Optimization of Logistic Processes in Supply-Chains Using Meta-heuristics

Silva, Carlos Antônio da; Runkler, Thomas A.; Sousa, João; Costa, J.M. Sá da

doi:10.1007/978-3-540-24580-3_9

Cited by 18 publications

(17 citation statements)

References 8 publications

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“…This problem is NP-Hard (Silva et al, 2003), therefore we use meta-heuristics, like GA and ACO, to optimize it. In principle, GA will perform well, since the optimization concerns the selection of a best set of items.…”

Section: The Scheduling Problem Consists Of Optimizing Daily the Costmentioning

confidence: 99%

“…However, to analyze the performance of the logistic system, it is necessary to consider a larger time window (weeks/months). In this case, we consider a sequence of 30 days in order to compare both methodologies, as described in (Silva et al, 2003). Table 2 presents the results in terms of best result, mean result and standard deviation.…”

Section: Comparison For the Logistic Problemmentioning

confidence: 99%

See 1 more Smart Citation

A Logistic Process Scheduling Problem: Genetic Algorithms or Ant Colony Optimization?

Silva

Sousa

Runkler

et al. 2005

IFAC Proceedings Volumes

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This paper compares the optimization of a logistic scheduling problem using two different optimization techniques; the genetic algorithms and the ant colony optimization. The comparison is preceded by a literature review that summarizes the available comparison results for different benchmark problems and tries to generalize the differences between the techniques. The simulation results for the logistic problem confirm the conclusions of the literature survey: both methods perform equally well, but in general the genetic algorithms are faster. However, the ant colonies give more information about the solution, which is advantage in some applications.

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Section: The Scheduling Problem Consists Of Optimizing Daily the Costmentioning

confidence: 99%

Section: Comparison For the Logistic Problemmentioning

confidence: 99%

A Logistic Process Scheduling Problem: Genetic Algorithms or Ant Colony Optimization?

Silva

Sousa

Runkler

et al. 2005

IFAC Proceedings Volumes

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“…To solve such hard problems, a new kind of algorithm has emerged which aimed at efficiently and effectively exploring a search space. In recent years, metaheuristic algorithms such as, Evolutionary Computation, Simulated Annealing, Tabu Search, Particle Swarm Optimization (PSO), and others, have been applied to various logistics optimization problem as a successful alternative to classical approaches (Silva et al, 2003) (Altiparmak et al, 2006). All metaheuristics share the elements of selecting candidate solution(s) from a neighbourhood of the current solution(s) and move at each iteration towards 'good solutions' in the search space (Olaffson, 2006).…”

Section: Introductionmentioning

confidence: 99%

Simulation-based optimization for a capacitated multi-echelon production-inventory system

Güller

Uygun

Noche

2015

Journal of Simulation

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One of the most important aspects affecting the performance of a supply chain is the management of inventories. Managing inventory in complex supply chains is typically difficult, and may have a significant impact on the customer service level and system-wide costs. The main challenge of inventory management is that almost every inventory problem involves multiple and conflicting objectives that need to be optimized simultaneously. In this paper, we present an efficient way using simulation-based optimization approach to determine the optimal inventory control parameters of a multi-echelon production-inventory system under a stochastic environment. The Pareto dominance concept is implemented to find a set of near optimal solutions for determining the best trade-off between objectives. The Multi-objective Particle Swarm Optimization (MOPSO) algorithm is used to determine the appropriate inventory control parameters to minimize the total inventory cost and maximize the service level. To evaluate the control parameters generated by the MOPSO, an object-oriented framework for developing the simulation model is presented. Finally, we provide a real-world case study of a major food product supply chain to demonstrate the use of proposed approach and enable decision making at inventory management. The proposed algorithm is compared with existing multi-objective genetic algorithm (NSGA-II).

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“…A fitness function is used to evaluate individuals, and reproductive success varies with fitness. Here, we used the algorithm and parameters presented in [7], 5 Model of the logistic process Fig. 2 presents a schematic view of the analyzed logistic process, that can be described in probabilistic terms.…”

Section: Other Optimization Algorithms Used In Schedulingmentioning

confidence: 99%

“…In order to avoid large volumes of stocks and to minimize the number of delayed orders, the scheduling program assigns components to orders every day, not considering their order of arrival but their volumes, requirements and dates of delivery. The objective function used is presented in [7], We impose as the most important objective that the highest number of orders is delivered at the correct date, although we consider also important to have a small tardiness variance (TJT) of the remaining orders. Thus, the objective function is given by 1…”

Section: (4)mentioning

confidence: 99%