A search heuristic for just-in-time scheduling in parallel machines

Laguna, Manuel; Velarde, José Luis González

doi:10.1007/bf01471113

Cited by 78 publications

(27 citation statements)

References 8 publications

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“…The hybridization of GRASP with tabu search was first studied by Laguna and González-Velarde (1991). Delmaire et al (1999) considered two approaches.…”

Section: Extensionsmentioning

confidence: 99%

GRASP: Greedy Randomized Adaptive Search Procedures

Resende

Ribeiro

2013

Search Methodologies

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others. The method described in this chapter represents another example of such a technique. Metaheuristics are based on distinct paradigms and offer different mechanisms to escape from locally optimal solutions. They are among the most effective solution strategies for solving combinatorial optimization problems in practice and have been applied to a wide array of academic and real-world problems. The customization (or instantiation) of a metaheuristic to a given problem yields a heuristic for that problem.In this chapter, we consider the combinatorial optimization problem of minimizing f (S) over all solutions S ∈ X, which is defined by a finite set E = {e 1 ,..., e n } (called the ground set), by a set of feasible solutions X ⊆ 2 E and by an objective function f : 2 E → R. The ground set E, the objective function f , and the constraints defining the set of feasible solutions X are specific for each problem. We seek an optimal solution S * ∈ X such that f (S * ) ≤ f (S), ∀S ∈ X.GRASP, which stands for greedy randomized adaptive search procedures Resende 1989, 1995), is a multistart, or iterative, metaheuristic in which

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“…The hybridization of GRASP with tabu search was first studied by Laguna and González-Velarde (1991). Delmaire et al (1999) considered two approaches.…”

Section: Extensionsmentioning

confidence: 99%

GRASP: Greedy Randomized Adaptive Search Procedures

Resende

Ribeiro

2013

Search Methodologies

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“…Laguna and Gonzalez-Velarde (1991) considered the scheduling ofparallel machines in a justin-time production environment. Feo et al (1991) developed a GRASP solution for a single machine scheduling problem.…”

Section: Grasp Heuristicmentioning

confidence: 99%

A Comparison of GRASP and an Exact Method for Solving a Production and Delivery Scheduling Problem

García

Smith

Lozano

et al. 2002

Hybrid Information Systems

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Abstract. In this paper we consider the production and delivery scheduling problem, a problem that deals with the selection of orders to be processed by a manufacturing plant and immediately delivered to the customer site. Orders have a fixed due date and must be prepared in a single plant with limited capacity. A limited number of vehicles are available for delivery. A ready-mix concrete manufacturing case study has motivated this research. We describe an exact method to find optimal solutions based on the construction of a graph that collects all feasible solutions. We also describe a greedy randomized adaptive search (GRASP) for the problem that finds good, though not necessarily optimal, solutions.

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“…In another application, it is utilized to minimize total earliness (Laguna and Velarde 1991). It is also applied to minimize total tardiness on a single machine with setup times (Armentano and Araujo 2006).…”

Section: A Mathematical Model For Minimization Of Total Energy Consummentioning

confidence: 99%

A framework to minimise total energy consumption and total tardiness on a single machine

Mouzon

Yildirim

2008

International Journal of Sustainable Engineering

276

128

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A great amount of energy is wasted in industry by machines that remain idle due to underutilization. A way to avoid wasting energy and thus reducing the carbon print of an industrial plant is to consider minimization of energy consumption objective while making scheduling decisions. In order to minimize energy consumption, the decision maker has to decide the timing and length of turn off /turn on operation (a setup) and also provide a sequence of jobs that minimizes the scheduling objective, assuming that all jobs are not available at the same time.In this paper, a framework to solve a multiobjective optimization problem that minimizes total energy consumption and total tardiness is proposed. Since total tardiness problem with release dates is an NP-hard problem, a new greedy randomized multiobjective adaptive search metaheuristic (GRASPTETT) is utilized to obtain an approximate pareto front (i.e., an approximate set of non-dominated solutions). Analytic Hierarchical Process is utilized to determine the "best" alternative among the solutions on the pareto front. The proposed framework is illustrated on a case study. It is shown that a wide variety of dispersed solutions can be obtained via the proposed framework, and as total tardiness decreases, total energy consumption increases.

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A search heuristic for just-in-time scheduling in parallel machines

Cited by 78 publications

References 8 publications

GRASP: Greedy Randomized Adaptive Search Procedures

GRASP: Greedy Randomized Adaptive Search Procedures

A Comparison of GRASP and an Exact Method for Solving a Production and Delivery Scheduling Problem

A framework to minimise total energy consumption and total tardiness on a single machine

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