THIS STUDY INVESTIGATES THE WORST-CASE PERFORMANCE GUARANTEE OF LOCALLY OPTIMAL SOLUTIONS TO MINIMIZE THE TOTAL WEIGHTED COMPLETION TIME ON UNIFORMLY RELATED PARALLEL MACHINES. THE INVESTIGATED NEIGHBORHOOD STRUCTURE IS JUMP, ALSO CALLED INSERTION OR MOVE. THIS RESEARCH FOCUSED ON ESTABLISHING THE LOCAL OPTIMALITY CONDITION EXPRESSED AS AN INEQUALITY AND MAPPING THAT MAPS A SCHEDULE INTO AN INNER PRODUCT SPACE SO THAT THE NORM OF THE MAPPING IS CLOSELY RELATED TO THE TOTAL WEIGHTED COMPLETION TIME OF THE SCHEDULE. WE DETERMINE TWO NEW UPPER BOUNDS FOR THE PERFORMANCE GUARANTEE, WHICH TAKE THE FORM OF AN EXPRESSION BASED ON PARAMETERS THAT DESCRIBE THE FAMILY OF INSTANCES: THE SPEED OF THE FASTEST MACHINE, THE SPEED OF THE SLOWEST MACHINE, AND THE NUMBER OF MACHINES. THESE NEW BOUNDS OUTPERFORM THE PARAMETRIC UPPER BOUND PREVIOUSLY ESTABLISHED IN THE EXISTING LITERATURE AND ENABLE A BETTER UNDERSTANDING OF THE PERFORMANCE OF THE SOLUTIONS OBTAINED FOR THE JUMP NEIGHBORHOOD IN THIS SCHEDULING PROBLEM, ACCORDING TO PARAMETERS THAT DESCRIBE THE FAMILY OF INSTANCES.

THIS ARTICLE INTRODUCES, MODELS, AND SOLVES THE TIME-DEPENDENT IN-BUILDING DELIVERY PROBLEM IN LAST-MILE LOGISTICS. IT DETERMINES EFFICIENT TRAVEL SEQUENCES FOR A WORKER (E.G., DELIVERY PERSON, DELIVERYMAN, MAILMAN, AGENT) WHO DELIVERS GOODS OR PROVIDES SERVICES DIRECTLY TO CUSTOMERS LOCATED WITHIN A BUILDING USING ITS ELEVATION SYSTEM. WE STUDY, IN DETAIL, ALL THE STEPS INVOLVED IN A TRAVEL SEQUENCE INSIDE A BUILDING: HORIZONTAL TRIPS, UNLOADING PRODUCTS TO THE CUSTOMERS, WAITING FOR ELEVATORS, AND VERTICAL TRIPS WITHIN ELEVATORS. THE SEQUENCES AND THEIR TOTAL TIMES VARY DEPENDING ON THE BUILDING TYPE, THE ELEVATION SYSTEM, THE MOMENT OF THE DAY, AND THE ARRIVAL TIME BECAUSE OF THE DAILY BUILDING TRAFFIC INTENSITY VARIATIONS. A MIXED-INTEGER LINEAR PROGRAMMING MODEL AND A GENETIC ALGORITHM-BASED METAHEURISTIC ARE PROPOSED TO SOLVE A SET OF INSTANCES IN TWO OFFICE BUILDINGS. THE RESULTS SHOW THAT IT IS VERY IMPORTANT TO DETERMINE THE BEST TIME TO VISIT A BUILDING BECAUSE OF ITS TIME DEPENDENCY. THE VARIATION IN DELIVERY TIME BETWEEN OFF-PEAK HOURS VERSUS PEAK HOURS IS BETWEEN 15% AND 30% FOR THE SET OF SOLVED INSTANCES. MOREOVER, THE ORDER OF CUSTOMER VISITS DIFFERS DRASTICALLY DEPENDING ON THE ARRIVAL TIME TO THE BUILDING.

LA PROGRAMACIÓN DE CITAS MÉDICAS ES UNA ACTIVIDAD DE GRAN IMPORTANCIA EN UN HOSPITAL, YA QUE SE DEBEN UTILIZAR DE FORMA EFICIENTE DIFERENTES CAPITALES, TANTO HUMANOS COMO MATERIALES. UNO DE LOS PROBLEMAS DE ESTE TRABAJO ES LA INASISTENCIA DE UN PACIENTE, LO QUE DISMINUYE LA EFICIENCIA DEL USO DE ESTOS RECURSOS. PARA HACER FRENTE A ESTO, DIVERSOS ESTUDIOS HAN PROPUESTO CONSIDERAR EL ?AUSENTISMO? PARA PROGRAMAR LAS CITAS MÉDICAS. SIN EMBARGO, PREDECIRLO ES UNA TAREA COMPLEJA. ESTA INVESTIGACIÓN PROPONE LA PREDICCIÓN DE LA NO ASISTENCIA A LA CITACIÓN PARA TRES ÁREAS MÉDICAS DEL HOSPITAL CLÍNICO REGIONAL DR. GUILLERMO GRANT BENAVENTE EN LA CIUDAD DE CONCEPCIÓN, CHILE. PARA ESTO SE ENTRENAN Y EVALÚAN CINCO ALGORITMOS DE MACHINE LEARNING. EL MEJOR MODELO ENTRENADO LOGRÓ SER UNA HERRAMIENTA PREDICTIVA DEL NIVEL DE AUSENTISMO DE UN PACIENTE PARA SU PRÓXIMA CONSULTA Y CARACTERIZAR A AQUELLOS PACIENTES CON MAYORES NIVELES DE AUSENTISMO.

MOBILE DENTAL CLINICS (MDCS) ARE SUITABLE SOLUTIONS FOR SERVICING PEOPLE LIVING IN RURAL AND URBAN AREAS THAT REQUIRE DENTAL HEALTHCARE. MDCS CAN PROVIDE DENTAL CARE TO THE MOST VULNERABLE HIGH-SCHOOL STUDENTS. HOWEVER, SCHEDULING MDCS TO VISIT PATIENTS IS CRITICAL TO DEVELOPING EFCIENT DENTAL PROGRAMS. HERE, WE STUDY A MOBILE DENTAL CLINIC SCHEDULING PROBLEM THAT ARISES FROM THE REAL-LIFE LOGISTICS MANAGEMENT CHALLENGE FACED BY A SCHOOL-BASED MOBILE DENTAL CARE PROGRAM IN SOUTHERN CHILE. THIS PROBLEM INVOLVES SCHEDULING MDCS TO TREAT HIGH-SCHOOL STUDENTS AT PUBLIC SCHOOLS WHILE CONSIDERING A FAIRNESS CONSTRAINT AMONG DISTRICTS. SCHOOLS ARE CIRCUMSCRIBED INTO DISTRICTS, AND BY PROGRAM REGULATIONS, AT LEAST 50% OF THE STUDENTS IN EACH DISTRICT MUST RECEIVE DENTAL CARE DURING THE FRST SEMESTER. FAIRNESS PREVENTS SOME DISTRICTS FROM WAITING MORE TIME TO RECEIVE DENTAL CARE THAN OTHERS. WE MODEL THE PROBLEM AS A PARALLEL MACHINE SCHEDULING PROBLEM WITH SEQUENCE-DEPENDENT SETUP COSTS AND BATCH DUE DATES AND PROPOSE A MATHEMATICAL MODEL AND A GENETIC ALGORITHM-BASED SOLUTION TO SOLVE THE PROBLEM. OUR COMPUTATIONAL RESULTS DEMONSTRATE THE EFECTIVENESS OF OUR APPROACHES IN OBTAINING NEAR-OPTIMAL SOLUTIONS. FINALLY, DENTAL PROGRAM MANAGERS CAN USE THE METHODOLOGIES PRESENTED IN THIS WORK TO SCHEDULE MOBILE DENTAL CLINICS AND IMPROVE THEIR OPERATIONS.

SCHEDULING MEDICAL APPOINTMENTS PLAYS A FUNDAMENTAL ROLE IN MANAGING PATIENT FLOW AND ENSURING HIGH-QUALITY CARE. HOWEVER, NO-SHOWS CAN SIGNIFICANTLY DISRUPT THIS PROCESS AND AFFECT PATIENT CARE. TO ADDRESS THIS CHALLENGE, HEALTHCARE FACILITIES CAN ADOPT DIFFERENT STRATEGIES, INCLUDING OVERBOOKING IN MEDICAL CONSULTATIONS. WHILE THIS REDUCES THE RISK OF UNUSED SLOTS, IT CAN GENERATE ASSOCIATED COSTS AND AFFECT THE PERCEPTION OF SERVICE QUALITY. IN THIS ARTICLE, WE PROPOSE AN INTEGER LINEAR OPTIMIZATION MODEL THAT MAXIMIZES THE EXPECTED UTILITY OF A MEDICAL CENTER, CONSIDERING THE RISK OF NO-SHOWS AND OVERBOOKING. FOR THIS PURPOSE, MACHINE LEARNING IS USED TO ESTIMATE THE PROPENSITY OF EACH PATIENT TO ATTEND THEIR MEDICAL APPOINTMENT, USING REAL DATA FROM THREE MEDICAL SPECIALTIES OF A HOSPITAL. THE RESULTS OF THE APPLICATION DEMONSTRATE THE MODEL?S ABILITY TO ASSIGN APPOINTMENTS AND PERFORM OVERBOOKING EFFICIENTLY AND IN AN ORGANIZED MANNER, IMPLYING AN IMPROVEMENT IN THE UTILITY OF A MEDICAL CENTER AND A POSITIVE IMPACT ON THE PERCEPTION OF THE QUALITY OF CARE.

THIS PAPER STUDIES THE FEEDER VEHICLE ROUTING PROBLEM (FVRP), A NEW VARIANT OF THE VEHICLE ROUTING PROBLEM (VRP), IN WHICH EACH CUSTOMER IS SERVED BY EITHER A LARGE (TRUCK) OR A SMALL VEHICLE (MOTORCYCLE). IN THIS PARTICULAR TYPE OF DELIVERY, THE TRUCKS AND THE MOTORCYCLES MUST DEPART FROM THE DEPOT, VISIT THE CUSTOMERS, AND EVENTUALLY RETURN TO THE DEPOT. DURING THE DELIVERY PROCESS, THE MOTORCYCLES TRAVEL TO THE TRUCK LOCATIONS FOR RELOADING. THE ANT COLONY OPTIMIZATION (ACO) ALGORITHM IS EMPLOYED FOR SOLVING THE PROBLEM WITH THE OBJECTIVE OF DETERMINING THE NUMBER OF DISPATCHING SUB-FLEETS AND OPTIMAL ROUTES TO MINIMIZE THE TOTAL COST (FIXED ROUTE AND TRAVEL COSTS). THREE BENCHMARK DATASETS ARE GENERATED TO EXAMINE THE PERFORMANCE OF THE FVPR. FOR COMPARISON PURPOSES, ALL INSTANCES ARE EXECUTED BY DISPATCHING ONLY TRUCKS AS IN THE TRADITIONAL VRP AND A FOUR-STAGE HIERARCHICAL HEURISTIC. ADDITIONALLY, ACO IS COMPARED TO OPTIMAL SOLUTIONS FOR SMALL INSTANCES. THE RESULTS INDICATE THAT THE PROPOSED ACO ALGORITHM YIELDS PROMISING SOLUTIONS PARTICULARLY FOR LARGE INSTANCES WITHIN A REASONABLE TIME FRAME IN AN EFFICIENT MANNER.