Vehicle routing problem (VRP) is a classic problem studied in logistic. One of the most important variations within this problem is called Green Vehicle Routing Problem (GVRP), in which environmental aspects are considered when designing product delivery routes. This variant arises due to the high levels of pollution produced by transport vehicles, so it is a variation whose study represents a vital impact nowadays. This project will consider a GVRP and will be developed considering the characteristics of multi-depot (MDVRP) and multi-product (MPVRP) to minimize the costs of assignation of vehicles and CO2 emissions. To solve the problem, this project proposes a hybridization between the classic tabu search (TS) metaheuristic and the PAES algorithm (TS+PAES) to generate the Pareto frontier of both objectives. An integer mixed linear programming model is formulated and developed for each objective function separately to have an optimal point of comparison for the efficiency of the proposed algorithm. Also, the TS+PAES algorithm is compared to the nearest neighbor algorithm for large instances. Two computational experiments were carried out, one for small and the other one for large instances. The experiment for small instances showed that the GAP of each extreme of the frontier compared to the MILP model is on average 0.73%. For large instances, the metaheuristic improves in 0.1% the results presented by the MILP model showing that the metaheuristic provides closer near-optimal solutions in less computational time. Besides, the metaheuristic, in comparison with the nearest neighborhood heuristic, improves in 44.21% the results of emissions and in 3.88% the costs. All these results demonstrate the effectiveness of the metaheuristic.

The Vehicle Routing Problem with Loading Constraints (VRPLC) is strongly related to real life applications in distribution logistics. It addresses the simultaneous loading and routing of vehicles, which are two crucial activities in transportation. Since treating these operations separately may result in impractical solutions, the development of applications for VRPLCs has gained the attention of researchers in recent years. Several heuristic methods have been proposed, but they consider only a limited group of practical characteristics that arise in real world situations. This study proposes a hybrid heuristic method based on the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic and the Clarke and Wright Savings algorithm, to solve a VRPLC with several loading and routing constraints that have not been considered simultaneously before. Experimental results show that the proposed procedure produces competitive solutions in short processing times. Lastly, the impact of the added operational constraints is also analyzed.

Patient Bed Assignment (PBA) consists of assigning patients to hospital beds according to specific requirements such as patient diagnosis, equipment requirements, age and gender policies, among others. We worked in conjunction with the Hospital Universitario San Ignacio (HUSI) with the goal of designing an application to support decision-making during the bed assignment process. We introduced a mathematical model for the PBA. We used Analytic Hierarchy Process (AHP) to determine the weights attributed to each part of the objective function. Due to the long execution time required, we used a Greedy Algorithm and Tabu Search (TS) to optimize the match between the patient’s requirements and the characteristics of the assigned bed. To test the algorithms, we created 15 test instances of various sizes. The results showed that the gap between the value of the objective function resulting from using the Greedy/TS in comparison with the optimal solution is on average 6.2%. Also, the TS takes 84% less time than the MILP for medium and large instances. We collected data from real life instances and compared the actual method with the designed metaheuristic. On average, the value of the objective function resulting from using the proposed Greedy/Tabu algorithm is 8.6% higher.

This work studies the scheduling of elective procedures, with stochastic durations, in surgery rooms. Given a set of rooms with limited availability and a set of procedures, it must be decided in which room and when each procedure will be performed. The problem’s objectives are to maximize the use of the operating rooms and to minimize the delays in starting the scheduled surgeries. A simulation-optimization approach is proposed. First, procedures’ durations are modeled as random variables and a set of test percentiles (i.e. it is assumed that all surgeries will last as many minutes as the 75th percentile of its probability density function) is selected. Subsequently, using these durations as a parameter, a greedy randomized adaptive search procedure (GRASP) is run. Consequently, as many solutions as selected test percentiles are generated. Finally, a Monte Carlo simulation is used to estimate three indicators: i) rooms utilization, ii) percentage of surgeries that had delays, and iii) average delay time of scheduled surgeries. The technique was tested by solving the elective procedures scheduling problem in a high-complexity hospital in Bogota. This hospital has 19 operating rooms and 35,000 surgeries performed annually. Currently, the scheduling process is manual. The simulation-optimization proposed approach allowed to determine the relation between utilization rate and delays in the service. As the occupation percentage increases, delay times also augment, implying a reduction of the service level. An average reduction of 5% in delay times entails a reduction between 3% and 9% of operating room occupancy.

Among the different tasks in production logistics, job scheduling is one of the most important at the operational decision-making level to enable organizations to achieve competiveness. Scheduling consists in the allocation of limited resources to activities over time in order to achieve one or more optimization objectives. Flow-shop (FS) scheduling problems encompass the sequencing processes in environments in which the activities or operations are performed in a serial flow. This type of configuration includes assembly lines and the chemical, electronic, food, and metallurgical industries, among others. Scheduling has been mostly investigated for the deterministic cases, in which all parameters are known in advance and do not vary over time. Nevertheless, in real-world situations, events are frequently subject to uncertainties that can affect the decision-making process. Thus, it is important to study scheduling and sequencing activities under uncertainties since they can cause infeasibilities and disturbances. The purpose of this paper is to provide a general overview of the FS scheduling problem under uncertainties and its role in production logistics and to draw up opportunities for further research. To this end, 100 papers about FS and flexible flow-shop scheduling problems published from 2001 to October 2016 were analyzed and classified. Trends in the reviewed literature are presented and finally some research opportunities in the field are proposed.

This paper tackles an extension to the Multi-activity Combined Timetabling and Crew Scheduling Problem (MCTCSP). The goal of the original problem is to schedule the minimum number of homogenous workers required, in order to visit a set of customers characterized by services needed against schedule availability. However, since in home services it is common to have specialized workers, a mathematical model considering a heterogeneous workforce is proposed. As a solution, a GRASP-based algorithm is designed. In order to test the metaheuristic performance, 110 instances from the literature are adapted to include categorical skills. In addition, another 10 instances are randomly generated to consider large problems. The results show that the proposed GRASP finds optimal solutions in 46% of the cases and saves 40–96% computational time.

The no-wait flow shop scheduling problem (NWFSP) plays a crucial role in the allocation of resources in multitudinous industries, including the steel, pharmaceutical, chemical, plastic, electronic, and food processing industries. The NWFSP consists of n jobs that must be processed in m machines in series, and no job is allowed to wait between consecutive operations. This project deals with NWFSP with sequence-dependent setup times for minimizing earliness and tardiness. From the literature review of the last five years in NWFSP, it is noticeable that only around 1.92% of the researchers have studied that multi-objective function, which could help to improve the productivity of industries where methods such as just in time are considered. Besides, there is no information about previous researchers that have solved this problem with sequence-dependent setup times. Firstly, a MILP model is proposed to solve small instances, and secondly, a genetic algorithm (GA) is developed as a solution method for medium and large instances. Compared with the mathematical model for small instances, the GA obtained the optimal solution in 100% of the cases. For medium and large instances, the GA improves in an average of 31.54%, 38.09%, 44.58%, 47.72%, and 37.33% the MDD, EDDP, ATC, SPT, and LPT dispatching rules, respectively.

This paper addresses the scheduling problem in a Permutation Flow Shop (PFS) environment, which is associated with many types of industries such as chemical, petrochemical, automobile manufacturing, metallurgical, textile, etc. Thus, this work intends to solve a PFS scheduling problem in order to minimize the total weighted tardiness, since it is an important sequencing criterion not only for on time delivery jobs but also for customer satisfaction. To solve the problem, GRASP (Greedy Randomized Adaptive Search Procedure) metaheuristic is proposed as a solution, which has shown competitive results compared with other combinatorial problems. In addition, two utility functions called Weighted Modified Due Date (WMDD) and Apparent Tardiness Cost (ATC) are proposed to develop GRASP. These are based on dynamic dispatching rules and also known for solving the problem of total weighted tardiness for single machine scheduling problem. Next, an experimental design was carried out for comparing the GRASP performance with both utility functions and against the WEDD dispatching rule results. The results indicate that GRASP-WMDD could improve the total weighted tardiness in 47.8% compared with WEDD results. Finally, the GRASP-WMDD performance for the PFS total tardiness problem was evaluated, obtaining a relative deviation index of 13.89% and ranking the method over 26 heuristics and metaheuristics.

Stochastic scheduling addresses several forms of uncertainty to represent better production environments in the real world. Stochastic scheduling has applications on several areas such as logistics, transportation, production, and healthcare, among others. This paper aims to evaluate the performance of various greedy functions for a GRASP-based approach, under stochastic processing times. Since simulation is used for estimating the objective function, two simulation techniques, Monte Carlo simulation and Common Random Numbers (CRN), are used to compare the performance of different greedy (utility) functions within the GRASP. In order to validate the proposed methodology, the expected total weighted tardiness minimization for a single machine problem was taken as case study. Results showed that both, CRN and Monte Carlo, are not statistically different regarding the expected weighted tardiness results. However, CRN showed a better performance in terms of simulation replications and the confidence interval size for the difference between means. Furthermore, the statistical analysis confirmed that there is a significant difference between greedy functions.

This paper addresses the stochastic permutation flow shop problem (SPFSP) in which the stochastic parameters are the processing times. This allows the modeling of setups and machine breakdowns. Likewise, it is proposed a multi-objective greedy randomized adaptive search procedure (GRASP) coupled with Monte-Carlo Simulation to obtain expected makespan and expected tardiness. To manage the bi-objective function, a sequential combined method is considered in the construction phase of the meta-heuristic. Moreover, the local Search combines 2-optimal interchanges with a Pareto Archived Evolution Strategy (PAES) to obtain the Pareto front. Also, some Taillard benchmark instances of deterministic permutation flow shop problem were adapted in order to include the variation in processing times. Accordingly, two coefficients of variation (CVs) were tested: one depending on expected processing times values defined as twice the expected processing time of a job, and a fixed value of 0.25. Thus, the computational results on benchmark instances show that the variable CV provided lower values of the expected makespan and tardiness, while the con-stant CV presented higher expected measures. The computational results present insights for further analysis on the behavior of stochastic scheduling problems for a better approach in real-life scenarios at industrial and service systems.