A particle swarm approach to solve vehicle routing problem with uncertain demand: A drug distribution case study

Moghadam, Babak Farhang

doi:10.5267/j.ijiec.2010.01.005

Cited by 17 publications

(3 citation statements)

References 12 publications

(13 reference statements)

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“…They proposed an improved multi-objective local search utilizing a combination of GA and variable neighborhood search (VNS) to improve the efficiency of pharmaceutical distribution. Moghadam (2010) studied the pharmaceutical distribution routing problem under demand uncertainty and unknown distributions, successfully using a particle swarm optimization (PSO) to address real world case. Redi et al (2020) presented a simulated annealing (SA) heuristic to finding a set of vehicle distribution routes with minimal transportation time for a pharmaceutical company in Jakarta.…”

Section: Introductionmentioning

confidence: 99%

Model and algorithm for pharmaceutical distribution routing problem considering customer priority and carbon emissions

Li,

Peng,

Deng

et al. 2024

DCE

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Pharmaceutical distribution routing problem is a key problem for pharmaceutical enterprises, since efficient schedules can enhance resource utilization and reduce operating costs. Meanwhile, it is a complicated combinatorial optimization problem. Existing research mainly focused on delivery route lengths or distribution costs minimization, while seldom considered customer priority and carbon emissions simultaneously. However, considering the customer priority and carbon emissions simultaneously will not only help to enhance customer satisfaction, but also help to reduce the carbon emissions. In this article, we consider the customer priority and carbon emission minimization simultaneously in the pharmaceutical distribution routing problem, the corresponding problem is named pharmaceutical distribution routing problem considering customer priority and carbon emissions. A corresponding mathematical model is formulated, the objectives of which are minimizing fixed cost, refrigeration cost, fuel consumption cost, carbon emission cost, and penalty cost for violating time windows. Moreover, a hybrid genetic algorithm (HGA) is proposed to solve the problem. The framework of the proposed HGA is genetic algorithm (GA), where an effective local search based on variable neighborhood search (VNS) is specially designed and incorporated to improve the intensification abilities. In the proposed HGA, crossover with adaptive probability and mutation with adaptive probability are utilized to enhance the algorithm performance. Finally, the proposed HGA is compared with four optimization algorithms, and experimental results have demonstrated the effectiveness of the HGA.

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Section: Introductionmentioning

confidence: 99%

Model and algorithm for pharmaceutical distribution routing problem considering customer priority and carbon emissions

Li,

Peng,

Deng

et al. 2024

DCE

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show abstract

“…Zhu and Ursavas (2018) designed a VRP model for drug distribution in the Netherlands, which included warehouse location, customer time windows and limited route time. The VRP models in the field of pharmaceutical distribution can be found in Moghadama and Seyedhosseinia (2010), Gulczynski et al (2011), Kramer et al (2019), Repolho et al (2019) and Campelo et al . (2019).…”

Section: Introductionmentioning

confidence: 99%

Solving vehicle routing problem with time windows using metaheuristic approaches

Birecik

Özgen

2022

IJICC

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PurposeDrugs are strategic products with essential functions in human health. An optimum design of the pharmaceutical supply chain is critical to avoid economic damage and adverse effects on human health. The vehicle-routing problem, focused on finding the lowest-cost routes with available vehicles and constraints, such as time constraints and road length, is an important aspect of this. In this paper, the vehicle routing problem (VRP) for a pharmaceutical company in Turkey is discussed.Design/methodology/approachA mixed-integer programming (MIP) model based on the vehicle routing problem with time windows (VRPTW) is presented, aiming to minimize the total route cost with certain constraints. As the model provides an optimum solution for small problem sizes with the GUROBI® solver, for large problem sizes, metaheuristic methods that simulate annealing and adaptive large neighborhood search algorithms are proposed. A real dataset was used to analyze the effectiveness of the metaheuristic algorithms. The proposed simulated annealing (SA) and adaptive large neighborhood search (ALNS) were evaluated and compared against GUROBI® and each other through a set of real problem instances.FindingsThe model is solved optimally for a small-sized dataset with exact algorithms; for solving a larger dataset, however, metaheuristic algorithms require significantly lesser time. For the problem addressed in this study, while the metaheuristic algorithms obtained the optimum solution in less than one minute, the solution in the GUROBI® solver was limited to one hour and three hours, and no solution could be obtained in this time interval.Originality/valueThe VRPTW problem presented in this paper is a real-life problem. The vehicle fleet owned by the factory cannot be transported between certain suppliers, which complicates the solution of the problem.

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“…Facility location decisions are found to be extremely important for the long-term planning of the manufacturing organizations. Improper selection of location may result in inadequate qualified work force, unavailability of raw materials, insufficient transportation facility, increased operating expenses or even disastrous impact on the organization due to political and societal interference (Farhang Moghadam & Seyedhosseini, 2010). Thus, it is very much necessary for the decision maker to select the appropriate location for a facility that will not only perform well, but also will be flexible enough to accommodate the necessary future changes.…”

Section: Introductionmentioning

confidence: 99%

Multi criteria decision making methods for location selection of distribution centers

Chakraborty¹,

Ray²,

Dan³

2013

10.5267/j.ijiec

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In recent years, major challenges such as, increase in inflexible consumer demands and to improve the competitive advantage, it has become necessary for various industrial organizations all over the world to focus on strategies that will help them achieve cost reduction, continual quality improvement, increased customer satisfaction and on time delivery performance. As a result, selection of the most suitable and optimal facility location for a new organization or expansion of an existing location is one of the most important strategic issues, required to fulfill all of these above mentioned objectives. In order to sustain in the global competitive market of 21st century, many industrial organizations have begun to concentrate on the proper selection of the plant site or best facility location. The best location is that which results in higher economic benefits through increased productivity and good distribution network. When a choice is to be made from among several alternative facility locations, it is necessary to compare their performance characteristics in a decisive way. As the facility location selection problem involves multiple conflicting criteria and a finite set of potential candidate alternatives, different multicriteria decision-making (MCDM) methods can be effectively applied to solve such type of problem. In this paper, four well known MCDM methods have been applied on a facility location selection problem and their relative ranking performances are compared. Because of disagreement in the ranks obtained by the four different MCDM methods a final ranking method based on REGIME has been proposed by the authors to facilitate the decision making process.

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A particle swarm approach to solve vehicle routing problem with uncertain demand: A drug distribution case study

Cited by 17 publications

References 12 publications

Model and algorithm for pharmaceutical distribution routing problem considering customer priority and carbon emissions

Model and algorithm for pharmaceutical distribution routing problem considering customer priority and carbon emissions

Solving vehicle routing problem with time windows using metaheuristic approaches

Multi criteria decision making methods for location selection of distribution centers

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