Research on rush order insertion rescheduling problem under hybrid flow shop based on NSGA-III

Rush order insertion is widespread in the enterprises that apply make-to-order production mode which affects the stability of production system. This article studies rush order insertion rescheduling problem (ROIRP) under hybrid flow shop (HFS) with multiple stages and multiple machines. A mathematical model simultaneously considering constraints such as lots, sequence-dependent set-up times and transportation times with objectives to minimise makespan, total transportation time and total machine deviation between the initial scheduling plan and the event-driven rescheduling plan is developed and NSGA-III is applied to solve the problem. Three groups of experiments are carried out which verify the suitability of NSGA-III for HFS scheduling problem with multi-objective and multi-constraint, the effectiveness of NSGA-III for the proposed ROIRP and the feasibility and effectiveness of the proposed model and algorithm in solving the ROIRP of a realistic ship pipe parts manufacturing enterprise.     

A multi-objective optimization model for hub network design under uncertainty: An inexact rough-interval fuzzy approach

This article proposes a multi-objective mixed-integer model to optimize the location of hubs within a hub network design problem under uncertainty. The considered objectives include minimizing the maximum accumulated travel time, minimizing the total costs including transportation, fuel consumption and greenhouse emissions costs, and finally maximizing the minimum service reliability. In the proposed model, it is assumed that for connecting two nodes, there are several types of arc in which their capacity, transportation mode, travel time, and transportation and construction costs are different. Moreover, in this model, determining the capacity of the hubs is part of the decision-making procedure and balancing requirements are imposed on the network. To solve the model, a hybrid solution approach is utilized based on inexact programming, interval-valued fuzzy programming and rough interval programming. Furthermore, a hybrid multi-objective metaheuristic algorithm, namely multi-objective invasive weed optimization (MOIWO), is developed for the given problem. Finally, various computational experiments are carried out to assess the proposed model and solution approaches.     

An integrated neural network–simulation algorithm for performance optimisation of the bi-criteria two-stage assembly flow-shop scheduling problem with stochastic activities

This paper presents an integrated computer simulation and Artificial Neural Network (ANN) algorithm for a stochastic Two-Stage Assembly Flow-Shop Scheduling Problem (TSAFSP) with setup times under a weighted sum of makespan and mean completion time (MCT) criteria, known as bi-criteria. Significantly, it should be noted that there is no mathematical model to analyse the stochastic model, therefore simulation is used to solve the problem. The simulation model enables decision makers to consider the influence of job scheduling on machines in order to examine both criteria simultaneously. Since it is not possible to evaluate all sequence combinations using the simulation model in a reasonable time, multilayered neural network meta-models have been trained and used to estimate objective function values composed of both makespan and mean completion time criteria for the stochastic TSAFSP. To the best of the authors’ knowledge, this is the first study that considers stochastic machine breakdown, processing times, setup times, makespan and mean completion time as objectives concurrently. The TSAFSP is modelled by Visual SLAM simulation software. The simulation output results are then given to the ANN as inputs to build the meta-model. This meta-model is then used to obtain the results with the optimum values. The advantage of these meta-model applications is a reduction in the number of simulation runs and consequently a reduced run time. Also, this is the first study that introduces an intelligent and flexible algorithm for handling stochastic TSAFSP.     

A fast procedure for computing the total system cost of an appointment schedule for medical and kindred facilities

Scheduling outpatients and medical operation rooms has the following structure: Nusers are given appointment times to use a facility, the duration required by the facility to service each user is stochastic. The system incurs a “user idle cost” if a user arriving at the appointed time finds the facility still engaged by preceding users, while a “facility idle cost” is incurred if the facility becomes free before the next user arrives. We develop an accurate procedure to compute the expected total system costs for any given appointment schedule. Compared to earlier related procedures, ours is much faster and can handle larger problems as well as very general service-time distributions. We then show that this fast computation procedure enables one to determine easily the “lowest-cost appointment schedule” for any given “job” (i.e., “user”) sequence. This in turn will enable one to search for the optimal job sequence that has the best “lowest-cost appointment schedule”.     

Integrated multi-site production-distribution planning in supply chain by hybrid modelling

Allocation of production volumes among multiple manufacturing sites and distribution of products among distribution channels involves many quantitative and qualitative variables and constraints. An integrated multi-product, multi-period, multi-site production-distribution planning subject to the production and distribution constraints, distribution system and local customers demand is considered in this paper. To do this, we took advantage of a hybrid mathematical-simulation model to solve the proposed problem. A mathematical model has been developed to solve the problem, aiming to decrease the costs of set up, production, inventory, distribution and transportation. Because of stochastic factors such as unexpected delays, queuing and machine failure, operation time provided by mathematical model cannot reflect dynamic characteristic of real-world systems and optimal solution of mathematical model is not acceptable in practice. Therefore, we took advantage of a hybrid mathematical-simulation approach to explore the behaviour of the real-world system. Through the computational experiments, we demonstrated that the number of iterations to converge hybrid procedure would lessen when we consider the production-distribution problem in an integrated manner. Also, supply chain overall costs will be reduced through the integration of production and distribution problems. Finally, the solution is a realistically optimal solution for the proposed integrated production-distribution planning problem.     

带时间窗的时变多目标危险化学品道路运输路径优化

研究了危险化学品道路运输路径优化（VRP）问题,考虑了该问题的3个方面：1）路径选择涉及运输时间与运输风险两个目标;2）运输时间与风险具有时变特性;3）道路节点的服务时间窗限制。本文以运输时间和风险为多目标,综合考虑以上约束,建立了该问题的数学模型并设计了蚁群算法进行求解。求解结果表明该算法可以有效计算帕累托最优路径,决策者可结合实际问题和决策偏好作出最合适的决策,同时运输企业可依据不同时刻的运输结果制定车辆的出发时刻表,监管部门可通过合理规划各路径的服务时间窗及允许停留等待的节点来调节各路径运输时间及风险。     

A multi-objective flow shop scheduling with resource-dependent processing times: trade-off between makespan and cost of resources

In traditional flow shop scheduling problems (FSSPs), the processing times are assumed to be pre-known and fixed parameters while in many practical environments, the processing times can be controlled by consumption of extra resources. In this paper, we propose resource-dependent processing times (RDPT) for permutation FSSP in which the processing time of a job depends on the amount of additional resources assigned to that job. To make a trade-off between makespan and required amount of resources, two conflict objective functions are considered: the minimisation of maximum completion time and the minimisation of total cost of resources. In order to solve the problem considered in this paper, a decomposition approach is suggested that strives to deal with the original model via two sub-problems: (i) sequencing problem; and (ii) resource allocation problem. A hybrid discrete differential evolution (HDDE) algorithm with an effective coordinate directions search and a variable neighbourhood search (VNS) are combined to solve two sub-problems. Furthermore, a statistical procedure is employed to adjust the significant parameters of the proposed HDDE and VNS algorithms. This procedure is based on the stepwise regression (SR) technique. The effectiveness of the suggested hybrid algorithm is investigated through a computational study and obtained results show the good performance of our approach with regard to the other algorithms.     

Toward a formalization of affordance modeling for engineering design

When developing an artifact, designers must first capture and represent user needs. These needs can then be transformed into system requirements or objectives. The contribution of this work is rooted in the formalization of the affordance-based approach for capturing user needs in the early stages of design. This formalization comes in three forms: the first affordance basis for engineering design (a defined set of affordances), a formal structure for affordance statements, and a new relational model structure. This formalization is intended to improve model quality and consistency, while managing model creation resources. Further, this affordance-based approach to capturing user needs imposes a level of abstraction that forces solution independence yet is capable of capturing the large range of user needs. As such, the approach provides a structured approach to problem abstraction—the process of specifying user needs without reference to specific solutions. This affordance-based problem representation relies on other design process tools to help develop the actual artifact, which is also discussed.     

Time-redundant system reliability under randomly constrained time resources

The paper considers the time-redundant system where the system total task is a sequence of n phases and the total task must be executed during constrained time. For every phase, there is its own server, which executes the phase task during randomly distributed time. The server is not perfectly reliable and two types of failure (“open” and “closed”) are possible. Redundant servers may be used in any phase. The time–probability characteristics are introduced for any task, based on which the system reliability is treated as a probability that the system total task will be correctly completed during a corresponding time resource, which also may be randomly distributed. The adequate model is presented and a semi-Markov process is used as a mathematical technique. The closed-form solution was derived based on an acyclic Semi-Markov process. The numerical example of the elaborated approach is presented.     

跨区域物流网络优化的模型与算法的改进研究

针对跨区域物流网络存在的非优问题,综合考虑物流运输成本、过城费、出入库费、装卸费、仓储费以及物流网络流量等决策因素,建立了双目标数学模型:最小化物流总成本和最大化物流网络流量。给出了改进的最小费用最大流算法,并结合有供需需求的网络流算法和典则型网络转化法对模型进行求解。最后,通过MATLAB仿真实验,证实了模型的价值和算法的有效性。     

Modeling an Augmented Lagrangian for Blackbox Constrained Optimization

Constrained blackbox optimization is a difficult problem, with most approaches coming from the mathematical programming literature. The statistical literature is sparse, especially in addressing problems with nontrivial constraints. This situation is unfortunate because statistical methods have many attractive properties: global scope, handling noisy objectives, sensitivity analysis, and so forth. To narrow that gap, we propose a combination of response surface modeling, expected improvement, and the augmented Lagrangian numerical optimization framework. This hybrid approach allows the statistical model to think globally and the augmented Lagrangian to act locally. We focus on problems where the constraints are the primary bottleneck, requiring expensive simulation to evaluate and substantial modeling effort to map out. In that context, our hybridization presents a simple yet effective solution that allows existing objective-oriented statistical approaches, like those based on Gaussian process surrogates and expected improvement heuristics, to be applied to the constrained setting with minor modification. This work is motivated by a challenging, real-data benchmark problem from hydrology where, even with a simple linear objective function, learning a nontrivial valid region complicates the search for a global minimum. Supplementary materials for this article are available online.     

Multi-objective integrated acyclic crew rostering and vehicle assignment problem in public bus transportation

In this study, we try to solve a real planning problem faced in public bus transportation. It is a multi-objective integrated crew rostering and vehicle assignment problem. We model this problem as a multi-objective set partitioning problem. Most of the time, crew rostering problem with a single-objective function is considered, and the output may not satisfy some transport companies. To minimize the cost and maximize the fairness of the workload among the drivers, we define many criteria. Although crew rostering problem and its integrated versions appear in the literature, it is the first time these two problems are integrated. We propose a new multi-objective tabu search algorithm to obtain near Pareto-optimal solutions. The algorithm works with a set of solutions using parallel search. We test our algorithm for the case with ten objectives and define a method to choose solutions from the approximated efficient frontier to present to the user. We discuss the performance of our meta-heuristic approach.

     

Ein Simulationsmodell für den Landeprozeß von Flugzeugen

Zusammenfassung Die Eliminierung von Wartezeiten aus einer empirischen Landezeitverteilung mit Wartezeiten wird mit Hilfe einer Monte-Carlo-Simulation vorgenommen. Zur Verifizierung einer echten Landezeitverteilung wird ein symbolisches Strukturmodell des Landevorgangs erstellt, das alle Komponenten des realen Prozesses abbildet, z. B. eine Reihenfolgenbildung von Flugzeugen im Endanflug und Zuweisungen von Flugzeugen in Warteräumen. Mit simulierten LFZ-Ankünften im Flughafen-Nahbereich werden synthetische Verteilungen von Landezeiten mit Wartezeiten erzeugt, die mit einem Anpassungstest (CHI-QUADRAT) bezüglich der empirischen Landezeitverteilung bewertet werden. Das Modell des Landeprozesses wird in drei Stufen entwickelt. Die Funktion der einzelnen Modelle werden erläutert und die Wirksamkeit der einzelnen Modellstrukturen anhand der erzielbaren Ergebnisse bewertet.

---

Summary In order to separate proportions of holding times from an empirical distribution of landing times, a symbolized model is set up to simulate the landing process. The model includes all structural components of approach procedures, e.g. sequencing of aircraft before final approach and allocation to holding areas. For simulation runs, a distribution of aircraft arrivals at airport terminal area is required as well as test distributions derived from the empirical landing time distribution which are used as potential landing times distributions. Applying the Monte-Carlo-method, synthetic landing time including hilding times are created. The adaptation of the simulated distributions to the empirical distribution is checked by the chi-square-test. The three stages in developing the model are described and the efficiency of the different model structures is approved by the results yielded.

     

The Single Period Stochastic Plant Layout Problem

In this paper we present an optimal solution procedure for the single period stochastic plant layout problem. The procedure only requires solving a “deterministic” from-to flow matrix. This “deterministic” matrix is a weighted-average of all from-to flow matrices. Since it may be difficult to estimate the probabilities of occurrence of the various states of nature, we developed a simulation model to test the robustness of our approach. For over 26,000 problems examined, the average error of using our approach was very small, only about one-sixth of one percent.     

Finite element modeling of the filament winding process

A finite element model of the wet filament winding process was developed. In particular, a general purpose software for finite element analysis was used to calculate the fiber volume fraction under different process conditions. Several unique user defined subroutines were developed to modify the commercial code for this specific application, and the numerical result was compared with experimental data for validation. In order to predict the radial distribution of the fiber volume fraction within a wet wound cylinder, three unique user defined subroutines were incorporated into the commercial finite element code: a fiber consolidation/compaction model, a thermochemical model of the resin and a resin mixing model. The fiber consolidation model describes the influence of the external radial compaction pressure of a new layer as it is wound onto the surface of existing layers. The thermochemical model includes both the cure kinetics and viscosity of the resin. This model analyzes the composite properties and tracks the viscosity of the resin, which is a function of the degree of cure of the resin. The resin mixing model describes the mixing of “old” and “new” resin as plies are compacted. Validations were made by comparing image analysis data of fiber volume fraction in each ply for filament wound cylinders with the FEM results. The good agreement of these comparisons demonstrated that the FEM approach has can predict fiber volume fraction over a range of winding conditions. This approach, then, is an invaluable tool for predicting the effects of winding parameters on cylinder structural quality.     

Balancing user preferences for aircraft schedule recovery during irregular operations

In this paper, the irregular operations problem is approached for the first time in a way that allows an airline to provide for schedule recovery with minimal deviations from the original aircraft routings. A network model with side constraints is presented in which delays and cancellations are used to deal with aircraft shortages in a way that ensures a significant portion of the original aircraft routings remain intact. The model is flexible, allowing user preferences in the objective, and thereby reflecting the immediate concerns of the decision-maker in the recovery schedule. The model can be tailored by airline operations personnel to emphasize differing solution characteristics. Fleet data provided by Continental Airlines are used to demonstrate the approach. Extensive testing indicates that optimal or near-optimal solutions are routinely obtained from the LP relaxation of the network formulation. When integrality is not achieved, a rounding heuristic is provided that finds feasible solutions within a small fraction of the optimum.     

Hybrid Monte Carlo for photon transport through optically thick scattering media

A Monte Carlo simulation code developed to model time-domain transillumination measurements with small-area detectors through an optically thick scattering slab is presented. A hybrid approach has been implemented to reduce calculation times. Most of the scattering slab is treated stochastically, albeit with variance reduction techniques and the isotropic diffusion similarity rule. The contribution to the output signal per unit area and time of photon packets propagating in a thin slice near the output face of the slab is calculated analytically after each propagation step. This approach drastically reduces the calculation time but produces spikes in the temporal signals.     

Models for nuclear smuggling interdiction

We describe two stochastic network interdiction models for thwarting nuclear smuggling. In the first model, the smuggler travels through a transportation network on a path that maximizes the probability of evading detection, and the interdictor installs radiation sensors to minimize that evasion probability. The problem is stochastic because the smuggler's origin-destination pair is known only through a probability distribution at the time when the sensors are installed. In this model, the smuggler knows the locations of all sensors and the interdictor and the smuggler “agree” on key network parameters, namely the probabilities the smuggler will be detected while traversing the arcs of the transportation network. Our second model differs in that the interdictor and smuggler can have differing perceptions of these network parameters. This model captures the case in which the smuggler is aware of only a subset of the sensor locations. For both models, we develop the important special case in which the sensors can only be installed at border crossings of a single country so that the resulting model is defined on a bipartite network. In this special case, a class of valid inequalities reduces the computation time for the identical-perceptions model.     

A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters

This paper presents a hybrid optimization method for optimizing the process parameters during plastic injection molding (PIM). This proposed method combines a back propagation (BP) neural network method with an intelligence global optimization algorithm, i.e. genetic algorithm (GA). A multi-objective optimization model is established to optimize the process parameters during PIM on the basis of the finite element simulation software Moldflow, Orthogonal experiment method, BP neural network as well as Genetic algorithm. Optimization goals and design variables (process parameters during PIM) are specified by the requirement of manufacture. A BP artificial neural network model is developed to obtain the mathematical relationship between the optimization goals and process parameters. Genetic algorithm is applied to optimize the process parameters that would result in optimal solution of the optimization goals. A case study of a plastic article is presented. Warpage as well as clamp force during PIM are investigated as the optimization objectives. Mold temperature, melt temperature, packing pressure, packing time and cooling time are considered to be the design variables. The case study demonstrates that the proposed optimization method can adjust the process parameters accurately and effectively to satisfy the demand of real manufacture.     

Modeling the Series Impedance of a Quad Cable for Common-Mode DSL Applications

New digital subscriber line (DSL) technologies are being developed to meet the ever-increasing bandwidth demand of the user. One very promising approach injects “common-mode” signals that superimpose a signal on two regular differential pairs. This technique requires new reliable multiconductor models for the telephony cables. Therefore, good approximations of the series impedance of the line are indispensable. In this paper, a model for this series impedance is theoretically derived and validated with measurements.