Two Stage Heuristic Algorithm for Logistics Network Optimization of Integrated Location-Routing-Inventory

2018 ◽  
pp. 209-217
Author(s):  
Hao Wang ◽  
Ming K. Lim
Keyword(s):  
Heuristic Algorithm ◽  
Network Optimization ◽  
Two Stage ◽  
Logistics Network ◽  
Location Routing

scholarly journals Model and Algorithm of Two-Stage Distribution Location Routing with Hard Time Window for City Cold-Chain Logistics

2020 ◽  
Vol 10 (7) ◽  
pp. 2564
Author(s):  
Liying Yan ◽  
Manel Grifoll ◽  
Pengjun Zheng
Keyword(s):  
Time Window ◽  
Hybrid Genetic Algorithm ◽  
Distribution Networks ◽  
Cold Chain ◽  
Two Stage ◽  
Logistics Network ◽  
Stage Distribution ◽  
Hard Time ◽  
Location Routing ◽  
Logistics Enterprises

Taking cold-chain logistics as the research background and combining with the overall optimisation of logistics distribution networks, we develop two-stage distribution location-routing model with the minimum total cost as the objective function and varying vehicle capacity in different delivery stages. A hybrid genetic algorithm is designed based on coupling and collaboration of the two-stage routing and transfer stations. The validity and feasibility of the model and algorithm are verified by conducting a randomly generated test. The optimal solutions for different objective functions of two-stage distribution location-routing are compared and analysed. Results turn out that for different distribution objectives, different distribution schemes should be employed. Finally, we compare the two-stage distribution location-routing to single-stage vehicle routing problems. It is found that a two-stage distribution location-routing system is feasible and effective for the cold-chain logistics network, and can decrease distribution cost for cold-chain logistics enterprises.

scholarly journals Research and Design of Reverse Logistics Network of Used Power Battery Based on Heuristic Algorithm

2021 ◽  
Vol 275 ◽  
pp. 03055
Author(s):  
Longyu He
Keyword(s):  
Heuristic Algorithm ◽  
Reverse Logistics ◽  
Two Stage ◽  
Logistics Network ◽  
Operating Modes ◽  
Power Battery ◽  
Reverse Logistics Network ◽  
New Energy ◽  
Research And Design ◽  
New Energy Vehicles

This paper starts with the rapidity of new energy vehicles and the hazards of power battery disposal, and puts forward the importance of the construction of a reverse logistics network for used power batteries, and then analyzes the characteristics and operating modes of the reverse logistics network. Based on the results of the analysis, a heuristic is proposed. The algorithm uses a two-stage algorithm to solve the problem of waste reverse logistics location-transportation, and proposes the design and construction of a reverse logistics network for waste power batteries, so that the recycling of waste power batteries can be better carried out.

scholarly journals A column-and-constraint generation algorithm for two-stage stochastic programming problems

Top ◽  
2021 ◽  
Author(s):  
Denise D. Tönissen ◽  
Joachim J. Arts ◽  
Zuo-Jun Max Shen
Keyword(s):  
Stochastic Programming ◽  
Benders Decomposition ◽  
Equivalent Model ◽  
Computational Time ◽  
Two Stage ◽  
Generation Algorithm ◽  
Constraint Generation ◽  
Deterministic Equivalent ◽  
Location Routing ◽  
Relative Tolerance

AbstractThis paper presents a column-and-constraint generation algorithm for two-stage stochastic programming problems. A distinctive feature of the algorithm is that it does not assume fixed recourse and as a consequence the values and dimensions of the recourse matrix can be uncertain. The proposed algorithm contains multi-cut (partial) Benders decomposition and the deterministic equivalent model as special cases and can be used to trade-off computational speed and memory requirements. The algorithm outperforms multi-cut (partial) Benders decomposition in computational time and the deterministic equivalent model in memory requirements for a maintenance location routing problem. In addition, for instances with a large number of scenarios, the algorithm outperforms the deterministic equivalent model in both computational time and memory requirements. Furthermore, we present an adaptive relative tolerance for instances for which the solution time of the master problem is the bottleneck and the slave problems can be solved relatively efficiently. The adaptive relative tolerance is large in early iterations and converges to zero for the final iteration(s) of the algorithm. The combination of this relative adaptive tolerance with the proposed algorithm decreases the computational time of our instances even further.

scholarly journals An evaluation of three DoE-guided meta-heuristic-based solution methods for a three-echelon sustainable distribution network

2020 ◽  
Vol 296 (1-2) ◽  
pp. 421-469
Author(s):  
Sahar Validi ◽  
Arijit Bhattacharya ◽  
P. J. Byrne
Keyword(s):  
Distribution Network ◽  
Np Hard ◽  
Nsga Ii ◽  
Solution Approach ◽  
Logistics Network ◽  
Location Routing ◽  
Solution Methods ◽  
Order Preference ◽  
Operational Constraints ◽  
Transportation Routes

AbstractThis article evaluates the efficiency of three meta-heuristic optimiser (viz. MOGA-II, MOPSO and NSGA-II)-based solution methods for designing a sustainable three-echelon distribution network. The distribution network employs a bi-objective location-routing model. Due to the mathematically NP-hard nature of the model a multi-disciplinary optimisation commercial platform, modeFRONTIER®, is adopted to utilise the solution methods. The proposed Design of Experiment (DoE)-guided solution methods are of two phased that solve the NP-hard model to attain minimal total costs and total CO2 emission from transportation. Convergence of the optimisers are tested and compared. Ranking of the realistic results are examined using Pareto frontiers and the Technique for Order Preference by Similarity to Ideal Solution approach, followed by determination of the optimal transportation routes. A case of an Irish dairy processing industry’s three-echelon logistics network is considered to validate the solution methods. The results obtained through the proposed methods provide information on open/closed distribution centres (DCs), vehicle routing patterns connecting plants to DCs, open DCs to retailers and retailers to retailers, and number of trucks required in each route to transport the products. It is found that the DoE-guided NSGA-II optimiser based solution is more efficient when compared with the DoE-guided MOGA-II and MOPSO optimiser based solution methods in solving the bi-objective NP-hard three-echelon sustainable model. This efficient solution method enable managers to structure the physical distribution network on the demand side of a logistics network, minimising total cost and total CO2 emission from transportation while satisfying all operational constraints.

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