Dipak Laha

855 total citations
35 papers, 637 citations indexed

About

Dipak Laha is a scholar working on Industrial and Manufacturing Engineering, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Dipak Laha has authored 35 papers receiving a total of 637 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Industrial and Manufacturing Engineering, 10 papers in Computer Networks and Communications and 3 papers in Artificial Intelligence. Recurrent topics in Dipak Laha's work include Advanced Manufacturing and Logistics Optimization (29 papers), Scheduling and Optimization Algorithms (28 papers) and Assembly Line Balancing Optimization (15 papers). Dipak Laha is often cited by papers focused on Advanced Manufacturing and Logistics Optimization (29 papers), Scheduling and Optimization Algorithms (28 papers) and Assembly Line Balancing Optimization (15 papers). Dipak Laha collaborates with scholars based in India, United States and Singapore. Dipak Laha's co-authors include Uday K. Chakraborty, Jatinder N.D. Gupta, Arindam Majumder, Ponnuthurai Nagaratnam Suganthan, Sanjiv Sarin, Ye Ren, Purnendu Mandal, Dhiren Kumar Behera and Simul Banerjee and has published in prestigious journals such as Expert Systems with Applications, Journal of the Operational Research Society and Knowledge-Based Systems.

In The Last Decade

Dipak Laha

35 papers receiving 611 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Dipak Laha India 15 480 80 79 73 71 35 637
Li Shi China 9 340 0.7× 103 1.3× 67 0.8× 51 0.7× 96 1.4× 27 550
Minghai Yuan China 14 579 1.2× 72 0.9× 45 0.6× 41 0.6× 74 1.0× 51 724
Lenz Belzner Germany 7 444 0.9× 72 0.9× 112 1.4× 21 0.3× 89 1.3× 29 620
Tianguo Jin China 11 226 0.5× 65 0.8× 46 0.6× 82 1.1× 38 0.5× 26 409
Jianlin Fu China 4 512 1.1× 54 0.7× 50 0.6× 19 0.3× 64 0.9× 8 584
Hong Jin China 7 220 0.5× 80 1.0× 54 0.7× 116 1.6× 46 0.6× 10 437
Janis S. Neufeld Germany 16 681 1.4× 74 0.9× 65 0.8× 15 0.2× 86 1.2× 24 747
Beatrice M. Ombuki Japan 6 398 0.8× 41 0.5× 145 1.8× 40 0.5× 45 0.6× 13 514
Alper Hamzadayı Türkiye 11 476 1.0× 67 0.8× 102 1.3× 16 0.2× 50 0.7× 22 580
A. Márkus Hungary 11 560 1.2× 32 0.4× 79 1.0× 150 2.1× 59 0.8× 30 695

Countries citing papers authored by Dipak Laha

Since Specialization
Citations

This map shows the geographic impact of Dipak Laha's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dipak Laha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dipak Laha more than expected).

Fields of papers citing papers by Dipak Laha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dipak Laha. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dipak Laha. The network helps show where Dipak Laha may publish in the future.

Co-authorship network of co-authors of Dipak Laha

This figure shows the co-authorship network connecting the top 25 collaborators of Dipak Laha. A scholar is included among the top collaborators of Dipak Laha based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dipak Laha. Dipak Laha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Gupta, Jatinder N.D., Arindam Majumder, & Dipak Laha. (2019). Flowshop scheduling with artificial neural networks. Journal of the Operational Research Society. 71(10). 1619–1637. 23 indexed citations
2.
Laha, Dipak, et al.. (2018). Genetic Algorithm approach for Machine Cell Formation with Alternative Routings. Materials Today Proceedings. 5(1). 1766–1775. 14 indexed citations
3.
Majumder, Arindam, Dipak Laha, & Ponnuthurai Nagaratnam Suganthan. (2018). A hybrid cuckoo search algorithm in parallel batch processing machines with unequal job ready times. Computers & Industrial Engineering. 124. 65–76. 20 indexed citations
4.
Laha, Dipak, et al.. (2017). Application of genetic algorithm in generalized machine cell formation problem. 1855–1860. 4 indexed citations
5.
Laha, Dipak & Dhiren Kumar Behera. (2017). A comprehensive review and evaluation of LPT, MULTIFIT, COMBINE and LISTFIT for scheduling identical parallel machines. International Journal of Information and Communication Technology. 11(2). 151–151. 3 indexed citations
6.
Laha, Dipak & Dhiren Kumar Behera. (2017). A comprehensive review and evaluation of LPT, MULTIFIT, COMBINE and LISTFIT for scheduling identical parallel machines. International Journal of Information and Communication Technology. 11(2). 151–151. 3 indexed citations
7.
Laha, Dipak, et al.. (2016). A Heuristic Approach for Machine Cell Formation Problems with Alternative Routings. Procedia Computer Science. 89. 228–242. 7 indexed citations
8.
Laha, Dipak, et al.. (2013). A penalty-shift-insertion-based algorithm to minimize total flow time in no-wait flow shops. Journal of the Operational Research Society. 65(10). 1611–1624. 13 indexed citations
9.
Laha, Dipak, et al.. (2013). A heuristic for no-wait flow shop scheduling. The International Journal of Advanced Manufacturing Technology. 68(5-8). 1327–1338. 29 indexed citations
10.
Laha, Dipak, et al.. (2012). Application of VAM to Manufacturing Scheduling Problems. Advanced materials research. 488-489. 578–582. 1 indexed citations
11.
Laha, Dipak, et al.. (2012). Searching for a Pareto Optimal Solution Set of EDM Responses Applying Multi-Objective Simulated Annealing on RSM Model. Advanced materials research. 622-623. 51–55. 2 indexed citations
12.
Laha, Dipak, et al.. (2012). Optimization Techniques for No-Wait Manufacturing Scheduling: A Review. Advanced materials research. 488-489. 1114–1118. 1 indexed citations
13.
Laha, Dipak & Uday K. Chakraborty. (2010). Minimising total flow time in permutation flow shop scheduling using a simulated annealing-based approach. International Journal of Automation and Control. 4(4). 359–359. 6 indexed citations
14.
Laha, Dipak, et al.. (2010). A new heuristic for minimizing total completion time objective in permutation flow shop scheduling. The International Journal of Advanced Manufacturing Technology. 53(9-12). 1189–1197. 8 indexed citations
15.
Laha, Dipak & Sanjiv Sarin. (2008). A heuristic to minimize total flow time in permutation flow shop☆. Omega. 37(3). 734–739. 60 indexed citations
16.
Laha, Dipak & Purnendu Mandal. (2008). Handbook of Computational Intelligence in Manufacturing and Production Management. IGI Global eBooks. 14 indexed citations
17.
Chakraborty, Uday K. & Dipak Laha. (2007). An improved heuristic for permutation flowshop scheduling. International Journal of Information and Communication Technology. 1(1). 89–89. 30 indexed citations
18.
Laha, Dipak & Purnendu Mandal. (2007). Improved heuristically guided genetic algorithm for the flow shop scheduling problem. International Journal of Services and Operations Management. 3(3). 316–316. 8 indexed citations
19.
Laha, Dipak & Uday K. Chakraborty. (2007). An efficient heuristic approach to total flowtime minimization in permutation flowshop scheduling. The International Journal of Advanced Manufacturing Technology. 38(9-10). 1018–1025. 11 indexed citations
20.
Laha, Dipak & Uday K. Chakraborty. (2006). An efficient stochastic hybrid heuristic for flowshop scheduling. Engineering Applications of Artificial Intelligence. 20(6). 851–856. 24 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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