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

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

Developing A Mathematical Model For Locating Facilities And Vehicles To Minimize Response Time

Traditional mathematical models for locating/allocating vehicles and facilities are reviewed and extended to illustrate how to formulate and solve a problem of minimized response time, given resource constraints.  Results indicate that the average response time can be significantly improved through strategically allocating vehicles throughout the service area.  Furthermore, the modified model was shown to outperform the traditional model as the number of vehicles allocated to a fixed number of facilities increase.  Implications are identified for applications such public transit systems, wholesale and distribution operations

A HEURISTIC APPROACH FOR LOCATING EMS FACILITIES AND VEHICLES

Many industries thrive on their ability to provide rapid and efficient services to customers. Attaining this goal is particularly critical for life sensitive service providers like fire departments and emergency medical services (EMS). This issue is complicated by the possibility that a vehicle may be unavailable when called for service. In this paper we analyze current techniques for locating EMS and highlight some of the problems associated with these methods. We propose a genetic algorithm to locate emergency facilities and vehicles to minimize the number of locations which cannot be covered in a specified time. Our proposed genetic algorithm is driven by the evaluation of the fitness of chromosomes through a discrete event simulation model. This method of evaluating the fitness of the chromosomes is used because of the difficulty of evaluating the required probabilities. We believe that our approach will lead to a general cost efficient technique for locating EMS