TIMBER EXTRACTION IS A VITAL PROCESS IN FOREST HARVESTING, PARTICULARLY IN AREAS WITH HIGH SLOPES WHERE TIMBER HARVESTING METHODS ARE NOT FEASIBLE. IN SUCH CASES, LOGGING TOWERS EMPLOYING EXTRACTION CABLES ARE OFTEN THE MOST EFFECTIVE SOLUTION. THIS INTRICATE TASK INVOLVES SEVERAL PHASES, WITH THE INSTALLATION OF THE TOWER BEING ONE OF THE MOST CRITICAL. IT SIGNIFICANTLY INFLUENCES THE PERFORMANCE AND FEASIBILITY OF TIMBER EXTRACTION. ANOTHER CRUCIAL PHASE INVOLVES STRATEGICALLY POSITIONING LOGGING LINES TO MINIMIZE THE INSTALLATION TIME WHILE MAXIMIZING THE LOAD CAPACITY EFFICIENCY. THIS ARTICLE PRESENTS AN INTEGER PROGRAMMING MATHEMATICAL MODEL FOR DETERMINING THE OPTIMAL POSITIONING OF YARDERS CONDITIONED TO LOGGING LINES, THE TIMBER LOGGING TIME, AND THE LOGGING CYCLE TIME. FURTHERMORE, A TWO-PHASE HEURISTIC ALGORITHM IS INTRODUCED TO ADDRESS THE PROBLEM. BOTH APPROACHES OFFER A PRELIMINARY PROPOSAL FOR THE LOCATION OF LOGGING TOWERS AND THE ARRANGEMENT OF LOGGING LINES WITHIN A TWO-DIMENSIONAL SPATIAL PLANE, THEREBY STREAMLINING THE TIMBER EXTRACTION PROCESS IN CHALLENGING TERRAINS. FINALLY, WE COMPARE MANUALLY GENERATED APPROXIMATE PLANNING (REFERRED TO AS THE MANUAL PLANNING APPROACH, MPA) WITH OUR PRESENTED APPROACHES. OUR METHODS OUTPERFORM THE MPA, AND NOTABLY, OUR TWO-PHASE APPROACH SURPASSES SOLVERS COMMONLY USED IN THE INDUSTRY BY UP TO 38% IN REAL CASE STUDIES.

ONE OF GRAPH OPTIMIZATION?S FUNDAMENTAL AND MOST CHALLENGING PROBLEMS IS DETERMINING THE MAXIMUM SET OF UNCONNECTED VERTICES IN A GRAPH, CALLED THE MAXIMUM INDEPENDENT SET PROBLEM. THIS PROBLEM CONSISTS OF FINDING THE LARGEST INDEPENDENT SET IN A GRAPH, WHERE AN INDEPENDENT SET IS A SET OF VERTICES SUCH THAT NO TWO VERTICES ARE ADJACENT. THIS PAPER PRESENTS A NEW ARTIFICIALLY GENERATED ALGORITHM FOR THE MAXIMUM INDEPENDENT SET PROBLEM. THE NEW ALGORITHM IS GENERATED BY THE AUTOMATIC GENERATION OF ALGORITHMS, A TECHNIQUE THAT ALLOWS THE CONSTRUCTION OF NEW HYBRID ALGORITHMS, TAKING ADVANTAGE OF EXISTING ALGORITHMS. THUS, THE AUTOMATIC GENERATION OF ALGORITHMS COMBINES BASIC HEURISTICS FOR THE PROBLEM, A TABU SEARCH METHOD SELECTED FROM THE LITERATURE, AND AN EXACT METHOD THAT SOLVES THE PROBLEM?S MATHEMATICAL FORMULATION WORKING FOR A LIMITED COMPUTATIONAL TIME. WITH THESE COMPONENTS, THE SPACE OF POSSIBLE ALGORITHMS IS TRAVERSED BY EMPLOYING GENETIC PROGRAMMING. ALGORITHMS OF SMALL SIZES ARE GENERATED TO STUDY THEIR STRUCTURE AND DISCOVER NEW ALGORITHMIC COMBINATIONS. THEN, WE SELECT THE ALGORITHM THAT FINDS SOLUTIONS WITH THE BEST COMPUTATIONAL PERFORMANCE AMONG ALL THE GENERATED ALGORITHMS. THIS BEST ALGORITHM IS COMPARED WITH THREE STATE-OF-THE-ART ALGORITHMS FOR THE PROBLEM, PRESENTING THE BEST COMPUTATIONAL PERFORMANCE FOR THE 131 INSTANCES IN THE LITERATURE.

THE EFFICIENT MOVEMENT OF RAW MATERIALS WITHIN ORGANIZATIONS IS FUNDAMENTAL TO MAINTAINING THE SEAMLESS PROGRESSION OF PRODUCTION PROCESSES. HOWEVER, THESE LOGISTICAL OPERATIONS CAN INADVERTENTLY COMPROMISE OVERALL COMPANY EFFICIENCY, PRIMARILY DUE TO THE SUBSTANTIAL TIME INVESTED IN TRANSPORTING MATERIALS. THIS PAPER INTRODUCES AN INNOVATIVE MATHEMATICAL MODEL SPECIFICALLY DESIGNED TO OPTIMIZE THE TRANSPORT OF RAW MATERIALS VIA DRONES ACROSS MULTIPLE WORKSTATIONS. THIS MODEL EMPLOYS A NOVEL MODIFICATION OF THE TRADITIONAL MULTI-LEVEL VEHICLE ROUTING PROBLEM BY INCORPORATING AN ADDITIONAL INDEX AND ACCOUNTING FOR THE DRONE?S ENERGY CONSUMPTION. WE EMPLOY A WIDELY-RECOGNIZED SOLVER FOR PRACTICAL RESOLUTION AND COMPARE IT WITH A HEURISTIC ALGORITHM. THE RESULTANT STRATEGIES OFFER PROMISING PROSPECTS FOR THE ORGANIZATION STUDIED, INTRODUCING ROBUST SOLUTIONS FOR ELEVATING THE EFFICIENCY OF RAW MATERIAL TRANSPORTATION.

THE LATENCY LOCATION ROUTING PROBLEM (LLRP), A COMBINATION OF THE FACILITY LOCATION PROBLEM AND THE CUMULATIVE CAPACITATED VEHICLE ROUTING PROBLEM, IS A RECENTLY PROPOSED VARIANT OF LOCATION ROUTING PROBLEMS. IT CORRESPONDS TO A CUSTOMER-CENTRIC PROBLEM, IN WHICH THE AIM IS TO MINIMIZE THE SUM OF THE ARRIVAL TIMES AT THE CUSTOMERS. THIS PAPER PROPOSES THREE NOVEL METAHEURISTIC ALGORITHMS TO SOLVE THE LLRP. THEY USE A SIMULATED ANNEALING FRAMEWORK, WHICH AFTER EACH TEMPERATURE REDUCTION IS INTENSIFIED THROUGH A VARIABLE NEIGHBORHOOD DESCENT PROCEDURE. EACH ALGORITHM USES A DIFFERENT SEARCH STRATEGY AS INTENSIFICATION. RESULTS ON 76 BENCHMARK INSTANCES INDICATE THAT THE PROPOSED METAHEURSTICS OUTPERFORM THE STATE-OF-THE-ART ALGORITHMS, FINDING NEW BEST SOLUTIONS FOR ALL THE LARGE-SIZED INSTANCES (OVER 100 CUSTOMERS), OR THE CURRENTLY KNOWN OPTIMAL ONES FOR MOST OF THE SMALL- AND MEDIUM-SIZED INSTANCES, IN COMPARABLE COMPUTING TIMES. FURTHERMORE, IN MORE THAN 80% OF THE INSTANCES THE AVERAGE VALUE OF THE SOLUTIONS FOUND BY THE PROPOSED ALGORITHMS IS BETTER THAN OR EQUAL TO THAT OF THE CURRENT BEST KNOWN SOLUTION.

DETERMINING THE DESIGN OF PUSHBACKS IN AN OPEN-PIT MINE IS A KEY PART OF OPTIMIZING THE ECONOMIC VALUE OF THE MINING PROJECT AND THE OPERATIONAL FEASIBILITY OF THE MINE. THIS PROBLEM REQUIRES BALANCING PUSHBACKS THAT HAVE GOOD GEOMETRIC PROPERTIES TO ENSURE THE SMOOTH OPERATION OF THE MINING EQUIPMENT AND SO THAT THE SCHEDULING OF EXTRACTION MAXIMIZES THE ECONOMIC VALUE BY PROVIDING EARLY ACCESS TO THE RICH PARTS OF THE DEPOSIT. HOWEVER, BECAUSE OF THE CHALLENGING NATURE OF THE PROBLEM, PRACTICAL APPROACHES FOR FINDING THE BEST PUSHBACKS STRONGLY DEPEND ON THE EXPERT CRITERIA TO ENSURE GOOD OPERATIONAL PROPERTIES. THIS PAPER INTRODUCES THE ADVANCED GEOMETRICALLY CONSTRAINED PRODUCTION SCHEDULING PROBLEM TO ACCOUNT FOR OPERATIONAL SPACE CONSTRAINTS, MODELED AS TRUNCATED CONES OF EXTRACTION. TO FIND THE BEST SOLUTION FOR THIS PROBLEM, WE PRESENT A PARALLEL GENETIC ALGORITHM BASED ON A GENOTYPE?PHENOTYPE MODEL SUCH THAT THE GENOTYPE SYMBOLIZES THE BASE BLOCK OF A TRUNCATED CONE, AND THE PHENOTYPE REPRESENTS THE CONE ITSELF. A CENTRAL COMPUTER NODE EVALUATES THESE SOLUTIONS, COLLABORATING WITH VARIOUS SECONDARY NODES THAT EVOLVE A POPULATION OF FEASIBLE SOLUTIONS. THE PGA?S EFFICACY WAS VALIDATED USING COMPREHENSIVE TEST INSTANCES FROM ESTABLISHED RESEARCH. THE PGA SOLUTION EXHIBITED A CONSISTENT AVERAGE COPPER GRADE ACROSS PERIODS, WITH ITS INCREMENTAL PHASES REFLECTING REAL-WORLD MINE GEOMETRY. MOREOVER, THE BENEFITS OF THE MEANSHIFT CLUSTERING TECHNIQUE WERE EVIDENT, SUGGESTING EFFECTIVE PHASE-BASED SCHEDULING. THE PGA?S APPROACH ENSURES OPTIMAL RESOURCE UTILIZATION AND OFFERS INSIGHTS INTO POTENTIAL AVENUES FOR FURTHER MODEL ENHANCEMENTS AND FINE-TUNING.

THE QUADRATIC KNAPSACK PROBLEM IS A WELL-KNOWN GENERALIZATION OF THE CLASSICAL 0-1 KNAPSACK PROBLEM, IN WHICH ANY PAIR OF ITEMS PRODUCES A PAIRWISE PROFIT IF BOTH ARE SELECTED. ANOTHER RELEVANT GENERALIZATION OF THE KNAPSACK PROBLEM IS THE KNAPSACK PROBLEM WITH SETUP, IN WHICH THE ITEMS ARE PARTITIONED INTO CLASSES, THE ITEMS OF A CLASS CAN ONLY BE INSERTED INTO THE KNAPSACK IF THE CORRESPONDING CLASS IS ACTIVATED, AND ACTIVATING A CLASS INVOLVES A SETUP COST AND A SETUP CAPACITY REDUCTION. DESPITE A RICH LITERATURE ON THESE TWO PROBLEMS, THEIR OBVIOUS GENERALIZATION, I.E., THE QUADRATIC KNAPSACK PROBLEM WITH SETUP, WAS NEVER INVESTIGATED SO FAR. WE DISCUSS APPLICATIONS, MATHEMATICAL MODELS, DETERMINISTIC MATHEURISTIC ALGORITHMS, AND COMPUTATIONALLY EVALUATE THEIR PERFORMANCE.

ESTIMATING VERTICAL HYDRAULIC CONDUCTIVITY (KV) IN SEMI-ARID BASINS IS CHALLENGING BECAUSE LITHOLOGICAL HETEROGENEITY LIMITS THE REPRESENTATIVENESS OF SPARSE HYDRAULIC MEASUREMENTS. THIS STUDY EVALUATES A SURFACE-BASED SEISMOELECTRIC WORKFLOW FOR INFERRING DEPTH-RESOLVED K V PROFILES WITHOUT USING DRILLING INFORMATION DURING MODEL DEVELOPMENT. BOREHOLE DATA WERE RESERVED FOR EXTERNAL VALIDATION. THE WORKFLOW INTEGRATES FIELD ACQUISITION, GEOSPATIAL DATA SYNTHESIS, SIGNAL CONDITIONING, PHYSICALLY GUIDED FEATURE ENGINEERING, SUPERVISED REGRESSION BENCHMARKING, AND HYDROGEOLOGICAL EXPERT APPRAISAL. ITS MAIN CONTRIBUTION IS A DRILLING-INDEPENDENT MODELING STRATEGY THAT TRANSLATES SURFACE SEISMOELECTRIC ATTRIBUTES INTO VERTICAL K V PROFILES. THIS PROVIDES PRE-DRILLING INFORMATION FOR WELL SITING, SCREEN-INTERVAL DESIGN, AND PLANNING OF DRILLING AND COMPLETION COSTS. A HIGH-DENSITY SURVEY WAS CONDUCTED IN THE ÑUBLE REGION OF CENTRAL CHILE USING A DUAL-CHANNEL SEISMOELECTRIC SYSTEM WITH INVESTIGATION DEPTHS OF UP TO 200 M. MORE THAN 500 GEOREFERENCED STATIONS WERE ACQUIRED. SEISMOELECTRIC ATTRIBUTES DERIVED FROM THE DUAL-CHANNEL RESPONSE, TOGETHER WITH TOPOGRAPHIC DESCRIPTORS, WERE USED TO TRAIN AND COMPARE MULTIPLE REGRESSION MODELS. COMPARED WITH A BASELINE CONFIGURATION BASED ONLY ON RAW VOLTAGE AND DEPTH, THE AUGMENTED MODEL SHOWED STRONGER CROSS-VALIDATED PERFORMANCE. THE COEFFICIENT OF DETERMINATION INCREASED FROM R 2 = 0.711 TO R 2 = 0.968 , RMSE DECREASED FROM 3.534 TO 1.305 M/D, AND MAE DECREASED FROM 1.368 TO 0.546 M/D. THE PREDICTED K V PROFILES BETTER PRESERVED PERSISTENT HIGH- AND LOW-CONDUCTIVITY INTERVALS AND ABRUPT VERTICAL TRANSITIONS THAN THE BASELINE MODEL. THEY WERE ALSO CONSISTENT WITH INDEPENDENT LITHOSTRATIGRAPHIC EVIDENCE AND POST-DRILLING YIELD VERIFICATION AT THE VALIDATION SITE. PROFILE-LEVEL SMOOTHING WITH SAVITZKY–GOLAY AND WAVELET FILTERS IMPROVED VISUAL CONTINUITY WHILE PRESERVING THE DOMINANT CONDUCTIVITY CONTRASTS. THESE RESULTS