Solving a Novel Multi-Objective Inventory Location Problem by means of a Local Search Algorithm

Lagos, Carolina; Moreno-Vega, J. Marcos; Guerrero, Guillermo; Rubio, José-Miguel

doi:10.24846/v25i2y201606

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…In the study of Huang & Lin (2010) In the study (Hnaien et al, 2016), a branch-andbound algorithm performed better than a heuristic algorithm. In the study (Lagos et al, 2016), an inventory model with objectives of minimizing warehouse location and general inventory management costs was solved by Pareto local search. It was concluded that Pareto local search is effective for such a problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel NSGA-II and SPEA2 Algorithms for Bi-Objective Inventory Optimization

HUSEYINOV¹,

BAYRAKDAR²

2022

STUD INFORM CONTROL

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Inventory optimization is a significant problem that is tied directly to financial gains. Its complexity has led to the development of new inventory models and optimization techniques. Evolutionary algorithms, particularly Pareto based evolutionary algorithms have been proven to be reliable for solving such problems. However, these evolutionary algorithms concentrate mostly on global search and have limited local search abilities. This leads to a poor convergence to the Pareto front. Among these algorithms the most studied are non-dominated sorting genetic algorithm-II and strength Pareto evolutionary algorithm2. This paper proposes a novel method that increases their convergence. The novelty is based on three techniques: Firstly, a time-based fitness assignment that favours solutions from previous generations is employed. Secondly, before the crossover process, the mating pool is updated with a positive bias towards better solutions. Finally, a more disruptive mutation scheme is used to prevent premature convergence. The novel algorithms were tested on a benchmark problem suite and two inventory problems. The performance of the algorithms is measured using hypervolume, generational distance and spacing metrics. The results illustrated by graphics indicate that the novel algorithms can obtain better convergence without increasing the time complexity.

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Section: Literature Reviewmentioning

confidence: 99%

Novel NSGA-II and SPEA2 Algorithms for Bi-Objective Inventory Optimization

HUSEYINOV¹,

BAYRAKDAR²

2022

STUD INFORM CONTROL

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“…A comparison of the proposed algorithm with other well-known heuristic algorithms, including nondominated sorting genetic algorithm-II (NSGA-II) [31] and multiobjective evolutionary algorithm (MOEA) [67], is performed in this section to test the proposed IMOPSO. Benchmark instances are utilized to solve and analyze with three objectives; in this case, the total waiting time and number of vehicles can be exchanged and represented by the penalty and vehicle maintenance costs on the basis of relative research that imbalances the multiobjective and single-objective problems [68,69]. The proposed algorithms along with two other algorithms are performed on a laptop with an Inter (R) Core (TM) i7-8565 1.8 GHz CPU and 8GB RAM using MATLAB programming language.…”

Section: Algorithm Comparisonmentioning

confidence: 99%

Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries

Wang

Guan

et al. 2020

Sustainability

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In this work, a multidepot multiperiod vehicle routing problem with pickups and deliveries (MDPVRPPD) is solved by optimizing logistics networks with collaboration and resource sharing among logistics service providers. The optimal solution can satisfy customer demands with periodic time characteristics and incorporate pickup and delivery services with maximum resource utilization. A collaborative mechanism is developed to rearrange both the open and closed vehicle routes among multiple pickup and delivery centers with improved transportation efficiency and reduced operational costs. The effects of resource sharing strategies combining customer information sharing, facility service sharing, and vehicle sharing are investigated across multiple service periods to maximize resource utilization and refine the resource configuration. A multiobjective optimization model is developed to formulate the MDPVRPPD so that the minimum total operational costs, waiting time, and the number of vehicles are obtained. A hybrid heuristic algorithm incorporating a 3D clustering and an improved multiobjective particle swarm optimization (IMOPSO) algorithm is introduced to solve the MDPVRPPD and find Pareto optimal solutions. The proposed hybrid heuristic algorithm is based on a selective exchange mechanism that enhances local and global searching capabilities. Results demonstrate that the proposed IMOPSO outperforms other existing algorithms. We also study profit allocation issues to quantify the stability and sustainability of long-term collaboration among logistics participants, using the minimum costs remaining savings method. The proposed model and solution methods are validated by conducting an empirical study of a real system in Chongqing City, China. This study contributes to the development of efficient urban logistics distribution systems, and facilitates the expansion of intelligent and sustainable supply chains.

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“…Therefore, papers are also continuously dealing with uncertainty in inventory control [8], [24]. As a result of the emergence of complex inventory control systems, scientist began to use the methods of multi-criteria optimization: [25], [14], [16], [19].…”

Section: Introductionmentioning

confidence: 99%

Comparison of SPEA2 and NSGA-II Applied to Automatic Inventory Control System Using Hypervolume Indicator

vlad¹,

Chołodowicz²,

Orłowski³

2017

SIC

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Abstract:The optimization of multi-objective problems is an area of important research. The importance attained by this type of problems has allowed the development of multiple algorithms. To determine which multi-objective algorithm has the best performance with respect to the problem of goods flow in the inventory, in this article an experimental comparison between two of the main multi-objective evolutionary algorithms is conducted: Nondominated Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The inventory model is optimized by taking into account two objectives: minimal cost of lost opportunities to make sales and minimal cost of used space in the inventory. The results obtained by both algorithms are compared and analysed based on hypervolume indicator that measures the volume of the dominated space.

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Solving a Novel Multi-Objective Inventory Location Problem by means of a Local Search Algorithm

Cited by 5 publications

References 20 publications

Novel NSGA-II and SPEA2 Algorithms for Bi-Objective Inventory Optimization

Novel NSGA-II and SPEA2 Algorithms for Bi-Objective Inventory Optimization

Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries

Comparison of SPEA2 and NSGA-II Applied to Automatic Inventory Control System Using Hypervolume Indicator

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