prove a lower bound of Ω(log n/log log n) on the competitive ratio of any (deterministic or randomized) distributed algorithm for solving the mobile user problem introduced by Awerbuch and Peleg (1989, 1990), on certain networks of n processors. Our lower bound holds for various networks, including the hypercube, any network with sufficiently large girth, and any highly expanding graph. A similar Ω(log n/log log n) lower bound is proved for the competitive ratio of the maximum job delay of any distributed algorithm for solving the distributed scheduling problem of Awerbuch, (1992) on any of these networks. The proofs combine combinatorial techniques with tools from linear algebra and harmonic analysis and apply, in particular, a generalization of the vertex isoperimetric problem on the hypercube, which may be of independent interest.

Task scheduling with interprocessor communication delays

Distributed memory architectures raise new and complex scheduling problems. n this paper, we first define a basic distributed memory computer scheduling problem issued from an ideal architecture. By proving that the corresponding decision problem is NP-complete, we show that unlike shared memory computer scheduling problems, these new problems do not become easy when the processor limitation constraint is removed. Finally, we improve the knowledge of the borderline between the easy and difficult subproblems of the basic one by giving polynomial special cases.