Tarek Y. ElMekkawy

Hybridized ant colony algorithm for the Multi Compartment Vehicle Routing Problem

Hybridized ant colony algorithm has been proposed to solve the Multi Compartment Vehicle Routing Problem.Numerical experiments were performed to evaluate the performance of the algorithm.The numerical results showed that the average total length improvement of the proposed HAC over the existing ACS is 5.1%. In addition, the proposed HAC maintains its high performance in large problems on contrary of the existing ACS.The numerical result for the effect of hybridizing the ant colony algorithm with local search schemes has been presented.Illustration of the benefit of using two-compartment vehicles instead of single-compartment vehicles has been presented. 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.

Bi-criteria appointment scheduling of patients with heterogeneous service sequences

We address scheduling of different patient type with stochastic service times.Different heterogeneous service sequences in multi-stage facilities are considered.We minimize the waiting time of patients and the completion time of the facility.Mathematical programming, simulation, and multiobjective tabu search are used in this work.We provide a real industrial case-study and analyze the results. This article addresses the challenges of scheduling patients with stochastic service times and heterogeneous service sequences in multi-stage facilities, while considering the availability and compatibility of resources with presence of a variety of patient types. The proposed method departs from existing literature by optimizing the scheduling of patients by integrating mathematical programming, simulation, and multiobjective tabu search methods to achieve our bi-objectives of minimizing the waiting time of patients and the completion time of the facility. Through intensive testing, the performance of the proposed approach is analyzed in terms of the solution quality and computation time, and is compared with the performance of the well-known method, Non-Dominated Sorting Genetic Algorithm (NSGA-II). The proposed method is then applied to actual data of a case study operating department in a major Canadian hospital and promising results have been observed. Based on this study, insights are provided for practitioners.

Integrated Optimization for Stock Levels and Cross-Training Schemes with Simulation-Based Genetic Algorithm

A spare part supply system for repairable spares in a repair shop is modeled as a set of heterogeneous parallel servers that have the ability to repair only certain types of repairables. The proposed model minimizes the total cost of holding inventory for spare parts, cost for backorder arising from downtime of the system due to the lack of spare parts and the cost of crosstraining for servers. Simulation-based Genetic Algorithm (GA) is proposed to optimize inventory levels and to determine the best skill assignments to servers, i.e., cross-training schemes. When methodologys performance is compared with total enumeration, tight optimality gaps are obtained.

Simulation based particle swarm optimization of cross-training policies in spare parts supply systems

We study a single location supply system for repairable spare parts. The system consists of a multi-server repair shop and inventory with ready-to-use spare parts. When a failed part is received, a new (or as-good-as-new) replacement part is sent back, and the failed part is forwarded to the repairshop. In the case of unavailability of spare parts, failed requests are backordered and fulfilled when a ready-for-use part of the same type is received from the repairshop. The repair shop has several multi-skilled parallel servers (technicians) that are capable of handling certain types of spares. In this paper, we propose a Particle Swarm Optimization heuristic combined with Discrete-Event Simulation for optimizing the cross-training policy (skill assignment scheme) while minimizing the total system cost (consisting of inventory costs, backorder penalty cost, server cost and skill cost).

A Pooling Strategy for Flexible Repair Shop Designs.

We discuss the design problem of a repair shop in a single echelon repairable multi-item spare parts supply system. The repair shop consists of several parallel multi-skilled servers, and storage facilities for the repaired items. The effectiveness of repair shops and the total cost of a spare part supply system depend highly on the design of repair facility and the management of inventory levels of the spare parts. In this paper, we concentrate on a design scheme known as pooling. A repair shop can be considered as a pooled structure if the spare parts can be divided into clusters such that each part type is unambiguously assigned to a single cluster (cell). Nonetheless, it is both an important and tough combinatorial optimization question to determine which type of spares to pool together. We propose a sequential solution heuristic to find the best pooled design by considering inventory allocation and capacity level designation of the repair shop. The numerical experiments show that the suggested solution approach has a reasonable algorithm run time and yields considerable cost reductions.

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

Abstract 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.

Multi-objective optimisation of facility location decisions within integrated forward/reverse logistics under uncertainty

Increasing interest to the environmental, social and economic aspects of the supply chains has motivated supply chain managers to optimise location-allocation decisions within closed-loop logistics networks. This paper presents a multi-objective model to optimise facility location decisions in integrated forward/reverse streams under uncertainty. The objectives of the model are to minimise total costs and simultaneously maximise customer satisfaction considering uncertainties in demand and return rate. The proposed model is solved by integrating genetic algorithm with sampling average method. The application of the model is examined in a real case study of car after sales network. The result of the model is compared to a deterministic model to identify how uncertainties affect the optimal configurations. The other experiment is carried out to study the effect of integrating forward and reverse logistics operations on the stakeholders objectives. Finally, a post-analysis is applied to help in choosing one solution among many different solutions.

Stochastic Optimization of Hybrid Renewable Energy Systems

The stochastic nature of energy demand and renewable energy (RE) resources make the design of hybrid renewable energy systems as a complex problem. In this paper, an innovative stochastic optimization approach is proposed for optimal sizing of hybrid renewable energy systems (HRES) incorporating existing uncertainties in RE resources and energy load. The design problem is formulated based on multiobjective optimization framework with three objective functions including minimize total net present cost (NPC), maximize renewable energy ratio (RER), and minimize fuel emission. The reliability index named loss of load probability (LLP) is considered as a constraint with a desirable level. The Pareto front (PF) of developed multi-objective optimization problem is approximated with the help of the integration of dynamic multi-objective particle swarm optimization (DMOPSO) algorithm, simulation module, and sampling average method. Synthetic data generation approaches are applied to tackle the randomness in wind speed, solar irradiation, ambient temperature, and energy load. A building located in Canada is used as the case study to assess the performance of the developed model. Finally, the obtained PF by the stochastic optimization approach is examined against the deterministic PF using the most famous performance metrics.Copyright

Stochastic Optimization of the Repair Shops Location Problem Using Particle Swarm Optimization Algorithm

The optimization of facility location decisions is critical for the success of a supply chain in a market since it can contribute to long-term performance of the supply chain. In the last two decades, the number of research in this field has been growing to address more realistic problems such as incorporating uncertainties in repair time and demand. In this paper, a particle swarm optimization algorithm (PSO) is employed to locate repair shops in a stochastic environment. The problem aim is to decide about the location and the capacity of local repair shops as well as identifying the capacity of central repair shop to minimize total expected cost. It is assumed that customers select the closest local repair shop. In the local repair shops, services are available to repair customer’s broken items and a number of spare parts are stored to supply customers’ needs. Additionally, each repair shop is allowed to open some servers, depending on the number of customers, to serve its customers. If a stock-out happens, a customer should wait until the part is repaired in that shop. When a local repair shop is unable to repair a part, the part is sent to the central repair shop to be repaired. The central repair shop follows similar strategy for spare part inventory. The contribution of this paper is to employ a meta-heuristic solution approach based on particle swarm optimization for locating repair shops problem. In order to evaluate the performance of the employed solutions approach, its result is compared to other methods and differences are highlighted.Copyright