Simulation-based Optimization Approach Research Articles

A simulation-based multi-objective optimisation approach in flexible manufacturing system planning

In this paper, an attempt has been made to address the optimum set of queues capacity, queues discipline, conveyors and transporter's speed and operational setup times in a common flexible manufacturing system (FMS) with objectives of minimisation of average stay time in queues and maximisation of throughput simultaneously. Analysis of such issues, especially when the objective functions cannot be described in mathematical terms is a complex task is focused in this paper. Here the mean of five objective functions estimated by fitness functions extracted experimentally using 36 scenarios on a valid computer simulation model. Under equal weights, the exact solution attained by the Lingo software and optimum process setting was achieved. The applicability of the new approach is ascertained through a case study involving the optimisation of real engineering system and presented using the proposed approach.

A simulation-based multi-objective optimisation approach in flexible manufacturing system planning

In this paper, an attempt has been made to address the optimum set of queues capacity, queues discipline, conveyors and transporter's speed and operational setup times in a common flexible manufacturing system (FMS) with objectives of minimisation of average stay time in queues and maximisation of throughput simultaneously. Analysis of such issues, especially when the objective functions cannot be described in mathematical terms is a complex task is focused in this paper. Here the mean of five objective functions estimated by fitness functions extracted experimentally using 36 scenarios on a valid computer simulation model. Under equal weights, the exact solution attained by the Lingo software and optimum process setting was achieved. The applicability of the new approach is ascertained through a case study involving the optimisation of real engineering system and presented using the proposed approach.

Towards a simulation-based optimization approach to integrate supply chain planning and control

Manufacturing organizations aim to adopt integrated supply chain planning to improve their operational performance. Technologies driving the 4th Industrial Revolution can support them by providing real-time data from physically distributed processes. However, in order to achieve an optimal allocation of interdependent production, inventory and transport resources, several planning and control problems have to be considered, which pose theoretical and computational challenges. In this direction, the challenges of dynamic supply chains can be addressed using adaptive simulation-based optimization and Industry 4.0 technologies. Therefore, this research proposes an adaptive simulation-based optimization approach to integrate manufacturing supply chain planning tasks. This approach is capable of dealing with complex systems as well as considering a dynamic environment with stochastic behavior. Finally, a test case is presented to evaluate the computation time feasibility and quality of the solutions provided by the method. In the present application, the method provided a convergence to a best solution in different experiments within a short amount of time, coping with the requirements of complex and uncertain scenarios.

A simulation-based optimisation approach for multi-objective inventory control of perishable products in closed-loop supply chains under uncertainty

A simulation-based optimisation approach for multi-objective inventory control of perishable products in closed-loop supply chains under uncertainty

A concept for simulation-based optimization in Vehicle Routing Problems

Transport processes directly influence the operational performance of distributed production systems. In this context, the Vehicle Routing Problem (VRP) aims for optimizing the delivery of goods, choosing the best route, which reduces travel time, distance or cost. This paper deals with a variant of the VRP, known as VRP with Time Windows (VRPTW) considering daily scheduling. Therefore, this paper proposes a simulation-based optimization approach for transport scheduling and vehicle routing. The approach was validated in a test case focus on minimize the total cost of the distance travelled in a scenario composed by 13 vehicles and 100 clients.

Simulation-based optimization approach for simultaneous scheduling of vehicles and machines with processing time uncertainty in FMS

Many stochastic factors, such as vehicle congestion, deadlock or conflict, or stochastic processing time have significant effects on performance in scheduling problem in flexible manufacturing system (FMS). This paper proposed a simulation-based optimization, L-GAOCBA, to address the simultaneous scheduling of vehicles and machines in FMS. The simulation model is constructed to evaluate the performance of scheduling decision, and includes stochastic elements, such as vehicle congestion, deadlock, and uncertain processing time. Genetic algorithm (GA) combined with local search, L-GA, plays important role in exploring the good design alternative based on simulation output. Optimal computing budget allocation (OCBA) embedded with L-GA is used to allocate the number of replications for reducing simulation replications. The design of experiments is used to analyze and set the parameters of L-GA and OCBA. This study shows that L-GAOCBA is superior for enhancing solution quality and search efficiency.

Simulation and optimization of a combined cycle gas turbine power plant for part-load operation

Combined cycle gas turbine (CCGT) power plants must often run at part-load conditions, as the electricity demand varies constantly. We present a method and necessary correlations for simulating the part-load operation of a typical CCGT plant in a commercial simulator (e.g. GateCycle). We show that assuming constant values for some equipment parameters (e.g. efficiencies) and ignoring the operating maps of key equipment can overestimate plant performance significantly at part-loads. Furthermore, a rise in the ambient temperature lowers the plant capacity, but increases the plant efficiency. Then, we propose a simulation-based optimization approach that yields an optimal operating strategy to maximize the overall plant efficiency for any part-load. Our strategy forms a basis for evaluating the two widely used operating policies (fuel flow control or FFC and inlet guide vane control or IGVC). Our proposed strategy increases the plant efficiency by as much as 2.63% (absolute) over FFC and 0.93% over IGVC. This work highlights the need for integrating the two cycles (gas turbine and steam) to optimize the plant performance. We find that FFC seems to prioritize the gas turbine and IGVC tends to prioritize the steam cycle, while our proposed strategy strikes an optimal balance between the two.

A discrete-event optimization framework for mixed-speed train timetabling problem

Railway scheduling is a complex task of rail operators that involves the generation of a conflict-free train timetable. This paper presents a discrete-event simulation-based optimization approach for solving the train timetabling problem to minimize total weighted unplanned stop time in a hybrid single and double track railway networks. The designed simulation model is used as a platform for generating feasible conflict-free train timetables. It includes detailed infrastructure information, such as station characteristics, trains running time and praying intervals. The proposed approach has the capability of scheduling trains in large-scale networks subject to the capacity constraints and infrastructure characteristics. In optimization procedure, a path relinking meta-heuristic algorithm is utilized to generate near-optimal train timetables. A case study of Iran railway network is selected for examining the efficiency of the meta-heuristic algorithm. The computational result shows that the proposed approach has the capability of generating near-optimal timetable in real-sized train scheduling problems.

A Simulation-Based Optimization Model for Scheduling New Product Development Projects in Research and Development Centers

a simulation-based optimization approach for the purpose of finding a near-optimal answer can be efficient and effective. In the present paper, first, the mathematical model for the project activity scheduling problem has been presented with a job shop approach. Then, using the Arena 14 software, the simulation model has been designed. Consequently, a numerical example has been solved via running the model and using variance analysis in order to find a near-optimal answer in terms of earliness profits subtracted by tardiness costs, with the purpose of choosing the best prioritization rule for activities in research teams. In the numerical example, the results reveal that the FCFS method has the highest value of the objective function, and possesses a significant difference from the other methods. After determining the best method, different scenarios regarding the number of resources in the workstations which possess long waiting times have been analyzed, whereby the results show that doubling the number of resources in these workstations can improve the objective function towards a positive output. In addition, the results from the present paper reveal that, contrary to other optimization methods, there is no need for an exact mathematical model in simulation and one can achieve optimal results via a conceptual mathematical model. Therefore, this issue can facilitate the solution of optimization problems, provided that they can be changed to a simulation model.

Simultaneous locomotive assignment and train scheduling on a single-track railway line: A simulation-based optimization approach

• Two optimization models for integrated problem are formulated. • We propose an efficient approach to assign locomotives and schedule trains. • Numerical experiments validate the effectiveness of the solution approach. This paper aims to assign locomotives to unscheduled trains with pre-planned departure times instead of trains with a pre-scheduled timetable on a single-track railway line. Considering both rigorous and relaxed situations, this paper formulates two mixed-integer programming models of the integrated optimization models, to simultaneously assign locomotives to trains and schedule trains, so that train delay is minimized. In order to solve these problems within reasonable computational times, we develop an efficient optimization approach on the basis of designed locomotive assignment rules and train movement simulation methods. For comparison purposes, two heuristics, including local branching method and relaxation induced neighborhood search approach which can be embedded into an optimization solver to effectively solve mixed-integer programming models, are also introduced. Finally, we implement extensive numerical experiments to validate the dependability and effectiveness of the proposed optimization models and approaches.

Integrating meta-heuristics, simulation and exact techniques for production planning of a failure-prone manufacturing system

This paper considers a real-world production planning problem in which production line failures cause uncertainty regarding the practical implementation of a given production plan. We provide a general formulation of this problem as an extended stochastic knapsack problem, in which uncertainty arises from non-trivial perturbations to the decision variables that cannot be represented in closed form.We then proceed by describing a combination of exact optimization, simulation and a meta-heuristic that can be employed in such a setting. Specifically, a discrete-event simulation (DES) of the production system is developed to estimate solution quality and to model perturbations to the decision variables. A genetic algorithm (GA) can then be used to search for optimal production plans, using a simulation-based optimization approach. To provide effective seeding to the GA, we propose initialization operators that exploit mathematical programming in combination with the DES model.The approach is benchmarked against integer linear programming and chance-constrained programming. We find that our approach significantly outperforms contestant techniques under various levels of uncertainty.

A multi-objective simulation-based optimization approach for inventory replenishment problem with premium freights in convergent supply chains

In this study, a multi-objective simulation-based optimization approach is developed to solve inventory replenishment problem with premium freights in convergent supply chains. In this context, a decomposition-based multi-objective differential evolution algorithm (MODE/D) is used to determine demand forecast adjustment factor, safety stock and supplier flexibility parameters that minimize total holding cost, inbound and outbound premium freight ratios simultaneously. The proposed approach is applied a set of problem instances and the performance of the proposed approach is evaluated in comparison with the performance of non-dominated sorting genetic algorithm-II (NSGA-II). Furthermore, the proposed approach is applied to a multi-national automotive supply chain spread on Europe. The results reveal that the proposed approach is effective in solving inventory replenishment problem with premium freights in convergent supply chains.

Flexible sustainable manufacturing via decision support simulation: A case study approach

The main purpose of this article is to illustrate a decision support system for the redesign of an existing assembly facility in an automotive factory for new product introduction in a flexible and sustainable way with the help of a simulation-based optimization approach. In our study, we have assigned equal weightage to each performance parameter of the facility, including economic, environmental, social, and manufacturing flexibility. Our analyses further helped validate the changes required before implementation. Implementation of the proposal based on our findings helped the organization achieve economic benefits, saving around USD 0.314 million, and environmental benefits, saving around 335 kW of energy per day compared to the previously estimated measures. Along with these benefits, our studies have also helped improve safety measures and working conditions. Finally, we have outlined the limitations of our study and noted future research directions.

Developing Simulation Based Optimization Mechanism for Novel Stochastic Reliability Centered Maintenance Problem

This research investigates joint scheduling of maintenance and production planning. This novel integrated problem takes benefit of reliability-centered maintenance (RCM) for monitoring and managing maintenance function of a stochastic complex production-planning problem namely flexible job shop scheduling problem (FJSP). The developed RCM works based on stochastic shocking of machines during their process time. In fact, it implements condition based maintenance approach regulated according to stochastic reliability concept. Comparison of the system reliability with critical levels determines the failure statues of the machines. It activates two main types of reaction called preventive and corrective maintenance. Considering breakdown of the system between inspection intervals makes the proposed model more realistic. Moreover, maintenance activity times and their duration are considered stochastically. Because of the high complexity level for this joint system, simulation-based optimization (SBO) approach is proposed for solving the problem. This SBO searches the feasible area through genetic algorithm (GA) and biogeography based optimization (BBO) algorithm. Different test problems, statistical methods, novel visualizations are used to discuss the problem and the algorithm explicitly.

A novel integrated condition-based maintenance and stochastic flexible job shop scheduling problem: simulation-based optimization approach

Integrated consideration of production planning and maintenance processes is a real world assumption. Specifically, by improving the monitoring equipment such as various sensors or product-embedded information devices in recent years, joint assessment of these processes is inevitable for enhancing the level of the system optimization. By means of this equipment, managers can benefit from a condition-based maintenance (CBM) for monitoring and managing their system. The chief aim of the paper is to develop a stochastic maintenance problem based on CBM activities engaged with a complex applied production problem called flexible job shop scheduling problem (FJSP). This integrated problem considers two maintenance scenarios in terms of corrective maintenance (CM) and preventive maintenance (PM). The activation of scenario is done by monitoring the degradation condition of the system and comparing the associated value by predetermined PM and CM levels. Moreover, to make it more realistic, the developed problem allows breakdown of the system between inspection intervals. The event time and duration of performing the maintenance activities are also considered stochastic. The developed methodology copes with high stochastic complexity of proposed problem through simulation-based optimization (SBO) approach, which works based on harmony search optimization algorithm. The developed items of SBO are discussed on different generated test problems and assessed through statistical methods and new visualization approach. Numerical example shows that the proposed method is practical for proposed integrated CBM and FJSP problem. It also offers a basis for any implementation of CBM on production problems.

A simulation-based optimization approach to reschedule train traffic in uncertain conditions during disruptions

Delays and disruptions reduce the reliability and stability of the rail operations. Railway traffic rescheduling includes ways to manage the operations during and after the occurrences of such disturbances. In this study, we consider the simultaneous presence of large disruptions (temporary full or partial blockage of tracks) as well as stochastic variation of operations, as a source of disturbance. The occurrence time of blockage and its recovery time are given. We designed a simulation-based optimization model that incorporates dynamic dispatch priority rules with the objective of minimizing the total delay time of trains. We moreover design a variable neighborhood search meta-heuristic scheme for handling traffic under the limited capacity close to the blockage. The new plan includes a set of new departure times; dwell times, train running times. We evaluate the proposed model on a set of disruption scenarios covering a large part of the Iranian rail network. The result indicates that the developed simulation-based optimization approach has substantial advantages in producing practical solution quickly, when compared to commercial optimization software. In addition, the solutions have a lower average and smaller standard deviation than currently accepted solutions, determined by human dispatcher or by standard software packages.

Integrated production and maintenance planning for parallel machine system considering cost of rejection

Production scheduling and maintenance planning have interdependencies but been often considered and optimized independently in practice and in the literature. Furthermore, product quality has direct relationship with maintenance planning. This paper proposes an integrated approach for production scheduling and maintenance planning for parallel machine system considering the effect of cost of rejection. The approach aims to determine optimal production schedule and maintenance plan such that overall operations cost is minimized. A simulation-based optimization approach is used to solve the problem. A numerical investigation is performed to illustrate the approach. The integrated approach shows between 0.6 and 35.8% improvement in term of overall operations cost over independent approach for various scenarios. The results indicate that simultaneous consideration of production scheduling and maintenance planning results into better system performance.

A life cycle approach to optimizing carbon footprint and costs of a residential building

Finding life cycle optimized building designs is a challenging task. It requires the inclusion of all phases of the building life cycle in a single optimization problem. The present study demonstrates a life cycle simulation-based optimization approach, by including the operational carbon footprint (OCF) and embodied carbon footprint (ECF) of a building. Particularly, finding and analyzing the difference between life cycle (OCF + ECF) optimized design and operational (only OCF) performance-based optimized design is the primary goal of the current study. The life cycle optimization method is applied to a townhouse in Finland to determine carbon-cost optimal designs. Different options of building envelope insulation thicknesses, window types, heating systems, heat recovery units, and PV area are explored as design variables. It has been found that the heating system is a dominant design variable, which results in clearly separated pareto fronts for each system. Generally, a majority of the design variables' optimal values, obtained from OCF + ECF optimization, suggest thinner insulation for the building envelope and a larger PV area, compared to the optimal solutions from OCF optimization. In a carbon optimal solution, the share of ECF is 39% of the life cycle carbon footprint, whereas in a cost optimal solution, the share of ECF is 28% of the life cycle carbon footprint.

Inventory control policy for a periodic review system with expediting

• The inventory control problem of an inventory system with expediting is studied. • We propose (S, e) policy that allows to expedite a part of order whenever needed. • An effective simulation optimization approach is designed to find the best (S, e). • (S, e) policy outperforms similar policies and is applicable to various areas. In this work, we examine a single stage, periodic review inventory system where two modes, namely regular mode and expediting mode are available for a firm to obtain its replenishment. The firm can choose expediting mode with shorter lead time at a higher cost when necessary. A two-replenishment-mode model, with random expediting points is established and an innovative ordering policy ( S, e ) which replenishes the inventory level to S in every cycle and expedites a part of the order using fast mode when the inventory level drops to or below a certain level e , is proposed. A simulation-based optimization approach is employed to solve the problem. To be specific, an infinitesimal perturbation analysis (IPA) method is applied to estimate the gradients of the objective function and a gradient search algorithm is then used to find the best ( S, e ). Numerical experiments have validated the IPA estimate of ( S, e ) and shown that our new ( S, e ) policy outperforms the pure order-up-to policy and two single review moment policies. Sensitivity analysis has been conducted to evaluate the impact of system parameters of interest and managerial insights have been drawn.

A simulation based optimization approach for spare parts forecasting and selective maintenance

Equipment of the Army encounters various modes of exploitation depending on the scenario in which it is used. Typically, the missions are followed by intervals which can be used for maintenance. This is a suitable condition for employment of selective maintenance strategy. However, this maintenance interval is bound by the constraints of time, resources and desired reliability before the start of the next mission. This calls for optimization of maintenance activities that can be fitted into the maintenance break. There is also a requirement of having a forecasting technique for reducing the supply lead times. This paper lays out a methodology to use simulation for predicting failures in the army equipment. A Genetic Algorithm (GA) based approach is then used for optimizing the maintenance activities before the start of the maintenance break. The process of Simulation plus GA Optimization is automated using a program in MATLAB. The novelty of the work lies in modifying the process of Simulation and GA Optimization to suit the exact modus operandi employed by the Army in deploying equipment for peace, training exercise and war (mission with or without some maintenance break) separately. In addition to optimizing the maintenance activities, the methodology also helps in forecasting the requirement of spare parts both before and during the mission.