Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm

This paper addresses the problem of finding robust and stable solutions for the flexible job shop scheduling problem with random machine breakdowns. A number of bi-objective measures combining the robustness and stability of the predicted schedule are defined and compared while using the same rescheduling method. Consequently, a two-stage Hybrid Genetic Algorithm (HGA) is proposed to generate the predictive schedule. The first stage optimizes the primary objective, minimizing makespan in this work, where all the data is considered to be deterministic with no expected disruptions. The second stage optimizes the bi-objective function and integrates machines assignments and operations sequencing with the expected machine breakdown in the decoding space. An experimental study and Analysis of Variance (ANOVA) is conducted to study the effect of different proposed measures on the performance of the obtained results. Results indicate that different measures have different significant effects on the relative performance of the proposed method. Furthermore, the effectiveness of the current proposed method is compared against three other methods; two are taken from literature and the third is a combination of the former two methods.Robust Stable Flexible job shop scheduling problem Machine breakdowns

Vehicle routing problem in omni-channel retailing distribution systems

This paper introduces a variant of the vehicle routing problem where a group of retail stores are served from a distribution center using a fleet of vehicles. Moreover, products are distributed to consumers from some of these retail stores based on product availability at inventory and by means of the same fleet of vehicles. This variant of the vehicle routing problem can be found in omni-channel retail distribution systems. Retail distribution systems are considered omni- or multi-channel systems when consumers can either place orders online or physically visit the stores to buy the products. In this problem, the decisions of assigning consumers to retail stores based on inventory availability are combined with finding the routes of vehicles. The new problem can be considered a generalization of both capacitated vehicle routing problem and the pickup and delivery problem. The paper presents a mathematical formulation to describe this problem and proposes two solution approaches (two-phase heuristic and multi-ant colony algorithm). We also generate new benchmark problem instances to evaluate the performance of the proposed solution approaches. 2017 Elsevier B.V.The authors acknowledge the funding received from the University of Manitoba and the NSERC discovery grants to support this research. The authors thank the anonymous referees for their valuable comments.Scopu

Hybridized ant colony algorithm for the Multi Compartment Vehicle Routing Problem

Multi Compartment Vehicle Routing Problem is an extension of the classical Capacitated Vehicle Routing Problem where different products are transported together in one vehicle with multiple compartments. Products are stored in different compartments because they cannot be mixed together due to differences in their individual characteristics. The problem is encountered in many industries such as delivery of food and grocery, garbage collection, marine vessels, etc. We propose a hybridized algorithm which combines local search with an existent ant colony algorithm to solve the problem. Computational experiments are performed on new generated benchmark problem instances. An existing ant colony algorithm and the proposed hybridized ant colony algorithm are compared. It was found that the proposed ant colony algorithm gives better results as compared to the existing ant colony algorithm.NSERCScopu

Optimal design of hybrid renewable energy systems in buildings with low to high renewable energy ratio

We develop a simulation-based meta-heuristic approach that determines the optimal size of a hybrid renewable energy system for residential buildings. This multi-objective optimization problem requires the advancement of a dynamic multi-objective particle swarm optimization algorithm that maximizes the renewable energy ratio of buildings and minimizes total net present cost and CO2 emission for required system changes. Three proven performance metrics evaluate the quality of the Pareto front generated by the proposed approach. The obtained results are compared against two reported multi-objective optimization algorithms in the related literature. Finally, an existing residential apartment located in a cold Canadian climate provides a test case to apply the proposed model and optimally size a hybrid renewable energy system. In this test application, the model investigates the potential use of a heat pump, a biomass boiler, wind turbines, solar heat collectors, photovoltaic panels, and a heat storage tank to produce renewable energy for the building. Furthermore, the utilization of plug-in electric vehicles for transportation reduces gasoline use where all power is generated by the building, and the utility provides the means to match intermittent renewable generation from solar and wind to the building electrical loads. Model results show that under the chosen meteorological conditions and building parameters a wind turbine, and plug-in electric vehicle technologies are consistently the optimal option to achieve a target renewable energy ratio. In particular, the optimization result shows that the renewable energy ratio can achieve near 100% by installing a 73kW wind turbine, a 200kW biomass boiler, and using plug-in electric vehicles. This option has a net present cost of C$705,180 and results in total CO2 emission of 2.4ton/year. Finally, a sensitivity analysis is performed to investigate the impact of economic constants on net present cost of the obtained non-dominated solutions.NSERC discovery grants (NSERC 315104-332300) and University of Manitoba Graduate Fellowship (UMGF)Scopu

Economic Energy Allocation of Conventional and Large-Scale PV Power Plants

During the past few decades, rapid progress in reducing the cost of photovoltaic (PV) energy has been achieved. At the megawatt (MW) to gigawatt (GW) scale, large PV systems are connected to the electricity grid to provide power during the daytime. Many PVs can be installed on sites with optimal solar radiation and other logistical considerations. However, the electricity produced by the PV power plant has to be transmitted and distributed by the grid, which leads to more power losses. With the widespread commissioning of the large-scale solar PV power plants connected to the grid, it is crucial to have an optimal energy allocation between the conventional and the PV power plants. The electricity cost represents the most significant part of the budget in the power distribution companies, which can reach in many countries billions of dollars. This optimal energy allocation is used to minimize the electricity cost from buyers' (distribution companies) point of view rather than sellers' (owners of power plants, i.e., investors) point of view. However, some constraints have to be considered and met, such as water demand, network limitations, and contractual issues such as minimum-take energy. This paper develops a model for the economic energy allocation of conventional and large-scale PV power plants, which considers both the operational aspects and the contractual provisions. The model can be used either in the design or operation phases to minimize the operating cost. Moreover, the proposed model can be used for budgeting tasks. The developed model is entirely generic and can be used for any country or electricity system regardless of the PV energy contribution. Furthermore, the Al-Karsaah power plant located in Qatar is discussed as a case study to validate the claimed contribution.Scopu

A clustering-based repair shop design for repairable spare part supply systems

In this study, we address the design problem of a single repair shop in a repairable multi-item spare part supply system. We propose a sequential solution heuristic to solve the joint problem of resource pooling, inventory allocation, and capacity level designation of the repair shop with stochastic failure and repair time of repairables. The pooling strategies to obtain repair shop clusters/cells are handled by a K-median algorithm by taking into account the repair time and the holding cost of each repairable spare part. We find that the decomposition of the repair shop in sub-systems by clustering reduces the complexity of the problem and enables the use of queue-theoretical approximations to optimize the inventory and capacity levels. The effectiveness of the proposed approach is analyzed with several numerical experiments. The repair shop designs suggested by the approach provide around 10% and 30% cost reductions on an average when compared to fully flexible and totally dedicated designs, respectively. We also explore the impact of several input parameters and different clustering rules on the performance of the methodology and provide managerial insights.Scopu

Cross-training policies for repair shops with spare part inventories

We study a spare part supply system for repairable spare parts where parallel repair servers may have multiple skills (can repair different failed parts). Demands for the spares occur according to Poisson processes with different rates. The failed spare parts are immediately replaced from the inventory. Otherwise, failed parts are backordered and fulfilled when a spare of the same type becomes available (repaired). The repair servers are heterogeneous and can process certain types of repairables only if they have the necessary skill. In this system, in contrast with the other skill-optimization models, there is a trade-off between adding extra skills to servers (training) or adding extra inventory. In this paper, we formulate a mathematical model to optimize the assignment of skills to servers taking into account inventories for the ready-to-use spares and backorder costs (penalties). To optimize the skill assignments and inventories, we use a hybrid approach combining a Genetic Algorithm (GA) with simulation modeling. The proposed simulation-based optimization heuristic is used for extensive analysis of optimal skill assignments where we show that partial flexibility for repair servers with limited cross-training will lead to lower total system cost.This publication was made possible by the NPRP award [ NPRP 7-308-2-128 ] from the Qatar National Research Fund (a member of The Qatar Foundation ).Scopu