Flow shop scheduling algorithm to optimize warehouse activities

Centobelli, Piera; Converso, Giuseppe; Murino, Teresa; Santillo, Liberatina Carmela

doi:10.5267/j.ijiec.2015.8.001

Cited by 9 publications

(4 citation statements)

References 67 publications

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“…Naturally, the time required by these operations will depend on the type of product being processed (quantity, weight, volume, and shape of parts, components and final products). Centobelli et al (2016) provide further insight on the subject material handling in warehouses.…”

Section: Problem Presentationmentioning

confidence: 96%

Solving a multi-objective manufacturing cell scheduling problem with the consideration of warehouses using a simulated annealing based procedure

Toncovich¹,

Rossit²,

Frutos³

et al. 2019

10.5267/j.ijiec

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The competition manufacturing companies face has driven the development of novel and efficient methods that enhance the decision making process. In this work, a specific flow shop scheduling problem of practical interest in the industry is presented and formalized using a mathematical programming model. The problem considers a manufacturing system arranged as a work cell that takes into account the transport operations of raw material and final products between the manufacturing cell and warehouses. For solving this problem, we present a multiobjective metaheuristic strategy based on simulated annealing, the Pareto Archived Simulated Annealing (PASA). We tested this strategy on two kinds of benchmark problem sets proposed by the authors. The first group is composed by small-sized problems. On these tests, PASA was able to obtain optimal or near-optimal solutions in significantly short computing times. In order to complete the analysis, we compared these results to the exact Pareto front of the instances obtained with augmented ε-constraint method. Then, we also tested the algorithm in a set of larger problems to evaluate its performance in more extensive search spaces. We performed this assessment through an analysis of the hypervolume metric. Both sets of tests showed the competitiveness of the Pareto Archived Simulated Annealing to efficiently solve this problem and obtain good quality solutions while using reasonable computational resources.

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Section: Problem Presentationmentioning

confidence: 96%

Solving a multi-objective manufacturing cell scheduling problem with the consideration of warehouses using a simulated annealing based procedure

Toncovich¹,

Rossit²,

Frutos³

et al. 2019

10.5267/j.ijiec

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show abstract

“…Cross-docking facilities play an important role in JIT/JIS trans-shipment processes, where inventory backups and delays must be avoided. Lot sizing is an important research field related to the topic of our research work; the most relevant publications are focusing on a flow shop scheduling algorithm for warehouse activities [52], inventory control optimization in the case of flexible demand [53] and optimization of just-in-time logistics network problems under uncertainties [54]. Researchers showed that trans-shipment with supplier and customer time windows where flow is constrained by transportation schedules and warehouse capacities can be optimized with the objective to minimize costs, including inventory costs [55].…”

Section: Content Analysismentioning

confidence: 99%

Optimization of Just-In-Sequence Supply: A Flower Pollination Algorithm-Based Approach

et al. 2019

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The just-in-sequence inventory strategy, as an important part of the supply chain solutions in the automotive industry, is based on feedback information from the manufacturer. The performance, reliability, availability and cost efficiency are based on the parameters of the members of the supply chain process. To increase the return on assets (ROA) of the manufacturer, the optimization of the supply process is unavoidable. Within the frame of this paper, the authors describe a flower pollination algorithm-based heuristic optimization model of just-in-sequence supply focusing on sustainability aspects, including fuel consumption and emission. After a systematic literature review, this paper introduces a mathematical model of just-in-sequence supply, including assignment and scheduling problems. The objective of the model is to determine the optimal assignment and schedule for each sequence to minimize the total purchasing cost, which allows improving cost efficiency while sustainability aspects are taken into consideration. Next, a flower pollination algorithm-based heuristic is described, whose performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to increase cost-efficiency in just-in-sequence solutions.

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“…The probability of state transfer is obtained by formula (1). In the calculation process, ij η represents the visibility of two pick-up locations.…”

Section: Operation Design Of Dynamic Ant Colony Genetic Algorithmmentioning

confidence: 99%

“…The manufacturers strictly control the production of related aspects, trying to reduce production costs. However, warehousing management is an important task of enterprises [1]. The problem of stock efficiency has gradually become the focus of attention of the enterprises, and the stocking work has become the hot spot of the research of the scholars at present [2].…”

Section: Introductionmentioning

confidence: 99%

Warehouse stocking optimization based on dynamic ant colony genetic algorithm

Xiao¹

2018

AIP Conference Proceedings

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Abstract. In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.

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Flow shop scheduling algorithm to optimize warehouse activities

Cited by 9 publications

References 67 publications

Solving a multi-objective manufacturing cell scheduling problem with the consideration of warehouses using a simulated annealing based procedure

Solving a multi-objective manufacturing cell scheduling problem with the consideration of warehouses using a simulated annealing based procedure

Optimization of Just-In-Sequence Supply: A Flower Pollination Algorithm-Based Approach

Warehouse stocking optimization based on dynamic ant colony genetic algorithm

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