• Algorytmy metaheurystyczne w optymalizacji tras transportu produktów leczniczych

Algorytmy metaheurystyczne w optymalizacji tras transportu produktów leczniczych

  • Autor: Skubisz Oskar
  • Wydawca: Difin
  • ISBN: 978-83-8270-449-5
  • Data wydania: 2025
  • Liczba stron/format: 246/B5
  • Oprawa: miękka

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Dostępność: Duża ilość w magazynie
Książka systematyzuje wiedzę o metodach metaheurystycznych, zaliczanych do algorytmów sztucznej inteligencji, stosowanych w planowaniu tras i prezentuje ich zastosowanie w logistyce farmaceutycznej. Po wprowadzeniu do zagadnień związanych z wyznaczaniem tras dostaw w transporcie drogowym Autor przedstawia implementację zmodyfikowanego algorytmu sztucznej kolonii pszczół. Algorytm ten został opracowany w celu optymalizacji wyznaczania tras z uwzględnieniem okien czasowych, priorytetów i opcjonalnego transportu powrotnego. Publikacja zawiera porównania wyników, wnioski aplikacyjne oraz propozycję modelu wsparcia planowania tras w hurtowniach farmaceutycznych. Łączy perspektywę metodyczną z ujęciem praktycznym i może stanowić podstawę dalszych prac nad integracją algorytmów sztucznej inteligencji z systemami decyzyjnymi.

Podmiot odpowiedzialny za bezpieczeństwo produktu: Difin sp z o.o., ul. F. Kostrzewskiego 1, 00-768 Warszawa (PL), adres e-mail: info@difin.pl, tel (22) 851 45 61

Recenzja

dr hab. Joanna Nowakowska-Grunt, prof. Wojskowej Akademii Technicznej w Warszawie:

Monografia dobrze wpisuje się w aktualny dorobek naukowy z zakresu badań operacyjnych, logistyki i informatyki stosowanej. Autor podejmuje temat dotychczas słabo eksplorowany w kontekście sektora farmaceutycznego, co świadczy o nowatorstwie podjętej problematyki. (…) Należy podkreślić dogłębne przedstawienie mechanizmów działania poszczególnych algorytmów oraz ich potencjalnych zastosowań w planowaniu tras dostaw leków. Autor rzetelnie omawia ograniczenia klasycznych modeli VRP i wskazuje, w jaki sposób algorytmy metaheurystyczne mogą stanowić skuteczne narzędzie do ich przezwyciężania. Szczególnie cenne jest praktyczne podejście autora, który implementuje wybrany algorytm w rzeczywistym środowisku branżowym oraz dokonuje symulacji z wykorzystaniem danych wejściowych pochodzących z sektora farmaceutycznego. Przeprowadzona analiza porównawcza z innymi metodami dostarcza wartościowych informacji dla potencjalnych odbiorców pracy, w tym menedżerów logistyki i projektantów systemów DSS.

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Autor książki

Skubisz Oskar
Doktor, adiunkt w Akademii Wojsk Lądowyc..

Spis treści:

Wstęp

Rozdział 1. Problem planowania tras transportowych w sieciach dystrybucji

Wprowadzenie do problematyki planowania tras
Problem marszrutyzacji

Rozdział 2. Algorytmy metaheurystyczne i ich zastosowanie w logistyce

Przegląd algorytmów metaheurystycznych
Algorytm optymalizacji rojem cząstek
Algorytm optymalizacji kolonii mrówek
Algorytm sztucznej kolonii pszczół
Algorytm świetlika
Algorytm nietoperzy
Algorytm kukułki
Algorytm żerowania bakteryjnego
Algorytm wyszukiwania wron
Algorytm stada słoni
Algorytm motyla monarchy

Rozdział 3. Uwarunkowania planowania tras transportowych w sieci dystrybucji produktów leczniczych


Uwarunkowania dystrybucji produktów leczniczych w Polsce
Wybrane aspekty zarządzania ryzykiem w sektorze farmaceutycznym
Problem marszrutyzacji w branży farmaceutycznej
Informatyzacja procesów logistycznych w dystrybucji farmaceutyków
Uwarunkowania operacyjne w planowaniu tras w sektorze transportu drogowego produktów leczniczych
Sformułowanie problemu decyzyjnego w transporcie produktów leczniczych

Rozdział 4. Analiza zastosowania wybranego algorytmu metaheurystycznego w rozwiązaniu problemu planowania tras transportowych w sieci dystrybucji produktów leczniczych

Implementacja wybranego algorytmu
Modelowanie, symulacje i analiza wyników
Symulacja 1. Sektor 1. Liczba iteracji 1000
Symulacja 2. Sektor 2. Liczba iteracji 1000
Symulacja 3. Sektor 3. Liczba iteracji 1000/5000
Symulacja 4. Sektor 4. Liczba iteracji 1000
Symulacja 5. Sektor 5. Liczba iteracji 2000
Porównanie wyników z innymi metodami

Rozdział 5. Koncepcja wsparcia planowania tras transportowych w sieci dystrybucji produktów leczniczych

Propozycja modelu usprawnień
Rekomendacje praktyczne
Możliwości zastosowania wyników badań

Zakończenie
Bibliografia
Spis tabel
Załączniki

Monografie
1.  Biggs N.L., Lloyd E.K., Wilson R.J., Graph Theory 1736–1936 , Clarendon Press, Oxford 1976.
2.  Christou I.T., Quantitative Methods in Supply Chain Management: Models and Algorithms, Springer, New York 2011.
3.  Cormen T.H., Leiserson C.E., Rivest R.L., Stein C., Introduction to Algorithms, The MIT Press, London 2009.
4.  Dorigo M., Optimization, Learning and Natural Algorithms, Ph.D. Thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1992.
5.  Glover F., Laguna M., Tabu Search, Springer, Boston 1997.
6.  Goyal D.P., Management Information Systems: Managerial Perspectives, Vikas Publishing House, New Delhi 2014.
7.  Grabińska A., Pawełoszek I., Ziora L., Informatyczne wspomaganie procesów logistycznych, Wydawnictwo Politechniki Częstochowskiej, Częstochowa 2020.
8.  Hölldobler B., Wilson E.O., The Ants, Springer, Berlin 1990.
9.  Katz V.J., A History of Mathematics: An Introduction, Addison-Wesley, Boston 2009.
10.  Kawa R., Algorytmy i struktury danych, Uniwersytet Jagielloński, Kraków 2011.
11.  Kellerer H., Pferschy U., Pisinger D., Knapsack Problems, Springer, Berlin 2004.
12.  Kennedy J., Eberhart R.C., Shi Y., Swarm Intelligence, Academic Press, London 2001.
13.  Korte B., Vygen J., Combinatorial Optimization: Theory and Algorithms, Springer, Berlin 2002.
14.  Lambert  D.M.,  Stock  J.R.,  Ellram  L.M.,  Fundamentals  of  Logistics  Management,  McGraw-Hill, Boston 1998.
15.  Martin H., Warehousing and Transportation Logistics: Systems, Planning, Application and Cost Effectiveness, Kogan Page, London 2018.
16.  Mehling M., Cabeza L.F., Heat and cold storage with PCM: An up to date introduction into basics and applications, Springer, 2008.
17.  Meyer M.D., Transportation Planning Handbook, Wiley, New York 2016.
18.  Nayyar A., Dac-Nhuong L., Nhu G.N., Advances in Swarm Intelligence for Optimizing Problems in Computer Science, CRC Press, New York 2019.
19.  Osman I.H., Kelly J.P., Meta-Heuristics: An Overview, Kluwer Academic Publishers,  Boston–London–Dordrecht 1996.
20.  Pearl J., Heuristics: Intelligent Search Strategies for Computer Problem Solving, Addison
21.  Polya G., How to Solve It, Princeton University Press, Princeton 1945.
22.  Price K., Storn R., Lampinen J., Differential Evolution: A Practical Approach to Global Optimization, Springer, Berlin 2005.
23.  Reed C., Cold Chains Are Hot! Mastering The Challenges Of Temperature-Sensitive Distribution In Supply Chains, Chain Link Research 2002–2005.
24.  Ross D.F., Distribution: Planning and Control, Chapman & Hall, London 1996.
25.  Schrijver A., Combinatorial Optimization: Polyhedra and Efficiency , Springer, Berlin 2003.
26.  Slowik A., Swarm Intelligence Algorithms Modifications and Applications, Taylor & Francis Group, Boca Raton 2020.
27.  Toth P., Vigo D., The Vehicle Routing Problem , SIAM, Philadelphia 2001.
28.  Yang X.-S., Engineering Optimization: An Introduction with Metaheuristic Applications, Wiley, Chichester 2010.
29.  Yang X.-S., Nature-Inspired Metaheuristic Algorithms, Luniver Press, Frome 2010.
30.  Yang X.-S., Slowik A., Bat Algorithm, CRC Press, 2020.
Prace zbiorowe
1.  Abraham A., Hassanien A.E., Siarry P., Engelbrecht A. (eds), Global Optimization, [w:] Abraham A., Hassanien A.E., Siarry P., Engelbrecht A. (eds), Foundations of Computational Intelligence Volume 3: Global Optimization, Springer, Berlin 2009.
2.  Adhi A., Santosa B., Siswanto N., A meta-heuristic method for solving scheduling problem: crow search algorithm, „IOP Conference Series: Materials Science and Engineering” 2018, vol. 337.
3.  Al-Hadi I.A.A., Mohd Hashim S.Z., Shamsuddin S.M., Bacterial Foraging Optimization Algorithm for Neural Network Learning Enhancement, 11th International Conference on Hybrid Intelligent Systems (HIS), Melacca, Malaysia, 2011.
4.  Alirdha A.H., Salman A.M., Al-Jilawi A.S., The Applications of NP-Hardness Optimizations Problem, „Journal of Physics: Conference Series” 2021, vol. 1818.
5.  Anwar ul Hassan C., Khan M.S., Ghafar A., Aimal S., Asif S., Javaid N., Energy optimization in smart grid using grey wolf optimization algorithm and bacterial foraging algorithm, [w:] Advances in Intelligent Networking and Collaborative Systems: The 9th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2017), Springer International Publishing, 2018.
6.  Baldacci R., Battarra M., Vigo D., Routing a Heterogeneous Fleet of Vehicles, [w:] Golden B.L., Raghavan S., Wasil E.A. (eds), The Vehicle Routing Problem: Latest Advances and New Challenges, Springer, Boston 2008.
7.  Cao W., Tan Y., Huang M., Luo Y., Adaptive Bacterial Foraging Optimization Based on Roulette Strategy, [w:] Advances in Swarm Intelligence, 11th International Conference, ICSI 2020, Belgrade, Serbia, 14–20 July 2020, Proceedings 11, Springer.
8.  Cruz-Mejía O., Márquez A., Monsreal-Barrera M.M., Product Delivery and Simulation for Industry 4.0, [w:] Gunal M.M. (ed.), Simulation for Industry 4.0: Past, Present, and Future, Springer, Cham 2019.
9.  Das S., Panigrahi B.K., Pattnaik S.S., Nature-Inspired Algorithms for Multi-objective Optimization, [w:] Olivas E.S., Martín-Guerrero J.D., Martínez M., Magdalena R., Serrano A.J. (eds), Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods and Techniques, vol. 1, IGI Global, Hershey 2009.
10.  Desaulniers G., Desrosiers J., Erdmann A., Solomon M.M., Soumis F., VRP with Pickup and Delivery, [w:] Toth P., Vigo D. (eds), The Vehicle Routing Problem , SIAM, Philadelphia 2002.
11.  Dianati M., An Introduction to Genetic Algorithms and Evolution Strategies, [w:] Parallel Problem Solving from Nature IV, Springer, Berlin 2002.
12.  Feng M., Guomin L., Wenrong W., Heterogeneous network resource allocation optimization based on improved bat algorithm, 2018 International Conference on Sensor Networks and Signal Processing (SNSP), 2018.
13.  Floudas C.A., Pardalos P.M. (eds), Metaheuristics, [w:] Encyclopedia of Optimization, Springer, Boston 2008.
14.  Galabov M., Fractal Image Compression, [w:] Proceedings of the 4th International Conference on Computer Systems and Technologies, ACM, Sofia 2003.
15.  Ge L., Ji E., An improved artificial bee colony algorithm and its application in machine learning, „Journal of Physics: Conference Series” 2020, vol. 1650, no. 3.
16.  Golden B.L., Wasil E.A., Kelly J.P., Chao I.-M., The Impact of Metaheuristics on Solving the Vehicle Routing Problem: Algorithms, Problem Sets, and Computational Results, [w:] Crainic T.G., Laporte G. (eds), Fleet Management and Logistics, Kluwer, Boston 1998.
17.  Goldfarb D., Todd M.J., Linear Programming, [w:] Nemhauser G.L., Rinnooy Kan A.H.G., Todd M.J. (eds), Handbooks in Operations Research and Management Science, vol. 1, North-Holland, Amsterdam 1989.
18.  Hasle G., Kloster O., Industrial Vehicle Routing, [w:] Hasle G., Lie K.-A., Quak E. (eds), Geometric Modelling, Numerical Simulation, and Optimization: Applied Mathematics at SINTEF, Springer, Berlin 2007.
19.  Heyns W., Van Jaarsveld S., Transportation Modelling in Practice: Connecting Basic Theory to Practice, [w:] Mulley C., Shrestha K. (eds), Transportation, Land Use and Integration: Applications in Developing Countries, vol. 100, WIT Press, Southampton 2017.
20.  Hoffman K.L., Padberg M., Rinaldi G.,  Traveling Salesman Problem, [w:] Gass S.I.,  Fu M.C. (eds), Encyclopedia of Operations Research and Management Science, Springer, Boston 2013.
21.  Hossam A., Bouzidi A., Riffi M.E., Elephants Herding Optimization for Solving the Travelling Salesman Problem, [w:] Advanced Intelligent Systems for Sustainable Development (AI2SD’2018), Vol. 2: Advanced Intelligent Systems Applied to Energy, Springer International Publishing, 2019.
22.  Janicki J., Nowomiejski B., Rasińska J., System dystrybucji na rynku farmaceutycznym, „Pielęgniarstwo Polskie” 2016, nr 2(60).
23.  Jauhar S.K., Pant M., Genetic Algorithms, a Nature-Inspired Tool: Review of Applications in Supply Chain Management, [w:] Proceedings of Fourth International Conference on Soft Computing for Problem Solving, Springer India, 2015.
24.  Jing Y.W., Ren T., Zhou Y.C., Neural Network Training Using PSO Algorithm in ATM Traffic Control, [w:] Huang D.S., Li K., Irwin G. (eds), Intelligent Control and Automation, LNCIS, vol. 344, Springer, Berlin 2006.
25.  Junjie P., Dingwei W., An Ant Colony Optimization Algorithm for Multiple Traveling Salesman Problem, [w:] Proceedings of the First International Conference on Innovative Computing, Information and Control – Volume I, Beijing, China, 30 August–1 September 2006.
26.  Kassem S.S., Korayem L., Khorsid M., A hybrid bat algorithm to solve the capacitated vehicle routing problem, [w:] Novel Intelligent and Leading Emerging Sciences Conference, Nile University, Giza 2019.
27.  Kennedy J., Eberhart R.C., Particle Swarm Optimization, [w:] Proceedings of ICNN’95 – International Conference on Neural Networks, vol. 4, IEEE, Perth 1995.
28.  Kennedy J., Eberhart R.C., Particle Swarm Optimization, [w:] Proceedings of IEEE International Conference on Neural Networks, IEEE Press, Piscataway, Los Alamitos 1995.
29.  Livingstone J.D., Artificial Neural Networks, [w:] Methods and Applications, Humana Press, Totowa 2009.
30.  Niu B., Wang H., Tan L.-J., Wang J.-W., Vehicle Routing Problem with Time Windows Based on Adaptive Bacterial Foraging Optimization, [w:] Proceedings of the 8th International Conference on Intelligent Computing Theories and Applications, Lecture Notes in Computer Science, 2012.
31.  Pant M., Thangaraj R., Abraham A., A New PSO Algorithm with Crossover Operator for Global Optimization Problems, [w:] Corchado E., Corchado J.M., Abraham A. (eds),  Innovations in Hybrid Intelligent Systems, Springer, Berlin 2007.
32.  Prinz A., What is the Natural Abstraction Level of an Algorithm?, [w:] Raschke A., Riccobene E., Schewe K.-D. (eds), Logic, Computation and Rigorous Methods, Springer International Publishing 2021.
33.  Rodawski B.,  Hanczar  P., Doskonalenie łańcucha dostaw hurtowni farmaceutycznej,  „Logistyka” 2010, nr 2.
34.  Roskoss A., Temperature-Controlled Packaging Systems: Active or Passive?, „Innovations in Pharmaceutical Technology” 2011, iss. 37.
35.  Rozenberg G., Salomaa A., Lindenmayer Systems: Impacts on Theoretical Computer Science, Computer Graphics, and Developmental Biology, Springer-Verlag, Berlin 1992.
36.  Sari R., Widianti R., Optimizing Employee Scheduling System with Firefly Algorithm (Case Study: MJ Store), [w:] 2020 3rd International Conference on Computer and Informatics Engineering (IC2IE), Yogyakarta, Indonesia, 2020.
37.  Sarwar M.A., Amin B., Ayub N., Faraz S.H., Khan S.U.R., Javaid N., Scheduling of Apces in Home Energy Management System Using Elephant Herding Optimization and Enhanced Differential Evolution , [w:] Advances in Intelligent Networking and Collaborative Systems, Proceedings of the 9th International Conference on Intelligent Networking and ollaborative Systems (INCoS 2017), Ryerson Univ, Toronto, Canada, 24–26 August 2017, Springer, Berlin 2018.
38.  Sbai I., Krichen S., Vehicle Routing Problems with Loading Constraints: An Overview of Variants and Solution Methods, [w:] Vasant S. (ed.), Optimization and Machine Learning: Optimization for Machine Learning and Machine Learning for Optimization, Wiley, Hoboken 2022.
39.  Schrijver A., On the History of Combinatorial Optimization (Till 1960), [w:] Aardal K., Nemhauser G.L., Weismantel R. (eds), Handbooks in Operations Research and Management Science, vol. 12, Elsevier, Amsterdam 2005.
40.  Shi W., Weise T., An Initialized ACO for the VRPTW, [w:] Yin H., Tang K., Gao Y., Klawonn F., Lee M., Weise T., Li B., Yao X. (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2013, LNCS, vol. 8206, Springer, Berlin 2013.
41.  Strumberger I., Tuba E., Bacanin N., Beko M., Tuba M., Modified Monarch Butterfly Optimization Algorithm for RFID Network Planning, [w:] 6th International Conference on Multimedia Computing and Systems (ICMCS), Rabat 2018.Bibliografia 231
42.  Strumberger I., Tuba E., Bacanin N., Tuba M., Designing Convolutional Neural Network Architecture by the Firefly Algorithm , [w:] 2019 International Young Engineers Forum (YEF-ECE), 2019.
43.  Taha A., Hachimi M., Moudden A., A discrete Bat Algorithm for the vehicle routing problem with time windows, [w:] 2017 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA), 2017.
44.  Tharwat A., Gaber T.A., Hassanien E.A., Elnaghi B.E., Particle Swarm Optimization: A Tutorial, [w:] Handbook of Research on Machine Learning Innovations and Trends, IGI Global, Manchester 2017.
45.  Trabelsi K., Sevaux M., Coussy P., Rossi A., Sörensen K., Metaheuristics, [w:] Advanced Metaheuristics for High-Level Synthesis, Springer, Berlin 2010.
46.  Waldock A., Corne D., Multiple objective optimisation applied to route planning, [w:] Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO 2011), ACM, Dublin 2011.
47.  Wang G., Deb S., dos S. Coelho L., Elephant Herding Optimization, [w:] Proceedings of the 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI 2015), IEEE, Bali, 2015.
48.  Wigand R.T., Mande D.M., Wood J.D., Information management and tracking of drugs in supply chains within the pharmaceutical industry, 2011 Eighth International Conference on Information Technology: New Generations, IEEE, 2011.
49.  Yang X.-S., Deb S., Cuckoo search via Lévy flights, [w:] Proceedings of the World Congress on Nature & Biologically Inspired Computing (NABIC ‘09), 2009.
50.  Yang X.-S., Firefly algorithms for multimodal optimization , [w:] Stochastic Algorithms: Foundations and Applications (SAGA 2009), Lecture Notes in Computer Science, vol 5792, Springer.
51.  Zak, J. Decision Support Systems in Transportation, [w:] Jain L.C., Lim C.P. (eds), Handbook on Decision Making: Vol. 1: Techniques and Applications, Springer, Berlin–Heidelberg 2010.
52.  Zarzycki H., Application of the Crow Search Algorithm for Dynamic Route Optimization, [w:] Intelligent and Fuzzy Systems, Springer Nature Switzerland, 2023.
Artykuły z czasopism naukowych
1.  Abdallah G.Y., Algamal Z.Y., An hybrid particle swarm optimization with crow search algorithm for feature selection, „Machine Learning with Applications” 2022.
2.  Akay B., Karaboga D., Artificial Bee Colony Algorithm for Large-Scale Problems and Engineering Design Optimization, „Journal of Intelligent Manufacturing” 2012, vol. 23, no. 4.
3.  Akwasi A.M., Wei X., Design of control system for solar power generation based on an improved bat algorithm for an island operation, „Energy Harvesting and Systems” 2023, vol. 10, no. 2.
4.  Aliahmadi A., Nozari H., Ghahremani-Nahr J., Big Data IoT-based agile-lean logistic in pharmaceutical industries, „International Journal of Innovation in Management, Economics and Social Sciences” 2022, vol. 2, no. 3.
5.  Alirezaeizanjani Z., Grobmann R., Pfeifer V., Hintsche M., Beta C., Chemotaxis strategies of bacteria with multiple run modes, „Science Advances” 2020, vol. 6, no. 22.
6.  Almeida C.M., Batty M., Monteiro A.M.V., Câmara G., Soares-Filho B.S., Cerqueira G.C., Pennachin C.L., Stochastic Cellular Automata Modeling of Urban Land Use Dynamics: Empirical Development and Estimation, „Computers, Environment and Urban Systems” 2003, vol. 27.
7.  Altabeeb A.M., Mohsen A.M., Ghallab A., An improved hybrid firefly algorithm for capacitated vehicle routing problem, „Applied Soft Computing” 2019, vol. 84.
8.  Alvarado-Iniesta A., Garcia-Alcaraz J.L., Rodriguez-Borbon M.I., Maldonado A., Optimization of the material flow in a manufacturing plant by use of artificial bee colony algorithm, „Expert Systems with Applications” 2013, vol. 40, no. 12.
9.  Alvina G.H., Kek R., Long C., Qiang M., Distance-Constrained Capacitated Vehicle Routing Problems with Flexible Assignment of Start and End Depots, „Mathematical and Computer Modelling” 2008, vol. 47, no. 1–2.
10.  Alzaqebah M., Abdullah S., Jawarneh S., Modified artificial bee colony for the vehicle routing problems with time windows, „Springer Plus” 2016, vol. 5, no. 1.
11.  Amon D.A., A modified bat algorithm for power loss reduction in electrical distribution system, „TELKOMNIKA Indonesian Journal of Electrical Engineering” 2015, vol. 14, no. 1.
12.  Amudha A., Saravanan K., Security and Scalability Measurement of Distributed Databases of Cloud Computing, „International Journal of Applied Engineering Research” 2019, vol. 14, no. 2.
13.  Anand N., Grover N., Measuring retail supply chain performance: Theoretical model using key performance indicators (KPIs), „Benchmarking: An International Journal” 2015, vol. 22, no. 1.
14.  Apostol T.M., Mnatsakanian M.A., A Fresh Look at the Method of Archimedes, „The American Mathematical Monthly” 2004, vol. 111, no. 6.
15.  Aprile D., Garavelli A.C., Giannoccaro I., Operations planning and flexibility in a supply chain, „Production Planning & Control” 2005, vol. 16, no. 1.
16.  Askarzadeh A., A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm, „Computers & Structures” 2016, vol. 169.
17.  Atikno W., Setiawan I., Taufik D.A., Key Performance Indicators Implementation: Literature Review and Development for Performance Measurement, „IJIEM – Indonesian Journal of Industrial Engineering & Management” 2021, vol. 2, no. 3.
18.  Ayan K., Kılıç U., Artificial bee colony algorithm solution for optimal reactive power flow , „Applied Soft Computing” 2012, vol. 12, no. 5.
19.  Bacanin N., Bezdan T., Tuba E., Strumberger I., Tuba M., Monarch butterfly optimization based convolutional neural network design, „Mathematics” 2020, vol. 8, no. 6.
20.  Bai Y., Cao L., Chen B., Chen Y., Yue Y., A Novel Topology Optimization Protocol Based on an Improved Crow Search Algorithm for the Perception Layer of the Internet of Things, „Biomimetics” 2023, vol. 8, no 2.
21.  Balachennaiah P., Suryakalavathi M., Nagendra P., Firefly algorithm based solution to minimize the real power loss in a power system, „Ain Shams Engineering Journal” 2018, vol. 9, no. 1.
22.  Bansal J.C., Sharma H., Jadon S.S., Artificial Bee Colony Algorithm: A Survey , „International Journal of Advanced Intelligence Paradigms” 2013, vol. 5, no. 1–2.
23.  Baykasoğlu A., Subulan K., Taşan A.S., Dudaklı N., A review of fleet planning problems in single and multimodal transportation systems, „Transportmetrica A: Transport Science” 2019, vol 15, no. 2.
24.  Bento P.M.R., Pombo J., Calado M.D.R.A., Mariano S., Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting, „Neurocomputing” 2019, vol. 358.
25.  Berrazouane S., Mohammedi K., Parameter optimization via cuckoo optimization algorithm of fuzzy controller for energy management of a hybrid power system, „Energy Conversion and Management” 2014, vol. 78.
26.  Brintha N.C., Benedict S., Winowlin Jappes J.T., Resource allocation in cloud manufacturing using bat algorithm, „International Journal of Manufacturing Technology and Management” 2020, vol. 34, no. 3.
27.  Burnwal S., Deb S., Scheduling optimization of flexible manufacturing system using cuckoo search-based approach, „The International Journal of Advanced Manufacturing Technology” 2013, vol. 64, no. 5–8
28.  Cai J., Liu X., Xiao Z., Liu J., Improving supply chain performance management: a systematic approach to analyzing iterative KPI accomplishment, „Decision Support Systems” 2009, vol. 46, no. 2.
29.  Cai X., Wang H., Cui Z., Cai J., Xue Y., Wang L., Bat algorithm with triangle-flipping strategy for numerical optimization, „International Journal of Machine Learning and Cybernetics” 2018, vol. 9, no. 2.
30.  Chakri A., Khelif R., Benouaret M., Yang X.-S., New directional bat algorithm for continuous optimization problems, „Expert Systems with Applications” 2016, vol. 69.
31.  Charan A.S., Manasa N.K., Sarma N.V.S., Out Performance of Cuckoo Search Algorithm Among Nature Inspired Algorithms in Planar Antenna Arrays, „International Journal of Artificial Intelligence & Applications” 2014, vol. 5, no. 4.
32.  Chatterjee A., Fakhfakh M., Siarry P., Design of second-generation current conveyors employing bacterial foraging optimization, „Microelectronics Journal” 2010, vol. 41, no. 10.
33.  Chen S., Chen R., Gao J., Monarch butterfly optimization for the dynamic vehicle routing problem, „Algorithms” 2017, vol. 10, no. 3.
34.  Choi T.M., Chiu C.H., Chan H.K., Risk management of logistics systems, „Transportation Research Part E: Logistics and Transportation Review” 2016, no. 90.
35.  Clarke G., Wright J.W., Scheduling of Vehicles from a Central Depot to a Number of Delivery Points, „Operations Research” 1964, vol. 12, no. 4.
36.  Claycamp C.H., Perspective on Quality Risk Management of Pharmaceutical Quality, „Drug Information Journal” 2007, vol. 41, no. 3.
37.  Cuevas E., Reyna-Orta A., A Cuckoo Search Algorithm for Multimodal Optimization,  „The Scientific World Journal” 2014, vol. 2014.
38.  Cuevas E., Sención F., Zaldivar D., Pérez-Cisneros M., Sossa H., A multi-threshold segmentation approach based on artificial bee colony optimization , „Applied Intelligence” 2012, vol. 37.
39.  Dantzig G.B., Ramser J.H., The Truck Dispatching Problem , „Management Science” 1959, vol. 6, no. 1.
40.  de Moraes C.H.V., Vilas Boas J.L., Lambert-Torres G., Andrade G.C., Costa C.I.A., Intelligent Power Distribution Restoration Based on a Multi-Objective Bacterial Foraging Optimization Algorithm, „Energies” 2022, vol. 15, no. 4.
41.  Desale S., Rasool A., Andhale S., Rane P., Heuristic and meta-heuristic algorithms and their relevance to the real world: a survey, „International Journal of Computer Engineering in Research Trends” 2015, vol. 2, no. 5.
42.  Dey B., Bhattacharyya B., Srivastava A., Shivam K., Solving energy management of renewable integrated microgrid systems using crow search algorithm, „Soft Computing” 2020, vol. 24, no. 3.
43.  Dingley J., Thatcher N., Williams D., A study of temperature control in different designs of emergency drug transport bags, „Anaesthesia” 2019, vol. 74, no. 7.
44.  Dorigo M., Birattari M., Stützle T., Ant Colony Optimization, „IEEE Computational Intelligence Magazine” 2006, vol. 1, no. 4.
45.  Dumas Y., Desrosiers J., Soumis F., The Pickup and Delivery Problem with Time Windows, „European Journal of Operational Research” 1991, no. 1.
46.  Eksioglu B., Vural A.V., Reisman A., The Vehicle Routing Problem: A Taxonomic Review, „Computers & Industrial Engineering” 2009, vol. 57, no. 4.
47.  El-Sherbeny N.A., Vehicle Routing with Time Windows: An Overview of Exact, Heuristic and Metaheuristic Methods, „Journal of King Saud University – Science” 2010, vol. 22, no. 3.
48.  El-Shorbagy M.A., Hassanien A.E., Particle Swarm Optimization from Theory to Applications, „International Journal of Rough Sets and Data Analysis” 2018, vol. 5, no. 2.
49.  Faris H., Aljarah I., Mirjalili S., Improved monarch butterfly optimization for unconstrained global search and neural network training, „Applied Intelligence” 2018, vol. 48.
50.  Farmer J.D., Packard N.H., Perelson A.S., The Immune System, Adaptation, and Machine Learning, „Physica D: Nonlinear Phenomena” 1986, vol. 22, iss. 1–3.
51.  Feng Y., Deb S., Wang G.-G., Alavi A.H., Monarch butterfly optimization: A comprehensive review, „Expert Systems with Applications” 2021, vol. 168.
52.  Fister I., Fister I. Jr., Yang X.-S., Brest J., A comprehensive review of firefly algorithms, „Swarm and Evolutionary Computation” 2013, vol. 13.
53.  Fleischmann B., Gnutzmann S., Sandvoß E., Dynamic vehicle routing based on online traffic information, „Transportation Science” 2004, vol. 38, no. 4.
54.  Folgarait P.J., Ant biodiversity and its relationship to ecosystem functioning: a review, „Biodiversity and Conservation” 1998, vol. 7, no. 9.
55.  Franks N.R., Wilby A., Silverman B.W., Tofts C., Self-organizing nest construction in ants: sophisticated building by blind bulldozing, „Animal Behaviour” 1992, vol. 44, part 2.
56.  Gandomi A.H., Yang X.-S., Alavi A.H., Talatahari S., Bat algorithm for constrained optimization tasks, „Neural Computing and Applications” 2013, vol. 22, no. 6.
57.  Gaur A.K., Search Techniques to Contain Combinatorial Explosion in Artificial Intelligence , „International Journal of Engineering Research & Technology” 2012, vol. 1, no. 7.
58.  Ghanem W., Jantan A., An enhanced bat algorithm with mutation operator for numerical optimization problems, „Neural Computing and Applications” 2019, vol. 31.
59.  Goli A., Aazami A., Jabbarzadeh A., Accelerated cuckoo optimization algorithm for capacitated vehicle routing problem in competitive conditions, „International Journal of Artificial Intelligence” 2018, vol. 16, no. 1.
60.  Gonzalez-Pascual E., Nosedal-Sanchez J., Garcia-Gutierrez J., Performance evaluation of a road freight transportation company through SCOR metrics, „Case Studies on Transport Policy” 2021, vol. 9, iss. 4.
61.  Goodarzian F., Hosseini-Nasab H., Munuzuri J., Fakhrzad M.B., A multi-objective pharmaceutical supply chain network based on a robust fuzzy model: A comparison of meta-heuristics, „Applied Soft Computing” 2020, no. 92.Bibliografia 235
62.  Gordon D.M., The Ecology of Collective Behavior in Ants, „Annual Review of Entomology” 2018, vol. 63.
63.  Gözacan N., Lafci C., Evaluation of Key Performance Indicators of Logistics Firms, „Logistics & Sustainable Transport” 2020, vol. 11, no. 1.
64.  Groer C., Golden B., Wasil E., A library of local search heuristics for the vehicle routing problem, „Mathematical Programming Computation” 2010, vol. 2, no. 2.
65.  Gu Z., Zhu Y., Wang Y., Du X., Guizani M., Tian Z., Applying artificial bee colony algorithm to the multidepot vehicle routing problem, „Software: Practice and Experience” 2022.
66.  Gunasekaran A., Patel C., Tirtiroglu E., Performance measures and metrics in a supply chain environment, „International Journal of Operations & Production Management” 2001, vol. 21, no. 1–2.
67.  Gupta D., Sundaram S., Rodrigues J.J., Khanna A., An Improved Fault Detection Crow Search Algorithm for Wireless Sensor Network, „International Journal of Communication Systems” 2023, vol. 36, no. 12.
68.  Hall R., Partyka J., Vehicle Routing Software Survey: Higher Expectations Drive Transformation, „OR/MS Today” 2016, vol. 45, no. 1.
69.  Han T., Kim S., Yoon H.J., Park I.G., Evolutionary history of species of the firefly subgenus Hotaria (Coleoptera, Lampyridae, Luciolinae, Luciola) inferred from DNA barcoding data, „Contributions to Zoology” 2020, vol. 89, no. 2.
70.  Handy S., Regional Transportation Planning in the US: An Examination of Changes in Technical Aspects of the Planning Process in Response to Changing Goals, „Transport Policy” 2008, vol. 15, no. 2.
71.  Hernandez J.R., García M.G., Hernandez G.G., Enterprise logistics, indicators and physical distribution manager, „Research in Logistics & Production” 2013, vol. 3, no. 1.
72.  Hussien A.G., Amin M., Wang M., Liang G., Alsanad A., Gumaei A., Crow search algorithm: theory, recent advances, and applications, „IEEE Access” 2020, vol. 8.
73.  Išoraite M., The analysis of strategic planning in transport, „Transport” 2006, vol. 21, no. 1.
74.  Jaberidoost M., Nikfar S., Abdollahiasl A., Dinarvand R., Pharmaceutical supply chain risks: a systematic review, „DARU Journal of Pharmaceutical Sciences” 2013, vol. 21.
75.  Jalaee S.A., Shakibaei A., Akbarifard H., Horry H.R., Ghasemi N.A., Nazari R.F., Amani Z.N., Derakhshani R., A novel hybrid method based on Cuckoo optimization algorithm and artificial neural network to forecast world’s carbon dioxide emission , „MethodsX” 2021, vol. 8.
76.  Janicki B., Nowomiejski J., Rasińska R., System dystrybucji na rynku farmaceutycznym, „Pielęgniarstwo Polskie” 2016, nr 2(60).
77.  Joshi A.S., Kulkarni O., Kakandikar G., Nandedkar V.M., Cuckoo Search Optimization – A Review, „Materials Today: Proceedings” 2017, vol. 4, no. 8.
78.  Kallehauge B., Formulations and Exact Algorithms for the Vehicle Routing Problem with Time Windows, „Computers & Operations Research” 2008, no. 7.
79.  Katiyar S., Khan R., Kumar S., Artificial bee colony algorithm for fresh food distribution without quality loss by delivery route optimization, „Journal of Food Quality” 2021.
80.  Kiran M.S., Babalik A., Improved Artificial Bee Colony Algorithm for Continuous Optimization Problems, „Journal of Computer and Communications” 2014, vol. 2, no. 4.
81.  Kirkpatrick S., Gelatt C.D. Jr., Vecchi M.P., Optimization by Simulated Annealing, „Science” 1983, vol. 220, iss. 4598.236 Bibliografia
82.  Koc C., Laporte G., Vehicle Routing with Backhauls: Review and Research Perspectives, „Computers & Operations Research” 2018, no. 91.
83.  Koza J.R., Genetic programming as a means for programming computers by natural selection, „Statistics and Computing” 1994, vol. 4.
84.  Li J., Lei H., Alavi A.H., Wang G.-G., Elephant Herding Optimization: Variants, Hybrids, and Applications, „Mathematics” 2020, vol. 8, no. 9.
85.  Li J., Wang D., Zhang J., Heterogeneous Fixed Fleet Vehicle Routing Problem Based on Fuel and Carbon Emissions, „Journal of Cleaner Production” 2018, vol. 201, no. 8.
86.  Li X.-Y., Aneja Y.P., Baki F., An ant colony optimization metaheuristic for single-path multicommodity network flow problems, „Journal of the Operational Research Society” 2010, vol. 61, no. 9.
87.  Liang X., Zhu W., Lv Z., Zou Q., Molecular Computing and Bioinformatics, „Molecules” 2019, vol. 24, no. 13.
88.  Lin C.J., Li T.H.S., Kuo P.H., Wang Y.H., Integrated particle swarm optimization algorithm based obstacle avoidance control design for home service robot, „Computers & Electrical Engineering” 2016, vol. 56.
89.  Lin S.-W., Lee Z.-J., Ying K.-C., Lee C.-Y., Applying Hybrid Meta-Heuristics for Capacitated Vehicle Routing Problem, „Expert Systems with Applications” 2009, vol. 36, no. 2.
90.  Lloyd J.E., Bioluminescent communication in insects, „Annual Review of Entomology” 1971, vol. 16, no. 1.
91.  Lu Q., Dessouky M.M., A New Insertion-Based Construction Heuristic for Solving the Pickup and Delivery Problem with Time Windows, „European Journal of Operational Research” 2006, vol. 175, no. 2.
92.  Ma Y., Zhao Y., Wu L., He Y., Yang X.S., Navigability analysis of magnetic map with projecting pursuit-based selection method by using firefly algorithm , „Neurocomputing” 2015, vol. 159.
93.  MacGregor J.N., Chu Y., Human performance on the traveling salesman and related problems: a review, „The Journal of Problem Solving” 2011, vol. 3, no. 2.
94.  Magalhaes J.M.D., de Sousa J.P., Dynamic VRP in pharmaceutical distribution – a case study, „Central European Journal of Operations Research” 2006, vol. 14.
95.  Mak K.L., Peng P., Wang X.X., Lau T.L., An Ant Colony Optimization Algorithm for Scheduling Virtual Cellular Manufacturing Systems, „International Journal of Computer Integrated Manufacturing” 2007, vol. 20, no. 6.
96.  Mandal S., Elephant swarm water search algorithm for global optimization, „Sadhana” 2018, vol. 43.
97.  Mann Z.A., The Top Eight Misconceptions about NP-Hardness, „Computer” 2017, vol. 50, no. 5.
98.  Mareli M., Twala B., An adaptive Cuckoo search algorithm for optimisation, „Applied Computing and Informatics” 2018, vol. 14, no. 2.
99.  Martinovic G., Aleksi I., Baumgartner A., Single-Commodity Vehicle Routing Problem with Pickup and Delivery Service, „Mathematical Problems in Engineering” 2008.
100.  Matthopoulos P.P., Sofianopoulou S., A firefly algorithm for the heterogeneous fixed fleet vehicle routing problem, „International Journal of Industrial and Systems Engineering” 2019, vol. 33, no. 2.
101.  Mazzeo S., Loiseau I., An Ant Colony Algorithm for the Capacitated Vehicle Routing, „Electronic Notes in Discrete Mathematics” 2004, vol. 18.Bibliografia 237
102.  Melnic I., Graur A., Key Performance Indicators in the Enterprise’s Logistics Activity, „Economica” 2022, no. 1(119).
103.  Miao C., Chen G., Yan C., Wu Y., Path Planning Optimization of Indoor Mobile Robot Based on Adaptive Ant Colony Algorithm, „Computers & Industrial Engineering” 2021, vol. 156, no. 8.
104.  MirHassani S.A., Abolghasemi N., A Particle Swarm Optimization Algorithm for Open Vehicle Routing Problem, „Expert Systems with Applications” 2011, vol. 38, no. 9.
105.  Mishra S., Tripathy M., Nanda J., Multi-machine power system stabilizer design by rule based bacteria foraging, „Electric Power Systems Research” 2007, vol. 77, no. 12.
106.  Morales-Castañeda B., Zaldivar D., Cuevas E., Rodríguez F.F., Rodríguez A., A better balance in metaheuristic algorithms: Does it exist?, „Swarm and Evolutionary Computation” 2020, vol. 54.
107.  Muthusamy H., Ravindran S., Yaacob S., Polat K., An improved elephant herding optimization using sine–cosine mechanism and opposition based learning for global optimization problems, „Expert Systems with Applications” 2021, vol. 172.
108.  Nash J.C., The (Dantzig) Simplex Method for Linear Programming , „Computing in Science & Engineering” 2000, vol. 2, no. 1.
109.  Okdem S., Karaboga D., Routing in Wireless Sensor Networks Using an Ant Colony Optimization (ACO) Router Chip, „Sensors” 2009, vol. 9, no. 2.
110.  Othman W.A.F.W., Wahab A.A.A., Alhady S.S.N., Wong H.N., Solving Vehicle Routing Problem using Ant Colony Optimisation (ACO) Algorithm, „International Journal of Research and Engineering” 2018, vol. 5, no. 9.
111.  Öztürk S., Ahmad R., Akhtar N., Variants of Artificial Bee Colony Algorithm and Its Ap plications in Medical Image Processing, „Applied Soft Computing” 2020, vol. 97, part A.
112.  Pakdel H., Fotohi R.A., Firefly algorithm for power management in wireless sensor networks (WSNs), „The Journal of Supercomputing” 2021, vol. 77.
113.  Palshikar G.K., Simulated annealing: A heuristic optimization algorithm, „Software Tools for the Professional Programmer” 2001, vol. 26, no. 9.
114.  Passino K.M., Biomimicry of bacterial foraging for distributed optimization and control, „IEEE Control Systems Magazine” 2002, vol. 22, no. 3.
115.  Pavlyukevich  I.,  Lévy  flights,  non-local  search  and  simulated  annealing ,  „Journal  of  Computational Physics” 2007, vol. 226, no. 2.
116.  Pisinger D., Ropke S., A general heuristic for vehicle routing problems, „Computers &  Operations Research” 2007, vol. 34, no. 8.
117.  Reddy N.S., Mareddy P.L., Ramamurthy D.V., Rao K.P., Simultaneous Scheduling of Machines and Tools in a Multi-Machine FMS with Alternate Machines Using Crow Search Algorithm, „Journal of Advanced Manufacturing Systems” 2022, vol. 21, no. 04.
118.  Ren T., Ren J., Matellini D.B., Ouyang W., A Comprehensive Review of Modern Cold Chain Shipping Solutions, „Sustainability” 2022, vol. 14, no. 22.
119.  Reyes-Sierra M., Coello C.A.C., Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art, „International Journal of Computational Intelligence Research” 2006, vol. 2, no. 3.
120.  Rodawski B., Hanczar P., Doskonalenie łańcucha dostaw hurtowni farmaceutycznej, „Logistyka” 2010, nr 2.
121.  Rushton A., Saw R., A methodology for logistics strategy planning, „The International Journal of Logistics Management” 1992, vol. 3, no. 1.238 Bibliografia
122.  Savelsbergh M.W.P., Local Search in Routing Problems with Time Windows, „Annals of Operations Research” 1985, vol. 4.
123.  Schiezaro M., Pedrini H., Data feature selection based on Artificial Bee Colony algorithm , „EURASIP Journal on Image and Video Processing” 2013.
124.  Schmidt G., Wilhelm W.E., Strategic, Tactical and Operational Decisions in Multinational Logistics Networks: A Review and Discussion of Modelling Issues, „International Journal of Production Research” 2000, vol. 38, no. 7.
125.  Selvakumar B., Muneeswaran K., Firefly algorithm based feature selection for network intrusion detection, „Computers & Security” 2019, vol. 81, no. 1.
126.  Shadkam E., Cuckoo optimization algorithm in reverse logistics: A network design for COVID-19 waste management, „Waste Management & Research” 2022, vol. 40, no. 4.
127.  Shah N., Pharmaceutical supply chains: Key issues and strategies for optimisation, „Computers & Chemical Engineering” 2004, vol. 28, no. 6–7.
128.  Shim J.-P., Warkentin M., Power J.-D., Courtney J.-F., Sharda R., Carlsson C., Past, Present, and Future of Decision Support Technology, „Decision Support Systems” 2002, vol. 33, no. 2.
129.  Singh P., Meena N.K., Yang J., Vega-Fuentes E., Bishnoi S., Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution networks, „Applied Energy” 2020, vol. 278, no. 2.
130.  Sinha A.K., Anand A., Optimizing supply chain network for perishable products using improved bacteria foraging algorithm, „Applied Soft Computing” 2020, vol. 86.
131.  Srivastava S., Sahana S.K., Application of bat algorithm for transport network design problem, „Applied Computational Intelligence and Soft Computing” 2019.
132.  Stegherr H., Heider M., Hähner J., Classifying Metaheuristics: Towards a unified multi-level classification system , „Natural Computing” 2022, vol. 21, no. 5
133.  Stojanovic V., Nedic N., Prsic D., Application of cuckoo search algorithm to constrained control problem of a parallel robot platform, „The International Journal of Advanced Manufacturing Technology” 2016, vol. 87.
134.  Sysło M.M., Stosowana teoria grafów. Zastosowanie teorii grafów w metodach numerycznych, „Matematyka Stosowana” 1975, nr 5.
135.  Tao Y., Lin C., Wei L., Metaheuristics for a Large-Scale Vehicle Routing Problem of SameDay Delivery in E-Commerce Logistics System, „Journal of Advanced Transportation” 2022.
136.  Tlili T., Faiz S., Krichen S., A Hybrid Metaheuristic for the Distance-Constrained Capacitated Vehicle Routing Problem, „Procedia – Social and Behavioral Sciences” 2014, no. 109.
137.  Verma O.P., Jain R., Chhabra V., Solution of travelling salesman problem using bacterial foraging optimisation algorithm, „International Journal of Swarm Intelligence” 2014, vol. 1, no. 2.
138.  Viloria A., Commercial strategies providers pharmaceutical chains for logistics cost reduction, „Indian Journal of Science and Technology” 2016, vol. 9, iss. 47.
139.  Wagiman K.R., Abdullah M.N., Hassan M.Y., Nur Radzi H.M., A new metric for optimal visual comfort and energy efficiency of building lighting system considering daylight using multi-objective particle swarm optimization, „Journal of Building Engineering” 2021, vol. 43.
140.  Wang G.-G., Deb S., Cui Z., Monarch butterfly optimization , „Neural Computing and Applications” 2015, vol. 31.
141.  Wei Y., Jiang N., Li Z., Zheng D., Chen M., Zhang M., An Improved Ant Colony Algorithm for Urban Bus Network Optimization Based on Existing Bus Routes, „ISPRS International Journal of Geo-Information” 2022, vol. 11, no. 5.Bibliografia 239
142.  Woxenius J., Directness as a key performance indicator for freight transport chains, „Research in Transportation Economics” 2012, vol. 36, no. 1.
143.  Xu X., Hao J., Zheng Y., Multi-objective artificial bee colony algorithm for multi-stage reso urce levelling problem in sharing logistics network, „Computers & Industrial Engineering” 2020, vol. 142, no. 3.
144.  Yang J., Jaillet P., Mahmassani H., Real-Time Multivehicle Truckload Pickup and Delivery Problems, „Transportation Science” 2004, vol. 38, no. 2.
145.  Yang X.-S., A New Metaheuristic Bat-Inspired Algorithm, „Studies in Computational Intelligence” 2010, vol. 284.
146.  Yang X.-S., Bat algorithm: literature review and applications, „International Journal of Bio-Inspired Computation” 2013, vol. 5, no. 3.
147.  Yi J.H., Wang J., Wang G.-G., Using monarch butterfly optimization to solve the emergency vehicle routing problem with relief materials in sudden disasters, „Open Geosciences” 2019, vol. 11, no. 1.
148.  Yokomori T., Molecular Computing Paradigm – Toward Freedom from Turing’s Charm, „Natural Computing” 2002, vol. 1, no. 4.
149.  Yu S., Yang S., Su S., Self-Adaptive Step Firefly Algorithm , „Journal of Applied Mathematics” 2013.
150.  Zhan S., Zhang W., Niitepold K., Hsu J., Haeger J.F., Zalucki M.P., Altizer S., de Roode J.C., Reppert S.M., Kronforst M.R., The genetics of monarch butterfly migration and warning colouration, „Nature” 2014, vol. 514, no. 7522.
151.  Zhang J.W., Wang G.-G., Image matching using a bat algorithm with mutation, „Applied Mechanics and Materials” 2012, vol. 203.
152.  Zhang Y.D., Wu L., Wang S., Magnetic resonance brain image classification by an improved artificial bee colony algorithm , „Progress In Electromagnetics Research” 2011, vol. 116.
153.  Zhao D., Luo L., Zhang K., An Improved Ant Colony Optimization for the Communication Network Routing Problem, „Mathematical and Computer Modelling” 2010, vol. 52, no. 11–12.
154.  Zhong Y., Cole M.H., A Vehicle Routing Problem with Backhauls and Time Windows: A Guided Local Search Solution, „Transportation Research Part E: Logistics and Transportation Review” 2005, no. 2.
155.  Zhu H., Wang Y., Wang K., Chen Y., Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem, „Expert Systems with Applications” 2011, vol. 38, no. 8.
Akty prawne
1.  Ustawa z dnia 6 września 2001 r. – Prawo farmaceutyczne (Dz. U. z 2001 r. Nr 126,  
poz. 1381).
2.  Rozporządzenie Ministra Zdrowia z dnia 13 marca 2015 r. w sprawie wymagań Dobrej 
Praktyki Dystrybucyjnej (Dz. U. z 2017 r. poz. 509).
3.  Traktat o funkcjonowaniu Unii Europejskiej (Dz. Urz. UE C 202 z 2016).
4.  Dyrektywa 65/65/EEC Rady Europejskiej z dnia 26 stycznia 1965 r. w sprawie zbliżenia 
przepisów ustawowych, wykonawczych i administracyjnych dotyczących produktów lecz-
niczych (Dz. Urz. UE L 22 z 1965, s. 369).240 Bibliografia
5.  Dyrektywa Parlamentu Europejskiego i Rady 2001/83/EC z dnia 6 listopada 2001 r. w spra-
wie wspólnotowego kodeksu dotyczącego produktów leczniczych stosowanych u ludzi 
(Dz. Urz. UE L 311 z 2001, s. 67).
6.  Dyrektywa Parlamentu Europejskiego i Rady 2004/24/EC z dnia 31 marca 2004 r. zmie-
niająca, w zakresie tradycyjnych produktów leczniczych roślinnych, dyrektywę 2001/83/
EC w sprawie wspólnotowego kodeksu dotyczącego produktów leczniczych stosowanych 
u ludzi (Dz. Urz. UE L 136 z 2004, s. 85).
7.  Dyrektywa Parlamentu Europejskiego i Rady 2004/27/EC z dnia 31 marca 2004 r. zmienia-
jąca dyrektywę 2001/83/EC w sprawie wspólnotowego kodeksu dotyczącego produktów 
leczniczych stosowanych u ludzi (Dz. Urz. UE L 136 z 2004, s. 34).
8.  Dyrektywa Parlamentu Europejskiego i Rady 2011/62/UE z dnia 8 czerwca 2011 r. zmie-
niająca dyrektywę 2001/83/WE.
Źródła internetowe
1.  Ambient temperature, Pharma Logistics IQ, https://rb.gy/ws8vat [dostęp: 09.05.2022].
2.  Arvato Polska, Transport produktów leczniczych. Wymagania, https://blog.arvato.pl/trans-
port-produktow-leczniczych/ [dostęp: 10.11.2022].
3.  ASHP, Cold Chain Management Resource Guide, https://www.ashp.org/-/media/assets/
innovation/docs/ASHP-Cold-Chain-Management-Resource-Guide-3.pdf  [dostęp: 
2.03.2023].
4.  Best Delivery Route Planning Software 2022, https://www.g2.com/categories/route-plan -
ning [dostęp: 1.03.2022].
5.  Capterra, Ranking obejmuje programy działające na terenie: USA, Wielkiej Brytanii, Kana-
dy, Meksyku, Niemiec, Południowej Afryki, Holandii, Zjednoczonych Emiratów Arabskich, 
Włoch i Francji, https://www.capterra.com/route-planning-software/ [dostęp: 7.08.2024].
6.  Deliver, Monitoring and Evaluation Indicators for Assessing Logistics Systems Performance, 
Deliver, Arlington, 2006, https://iaphl.org/resources/publications/m_e_indicators_hdbk/ 
[dostęp: 23.04.2024].
7.  Dower R., Least Time Principle and Geometric Optics, https://quarknet.org/sites/default/
files/content/report/file/2024-11/Least%20Time.pdf [dostęp: 25.11.2024].
8.   Encyklopedia  PWN,  Optymalizacja,  https://encyklopedia.pwn.pl/haslo/optymaliza-
cja;3951487.html [dostęp: 22.08.2022].
9.  Erickson  J.,  Algorithms,  https://jeffe.cs.illinois.edu/teaching/algorithms/  [dostęp: 
18.08.2022].
10.  European Medicines Agency (EMA), https://www.ema.europa.eu/en/about-us [dostęp: 
12.11.2023].
11.  European Medicines Agency, ICH guideline Q9 on quality risk management, https://shor-
turl.at/mo7hA [dostęp: 15.06.2023].
12.  Good Storage and Distribution Practices for Drug Products, https://shorturl.at/QFdmh 
[dostęp: 2.03.2023].
13.  Karaboga D., An Idea Based on Honey Bee Swarm for Numerical Optimization, Technical 
Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Depart-
ment, 2005, https://abc.erciyes.edu.tr/pub/tr06_2005.pdf [dostęp: 15.06.2024].14.  Kunysz A., Logistyka leków a prawo farmaceutyczne, http://logistyczny.com/artykul_ogo.
php?id=3582 [dostęp: 14.04.2024].
15.  Litman T., Planning Principles and Practices, Victoria Transport Policy Institute, https://
www.vtpi.org/planning.pdf [dostęp: 30.04.2024].
16.  Microsoft Corporation, C# Language Specification Version 5.0: chapter 2.4.4.1 Boolean liter-
als, http://www.microsoft.com/en-us/download/details.aspx?id=7029 [dostęp: 2.03.2025].
17.  Microsoft  Corporation,  https://learn.microsoft.com/en-us/previous-versions/visu-
alstudio/visual-studio-2017/xml-tools/xml-document-properties-properties-win-
dow?view=vs-2017 [dostęp: 2.03.2025].
18.  Microsoft  Corporation,  https://learn.microsoft.com/pl-pl/visualstudio/win-
dows/?view=vs-2022 [dostęp: 2.03.2025].
19.  Parlament Europejski, Produkty lecznicze i wyroby medyczne, https://www.europarl.europa.
eu/factsheets/pl/sheet/50/produkty-lecznicze-i-wyroby-medyczne [dostęp: 12.11.2023].
20.   Encyklopedia PWN, System, https://encyklopedia.pwn.pl/haslo/system;3982198.html 
[dostęp: 24.04.2022].
21.  Ranking programów optymalizacji tras, https://developers.google.com/optimization/
routing/vrptw#c [dostęp: 15.12.2022].
22.  Rosen F., The Algebra of Mohammed ben Musa , Oriental Translation Fund of Great Britain 
and Ireland, 1831, https://legacy-www.math.harvard.edu/~knill/teaching/summer2019/
exhibits/algebra/AlgebraMohammedBenMusa.pdf [dostęp: 30.03.2024].
23.   The Britannica Dictionary , https://www.britannica.com/dictionary/data [dostęp: 24.04.2022].
24.   The Editors of Encyclopaedia Britannica , Algorithm, https://www.britannica.com/science/
algorithm [dostęp: 18.08.2022].
25.  The World Bank, IRU, Road Freight Transport Services Reform: Guiding Principles for 
Practitioners and Policy Makers, https://www.iru.org/sites/default/files/2017-01/iru-world-
bank-road-freight-transport-services-reform-en.pdf [dostęp: 30.04.2024].
26.  Urząd Rejestracji Produktów Leczniczych, Wyroby medyczne – wprowadzenie wyrobów 
medycznych do obrotu i do używania, https://www.urpl.gov.pl/pl/wyroby-medyczne/wpro-
wadzenie-wyrob%C3%B3w-medycznych-do-obrotu-i-do-u%C5%BCywania/informacje-
-dotycz%C4%85ce-0 [dostęp: 12.11.2023].
27.  W. Keens, Optimization Problems: Maximizing or Minimizing Functions, „The Mathsy Way” 
2023, https://medium.com/the-mathsy-way/optimization-problems-maximizing-or-min-
imizing-functions-4dda5ac6d2b6 [dostęp: 18.08.2022].