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.

Additive Manufacturing (AM) is a process of joining materials to make objects from 3D model data, usually layer by layer, as opposed to subtractive manufacturing methodologies. Selective Laser Melting, commercially known as Direct Metal Laser Sintering (DMLS®), is the most diffused additive process in today’s manufacturing industry. Introduction of a DMLS® machine in a production department has remarkable effects not only on industrial design but also on production planning, for example, on machine scheduling. Scheduling for a traditional single machine can employ consolidated models. Scheduling of an AM machine presents new issues because it must consider the capability of producing different geometries, simultaneously. The aim of this paper is to provide a mathematical model for an AM/SLM machine scheduling. The complexity of the model is NP-HARD, so possible solutions must be found by metaheuristic algorithms, e.g., Genetic Algorithms. Genetic Algorithms solve sequential optimization problems by handling vectors; in the present paper, we must modify them to handle a matrix. The effectiveness of the proposed algorithms will be tested on a test case formed by a 30 Part Number production plan with a high variability in complexity, distinct due dates and low production volumes.

During the past few years, transportation of agricultural products is increasingly becoming a crucial problem in supply chain logistics. In this paper, we present a new mathematical formulation and two solution approaches for an intermodal transportation problem. The proposed bi-objective model is applied to the transportation of agricultural products from Morocco to Europe to minimise both the transportation cost either in the form of uni-modal or intermodal, as well as the maximal overtime to delivery products. The first solution approach is based on a non-dominated sorting genetic algorithm improved by a local search heuristic and the second one is the GRASP algorithm (Greedy Randomised Adaptive Search Procedure) with iterated local search heuristics. They are tested on theoretical and real case benchmark instances and compared with the standard NSGA-II. Results are analysed and the efficiency of algorithms is discussed using some performance metrics.

This paper proposes a metaheuristic algorithm to solve the Multi-Depot Vehicle Routing Problem with a Heterogeneous Fleet (MDHFVRP). The problem consists of determining the customers and the vehicles to be assigned to each used depot and the routes to be performed to fulfill the demands of a set of customers. The objective is to minimize the sum of the fixed cost associated with the used vehicles and of the variable traveling costs related to the performed routes. The proposed approach is based on a modified genetic algorithm, which generates an initial population with heuristic solutions obtained from the well-known (LKH) heuristic algorithm for the TSP together with the solution of a mathematical model for the shortest path problem. In addition, two recombination methods and a mutation operator are considered. Computational experiments on benchmark instances show that the proposed algorithm can obtain high-quality solutions within short computing times.

This study employs mathematical modeling to explore the effects of overtime option, rework, and discontinuous end-item issuing policy on the economic manufacturing quantity (EMQ) model. Conventional EMQ model assumed that all products fabricated are of good quality and are issued under continuous policy. In real world, however, nonconforming items are randomly produced, due to diverse unexpected factors in fabrication process. When finished items are to be distributed to outside locations, discontinuous multi-shipment policy is often used rather than continuous rule. In addition, with the intention of increasing short-term capacity or shortening replenishment cycle length to smooth the production planning, adopting overtime option can be an effective strategy. To cope with the aforementioned features in real production systems, this study incorporates overtime option, rework, and multi-shipment policy into the EMQ model and explores their joint effects on optimal lot size and number of shipments, and on other relevant system parameters. Mathematical modeling and Hessian matrix equations enable us to derive the optimal policies to the problem. Through the use of numerical example, the applicability of research result is exhibited and a variety of significant effects of these features on the proposed system are revealed.

Particle swarm optimization has been established to be one of the efficient algorithms for finding solutions for continuous optimization problems. The discretized form of particle swarm optimization, known as the discrete particle swarm optimization is an efficient tool for solving combinatorial optimization problems and other problems involving discrete variables. In this paper, a revised version of the discrete particle swarm optimization algorithm is proposed for solving Quadratic Assignment Problems (QAP). Instead of using the general velocity and position update procedures in particle swarm optimization algorithms, four different possible positions are found out for each particle and the best among them is accepted as the updated position. The algorithm is applied to solve some benchmark instances of QAP taken from QAP Library and the results show minute deviations from best-known solutions.

The present study reports on the application of accelerated cooling environment (ACE) in hard turning of AISI D2 steel (55 ± 1HRC) using mixed ceramic insert (Al2O3 + TiCN) which is rarely being investigated and to address the major problems of brittle fracture of tool tip that arises through cutting forces and friction at tool-work and chip-tool interface. In spraying process, some portion of spraying coolant vaporize due to heat when it reaches to cutting zone where as remaining portion of coolant easily penetrate in cutting zone through capillary action and reduces friction as well as heat in cutting zone. Abrasion and chipping are noticed to be dominant wear mechanism. Cutting speed and depth of cut are significant for flank wear as well as cutting temperature whereas feed is significant for average surface roughness. Serrated chips have been identified at higher cutting speed and higher feeds. Optimal parametric combination is found to be d1-f1-v2 (0.1mm-0.04 m/min-108 m/min) and tool life and machining cost per part are 70 minutes and Rs 76.76 respectively. Investigation shows the worthy of application of ACE in hard turning in industrial sectors ecologically and economically. Empirical models reveal statistically significance due to higher coefficient of correlations.

In this paper, we introduce an inventory routing problem with network disruptions. In this problem, not only decisions on inventory levels and vehicle routing are made simultaneously, but also, we consider disruptions over the networks in which a number of arcs are vulnerable to these disruptions, leading to an increase in travel times. We develop a dynamic programming approach to deal with this situation, and we also evaluate some policies adapting well-known instances from the literature.

Electric Vehicles (EVs) represent a significant option that contributes to improve the mobility and reduce the pollution, leaving a future expectation in the merchandise transportation sector, which has been demonstrated with pilot projects of companies operating EVs for products delivering. In this work a new approach of EVs for merchandise transportation considering the location of Electric Vehicle Charging Stations (EVCSs) and the impact on the Power Distribution System (PDS) is addressed. This integrated planning is formulated through a mixed integer non-linear mathematical model. Test systems of different sizes are designed to evaluate the model performance, considering the transportation network and PDS. The results show a trade-off between EVs routing, PDS energy losses and EVCSs location.

Silicon carbide (SiC) particulate impregnated Al 7075 matrix composite was fabricated by stir casting method and then heat treated to T6 condition. It was then machined with multiple layer of TiN coated tungsten carbide (WC) inserts in dry environment and pollution free Spray Impingement Cooling (SIC) environment to compare the machining performance. SIC environment presented better machining performance with respect to cutting tool temperature (T), average roughness of the machined surface (Ra) and tool flank wear (VBc). Quadratic response surface models were developed by computing the experimental data. Weighted Principal Component Analysis (WPCA) based Taguchi technique was adopted to optimize the multiple responses simultaneously, which resulted 40 m/min of cutting speed (V), 0.05 mm/rev of feed (f) and 0.2 mm of cutting depth (d) was the optimal combination of process parameters.