Novel modifications of social engineering optimizer to solve a truck scheduling problem in a cross-docking system

Fathollahi-Fard, Amir M.; Ranjbar-Bourani, Mehdi; Cheikhrouhou, Naoufel; Hajiaghaei–Keshteli, Mostafa

doi:10.1016/j.cie.2019.106103

Cited by 76 publications

(37 citation statements)

References 63 publications

(87 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the related articles, scholars have studied and employed this technique to encode the metaheuristics in a mathematical model [2,5,[39][40]. The main reason of using this encoding strategy is to have less computational time with no repairing of the solutions' feasibility to encode the problem through a two-stage plan, comprehensively [40][41][42]. The first stage is to generate a set of random numbers due to continuous search space of the metaheuristics.…”

Section: Encoding Planmentioning

confidence: 99%

“…The first stage is to generate a set of random numbers due to continuous search space of the metaheuristics. After that this solution would be converted to a feasible discrete solution by a procedure [41][42]. In our encoding plan, as shown in Figure 2, at first, a matrix with |n| elements obtained by uniform distribution U (0, 1) is made.…”

Section: Encoding Planmentioning

confidence: 99%

See 1 more Smart Citation

Metaheuristics for a bi-objective green vendor managed inventory problem in a two-echelon supply chain network

et al. 2020

Self Cite

View full text Add to dashboard Cite

Generally, finding an alternative solution for reducing fossil fuel consumption and green emissions of supply chain networks can shift the Vendor Managed Inventory (VMI) to the Green VMI (GVMI). Our literature search also confirms that the issue of environmental pollution and green emissions, is still scarce and required to be explored. This motivates our attempt to offer the issue of green backorder for the VMI in a two-echelon supply chain network among the first studies. To this end, a bi-objective non-linear optimization model with the goal of maximizing the profit of inventory and minimizing the carbon emissions of transportation, simultaneously, is developed. Another contribution of this work is to propose three capable metaheuristics to solve it optimality in large-scale samples. In this regard, the Nondominated Sorting Genetic Algorithm (NSGA-II) as a well-known method as well as Multi-Objective of Keshtel Algorithm (MOKA) and Multi-Objective of Red Deer Algorithm (MORDA) as two recent nature-inspired algorithms are firstly applied. The outputs confirm that the allowed shortage situation along with the lack of cost reduction shows a greater amount of shipping and orders based on sensitivities. With regards to the comparison among algorithms, the performance of MORDA is highly better than MOKA and NSGA-II.

show abstract

Section: Encoding Planmentioning

confidence: 99%

Section: Encoding Planmentioning

confidence: 99%

Metaheuristics for a bi-objective green vendor managed inventory problem in a two-echelon supply chain network

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Constraint (15) shows that cargo on board ship does not exceed the ship capacity. Constraints (16) to (22) determine the limits of decision variables.…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…To solve the proposed model as an NP-hard problem, we apply a Genetic Algorithm (GA). Nature-inspired optimization methods differ significantly from conventional optimization methods [22].…”

Section: Genetic Algorithmmentioning

confidence: 99%

A Vehicle Routing Problem with Two Types of Ships in Container Terminals

2020

View full text Add to dashboard Cite

The Vehicle Routing Problem (VRP) is one of well-established and important topic in supply chain and logistics networks. The location-routing decision-making has a significant role to optimize the traveling cost per distance, total traveling time and the number of vehicles in the supply chain networks. One of the recent advances in these systems refers to container terminals in the global trade centers. The new contributions of this model are to consider the routes of two types of ships (Tramp and Liner) with different properties and the time windows in order to minimize costs, simultaneously. The proposed model is solved by an exact solver by using LINGO software and because of inherent complexity of problem proposed in the real-world cases, the Genetic Algorithm (GA) is used to find an optimal/global solution in a reasonable time. Finally, an in-depth analysis and discussion is provided to conclude the main findings and practical implications of the results.

show abstract

“…Baliarsingh et al [43] developed a memetic algorithm-based method in which they used SEO algorithm for local search to deal with the medical data classification problem. Fathollahi-Fard et al [44] proposed a truck scheduling problem and solved the model by using different versions of SEO by putting different weights on the SEO features. In SEO algorithm, the solutions are regarded as different persons with various abilities.…”

Section: Social Engineering Optimizer (Seo)mentioning

confidence: 99%

A hybrid metaheuristic algorithm for a data driven leagile sustainable closed-loop supply chain model under disruption risks

2020

View full text Add to dashboard Cite

In the current situation, taking into consideration the environmental and social issues are related with the production and distribution of products in supply chain systems, due to the global concerns related with emitting lots of greenhouse gases within the manufacturing process and overlooking the major needs of public. This paper proposes a new multi objective model in the area of closed loop supply chain problem integrated with lot sizing by considering lean, agility and sustainability factors simultaneously. Next, a robust possibilistic programming approach is applied to handle the uncertainty of the model. To increase the responsiveness of the system, a fuzzy c-means clustering method is applied to select the potential locations based on the proximity to local customers. In the following, a new hybrid metaheuristic algorithm is developed to deal with large size problems efficiency and to assess the impact of using a single-based initial solution as the income for the second phase of the proposed hybrid algorithm. To ensure about the effectiveness of the proposed algorithm, another well-known metaheuristic algorithm is developed too. The results reached from experiments on different test problems approve the superiority of the hybrid metaheuristic algorithm to find better solutions.

show abstract

Novel modifications of social engineering optimizer to solve a truck scheduling problem in a cross-docking system

Cited by 76 publications

References 63 publications

Metaheuristics for a bi-objective green vendor managed inventory problem in a two-echelon supply chain network

Metaheuristics for a bi-objective green vendor managed inventory problem in a two-echelon supply chain network

A Vehicle Routing Problem with Two Types of Ships in Container Terminals

A hybrid metaheuristic algorithm for a data driven leagile sustainable closed-loop supply chain model under disruption risks

Contact Info

Product

Resources

About