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Dr. Yuvraj Gajpal is an Associate Professor of Supply Chain Management at Asper School of Business, University of Manitoba Winnipeg, Canada. He holds a Ph.D. in Management Science from DeGroote School of Business at McMaster University Hamilton, Canada, and a Masters in Industrial Management from the Indian Institute of Technology (IIT) Madras, India. His research interests mainly lie on the application of heuristics and meta-heuristics on transportation and logistics management. He has published papers in leading research journals such as Computers and Operations Research, the European Journal of Operations Research, the International Journal of Production Economics, the Annals of Operations Research, Reliability Engineering and Systems Safety, Construction Management and Economics, and the Journal of the Operational Research Society. He is a reviewer of many international journals such as Computers and Operations Research, the European Journal of Operations Research, Computers and Industrial Engineering, the Journal of Heuristics, and Transportation Research Part E.
This paper considers a variant of the classical capacitated vehicle routing problem called clustered vehicle routing problem (CluVRP). In CluVRP, customers are grouped into different clusters. A vehicle visiting a cluster cannot leave the cluster until all customers in the same cluster have been served. Each cluster and customer have to be served only once. A new hybrid metaheuristic, combining the particle swarm optimization (PSO) and variable neighborhood search (VNS) for the specific problem, is proposed to solve the CluVRP. In the hybrid PSO, the basic PSO principle ensures the solution diversity and VNS ensures solution intensity to bring the solution to the local optima. Extensive computational experiments have been performed on numerous benchmark instances with various sizes obtained from the CluVRP literature to evaluate the performance of the proposed hybrid PSO. The obtained results of the proposed algorithm are compared with the results found in the literature to validate the effectiveness of the proposed hybrid PSO. The proposed algorithm is proven to be superior to the state-of-the-art algorithms on the CluVRP. The proposed algorithm obtains 138 new best-known solutions among the 293 benchmark instances.
Anisul Islam; Yuvraj Gajpal; Tarek Y. Elmekkawy. Hybrid particle swarm optimization algorithm for solving the clustered vehicle routing problem. Applied Soft Computing 2021, 110, 107655 .
AMA StyleAnisul Islam, Yuvraj Gajpal, Tarek Y. Elmekkawy. Hybrid particle swarm optimization algorithm for solving the clustered vehicle routing problem. Applied Soft Computing. 2021; 110 ():107655.
Chicago/Turabian StyleAnisul Islam; Yuvraj Gajpal; Tarek Y. Elmekkawy. 2021. "Hybrid particle swarm optimization algorithm for solving the clustered vehicle routing problem." Applied Soft Computing 110, no. : 107655.
The CO2 emission of transportation is significantly reduced by the employment of green vehicles to the existing vehicle fleet of the organizations. This paper intends to optimize the composition of conventional and green vehicles for a logistics distribution problem operating under a carbon emission cap imposed by the government. The underlying problem involves product delivery by the vehicles starting from a single depot to geographically distributed customers. The delivery occurs within specified time windows. To solve the proposed problem, we design a hybrid metaheuristic solution based on ant colony optimization (ACO) and variable neighborhood search (VNS) algorithms. Extensive computational experiments have been performed on newly generated problem instances and benchmark problem instances adopted from the literature. The proposed hybrid ACO is proven to be superior to the state-of-the-art algorithms available in the literature. We obtain 21 new best-known solutions out of 56 benchmark instances of vehicle routing problem with time windows (VRPTW). The proposed mixed fleet model obtains the best composition of conventional and green vehicles with a 6.90% reduced amount of CO2 emissions compared to the case when the fleet consists of conventional vehicles only.
Anisul Islam; Yuvraj Gajpal. Optimization of Conventional and Green Vehicles Composition under Carbon Emission Cap. Sustainability 2021, 13, 6940 .
AMA StyleAnisul Islam, Yuvraj Gajpal. Optimization of Conventional and Green Vehicles Composition under Carbon Emission Cap. Sustainability. 2021; 13 (12):6940.
Chicago/Turabian StyleAnisul Islam; Yuvraj Gajpal. 2021. "Optimization of Conventional and Green Vehicles Composition under Carbon Emission Cap." Sustainability 13, no. 12: 6940.
In recent times, organizations are increasingly adopting blockchain technology in their supply chains due to various advantages such as cost optimization, effective and verified record-keeping, transparency, and route tracking. This paper aims to examine the factors influencing the intention of small and medium enterprises (SMEs) in India to adopt blockchain technology in their supply chains. A questionnaire-based survey was used to collect data from 216 SMEs in the northern states of India. The study has considered an integrated technology adoption framework consisting of the Technology Acceptance Model (TAM), Diffusion of Innovation (DOI), and Technology-Organization-Environment (TOE). Using this integrated TAM-TOE-DOI framework, the study has proposed eleven hypotheses related to factors of blockchain technology adoption. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) have been used to test the hypotheses. The results show that relative advantage, technology compatibility, technology readiness, top management support, perceived usefulness, and vendor support have a positive influence on the intention of Indian SMEs to adopt blockchain technology in their supply chains. The complexity of technology and cost concerns act as inhibitors to the technology adoption by SMEs. Furthermore, the three factors, namely, security concerns, perceived ease of use, and regulatory support, do not influence the intention to adopt the technology. The study contributes to filling a significant gap in the academic literature since only a few studies have endeavored to ascertain the technology adoption factors by supply chains of SMEs in a developing country like India. The study has also proposed a novel integrated technology adoption framework that can be employed by future studies. The findings are expected to enable SMEs to understand important factors to be considered for adopting blockchain technology in their supply chains. Furthermore, the study may benefit the blockchain technology developers and suppliers as they can offer customized solutions based on the findings.
Amit Kumar Bhardwaj; Arunesh Garg; Yuvraj Gajpal. Determinants of Blockchain Technology Adoption in Supply Chains by Small and Medium Enterprises (SMEs) in India. Mathematical Problems in Engineering 2021, 2021, 1 -14.
AMA StyleAmit Kumar Bhardwaj, Arunesh Garg, Yuvraj Gajpal. Determinants of Blockchain Technology Adoption in Supply Chains by Small and Medium Enterprises (SMEs) in India. Mathematical Problems in Engineering. 2021; 2021 ():1-14.
Chicago/Turabian StyleAmit Kumar Bhardwaj; Arunesh Garg; Yuvraj Gajpal. 2021. "Determinants of Blockchain Technology Adoption in Supply Chains by Small and Medium Enterprises (SMEs) in India." Mathematical Problems in Engineering 2021, no. : 1-14.
Sustainable transportation is an ever-demanding matter for cities and societies in light of minimizing global CO2 emissions. This paper introduces the mixed fleet based green clustered logistics problem (MFGCLP) under CO2 emission cap to deal with the sustainable development effort of the transportation industry. The mixed fleet consists of hydrogen vehicles and conventional vehicles. In the proposed distribution problem, customers are clustered in different segments based on similar characteristics. The customers belonging to a cluster must be served by the same vehicle before it visits customers from a different cluster or before it returns to the depot. The CO2 emission of the vehicles is realistically considered as a function of traveled distance, speed, and on-board cargo load. The problem also includes time windows for customers and maximum tour length for the routes. A new hybrid metaheuristic, combining particle swarm optimization (PSO) and neighborhood search, is proposed to solve the problem. Extensive computational experiments have been performed on newly generated problem instances, and benchmark problem instances adopted from the literature. The proposed hybrid PSO proved to be superior to the state-of-the-art algorithms available in the literature.
Anisul Islam; Yuvraj Gajpal; Tarek Y. Elmekkawy. Mixed fleet based green clustered logistics problem under carbon emission cap. Sustainable Cities and Society 2021, 72, 103074 .
AMA StyleAnisul Islam, Yuvraj Gajpal, Tarek Y. Elmekkawy. Mixed fleet based green clustered logistics problem under carbon emission cap. Sustainable Cities and Society. 2021; 72 ():103074.
Chicago/Turabian StyleAnisul Islam; Yuvraj Gajpal; Tarek Y. Elmekkawy. 2021. "Mixed fleet based green clustered logistics problem under carbon emission cap." Sustainable Cities and Society 72, no. : 103074.
The aim of each company/industry is to provide a final product to customers at the minimum possible cost, as well as to protect the environment from degradation. Ensuring the shortest travel distance between involved locations plays an important role in achieving the company’s/industry’s objective as (i) the cost of a final product can be minimized by minimizing the total distance travelled (ii) finding the shortest distance between involved locations will require less fuel than the longest distance between involved locations. This will eventually result in lesser degradation of the environment. Hence, in the last few years, various algorithms have been proposed to solve different types of shortest path problems. A recently proposed algorithm for solving interval-valued Pythagorean fuzzy shortest path problems requires excessive computational efforts. Hence, to reduce the computational efforts, in this paper, firstly, an alternative lexicographic method is proposed for comparing interval-valued Pythagorean fuzzy numbers. Then, using the proposed lexicographic comparing method, a new approach (named as Mehar approach) is proposed to solve interval-valued Pythagorean fuzzy shortest path problems. Furthermore, the superiority of the proposed lexicographic comparing method, as well as the proposed Mehar approach, is discussed.
Tanveen Bhatia; Amit Kumar; Srimantoorao Appadoo; Yuvraj Gajpal; Mahesh Sharma. Mehar Approach for Finding Shortest Path in Supply Chain Network. Sustainability 2021, 13, 4016 .
AMA StyleTanveen Bhatia, Amit Kumar, Srimantoorao Appadoo, Yuvraj Gajpal, Mahesh Sharma. Mehar Approach for Finding Shortest Path in Supply Chain Network. Sustainability. 2021; 13 (7):4016.
Chicago/Turabian StyleTanveen Bhatia; Amit Kumar; Srimantoorao Appadoo; Yuvraj Gajpal; Mahesh Sharma. 2021. "Mehar Approach for Finding Shortest Path in Supply Chain Network." Sustainability 13, no. 7: 4016.
The paper considers two-agent order acceptance scheduling problems with different scheduling criteria. Two agents have a set of jobs to be processed by a single machine. The processing time and due date of each job are known in advance. In the order accepting scheduling problem, jobs are allowed to be rejected. The objective of the problem is to maximize the net revenue while keeping the weighted number of tardy jobs for the second agent within a predetermined value. A mixed-integer linear programming (MILP) formulation is provided to obtain the optimal solution. The problem is considered as an NP-hard problem. Therefore, MILP can be used to solve small problem instances optimally. To solve the problem instances with realistic size, heuristic and metaheuristic algorithms have been proposed. A heuristic method is used to determine and secure a quick solution while the metaheuristic based on particle swarm optimization (PSO) is designed to obtain the near-optimal solution. A numerical experiment is piloted and conducted on the benchmark instances that could be obtained from the literature. The performances of the proposed algorithms are tested through numerical experiments. The proposed PSO can obtain the solution within 0.1% of the optimal solution for problem instances up to 60 jobs. The performance of the proposed PSO is found to be significantly better than the performance of the heuristic.
Jiaji Li; Yuvraj Gajpal; Amit Kumar Bhardwaj; Huangen Chen; Yuanyuan Liu. Two-Agent Single Machine Order Acceptance Scheduling Problem to Maximize Net Revenue. Complexity 2021, 2021, 1 -14.
AMA StyleJiaji Li, Yuvraj Gajpal, Amit Kumar Bhardwaj, Huangen Chen, Yuanyuan Liu. Two-Agent Single Machine Order Acceptance Scheduling Problem to Maximize Net Revenue. Complexity. 2021; 2021 ():1-14.
Chicago/Turabian StyleJiaji Li; Yuvraj Gajpal; Amit Kumar Bhardwaj; Huangen Chen; Yuanyuan Liu. 2021. "Two-Agent Single Machine Order Acceptance Scheduling Problem to Maximize Net Revenue." Complexity 2021, no. : 1-14.
This paper delves into a two-agent scheduling problem in which two agents are competing for a single resource. Each agent has a set of jobs to be processed by a single machine. The processing time, release time, weight, and the due dates of each job are known in advance. Both agents have their objectives, which are conflicting in nature. The first agent tries to minimize the total completion time, while the second agent tries to minimize the number of tardy jobs. The two agents’ scheduling problem, an NP-hard problem, has a wide variety of applications ranging from the manufacturing industry to the cloud computing service provider. Due to the wide applicability, each variation of the problem requires a different algorithm, adapted according to the user’s requirements. This paper provides mathematical models, heuristic algorithms, and two nature-based metaheuristic algorithms to solve the problem. The algorithm’s performance was gauged against the optimal solution obtained from the AMPL-CPLEX solver for both solution quality and computational time. The outlined metaheuristics produce a solution that is comparable with a short computational time. The proposed metaheuristics even have a better solution than the CPLEX solver for medium-size problems, whereas the computation times are much less than the CPLEX solvers.
Hongwei Li; Yuvraj Gajpal; Chirag Surti; Dongliang Cai; Amit Kumar Bhardwaj. Nature-Inspired Metaheuristics for Two-Agent Scheduling with Due Date and Release Time. Complexity 2020, 2020, 1 -13.
AMA StyleHongwei Li, Yuvraj Gajpal, Chirag Surti, Dongliang Cai, Amit Kumar Bhardwaj. Nature-Inspired Metaheuristics for Two-Agent Scheduling with Due Date and Release Time. Complexity. 2020; 2020 ():1-13.
Chicago/Turabian StyleHongwei Li; Yuvraj Gajpal; Chirag Surti; Dongliang Cai; Amit Kumar Bhardwaj. 2020. "Nature-Inspired Metaheuristics for Two-Agent Scheduling with Due Date and Release Time." Complexity 2020, no. : 1-13.
This paper considers a two-agent single machine scheduling problem, with a weighted number of tardy jobs as objectives. Each agent has a finite set of jobs to be processed on a single machine. Parameters like the processing time, the due date, and the weight of jobs are known in advance. The objective is to minimize the weighted number of tardy jobs of the first agent, subject to the upper bound of the weighted number of tardy jobs of the second agent. Two metaheuristics based on Particle Swarm Optimization and Tabu Search are introduced to solve this problem. To establish the performance of the proposed meta-heuristics, a dynamic programming based exact algorithm is developed. To test the validity of the proposed algorithm, a numerical experiment is performed on randomly generated problem instances to compare the performance of the proposed algorithms.
Jiaji Li; Yuvraj Gajpal; Srimantoorao S. Appadoo. Algorithms for a two-agent single machine scheduling problem to minimize weighted number of tardy jobs. Journal of Information and Optimization Sciences 2020, 42, 785 -811.
AMA StyleJiaji Li, Yuvraj Gajpal, Srimantoorao S. Appadoo. Algorithms for a two-agent single machine scheduling problem to minimize weighted number of tardy jobs. Journal of Information and Optimization Sciences. 2020; 42 (4):785-811.
Chicago/Turabian StyleJiaji Li; Yuvraj Gajpal; Srimantoorao S. Appadoo. 2020. "Algorithms for a two-agent single machine scheduling problem to minimize weighted number of tardy jobs." Journal of Information and Optimization Sciences 42, no. 4: 785-811.
This paper considers the distributed permutation flowshop scheduling problem (DPFSP) which is an extension of permutation flowshop scheduling problem (PFSP). In DPFSP, there are multiple parallel factories instead of one factory as in PFSP. Each factory consists of same number of machines, and jobs can be processed in either of the factories to perform all necessary operations. This paper considers DPFSP for minimizing the total completion time objective. An MILP formulation is developed to find the optimal solution. To solve the problem, a metaheuristic, tabu search (TS) is proposed. Numerical experiments are performed on benchmark problem instances from the literature, and results of the proposed method are compared with current metaheuristics in the literature for this problem. The tabu search outperforms all existing metaheuristics in terms of solution quality.
Arshad Ali; Yuvraj Gajpal; Tarek Y. Elmekkawy. Distributed permutation flowshop scheduling problem with total completion time objective. OPSEARCH 2020, 58, 425 -447.
AMA StyleArshad Ali, Yuvraj Gajpal, Tarek Y. Elmekkawy. Distributed permutation flowshop scheduling problem with total completion time objective. OPSEARCH. 2020; 58 (2):425-447.
Chicago/Turabian StyleArshad Ali; Yuvraj Gajpal; Tarek Y. Elmekkawy. 2020. "Distributed permutation flowshop scheduling problem with total completion time objective." OPSEARCH 58, no. 2: 425-447.
The term “green products” is used commonly to describe the products that seek to protect or enhance the environment during production, use, or disposal by conserving resources and minimizing the use of toxic agents, pollution, and waste. Hence, green products offer potential benefits to the environment and human health. Therefore, environmentally conscious consumers have shown an enhanced inclination for them. Consumer preferences, environmental activism, and stringent regulations have forced sustainability-oriented firms to shift their focus to producing green products. The present study uses bibliometric tools and various indicators to discern research progress in the field of green products over the period 1964–2019. Further, VOSviewer software is applied to map the main trends. A total of 1619 publications during the study period were extracted from the SCOPUS database using different keywords related to the green products. The data analysis indicates that the field of green products has experienced significant growth since 1964, especially in the last 14 years. In terms of publications and citations, the United States is the leading country. The field of research concerning green products has evolved from the early debates on sustainable design, green marketing, sustainable development, and sustainability. The topic seems to be advancing into a variety of green themes related to consumer trust and purchase intentions, branding and loyalty, and environmental and health consciousness.
Amit Kumar Bhardwaj; Arunesh Garg; Shri Ram; Yuvraj Gajpal; Chengsi Zheng. Research Trends in Green Product for Environment: A Bibliometric Perspective. International Journal of Environmental Research and Public Health 2020, 17, 8469 .
AMA StyleAmit Kumar Bhardwaj, Arunesh Garg, Shri Ram, Yuvraj Gajpal, Chengsi Zheng. Research Trends in Green Product for Environment: A Bibliometric Perspective. International Journal of Environmental Research and Public Health. 2020; 17 (22):8469.
Chicago/Turabian StyleAmit Kumar Bhardwaj; Arunesh Garg; Shri Ram; Yuvraj Gajpal; Chengsi Zheng. 2020. "Research Trends in Green Product for Environment: A Bibliometric Perspective." International Journal of Environmental Research and Public Health 17, no. 22: 8469.
Cooperation between rescue teams is important to improve rescue performance. Vehicles outside of the disaster area usually deliver rescue resources. A two-echelon rescue delivery model is proposed, considering the isolated island of the disaster area where all the roads to the outside are interrupted. This paper first presents a non-cooperation scenario and then a cooperation scenario in an uncertain environment. Furthermore, two types of cooperative strategies to improve rescue performance are provided in the paper. The two cooperative strategies are a reactive cooperative strategy and an anticipatory cooperative strategy. Numerical experiments are used to evaluate the rescue performances of the two cooperation strategies by comparing them with the non-cooperation scenario. The results reveal that the anticipatory cooperative strategy performs the best in different cases varying in size.
Hanpeng Zhang; Yuxin Wu; Yi Liao; Yuvraj Gajpal. Cooperative Strategies in Two-Echelon Rescue Delivery Environment with Accessibility Uncertainty. Sustainability 2020, 12, 5333 .
AMA StyleHanpeng Zhang, Yuxin Wu, Yi Liao, Yuvraj Gajpal. Cooperative Strategies in Two-Echelon Rescue Delivery Environment with Accessibility Uncertainty. Sustainability. 2020; 12 (13):5333.
Chicago/Turabian StyleHanpeng Zhang; Yuxin Wu; Yi Liao; Yuvraj Gajpal. 2020. "Cooperative Strategies in Two-Echelon Rescue Delivery Environment with Accessibility Uncertainty." Sustainability 12, no. 13: 5333.
A Multi-Depot Green Vehicle Routing Problem (MDGVRP) is considered in this paper. In MDGVRP, Alternative Fuel-powered Vehicles (AFVs) start from different depots, serve customers, and, at the end, return to the original depots. The limited fuel tank capacity of AFVs forces them to visit Alternative Fuel Stations (AFS) for refueling. The objective is to minimize the total carbon emissions. A Two-stage Ant Colony System (TSACS) is proposed to find a feasible and acceptable solution for this NP-hard (Non-deterministic polynomial-time) optimization problem. The distinct characteristic of the proposed TSACS is the use of two distinct types of ants for two different purposes. The first type of ant is used to assign customers to depots, while the second type of ant is used to find the routes. The solution for the MDGVRP is useful and beneficial for companies that employ AFVs to deal with the various inconveniences brought by the limited number of AFSs. The numerical experiments confirm the effectiveness of the proposed algorithms in this research.
Weiheng Zhang; Yuvraj Gajpal; Srimantoorao. S. Appadoo; Qi Wei. Multi-Depot Green Vehicle Routing Problem to Minimize Carbon Emissions. Sustainability 2020, 12, 3500 .
AMA StyleWeiheng Zhang, Yuvraj Gajpal, Srimantoorao. S. Appadoo, Qi Wei. Multi-Depot Green Vehicle Routing Problem to Minimize Carbon Emissions. Sustainability. 2020; 12 (8):3500.
Chicago/Turabian StyleWeiheng Zhang; Yuvraj Gajpal; Srimantoorao. S. Appadoo; Qi Wei. 2020. "Multi-Depot Green Vehicle Routing Problem to Minimize Carbon Emissions." Sustainability 12, no. 8: 3500.
Hongwei Li; Yuvraj Gajpal; C. R. Bector. A survey of due-date related single-machine with two-agent scheduling problem. Journal of Industrial & Management Optimization 2020, 16, 1329 -1347.
AMA StyleHongwei Li, Yuvraj Gajpal, C. R. Bector. A survey of due-date related single-machine with two-agent scheduling problem. Journal of Industrial & Management Optimization. 2020; 16 (3):1329-1347.
Chicago/Turabian StyleHongwei Li; Yuvraj Gajpal; C. R. Bector. 2020. "A survey of due-date related single-machine with two-agent scheduling problem." Journal of Industrial & Management Optimization 16, no. 3: 1329-1347.
In supply chain operation practices, lead time uncertainty is a common management issue. Uncertain lead time can lead to increased inventory costs and unstable service levels, which will directly affect the overall operation performance of the supply chain. While considering environmental performance in supply chain, it is important to understand how an uncertain lead time will affect sustainable performance. In this paper, we constructed a supply chain model with stochastic lead time and explored the relationship between uncertain lead time and supply chain performance. We considered carbon cost, inventory cost, and service level as a supply chain performance. System dynamics methodology was employed to observe and explore the dynamic change trend of the overall performance in the complicated supply chain model. This was done under both different levels of lead time standard deviation and different order policies. The results demonstrate how stochastic lead times can significantly increase inventory costs and carbon costs. Therefore, we propose appropriate ordering policies which mitigate the negative impacts of stochastic lead times.
Zhuoqun Li; Weiwei Fei; Ermin Zhou; Yuvraj Gajpal; Xiding Chen. The Impact of Lead Time Uncertainty on Supply Chain Performance Considering Carbon Cost. Sustainability 2019, 11, 6457 .
AMA StyleZhuoqun Li, Weiwei Fei, Ermin Zhou, Yuvraj Gajpal, Xiding Chen. The Impact of Lead Time Uncertainty on Supply Chain Performance Considering Carbon Cost. Sustainability. 2019; 11 (22):6457.
Chicago/Turabian StyleZhuoqun Li; Weiwei Fei; Ermin Zhou; Yuvraj Gajpal; Xiding Chen. 2019. "The Impact of Lead Time Uncertainty on Supply Chain Performance Considering Carbon Cost." Sustainability 11, no. 22: 6457.
The green vehicle routing problem is a variation of the classic vehicle routing problem in which the transportation fleet is composed of electric vehicles with limited autonomy in need of recharge during their duties. As an NP-hard problem, this problem is very difficult to solve. In this paper, we first propose a memetic algorithm (MA)—a population-based algorithm—to tackle this problem. To be more specific, we incorporate an adaptive local search procedure based on a reward and punishment mechanism inspired by reinforcement learning to effectively manage the multiple neighborhood moves and guide the search, an effective backbone-based crossover operator to generate the feasible child solutions to obtain a better trade-off between intensification and diversification of the search, and a longest common subsequence-based population updating strategy to effectively manage the population. The purpose of this research is to propose a highly effective heuristic for solving the green vehicle routing problem and bring new ideas for this type of problem. Experimental results show that our algorithm is highly effective in comparison with the current state-of-the-art algorithms. In particular, our algorithm is able to find the best solutions for 84 out of the 92 instances. Key component of the approach is analyzed to evaluate its impact on the proposed algorithm and to identify the appropriate search mechanism for this type of problem.
Bo Peng; Yuan Zhang; Yuvraj Gajpal; Xiding Chen. A Memetic Algorithm for the Green Vehicle Routing Problem. Sustainability 2019, 11, 6055 .
AMA StyleBo Peng, Yuan Zhang, Yuvraj Gajpal, Xiding Chen. A Memetic Algorithm for the Green Vehicle Routing Problem. Sustainability. 2019; 11 (21):6055.
Chicago/Turabian StyleBo Peng; Yuan Zhang; Yuvraj Gajpal; Xiding Chen. 2019. "A Memetic Algorithm for the Green Vehicle Routing Problem." Sustainability 11, no. 21: 6055.
A major goal for port authorities, operators, and investors is to achieve efficient operations and effective environmental protection. This is because the environmental performance of a container port is important for its competitiveness and sustainable development. However, the container ports along the Maritime Silk Road (MSR) have caused numerous problems with the rapid development, among which the most significant problem is environmental pollution. In this paper, we aim to measure and compare the environmental performance and operational efficiency of ten major container ports along the MSR, including the ports of Shanghai, Hong Kong, Singapore, Kelang, Laem Chabang, Colombo, Dubai, Barcelona, Antwerp, and Hamburg. We develop an improved, inseparable data envelopment analysis (DEA) model with slack-based measures (SBMs) to evaluate and compare the environmental performance and operational efficiency, and we incorporate the desirable output of container throughput as well as the undesirable output of CO2 emission. Our results show that. Overall. these container ports perform better in terms of operational efficiency than environmental performance. We also provide insights for management and policy makers for container ports with different levels of operational efficiency and environmental performance.
Gang Dong; Jing Zhu; Jin Li; Handong Wang; Yuvraj Gajpal; Dong; Zhu; Li; Wang. Evaluating the Environmental Performance and Operational Efficiency of Container Ports: An Application to the Maritime Silk Road. International Journal of Environmental Research and Public Health 2019, 16, 2226 .
AMA StyleGang Dong, Jing Zhu, Jin Li, Handong Wang, Yuvraj Gajpal, Dong, Zhu, Li, Wang. Evaluating the Environmental Performance and Operational Efficiency of Container Ports: An Application to the Maritime Silk Road. International Journal of Environmental Research and Public Health. 2019; 16 (12):2226.
Chicago/Turabian StyleGang Dong; Jing Zhu; Jin Li; Handong Wang; Yuvraj Gajpal; Dong; Zhu; Li; Wang. 2019. "Evaluating the Environmental Performance and Operational Efficiency of Container Ports: An Application to the Maritime Silk Road." International Journal of Environmental Research and Public Health 16, no. 12: 2226.
A Multi-depot Green Vehicle Routing Problem (MDGVRP) is considered in this paper. An Ant Colony System-based metaheuristic is proposed to find the solution to this problem. The solution for MDGVRP is useful for companies, who employ the Alternative Fuel-Powered Vehicles (AFVs) to deal with the obstacles brought by the limited number of the Alternative Fuel Stations. This paper adds an important constraint, vehicle capacity to the model, to make it more meaningful and closer to real-world case. The numerical experiment is performed on randomly generated problem instances to understand the property of MDGVRP and to bring the managerial insights of the problem.
Shuai Zhang; Weiheng Zhang; Yuvraj Gajpal; S. S. Appadoo. Ant Colony Algorithm for Routing Alternate Fuel Vehicles in Multi-depot Vehicle Routing Problem. Decision Science in Action 2018, 251 -260.
AMA StyleShuai Zhang, Weiheng Zhang, Yuvraj Gajpal, S. S. Appadoo. Ant Colony Algorithm for Routing Alternate Fuel Vehicles in Multi-depot Vehicle Routing Problem. Decision Science in Action. 2018; ():251-260.
Chicago/Turabian StyleShuai Zhang; Weiheng Zhang; Yuvraj Gajpal; S. S. Appadoo. 2018. "Ant Colony Algorithm for Routing Alternate Fuel Vehicles in Multi-depot Vehicle Routing Problem." Decision Science in Action , no. : 251-260.
This paper considers two-agent scheduling problem with a single machine which is responsible for processing jobs from two agents. The objective is to minimize the objective function of one agent, subject to an upper bound on the objective function of the other agent. The objectives considered in this paper are, (1) the minimization of total completion time and (2) the minimization of total weighted completion time. To solve these problems, one heuristic and an Ant Colony Optimization algorithm are proposed. The heuristic suggested in the paper are motivated by the Weighted Shortest Processing Time first (WSPT) rule. A numerical experiment is performed on randomly generated problem instances. The performance of the algorithm is evaluated by comparing it with the lower bound value of all three problems considered in the present paper.
Hongwei Li; Yuvraj Gajpal; C.R. Bector. Single machine scheduling with two-agent for total weighted completion time objectives. Applied Soft Computing 2018, 70, 147 -156.
AMA StyleHongwei Li, Yuvraj Gajpal, C.R. Bector. Single machine scheduling with two-agent for total weighted completion time objectives. Applied Soft Computing. 2018; 70 ():147-156.
Chicago/Turabian StyleHongwei Li; Yuvraj Gajpal; C.R. Bector. 2018. "Single machine scheduling with two-agent for total weighted completion time objectives." Applied Soft Computing 70, no. : 147-156.
M.M.S. Abdulkader; Yuvraj Gajpal; Tarek Y. ElMekkawy. Vehicle routing problem in omni-channel retailing distribution systems. International Journal of Production Economics 2018, 196, 43 -55.
AMA StyleM.M.S. Abdulkader, Yuvraj Gajpal, Tarek Y. ElMekkawy. Vehicle routing problem in omni-channel retailing distribution systems. International Journal of Production Economics. 2018; 196 ():43-55.
Chicago/Turabian StyleM.M.S. Abdulkader; Yuvraj Gajpal; Tarek Y. ElMekkawy. 2018. "Vehicle routing problem in omni-channel retailing distribution systems." International Journal of Production Economics 196, no. : 43-55.
The capacitated green vehicle routing problem is considered in this paper as a new variant of the vehicle routing problem. In this problem, alternative fuel-powered vehicles (AFVs) are used for distributing products. AFVs are assumed to have low fuel tank capacity. Therefore, during their distribution process, AFVs are required to visit alternative fuel stations (AFSs) for refueling. The design of the vehicle routes for AFVs becomes difficult due to the limited loading capacity, the low fuel tank capacity and the scarce availability of AFSs. Two solution methods, the two-phase heuristic algorithm and the meta-heuristic based on ant colony system, are proposed to solve the problem. The numerical experiment is performed on the randomly generated problem instances to evaluate the performance of the proposed algorithms.
Shuai Zhang; Yuvraj Gajpal; S. S. Appadoo. A meta-heuristic for capacitated green vehicle routing problem. Annals of Operations Research 2017, 269, 753 -771.
AMA StyleShuai Zhang, Yuvraj Gajpal, S. S. Appadoo. A meta-heuristic for capacitated green vehicle routing problem. Annals of Operations Research. 2017; 269 (1-2):753-771.
Chicago/Turabian StyleShuai Zhang; Yuvraj Gajpal; S. S. Appadoo. 2017. "A meta-heuristic for capacitated green vehicle routing problem." Annals of Operations Research 269, no. 1-2: 753-771.