Two enhanced differential evolution algorithms for job shop scheduling problems

Wisittipanich, Warisa; Kachitvichyanukul, Voratas

doi:10.1080/00207543.2011.588972

Cited by 30 publications

(9 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, DE showed faster convergence behavior than GLNPSO, since it yielded better solutions, while using a fewer number of function evaluations. The superiority of DE to PSO was also demonstrated in other research fields, such as numerical benchmark problems (see Wisittipanich and Kachitvichyanukul (2014)), production scheduling problems (see Wisittipanich and Kachitvichyanukul (2011, 2015, Chakaravarthy et al (2013)), and vehicle routing problems (see Kunnapapdeelert and Kachitvichyanukul (2013), Liao et al (2012)).…”

Section: Discussionmentioning

confidence: 84%

Metaheuristics for Warehouse Storage Location Assignment Problems

Wisittipanich¹,

Kasemset²

2015

CMUJNS

View full text Add to dashboard Cite

This study addressed warehouse storage location assignment problems (SLAP) to minimize total travelling distances in an order-picking process. The problem was formulated and presented as a mixed integer programming model. The LINGO optimization solver was then used to find solutions for a set of generated problems. The results showed that the LINGO optimization solver easily attained optimal solutions for small-sized problems; however, as the problem size increased, computational time increased rapidly. Eventually, when the problem size became very large, LINGO was unable to find solutions. Due to the competence limitations of the exact solution method, this research presented two effective metaheuristic approaches -Differential Evolution (DE) and Global Local and Near-Neighbor Particle Swarm Optimization (GLNPSO) -to solve SLAP. To illustrate the performance of the algorithms, the numerical results were evaluated and compared with a set of generated problems. The experimental results showed that, for small-sized problems, DE and GLNPSO found optimal solutions equal to those obtained from LINGO, with fast computing times. For medium-sized problems, DE and GLNPSO were not significantly different in terms of solution quality, but DE found solutions approximately twice as fast as GLNPSO. For large-sized problems, DE was significantly superior to GL-NPSO in terms of both solution quality and computational times. The average DE solutions, obtained from five independent runs, were equal to or better than those obtained from GLNPSO in all large-sized instances. In addition, DE showed faster convergence behavior than GLNPSO, since it yielded better solutions while using a fewer number of function evaluations.

show abstract

Section: Discussionmentioning

confidence: 84%

Metaheuristics for Warehouse Storage Location Assignment Problems

Wisittipanich¹,

Kasemset²

2015

CMUJNS

View full text Add to dashboard Cite

show abstract

“…This method can be classified as an iterative and stochastic type. Wisittipanich and Kachitvichyanukul (2012) proposed a concept makespan problem to solve using two enhanced differential evolution algorithms for, minimize job scheduling makespan tardiness by doing an equal balance of analysis and consumption ability in differential evolution algorithms [17]. Ziaee et al, (2014) proposed Job shop scheduling with makespan objective: A heuristic approach, this method minimizing the makespan of jobs for JSSP and discovered a solution for good schedules frequently [18].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Algorithm using Grey Wolf and Chicken Swarm for Flexible Job Shop Scheduling

2019

IJRTE

View full text Add to dashboard Cite

The Job Shop Scheduling Problem (JSSP) is one of the major inconvenience models because it is the hardest combinatorial change in nature. So far number of algorithms developed to solve JSSP and still there is scope to develop an efficient algorithm. Hence, this paper considered a unique hybrid algorithm which is the combination of the Grey Wolf and Chicken Swarm Optimization algorithm with the objective of minimization of makespan time and cost. The most extraordinary compilation (makespan), and the tardiness is considered in the meantime. The hybrid algorithm count relies upon the reenactment of the swarming behavior of wolves’ individuals. The base objectives of each wolf and swarms from most bewildering thicknesses of the group are considered as the objective work for the improvement. The results show that the proposed hybrid Grey Wolf and Chicken Swarm Optimization algorithm can obtain better results in terms of various iteration levels. The accuracy and robustness are also applied in real industrial conditions and for large size problems. The proposed method results show that very less time duration when compared with the Genetic Algorithm (GA) and Ants Colony Optimization (ACO) algorithm.

show abstract

“…, and Mingyong and Erbao applied DEA to solve VRP and its extensions. Wisittipanich and Kachitvichyanukul proposed two enhanced DEA for job shop scheduling problems to minimize makespan and total weighted tardiness. This paper represents a different encoding scheme to construct integer solution vectors and improve the performance of the algorithm.…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

A differential evolution approach for the vehicle routing problem with backhauls and time windows

Küçükoğlu

Öztürk

2013

J of Advced Transportation

View full text Add to dashboard Cite

SUMMARY This paper presents a differential evolution algorithm (DEA) to solve a vehicle routing problem with backhauls and time windows (VRPBTW) and applied for a catering firm. VRPBTW is an extension of the vehicle routing problem, which includes capacity and time window constraints. In this problem, customers are divided into two subsets: linehaul and backhaul. Each vehicle starts from a depot and goods are delivered from the depot to the linehaul customers. Goods are subsequently brought back to the depot from the backhaul customers. The objective is to minimize the total distance that satisfies all of the constraints. The problem is formulated using mixed integer programming and solved using DEA. Proposed algorithm is tested with several benchmark problems to demonstrate effectiveness and efficiency of the algorithm and results show that our proposed algorithm can find superior solutions for most of the problems in comparison with the best known solutions. Hence, DEA was carried out for catering firm to minimize total transportation costs. Copyright © 2013 John Wiley & Sons, Ltd.

show abstract

Two enhanced differential evolution algorithms for job shop scheduling problems

Cited by 30 publications

References 39 publications

Metaheuristics for Warehouse Storage Location Assignment Problems

Metaheuristics for Warehouse Storage Location Assignment Problems

A Hybrid Algorithm using Grey Wolf and Chicken Swarm for Flexible Job Shop Scheduling

A differential evolution approach for the vehicle routing problem with backhauls and time windows

Contact Info

Product

Resources

About