A developed genetic algorithm for solving the multi-objective supply chain scheduling problem

Borumand, Ali; Beheshtinia, Mohammad Ali

doi:10.1108/k-07-2017-0275

Cited by 26 publications

(13 citation statements)

References 22 publications

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“…The implementation of Pharma 4.0 is well beyond basic process control compared to traditional quality control approach focused on automatic process control (APC). Pharmacological logistics also faced many issues in terms of sustainable development some of which includes: The socially anxious members should guarantee that complete service levels for the prevention of future inventory outs (Nematollahi et al, 2017;Borumand and Beheshtinia, 2018). Maintain high inventories for wholesalers and distributors and thus obstruct tight logistics.…”

Section: Features Of Industry 40mentioning

confidence: 99%

Challenges and Research Opportunities faced in Pharma Industries: 4.0

Goshain¹,

Sharma²

2021

ijrps

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The use of new technology from Pharma Industry 4.0 promotes sustainable production of value, makes pharmaceutical industry more flexible, clever and customized and thereby helps pharmaceutical companies to achieve economic benefit in long term. The present review discusses about the implications caused in enhancing pharmaceutical production and also investigate that how industry 4.0 aids in increasing the production. The review also discusses about the challenges faced by pharma industries 4.0 and addresses research opportunities faced in pharma industries 4.0. From the review it was observed that, main barriers which come in the way of increasing the production are high cost, time consuming design and framework development, seniors unsupportiveness, lack of good training skills, inadequate business inducement, lack of co-operation and communications. Further it was observed that, smart production system has led to the development of the increased production, but still difficulties and impediment to smart production should be considered in order to achieve expertize in supply chain managerial and sustainable practices.

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Section: Features Of Industry 40mentioning

confidence: 99%

Challenges and Research Opportunities faced in Pharma Industries: 4.0

Goshain¹,

Sharma²

2021

ijrps

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“…Therefore, metaheuristic algorithms are frequently used to solve this problem (Escobar et al, 2014;Koç et al, 2015). Evolutionary algorithms, specifically GAs are used recurrently to solve VRPs (Borumand and Beheshtinia, 2018;Karakatič and Podgorelec, 2015;Vidal et al, 2013). Genetic algorithm provides competitive solutions with less CPU time compared with other algorithms used in this area such as particle swarm, ant colony and simulated annealing (Arostegui et al, 2006;Youssef et al, 1998Youssef et al, , 2001.…”

Section: Ga For Vrpsmentioning

confidence: 99%

Post-disaster transportation of seriously injured people to hospitals

Shavarani

Vízvári

2018

JHLSCM

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Purpose The purpose of this paper is to deal with the transportation of a high number of injured people after a disaster in a highly populated large area. Each patient should be delivered to the hospital before the specific deadline to survive. The objective of the study is to maximize the survival rate of patients by proper assignment of existing emergency vehicles to hospitals and efficient generation of vehicle routes. Design/methodology/approach The concepts of non-fixed multiple depot pickup and delivery vehicle routing problem (MDPDVRP) is utilized to capture an image of the problem encountered in real life. Due to NP-hardness of the problem, a hybrid genetic algorithm (GA) is proposed as the solution method. The performance of the developed algorithm is investigated through a case study. Findings The proposed hybrid model outperforms the traditional GA and also is significantly superior compared to the nearest neighbor assignment. The required time for running the algorithm on a large-scale problem fits well into emergency distribution and the promptness required for humanitarian relief systems. Originality/value This paper investigates the efficient assignment of emergency vehicles to patients and their routing in a way that is most appropriate for the problem at hand.

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“…Zhao et al (2017) presented a multi-objective optimization model for green supply chain network by the minimization of inherent risk, carbon emission and economic cost. Borumand and Beheshtinia (2018) proposed an integrated decision-making model for integrated transportation and production scheduling problem in a two-stage supply chain, which aims at minimizing the total delivery tardiness, production cost and the emission by suppliers and vehicles and maximizing the production quality. Although above-mentioned literature considered carbon emissions in supply chain network optimization, the main difference between our paper and above papers lies in that the latter does not consider the substitution effects of final products or raw materials or the influences of substitution effects on low-carbon supply chain network optimization.…”

Section: K 478mentioning

confidence: 99%

Supply chain network optimization with consideration of raw material and final product substitutions driven by price and carbon emissions

Wang

2018

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Purpose This paper aims to develop a bi-objective mixed integer non-linear programing model to optimize the supply chain networks consisting of raw material providers, final product manufacturers and distribution centers. Raw material substitution caused by varying raw material supply amounts, prices and carbon emissions and final product substitution due to different product prices and carbon emissions are considered. Design/methodology/approach The proposed model aims to achieve total profit maximization and total carbon emission minimization. The objective function of carbon emissions is converted into a maximization problem by changing minimum to maximum. The composite objective function is the weighted sum of the bias value of each objective function. The model is then solved using software Lingo12. Findings Numerical analysis results show that an increase in the number of alternate raw materials for original raw material helps improve supply chain network performance, and variation in that number causes detectable but not significant changes in downstream final product substitution results. Originality/value The major differences between this work and existing research are as follows: first, although previous research considered carbon emissions in supply chain network optimization, it has not considered the substitution effects of products or raw materials. This paper considers the substitution of both raw material and productions. Second, the item substitution considered by previous research is derived from inventory shortage or price difference of original items. However, the substitution considered in the present paper is a response to differences in purchase price, production cost and carbon emissions for items.

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A developed genetic algorithm for solving the multi-objective supply chain scheduling problem

Cited by 26 publications

References 22 publications

Challenges and Research Opportunities faced in Pharma Industries: 4.0

Challenges and Research Opportunities faced in Pharma Industries: 4.0

Post-disaster transportation of seriously injured people to hospitals

Supply chain network optimization with consideration of raw material and final product substitutions driven by price and carbon emissions

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