A Bi-Level Optimization Model for Grouping Constrained Storage Location Assignment Problems

Xie, Jing; Mei, Yi; Ernst, Andreas T.; Li, Xiaodong; Song, Andy

doi:10.1109/tcyb.2016.2638820

Cited by 28 publications

(12 citation statements)

References 43 publications

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“…Considering the above two challenges, we first transform P into a bilevel optimization problem. Bilevel optimization involves addressing the upper level optimization problem under the premise of ensuring the optimality of the lower level optimization problem [32], [33]. In this paper, the offloading decision is regarded as the upper level optimization problem with the aim of minimizing the total energy consumption of all mobile users, and the computation resource allocation is considered as the lower level optimization problem with the purpose of minimizing the total computation energy consumption of all mobile users.…”

Section: A Problem Transformationmentioning

confidence: 99%

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A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing

Huang

Wang

et al. 2020

IEEE Trans. Cybern.

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This paper studies a multi-user cooperative mobile edge computing offloading (CoMECO) system in a multi-user interference environment, in which delay-sensitive tasks may be executed on local devices, cooperative devices, or the primary MEC server. In this system, we jointly optimize the offloading decision and computation resource allocation for minimizing the total energy consumption of all mobile users under the delay constraint. If this problem is solved directly, the offloading decision and computation resource allocation are generally generated separately at the same time. Note, however, that they are closely coupled. Therefore, under this condition, their dependency is not well considered, thus leading to poor performance. We transform this problem into a bilevel optimization problem, in which the offloading decision is generated in the upper level, and then the optimal allocation of computation resources is obtained in the lower level based on the given offloading decision. In this way, the dependency between the offloading decision and computation resource allocation can be fully taken into account. Subsequently, a bilevel optimization approach, called BiJOR, is proposed. In BiJOR, candidate modes are first pruned to reduce the number of infeasible offloading decisions. Afterward, the upper level optimization problem is solved by ant colony system (ACS). Furthermore, a sorting strategy is incorporated into ACS to construct feasible offloading decisions with a higher probability and a local search operator is designed in ACS to accelerate the convergence. For the lower level optimization problem, it is solved by the monotonic optimization method. In addition, BiJOR is extended to deal with a complex scenario with the channel selection. Extensive experiments are carried out to investigate the performance of BiJOR on two sets of instances with up to 400 mobile users. The experimental results demonstrate the effectiveness of BiJOR and the superiority of the CoMECO system.

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Section: A Problem Transformationmentioning

confidence: 99%

“…We can obtain the transmission energy consumption of U i in the P-MEC computing mode and cooperative computing mode via channel l. They are expressed as (32) and (33), respectively…”

Section: Extensionmentioning

confidence: 99%

A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing

Huang

Wang

et al. 2020

IEEE Trans. Cybern.

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“…The objective of storage location assignment is to assign a set of SKUs to slots to reduce the total travel distance or time for many picking orders. This objective can be achieved by applying mathematical programming model [7] [4], and tabu search [29], to solve SLAP, since it is an NP-hard combinatorial optimization problem. The problem with these methods is that they can be computationally expensive for large DCs with thousands of SKUs and slots.…”

Section: Figure 2 Simple Routing Policiesmentioning

confidence: 99%

Storage Location Assignment Heuristics Based on Slot Selection and Frequent Itemset Grouping for Large Distribution Centers

2020

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This paper considers heuristic approaches that can be used to assign stock keeping units (SKU) to individual slots in distribution center. Firstly, we propose two novel strategies, slot selection and frequent itemset grouping. The former is used to find the most suitable slot for a single SKU, while the latter is for sequencing SKUs in an appropriate order. Secondly, we develop several storage location assignment heuristics by applying the two strategies. Especially, the heuristics are designed to assign frequently ordered SKU to a slot close to I/O (input/output) point and SKUs frequently ordered together to slots close to each other. Consequently, the proposed heuristics are helpful to reduce the travel distance of order picker in distribution center. In this paper, travel distance of order picker is calculated based on a routing policy that enables order picker to move along a flexible route. For illustration, we applied the heuristics to real data collected from a large distribution center. The experiment results reveal that slot selection strategy is very helpful to reduce average travel distance of order picker, especially under greedy routing policy. Also, frequent itemset grouping strategy can provide additional reduction in average travel distance if it is applied together with slot selection strategy.

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“…The experimental results showed that the method could improve the efficiency of goods in and out of warehouse. Xie et al [7] proposed a new two-layer grouping optimization model for the location allocation with grouping constraints and solved the model using the multi-stage random search method and the tabu algorithm. The experimental results showed the effectiveness of the model and algorithm.…”

Section: Introductionmentioning

confidence: 99%

The optimization of automated goods dynamic allocation and warehousing model

Hou

2020

Computer Optics

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In the development of modern logistics, the role of automated cargo warehousing is gradually reflected, which is essential for the automatic distribution of goods. This paper briefly introduced the automatic location allocation model and the particle swarm optimization (PSO) algorithm used to optimize the model. At the same time, it introduced the concept of genetic operator and multi-group co-evolution to improve the algorithm, and then the simulation analysis of standard PSO and improved PSO was performed on MATLAB software. The results showed that the improved PSO iterated fewer times and get better solution sets; compared with the manual allocation scheme, the improved PSO calculation reduced more warehousing time, lowered more center of gravity height, and improved shelf stability. In summary, the improved PSO algorithm can effectively optimize the automated goods dynamic allocation and warehousing model.

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A Bi-Level Optimization Model for Grouping Constrained Storage Location Assignment Problems

Cited by 28 publications

References 43 publications

A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing

A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing

Storage Location Assignment Heuristics Based on Slot Selection and Frequent Itemset Grouping for Large Distribution Centers

The optimization of automated goods dynamic allocation and warehousing model

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