Hybrid Genetic Algorithms and Simulated Annealing for Multi-trip Vehicle Routing Problem with Time Windows

This paper introduces a grouped genetic algorithm (GGA) to solve the order batching and sequencing problem with multiple pickers (OBSPMP) with the objective of minimizing total completion time. To the best of our knowledge, for the first time, an OBSPMP is solved by means of GGA considering picking devices with heterogeneous load capacity. For this, an encoding scheme is proposed to represent in a chromosome the orders assigned to batches, and batches assigned to picking devices. Likewise, the operators of the proposed algorithm are adapted to the specific requirements of the OBSPMP. Computational experiments show that the GGA performs much better than six order batching and sequencing heuristics, leading to function objective savings of 18.3% on average. As a conclusion, the proposed algorithm provides feasible solutions for the operations planning in warehouses and distribution centers, improving margins by reducing operating time for order pickers, and improving customer service by reducing picking service times.

Section: Obspmp Features and Assumptionsmentioning

confidence: 99%

Solving the order batching and sequencing problem with multiple pickers: A grouped genetic algorithm

Cano

Cortés

Campo³

et al. 2021

“…A hybrid algorithm is an algorithm that combines two or more other algorithms that solve the same problem. The hybridization using GA used by a lot of researches to get the best optimal solution [22][23][24][25][26]. Although GA's are effective complete search algorithms with crossover and mutation operators, GA can be improved using local search methods and they can be made competitive with others when the search space is too large to explore.…”

Section: Hybrid Algorithms To Size Hospital Resourcesmentioning

confidence: 99%

A hybrid algorithm to size the hospital resources in the case of a massive influx of victims

Kacem¹,

Kamach²,

Samir³

et al. 2020

Disaster situations either natural or made-man caused a large number of deaths and injured people. Morocco has experienced several disasters recently, the last one was the railway accident on 16 October 2018, which caused 127 serious injuries and 7 deaths. This large number was a big problem for the hospital to manage the received victims in right direction, which caused lives lost and disability. In this article, in collaboration with Mohammed (V) hospital in Casablanca city in Morocco, we suggested a solution that saves lives and eliminates number of disability by using a hybrid algorithm to size the hospital resources in the case of a massive influx of victims. We also suggested a support decision tool that is called Emergency Support Decision Tool. This helpful tool gives an idea about the needed resources that support these emergencies according to the victim's number. The proposed solution consisted in making a hybrid algorithm that mixed the theoretical simulation process and the experience feedback by developing hybrid genetic and hybrid heuristic algorithms. These algorithms using as an input the matrix solutions that generated under ARENA software and the solution generated by neural networks that based on experiences feedback. The objective was to provide a solution based on available resources. In fact, the results showed that the hybrid heuristic algorithm is more performant than the hybrid genetic algorithm. . He holds a doctorate in medicine from the Faculty of Medicine and Pharmacy of Rabat in 1993 and a certificate in emergency medicine from the Faculty of Medicine and Pharmacy of Casablanca in 2005, he held several positions such as Acting Director of the AL HASSANNI prefectoral hospital centre and Chief Medical Officer of the Medical Affairs Department of the Mohammed V

“…Neither of these classes of algorithms will be leveraged in the current work. In the former class of algorithms large memory sizes are required [14,15], while in the latter category certain disadvantages are commonly encountered. These deal with the fact that the optimal solution usually depends on the initial state where the speed of convergence is rather too slow than many other algorithms [16,17].…”

Section: Introductionmentioning

confidence: 99%

Courses timetabling based on hill climbing algorithm

Rjoub

2020

In addition to its monotonous nature and excessive time requirements, the manual school timetable scheduling often leads to more than one class being assigned to the same instructor, or more than one instructor being assigned to the same classroom during the same slot time, or even leads to exercise in intentional partialities in favor of a particular group of instructors. In this paper, an automated school timetable scheduling is presented to help overcome the traditional conflicts inherent in the manual scheduling approach. In this approach, hill climbing algorithms have been modified to transact hard and soft constraints. Soft constraints are not easy to be satisfied typically, but hard constraints are obligated. The implementation of this technique has been successfully experimented in different schools with various kinds of side constraints. Results show that the initial solution can be improved by 72% towards the optimal solution within the first 5 seconds and by 50% from the second iteration while the optimal solution will be achieved after 15 iterations ensuring that more than 50% of scientific courses will take place in the early slots time while more than 50% of non-scientific courses will take place during the later time's slots.