A model for the time dependent vehicle routing problem with time windows under traffic conditions with intelligent travel times

Khanchehzarrin, Saeed; Shahmizad, Maral; Mahdavi, Iraj; Mahdavi–Amiri, Nezam; Ghasemi, Peiman

doi:10.1051/ro/2021098

Cited by 13 publications

(15 citation statements)

References 33 publications

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“…2 Continuous driving speed-dependent function The processing method of the vehicle speed of transporting logistics. According to Khanchehzarrin's speed-dependent function model [ 18 ], the change of vehicle speed is a continuous and uniform change, rather than a jumping change. This driving speed dependence function is shown in Fig.…”

Section: Design Of Optimization Model For Urban Emergency Resource Sc...mentioning

confidence: 99%

Deep Q networks-based optimization of emergency resource scheduling for urban public health events

Zhao

Wang

2022

Neural Comput & Applic

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In today's severe situation of the global new crown virus raging, there are still efficiency problems in emergency resource scheduling, and there are still deficiencies in rescue standards. For the happiness and well-being of people's lives, adhering to the principle of a community with a shared future for mankind, the emergency resource scheduling system for urban public health emergencies needs to be improved and perfected. This paper mainly studies the optimization model of urban emergency resource scheduling, which uses the deep reinforcement learning algorithm to build the emergency resource distribution system framework, and uses the Deep Q Network path planning algorithm to optimize the system, to achieve the purpose of optimizing and upgrading the efficient scheduling of emergency resources in the city. Finally, through simulation experiments, it is concluded that the deep learning algorithm studied is helpful to the emergency resource scheduling optimization system. However, with the gradual development of deep learning, some of its disadvantages are becoming increasingly obvious. An obvious flaw is that building a deep learning-based model generally requires a lot of CPU computing resources, making the cost too high.

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Section: Design Of Optimization Model For Urban Emergency Resource Sc...mentioning

confidence: 99%

Deep Q networks-based optimization of emergency resource scheduling for urban public health events

Zhao

Wang

2022

Neural Comput & Applic

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“…e results indicate the efficiency of the proposed model in comparison with the existing system. Khanchehzarrin et al [4] presented a new mixed-integer nonlinear programming model for the timedependent vehicle routing problem with time windows and intelligent travel times. e aim is to minimize fixed and variable costs with the assumption that the travel time between any two nodes depends on traffic conditions and is considered to be a function of vehicle departure time.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many countries have based their development plans on increasing productivity. Improving productivity in organizations enables human resources to think better, innovate, and nd a more systematic attitude, a thought that aims to create and build the future instead of the present and the past [4]. One of the issues that the tra c police always face is the optimal scheduling of personnel and equipment available to protect road safety at main and mobile police stations.…”

Section: Introductionmentioning

confidence: 99%

Designing a Multiobjective Human Resource Scheduling Model Using the Tabu Search Algorithm

olya

Shayannia

Movahedi

2022

Discrete Dynamics in Nature and Society

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Nowadays, the major and substantial challenge and constraint of organizations, specifically in developing countries, are not the lack of raw materials, energy, or even technology but cases that are directly or indirectly associated with human resource management. The most significant problems that organizations face include dissatisfaction, lack of motivation, inefficiency of human resources, high amount of job turnovers, and low productivity. Today’s organizations and institutions should realize true perfection and health create the conditions for survival, order, excellence, and growth. In this research, considering the high importance of these issues, the problems of scheduling and allocation of manpower in a real place are solved. To this end, the metaheuristic Tabu search algorithm is used with the aim of minimizing the duration of activity and the presence of all manpower. For this purpose, to achieve the objectives of the research, three different scenarios related to the required manpower and the minimum-maximum presence of manpower according to whether they are on leave or not are used.

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“…Minimizing the transportation costs was the main objectives of their research. Khanchehzarrin et al [23] presented a model for the time-dependent vehicle routing problem. Considering traffic condition is the main contribution of their paper.…”

Section: Outbound Trucks Inbound Trucksmentioning

confidence: 99%

A Multiobjective Mathematical Model for Truck Scheduling Problem in Multidoor Cross‐Docking System

Taghizadeh

Shojaie

Sarfaraz

et al. 2022

Discrete Dynamics in Nature and Society

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Cross-docking is the main operation of unloading products from incoming trucks, regrouping products in relation to their destination, and loading directly onto shipping trucks, reducing warehousing, picking, transportation costs, and delivery times. This is the intended logistics technology. In this paper, we present a new bi-objective mixed-integer mathematical model for truck scheduling problems in cross-docking systems. The goal of the proposed mixed-integer mathematical model is to minimize the total operation time (makespan) and cost of moving cargo within the terminal. The performance of the proposed model is compared with that of the available model to solve small instances. The results showed that in solving small size of problem, the proposed model in this study is more efficient and we found better solutions. An evolutionary algorithm called the nondominated sorting genetic algorithm (NSGA-II) has been proposed to solve larger instances due to computational complexity. To evaluate the proposed algorithm, a comparative analysis of benchmark instances was performed and the efficiency of the above algorithm was compared to the nondominated ranked algorithm (NRGA) based on the index designed in the literature. The statistical hypothesis testing (t-test) is used for determining the best algorithm based on the average runtime and average number of Pareto solutions. Using the Taguchi method, the proposed algorithms are tuned. Considering a temporary storage space and the multiple receiving and shipping docks is the main contribution of the paper. Finally, for evaluating algorithms, multicriteria decision-making (MCDM) technique and statistical method are used. The results show the suitable performance of presented model.

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A model for the time dependent vehicle routing problem with time windows under traffic conditions with intelligent travel times

Cited by 13 publications

References 33 publications

Deep Q networks-based optimization of emergency resource scheduling for urban public health events

Deep Q networks-based optimization of emergency resource scheduling for urban public health events

Designing a Multiobjective Human Resource Scheduling Model Using the Tabu Search Algorithm

A Multiobjective Mathematical Model for Truck Scheduling Problem in Multidoor Cross‐Docking System

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