This article analyses the impact that the confinement measures or quarantine imposed in Spain on 15 March 2020 had on urban mobility in the northern city of Santander. Data have been collected from traffic counters, public transport ITS, and recordings from traffic control cameras and environmental sensors to make comparisons between journey flows and times before and during the confinement. This data has been used to re-estimate Origin-Destination trip matrices to obtain an initial diagnostic of how daily mobility has been reduced and how the modal distribution and journey purposes have changed. The impact on externalities such as NO2 emissions and traffic accidents have also been quantified. The analysis revealed an overall mobility fall of 76%, being less important in the case of the private car. Public transport users dropped by up to 93%, NO2 emissions were reduced by up to 60%, and traffic accidents were reduced by up to 67% in relative terms.

https://www.mdpi.com/2071-1050/12/9/3870/pdf

Effects of the COVID-19 lockdown on urban mobility: Empirical evidence from the city of Santander (Spain)

Differential Evolution: A review of more than two decades of research

Flexible job shop scheduling problems &#x0028 FJSP &#x0029 have received much attention from academia and industry for many years. Due to their exponential complexity, swarm intelligence &#x0028 SI &#x0029 and evolutionary algorithms &#x0028 EA &#x0029 are developed, employed and improved for solving them. More than 60&#x0025 of the publications are related to SI and EA. This paper intents to give a comprehensive literature review of SI and EA for solving FJSP. First, the mathematical model of FJSP is presented and the constraints in applications are summarized. Then, the encoding and decoding strategies for connecting the problem and algorithms are reviewed. The strategies for initializing algorithms? population and local search operators for improving convergence performance are summarized. Next, one classical hybrid genetic algorithm &#x0028 GA &#x0029 and one newest imperialist competitive algorithm &#x0028 ICA &#x0029 with variables neighborhood search &#x0028 VNS &#x0029 for solving FJSP are presented. Finally, we summarize, discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.

A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems

Artificial intelligence (AI) emphasises the creation of intelligent machines/systems that function like humans. AI has been applied to many real-world applications. Machine learning is a branch of ...

/pdf/a-survey-on-evolutionary-machine-learning-41xxfv2woy.pdf

A survey on evolutionary machine learning

The neural network model of Job shop scheduling problem is built. The characteristics and properties of its solutions are studied. A Hybrid Genetic Algorithm, combining genetic algorithm with neural network, for Job shop scheduling problem is described. The corresponding simulation shows that our method is good for complex production scheduling, at calculation time and goodness.

A hybrid genetic algorithm for job shop scheduling problem

Semiconductor manufacturing is a complicated flexible job-shop scheduling problem (FJSP) of combinatorial complexity. Because of the adoption of advanced process control and advanced equipment control, the processing time in advanced wafer fabs become uncertain. Existing approaches considering constant processing time may not be appropriate to address the present problem in a real setting. In practice, processing times can be represented as intervals with the most probable completion time somewhere near the middle of the interval. A fuzzy number that is a generalized interval can represent this processing time interval exactly and naturally. This paper developed a hybrid approach integrating a particle swarm optimization algorithm with a Cauchy distribution and genetic operators (HPSO+GA) for solving an FJSP by finding a job sequence that minimizes the makespan with uncertain processing time. In particular, the proposed hybridized HPSO+GA approach employs PSO for creating operation sequences and assigning the time and resources for each operation, and then uses genetic operators to update the particles for improving the solution. To estimate the validity of the proposed approaches, experiments were conducted to compare the proposed approach with conventional approaches. The results show the practical viability of this approach. This paper concludes with discussions of contributions and recommends directions for future research.

Hybrid Particle Swarm Optimization Combined With Genetic Operators for Flexible Job-Shop Scheduling Under Uncertain Processing Time for Semiconductor Manufacturing

Multi-objective particle swarm optimization for mechanical harvester route planning of sugarcane field operations

Differential evolution algorithms for scheduling raw milk transportation

In this paper, we propose two heuristics to solve the General Q-Delivery Vehicle Routing Problem with consideration of flexibility of mixing pickup, delivery services and a maximum duration of a route constraint which is the extending version of the well-known VRP with pickup and delivery problem. Firstly, the heuristic called DE_G-Q-DVRP-FD is presented to determine the routing of transferring pullets from pullet houses to hen houses. Since the problem considered is very complicated, the DE_G-Q-DVRP-FD is extended to the two-phase heuristic called MESOMDE_G-Q-DVRP-FD. The difference between two heuristics is that in the MESOMDE_G-Q-DVRP-FD algorithm, the customer vertices (pullet houses) will be clustered before determining routes. The clustering of customer vertices method called the Multifactor Based Evolving Self-Organizing Map is proposed in the first phase in order to completely utilize the vehicle. Finally, in the second phase, the DE_G-Q-DVRP-FD is used to execute the routing. To demonstrate the algorithm efficiency, flock allocation from pullet houses to hen houses in the egg industry is used as the case study. The results obtained from this study show that the MESOMDE_G-Q-DVRP-FD algorithm provides lower total cost values than that of the firm’s current practice by 7.59–31.28 and 0.84–13.15 % better than the DE_G-Q-DVRP-FD algorithm. Additionally, the MESOMDE_G-Q-DVRP-FD is adjusted to solve the benchmark problem found in the literature. The experimental results show that the MESOMDE_G-Q-DVRP-FD algorithm yields better total cost values by 5.72–61.60 % (with an average of 31.46 %).

A differential evolution algorithm for the capacitated VRP with flexibility of mixing pickup and delivery services and the maximum duration of a route in poultry industry

DE is first designed to solve generalized assignment problem (GAP).The possibility of using new local search in DE for GAP is designed in order to get better solution.The computational results revealed that the proposed heuristic lies among the best algorithms found in the literature. This paper presents algorithms based on differential evolution (DE) to solve the generalized assignment problem (GAP) with the objective to minimize the assignment cost under the limitation of the agent capacity. Three local search techniques: shifting, exchange, and k-variable move algorithms are added to the DE algorithm in order to improve the solutions. Eight DE-based algorithms are presented, each of which uses DE with a different combination of local search techniques. The experiments are carried out using published standard instances from the literature. The best proposed algorithm using shifting and k-variable move as the local search (DE-SK) techniques was used to compare its performance with those of Bee algorithm (BEE) and Tabu search algorithm (TABU). The computational results revealed that the BEE and DE-SK are not significantly different while the DE-SK outperforms the TABU algorithm. However, even though the statistical test shows that DE-SK is not significantly different compared with the BEE algorithm, the DE-SK is able to obtain more optimal solutions (87.5%) compared to the BEE algorithm that can obtain only 12.5% optimal solutions. This is because the DE-SK is designed to enhance the search capability by improving the diversification using the DE's operators and the k-variable moves added to the DE can improve the intensification. Hence, the proposed algorithms, especially the DE-SK, can be used to solve various practical cases of GAP and other combinatorial optimization problems by enhancing the solution quality, while still maintaining fast computational time.

Kanchana Sethanan

Papers

Hybrid Particle Swarm Optimization Combined With Genetic Operators for Flexible Job-Shop Scheduling Under Uncertain Processing Time for Semiconductor Manufacturing

Multi-objective particle swarm optimization for mechanical harvester route planning of sugarcane field operations

Differential evolution algorithms for scheduling raw milk transportation

A differential evolution algorithm for the capacitated VRP with flexibility of mixing pickup and delivery services and the maximum duration of a route in poultry industry

Improved differential evolution algorithms for solving generalized assignment problem