Heuristic for a new multiobjective scheduling problem

Setämaa-Kärkkäinen, Anne; Miettinen, Kaisa; Vuori, Jarkko

doi:10.1007/s11590-006-0020-7

Cited by 10 publications

(8 citation statements)

References 13 publications

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“…When a multiobjective optimization problem has been scalarized, methods developed for single objective optimization can be used to solving the problem. The objective function of the single objective problem is called a scalarizing function (Setämaa-Kärkkäinen et al 2007). …”

Section: No Preference Articulationmentioning

confidence: 99%

Bi-objective parallel machines scheduling with sequence-dependent setup times using hybrid metaheuristics and weighted min–max technique

2010

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In the literature of multi-objective problem, there are different algorithms to solve different optimization problems. This paper presents a min-max multiobjective procedure for a dual-objective, namely make span, and sum of the earliness and tardiness of jobs in due window machine scheduling problems, simultaneously. In formulation of min-max method when this method is combined with the weighting method, the decision maker can have the flexibility of mixed use of weights and distance parameter to yield a set of Pareto-efficient solutions. This research extends the new hybrid metaheuristic (HMH) to solve parallel machines scheduling problems with sequence-dependent setup time that comprises three components: an initial population generation method based on an ant colony optimization (ACO), a simulated annealing (SA) as an evolutionary algorithm employs certain probability to avoid becoming trapped in a local optimum, and a variable neighborhood search (VNS) which involves three local search procedures to improve the population. In addition, two VNS-based HMHs, which are a combination of two methods, SA/VNS and ACO/VNS, are also proposed to solve the addressed scheduling problems. A design of experiments approach is employed to calibrate the parameters. The non-dominated sets obtained from HMH and two best existing bi-criteria scheduling algorithms are compared in terms of various indices and the computational results show that the proposed algorithm is capable of producing a number of high-quality Pareto optimal scheduling plans. Aside, an extensive computational experience is carried out to analyze the different parameters of the algorithm.

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Section: No Preference Articulationmentioning

confidence: 99%

Bi-objective parallel machines scheduling with sequence-dependent setup times using hybrid metaheuristics and weighted min–max technique

2010

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“…Minimizing the objective function (3) is computationally demanding in the case of the NCS problem because of the integer variables [11]. In other words, it is time-consuming to solve large problem instances using exact methods.…”

Section: Solving the Problemmentioning

confidence: 99%

“…In mobile terminals, there is not much computational capacity available for this kind of optimization, and the users of mobile terminals are rarely prepared to wait more than a very short time. Therefore, we have developed a heuristic to solve the NCS problem [11]. The heuristic minimizes the objective function (3) approximately, achieving a good compromise solution.…”

Section: Solving the Problemmentioning

confidence: 99%

“…The NCS problem was first introduced in [10], in which the problem was formulated as a multiobjective optimization problem and different multiobjective optimization methods were compared for solving the problem. In [11] a heuristic for solving the problem in a static environment was presented. In this paper, we present an improved dynamic version of the heuristic that reacts to changing network conditions.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Usage of Multiple Network Connections

Setämaa-Kärkkäinen

Kurhinen

2008

Proceedings of the 1st International ICST Conference on Mobile Wireless Middleware, Operating Systems and Applications

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In the future mobile networks, a mobile terminal is able to select the best suitable network for each data transmission. The selection of a network connection to be used has been under a lot of study. In this paper, we consider a more extensive case in which we do not select a network connection but use several network connections simultaneously to transfer data. When data is transferred using multiple network connections, a network connection has to be selected for each component of the data. We have modelled this problem as a multiobjective optimization problem and developed a heuristic to solve the problem fast in a static network environment. In this paper, we discuss solving the problem in a dynamic network environment in which the availability as well as the rate and prizing of connections vary. We introduce an improved version of the heuristic that reacts to changing network conditions and improves the solution when possible.

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“…In many branches of industry and logistics, there arise problems of ordering jobs on machines (Pardalos and Resende [20], Setamaa-Karkkainen et al [21], Lee and Yu [14], Hadda [11], and Gawiejnowicz [6]). Scheduling jobs with variable processing times has received increasing attention.…”

Section: Introductionmentioning

confidence: 99%

Single-machine scheduling with nonlinear deterioration

Wang

2010

Optim Lett

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In this paper, we consider the single-machine scheduling problems with nonlinear deterioration. By the nonlinear deterioration effect, we mean that the processing times of jobs are nonlinear functions of their starting times. We show that even with the introduction of nonlinear deterioration to job processing times, single machine makespan minimization problem remains polynomially solvable. We also show that an optimal schedule of the total completion time minimization problem is V-shaped with respect to job normal processing times. A heuristic algorithm utilizing the V-shaped property is proposed, and computational experiments show that it performs effectively and efficiently in obtaining near-optimal solutions.

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Heuristic for a new multiobjective scheduling problem

Cited by 10 publications

References 13 publications

Bi-objective parallel machines scheduling with sequence-dependent setup times using hybrid metaheuristics and weighted min–max technique

Bi-objective parallel machines scheduling with sequence-dependent setup times using hybrid metaheuristics and weighted min–max technique

Optimal Usage of Multiple Network Connections

Single-machine scheduling with nonlinear deterioration

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