Genetic algorithm to production planning and scheduling problems for manufacturing systems

Fundamental and extended multi-objective (MO) models are designed to address earliness/tardiness production scheduling planning (ETPSP) problems with multi-process capacity balance, multi-product production and lot-size consideration. A canonical genetic algorithm (GA) approach and a prospective multi-objective GA (MOGA) approach are proposed as solutions for different practical problems. Simulation results as well as comparisons with other techniques demonstrate the effectiveness of the MOGA approach, which is a noted improvement to any of the existing techniques, and also in practice provides a new trend of integrating manufacturing resource planning (MRPII) with just-in-time (JIT) in the production planning procedure.     

Solving the Stochastic Growth Model by Backsolving With a Particular Nonlinear Form for the Decision Rule

Backsolving is a class of methods that generate simulated values for exogenous forcing processes in a stochastic equilibrium model from specified assumed distributions for Euler-equation disturbances. It can be thought of as a way to force the approximation error generated by inexact choice of decision rule or boundary condition into distortions of the distribution of the exogenous shocks in the simulations rather than into violations of the Euler equations as with standard approaches. Here it is applied to a one-sector neoclassical growth model with decision rule generated from a linear-quadratic approximation.     

Quota fulfilment and errors in stratification

It is desired to draw a random sample containing specified numbers of individuals from each stratum of a population. First a random sample of size N is chosen from the whole populations and the stratum of each individual ascertained; then any shortfall is made up by selecting individuals with known stratum affiliation. Optimal values of N are sought allowing for cost structure and also the possibility of error in ascertaining the strata to which individuals in the first sample belong.     

An improvement on “Integrated production strategy and reuse scenario: A CoFAQ model and case study of mail server system development”

The authors (Tang et al. (2013) [1] developed a CoFAQ model to formulate a solution for the problem of production strategy decision and reuse scenario selection for a software product family. In the previous research, we stated that the CoFAQ model was a 0–1 mixed integer nonlinear program, where only a local optimal solution might be found. In a recent study, we found that the CoFAQ could be transformed into a 0–1 mixed integer linear programming model. By solving the model, a global optimal solution can be obtained. In this paper, we present the improved formulation and the optimal solution for the case study.     

Supply Performance in Multi‐Echelon Inventory Systems with Intermediate Product Demand: A Perspective on Allocation

In this article, we study the performance of multi‐echelon inventory systems with intermediate, external product demand in one or more upper echelons. This type of problem is of general interest in inventory theory and of particular importance in supply chain systems with both end‐product demand and spare parts (subassemblies) demand. The multi‐echelon inventory system considered here is a combination of assembly and serial stages with direct demand from more than one node. The aspect of multiple sources of demands leads to interesting inventory allocation problems. The demand and capacity at each node are considered stochastic in nature. A fixed supply and manufacturing lead time is used between the stages. We develop mathematical models for these multi‐echelon systems, which describe the inventory dynamics and allow simulation of the system. A simulation‐based inventory optimization approach is developed to search for the best base‐stock levels for these systems. The gradient estimation technique of perturbation analysis is used to derive sample‐path estimators. We consider four allocation schemes: lexicographic with priority to intermediate demand, lexiographic with priority to downstream demand, predetermined proportional allocation, and proportional allocation. Based on the numerical results we find that no single allocation policy is appropriate under all conditions. Depending on the combinations of variability and utilization we identify conditions under which use of certain allocation polices across the supply chain result in lower costs. Further, we determine how selection of an inappropriate allocation policy in the presence of scarce on‐hand inventory could result in downstream nodes facing acute shortages. Consequently we provide insight on why good allocation policies work well under differing sets of operating conditions.     

方形铝箔碎屑收集管数值优化设计

为了解决铝箔剪切机工作中存在的碎屑收集难题，课题组使用CFD方法计算了不同设计结构时的管路流场，在深入分析挡板布置及支管至出口距离设置对双支管吸力影响规律的基础上，重点探讨了不同压力 速度耦合分布现象出现的原因，并给出管路优化建议。数值模拟结果表明：2个支管前设置挡板时，第2个挡板会阻碍第1个支管后部气流运动，形成较大低速涡流区，无法形成负压及吸力；当2个支管使用连通挡板时，可以产生负压及吸力，但由于支管至出口距离不一致导致速度差异达62.24%；2个支管至出口距离相同时，在支管口形成均匀负压及吸力，可满足工业需求。此外，支管与主管倒圆角设置时对气流有良好的“导向”作用，支管速度可提高6%左右。     

Satellite downlink scheduling problem: A case study

The synthetic aperture radar (SAR) technology enables satellites to efficiently acquire high quality images of the Earth surface. This generates significant communication traffic from the satellite to the ground stations, and, thus, image downlinking often becomes the bottleneck in the efficiency of the whole system. In this paper we address the downlink scheduling problem for Canada׳s Earth observing SAR satellite, RADARSAT-2. Being an applied problem, downlink scheduling is characterised with a number of constraints that make it difficult not only to optimise the schedule but even to produce a feasible solution. We propose a fast schedule generation procedure that abstracts the problem specific constraints and provides a simple interface to optimisation algorithms. By comparing empirically several standard meta-heuristics applied to the problem, we select the most suitable one and show that it is clearly superior to the approach currently in use.     

Traffic engineering and congestion control for open shortest path first networks

Open shortest path first (OSPF) is the most widely used intra-domain Internet routing protocol. The OSPF protocol directs the Internet traffic along the shortest paths that are defined by the links weight. Traffic engineering is responsible for improving the network performance, for instance, the objective function that minimizes the maximal link utilization which is usually adopted to avoid the network congestion. This paper formulates the model of OSPF routing, and further evaluates the performance of different approaches. We also propose a heuristic algorithm to solve the routing problems by optimizing OSPF weights. Computational results indicated that the proposed algorithm could lead to good load balancing, and make efficient utilization of network resources.     

Two-dimensional density estimation using smooth invertible transformations

We investigate the problem of estimating a smooth invertible transformation f when observing independent samples X₁,…,X_n∼P°f where P is a known measure. We focus on the two-dimensional case where P and f are defined on R². We present a flexible class of smooth invertible transformations in two dimensions with variational equations for optimizing over the classes, then study the problem of estimating the transformation f by penalized maximum likelihood estimation. We apply our methodology to the case when P°f has a density with respect to Lebesgue measure on R² and demonstrate improvements over kernel density estimation on three examples.     

Variable Royalty Rates for Improving Franchise Channel Coordination*

A business format franchisor obtains a major part of its revenues from franchise royalties, which are typically a fixed percentage of franchisee gross sales. When a fixed royalty rate is used and the marginal costs of operating the franchise are increasing, the franchisee does not have an incentive to increase sales beyond a certain “optimal” volume. We present a model that recommends the use of a variable franchise royalty rate for extending this optimal sales volume. For a general convex cost function, we show that a new lower rate can be applied to incremental sales beyond the original optimal level. We show that this new rate should be less than half of the original rate when a quadratic cost function is applicable. Adopting a variable royalty rate increases franchisor royalty revenues and franchisee profits.