From studies, reports, and information, we can see that the Supply Chain Management System (SCMS) has a major impact on project success. The Supply Chain Management (SCM) plays an important role in achieving potential cross-company success because the SCMS allows us to control the development, design, management and identification of the most effective and efficient inputs, information and other resources required to complete the project. An effective SCMS is of essential importance for the strength of the competition and for the success of the entire organization. Therefore, the competitive advantage in this new environment no longer depends on the company's performance, but also on the entire supply chain partners, including competitors. The main objective of this research is to conduct the relationship between the supply chain management system, project success, project performance, to identify, and to determine the potential impact of SCM on project completion and success. In this study, a random sample was drawn from a population made up exclusively of construction companies, and project departments from other companies in Amman, the capital of Jordan. As a tool for data collection, we chose a questionnaire. Based on this, many statistical analyzes were carried out and the hypotheses of this study were tested. As a result of this test, we found out that in general there is a clear and positive relationship between supply chain management and project completion. The dimensions of supply chain management (knowledge, skills, and tools) have different effects on the project closure process. The knowledge dimension has the strongest effect.
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The present study deals with the management of supply chain using an updated Artificial Bee Colony (ABC) algorithm named UABC. UABC employs a linear combination of Gaussian and Cauchy distributions to update the candidate food positions from the older ones in memory. Optimization of a supply chain model is an integer programming problem or a constrained integer-mixed problem, for which suitable modifications are done in the algorithm. Statistical analysis of the proposed variant when compared with three ABC based algorithms indicates its efficiency and validity.
PL
Opisane w pracy badania dotyczą zarządzania łańcuchem dostaw przy zastosowaniu zmodyfikowanego algorytmu sztucznej kolonii pszczół (ang: Artificial Bee Colony - ABC), zwanego UABC (ang. Updated Artificial Bee Colony). Algorytm ten wykorzystuje liniową kombinację rozkładów Gaussa i Cauchy’ego w celu uaktualnienia pozycji rozmieszczenia pożywienia. Optymalizacja modelu łańcucha dostaw jest problemem programowania całkowitoliczbowego lub problemem programowania mieszanego z ograniczeniami. Opracowany algorytm UABC uwzględnia te informacje. Analiza statystyczna algorytmu UABC, w porównaniu z trzema innymi algorytmami opartymi na ABC, wskazuje na jego wydajność i poprawność.
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