Developing A Sustainable AoL Framework Using Supply Chain Principles

Many accreditation agencies have adopted Assurance of Learning (AoL)-based paradigms for assessing educational institutions. Colleges/universities transitioning to an Assurance of Learning (AoL) system encounter common challenges while implementing new standards. In this research, the authors develop a stakeholder driven AoL framework which addresses common transitional issues while maintaining the Southern Association of Colleges and Schools (SACS) and Association to Advance Collegiate Schools of Business (AACSB) accreditation standards. The model incorporates supply chain practices by best in class (BIC) companies to optimize overall assessment efforts. The model decreases the number of redundant processes, improves collaboration throughout the university, and promotes a more comprehensive curriculum. After the model implementation, the authors examine mission statements and tenure, promotion and reappointment documents to gain insight about how to sustain accreditation

An Examination of Academic-Practitioner Co-Authorship Trends in Supply Chain Management Journals

The purpose of this research is to explore the extent that academics and practitioners collaborate to publish research in academic journals as evidenced by co-authorship. Using journal rankings studies, fourteen top-ranked peer reviewed academic journals that publish supply chain management (SCM) research are identified. Each article within our journal sample is examined over an eleven year period beginning in 2000 for academic-practitioner co-authorship. Results indicate that approximately nine percent of a near census of 5,064 articles are co-authored between academics and practitioners. Finally, practitioner authors are classified into five different groups in order to have a more fine-grain view of the distribution of author-type by journal. Analysis shows that some journals are more inclined to publish certain types of academic-practitioner co-authored articles over others. Implications for future supply chain management research are discussed, advocating for more collaborative research between academics and practitioners within the field

A bi-criteria evolutionary algorithm for a constrained multi-depot vehicle routing problem

Most research about the vehicle routing problem (VRP) does not collectively address many of the constraints that real-world transportation companies have regarding route assignments. Consequently, our primary objective is to explore solutions for real-world VRPs with a heterogeneous fleet of vehicles, multi-depot subcontractors (drivers), and pickup/delivery time window and location constraints. We use a nested bi-criteria genetic algorithm (GA) to minimize the total time to complete all jobs with the fewest number of route drivers. Our model will explore the issue of weighting the objectives (total time vs. number of drivers) and provide Pareto front solutions that can be used to make decisions on a case-by-case basis. Three different real-world data sets were used to compare the results of our GA vs. transportation field experts’ job assignments. For the three data sets, all 21 Pareto efficient solutions yielded improved overall job completion times. In 57 % (12/21) of the cases, the Pareto efficient solutions also utilized fewer drivers than the field experts’ job allocation strategies