Pallet Picking Strategy in Food Collecting Center

Facchini, Francesco; Pascale, Gianluigi De; Faccilongo, Nicola

doi:10.3390/app8091503

Cited by 15 publications

(10 citation statements)

References 44 publications

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“…This paper considers important matters from the point of view of stochastic simulation models creation and experimentations employing them. Most of the analytical or simulation models assume unified load units for the whole system ([ 29 ]; e.g., in [ 110 ]). Moreover, simulation models ensure more detailed and more understandable representation of order picking processes and operations in comparison to analytical models [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

Sensitivity Analysis of Selected Parameters in the Order Picking Process Simulation Model, with Randomly Generated Orders

Kostrzewski

2020

Entropy

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Sensitivity analysis of selected parameters in simulation models of logistics facilities is one of the key aspects in functioning of self-conscious and efficient management. In order to develop simulation models adequate of real logistics facilities’ processes, it is important to input actual data connected to material flows on entry to models, whereas most models assume unified load units as default. To provide such data, pseudorandom number generators (PRNGs) are used. The original generator described in the paper was employed in order to generate picking lists for order picking process (OPP). This ensures building a hypothetical, yet close to reality process in terms of unpredictable customers’ orders. Models with applied PRNGs ensure more detailed and more understandable representation of OPPs in comparison to analytical models. Therefore, the author’s motivation was to present the original model as a tool for enterprises’ managers who might control OPP, devices and means of transport employed therein. The outcomes and implications of the contribution are connected to presentation of selected possibilities in OPP analyses, which might be developed and solved within the model. The presented model has some limitations. One of them is assumption that one mean of transport per one aisle is taken into consideration. Another limitation is the indirectly randomization of certain model’s parameters.

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Section: Discussionmentioning

confidence: 99%

Sensitivity Analysis of Selected Parameters in the Order Picking Process Simulation Model, with Randomly Generated Orders

Kostrzewski

2020

Entropy

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“…The proposed method can frequently determine an excellent solution even if it is not a global optimal solution. Actually, the proposed OOTSA approach can be applied to solve most industrial optimization problems, such as picking strategy in food supply chain [31], ergonomic assessment in infrequent job rotations [32], production-distribution planning in supply chain systems [33], dynamic job shop scheduling problems [34], and minimal cost of supply chain management [35]. Further research will address extension of the proposed method to consider more complex probabilistic constrained model, such as perishable inventory management problem and portfolio financial optimization problem.…”

Section: Discussionmentioning

confidence: 99%

Embedding Ordinal Optimization into Tree–Seed Algorithm for Solving the Probabilistic Constrained Simulation Optimization Problems

Horng

Lin

2018

Applied Sciences

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Probabilistic constrained simulation optimization problems (PCSOP) are concerned with allocating limited resources to achieve a stochastic objective function subject to a probabilistic inequality constraint. The PCSOP are NP-hard problems whose goal is to find optimal solutions using simulation in a large search space. An efficient “Ordinal Optimization (OO)” theory has been utilized to solve NP-hard problems for determining an outstanding solution in a reasonable amount of time. OO theory to solve NP-hard problems is an effective method, but the probabilistic inequality constraint will greatly decrease the effectiveness and efficiency. In this work, a method that embeds ordinal optimization (OO) into tree–seed algorithm (TSA) (OOTSA) is firstly proposed for solving the PCSOP. The OOTSA method consists of three modules: surrogate model, exploration and exploitation. Then, the proposed OOTSA approach is applied to minimize the expected lead time of semi-finished products in a pull-type production system, which is formulated as a PCSOP that comprises a well-defined search space. Test results obtained by the OOTSA are compared with the results obtained by three heuristic approaches. Simulation results demonstrate that the OOTSA method yields an outstanding solution of much higher computing efficiency with much higher quality than three heuristic approaches.

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“…Strandhagen et al proposed the importance of maintaining sustainability and reducing negative impact on the environment in logistics 4.0 [11]; Boenzi et al evaluated the overall impact due to all energy-consumptive factors of the warehousing activities and provide the best logistic solutions with lower environmental impact [13]; Facchini et al built a maturity model for Logistics 4.0 focusing on the environmental factors [12]; and Facchini et al analyzed the optimal warehouse layout for agricultural and food collecting centers, and built an analytical model to minimize the travelling time of the goods from picking to delivery area with consideration of carbon footprint and sustainability [14].…”

Section: Green Logisticsmentioning

confidence: 99%

Optimization of Green Fresh Food Logistics with Heterogeneous Fleet Vehicle Route Problem by Improved Genetic Algorithm

Cao

Zuo

et al. 2020

Sustainability

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In order to reduce the distribution cost of fresh food logistics and achieve the goal of green distribution at the same time, the Green Fresh Food Logistics with Heterogeneous Fleet Vehicle Route Problem (GFLHF-VRP) model is established. Based on the particularity of the model, an improved genetic algorithm called Genetic Algorithm with Adaptive Simulated Annealing Mutation (GAASAM) is proposed in which the mutation operation is upgraded to a simulated annealing mutation operation and its parameters are adjusted by the adaptive operation. The experimental results show that the proposed GAASAM can effectively solve the vehicle routing problem of the proposed model, achieve better performance than the genetic algorithm, and avoid falling into a local optimal trap. The distribution routes obtained by GAASAM are with lower total distribution cost, and achieve the goal of green distribution in which energy, fuel consumption and carbon emissions are reduced at the same time. On the other hand, the proposed GFLHF-VRP and GAASAM can provide a reliable distribution route plan for fresh food logistics enterprises with multiple types of distribution vehicles in real life, which can further reduce the distribution cost and achieve a greener and more environment-friendly distribution solution. The results of this study also provide a managerial method for fresh food logistics enterprises to effectively arrange the distribution work with more social responsibility.

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Pallet Picking Strategy in Food Collecting Center

Cited by 15 publications

References 44 publications

Sensitivity Analysis of Selected Parameters in the Order Picking Process Simulation Model, with Randomly Generated Orders

Sensitivity Analysis of Selected Parameters in the Order Picking Process Simulation Model, with Randomly Generated Orders

Embedding Ordinal Optimization into Tree–Seed Algorithm for Solving the Probabilistic Constrained Simulation Optimization Problems

Optimization of Green Fresh Food Logistics with Heterogeneous Fleet Vehicle Route Problem by Improved Genetic Algorithm

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