পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| বহু-উদ্দেশ্যমূলক অপ্টিমাইজেশান× | গোল প্রোগ্রামিং× | মিশ্র-পূর্ণসংখ্যা প্রোগ্রামিং× | |
|---|---|---|---|
| ক্ষেত্র≠ | অনুকরণ | সিদ্ধান্ত গ্রহণ | অনুকরণ |
| পরিবার≠ | Process / pipeline | MCDM | Process / pipeline |
| উদ্ভবের বছর≠ | 1896 (concept); 1989–2002 (evolutionary algorithms era) | 1955 | 1958–1960 |
| প্রবর্তক≠ | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. | Charnes, A., Cooper, W. W. | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) |
| ধরন≠ | Optimization framework | Multi-objective optimisation — weighted/lexicographic goal deviation minimisation | Mathematical optimization |
| মৌলিক উৎস≠ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 | Charnes, A., Cooper, W. W. (1955). Optimal estimation of executive compensation by linear programming. Management Science DOI ↗ | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432 |
| অপর নাম≠ | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization | — | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming |
| সম্পর্কিত≠ | 3 | 8 | 6 |
| সারসংক্ষেপ≠ | Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis. | GOAL-PROGRAMMING (Goal Programming — Minimise deviations from multiple aspiration levels) is a ranking multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W. in 1955. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. | Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally. |
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