Simulated Annealing for the Multi-Vehicle Cyclic Inventory Routing Problem

Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment.

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A Pseudo-Parallel Genetic Algorithm Integrating Simulated Annealing for Stochastic Location-Inventory-Routing Problem with Consideration of Returns in E-Commerce

Discrete Dynamics in Nature and Society ◽

10.1155/2015/586581 ◽

2015 ◽

Vol 2015 ◽

pp. 1-15 ◽

Cited By ~ 4

Author(s):

Bailing Liu ◽

Hui Chen ◽

Yanhui Li ◽

Xiang Liu

Keyword(s):

Genetic Algorithm ◽

Simulated Annealing ◽

Computing Time ◽

Single Type ◽

Inventory Routing ◽

Parallel Genetic Algorithm ◽

Routing Problem ◽

Inventory Routing Problem ◽

Stochastic Location ◽

Pseudo Parallel Genetic Algorithm

Facility location, inventory control, and vehicle routes scheduling are three key issues to be settled in the design of logistics system for e-commerce. Due to the online shopping features of e-commerce, customer returns are becoming much more than traditional commerce. This paper studies a three-phase supply chain distribution system consisting of one supplier, a set of retailers, and a single type of product with continuous review (Q, r) inventory policy. We formulate a stochastic location-inventory-routing problem (LIRP) model with no quality defects returns. To solve the NP-hand problem, a pseudo-parallel genetic algorithm integrating simulated annealing (PPGASA) is proposed. The computational results show that PPGASA outperforms GA on optimal solution, computing time, and computing stability.

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Hybrid Harmony Search-Simulated Annealing Algorithm for Location-Inventory-Routing Problem in Supply Chain Network Design with Defect and Non-Defect Items

Applied Sciences ◽

10.3390/app10186625 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6625

Author(s):

Farahanim Misni ◽

Lai Soon Lee ◽

Hsin-Vonn Seow

Keyword(s):

Supply Chain ◽

Simulated Annealing ◽

Network Design ◽

Harmony Search ◽

Supply Chain Network Design ◽

Supply Chain Network ◽

Inventory Routing ◽

Routing Problem ◽

Inventory Routing Problem ◽

The Cost

This paper considers a location-inventory-routing problem (LIRP) that integrates the strategic, tactical, and operational decision planning in supply chain network design. Both defect and non-defect items of returned products are considered in the cost of reverse logistics based on the economic production quantity model. The objective of the LIRP is to minimize the total cost of location-allocation of established depots, the cost of inventory, including production setup and holding cost, as well as the cost of travelled distance by the vehicles between the open depots and assigned customers. A Hybrid Harmony Search-Simulated Annealing (HS-SA) algorithm is proposed in this paper. This algorithm incorporates the dynamic values of harmony memory considering rate and pitch adjustment rate with the local optimization techniques to hybridize with the idea of probabilistic acceptance rule from simulated annealing, to avoid the local extreme points. Computational experiments on popular benchmark data sets show that the proposed hybrid HS-SA algorithm outperforms a standard HS and an improved HS for all cases.

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