Stability evaluation of a railway timetable at station level

This research deals with a real-world planning problem in railway infrastructure operations. It is part of the RECIFE project, which seeks to develop a decision support software to help evaluate the capacity of a rail junction or station. To this end, the project is working on a timetable optimization model, as well as timetable evaluation modules. This paper presents a module for evaluating timetable stability, which uses an original method based on delay propagation and using shortest path problem resolution. A didactic example and a complete case study applying this method to the Pierrefitte-Gonesse junction are also presented.     

Combined pricing and supply chain operations under price-dependent stochastic demand

In this study, we determined product prices and designed an integrated supply chain operations plan that maximized a manufacturer’s expected profit. The computational results of this study revealed that as the variance of the demand distribution increases, a manufacturer will increase its inventory to levels that are greater than the anticipated demand to prevent the potential loss of sales and will simultaneously raise product prices to obtain a greater profit. In the cost minimization approach, the manufacturer may earn the highest possible profits, as determined by the profit optimization approach, only if this firm precisely forecasts the mean market demand for its products. Greater inaccuracies in this forecast will produce lower levels of expected profit.     

Hierarchical game joint optimization for product family-driven modular design

Product family design takes advantage of modularity to enable product variety while maintaining mass production efficiency. Focusing on a set of similar product variants, product family modularity (PFM) is achieved by reusing common components and minimizing fulfillment costs throughout the product realization process. On the other hand, traditional modular design emphasizes technical system modularity (TSM) that focuses on a single product and is geared towards product decomposition in light of technical feasibility. While it is appealing to incorporate product family considerations into the prevailing modularization theories and methods, the key challenge lies in that TSM and PFM are essentially associated with different goals and decision criteria. This leads to a dilemma that TSM and PFM are competing in decision making for identification of modules by grouping similar components. Realizing the importance of game-theoretic decision making underlying product family-driven modular design, this paper proposes to leverage TSM and PFM within a coherent framework of joint optimization. A hierarchical game joint optimization model is developed in line with bilevel programming. A two-dimension evaluation criteria taxonomy is presented for TSM and PFM criteria measure. A bilevel nested genetic algorithm is put forward for efficient solution of the non-linear hierarchical joint optimization model. A case study of robotic vacuum cleaner modular design is reported to gain insight into joint optimization of TSM and PFM. Results and analyses demonstrate that the proposed hierarchical joint optimization model is robust and can empower modular design in cohesion with product family concerns.     

凸概率密度分布簇下的多损失鲁棒优化等价模型及在直营连锁企业中的应用

首先,证明了凸概率密度分布簇的单周期期望均值下单损失鲁棒优化等价模型定理,以及凸概率密度分布簇的单周期期望均值下多损失鲁棒优化等价模型。然后,提出了直营连锁企业的产品在凸概率密度分布簇下的期望均值的单周期生产分配供应问题,建立了直营连锁企业的单周期生产分配供应期望均值鲁棒模型,在获得近似周期概率分布簇情形下给出了单周期生产分配供应鲁棒模型,这种近似鲁棒模型等价于一个线性规划问题。最后,通过已知一个产品的4个周期构成的混合分布簇进行了数值实验,数值结果表明了期望均值准则下的生产分配供应鲁棒模型的生产分配供应策略更加稳健。     

Optimization of a fully adaptive quality and maintenance model in the presence of multiple location and scale quality shifts

This paper presents a new model for the economic-statistical optimization of a Variable-Parameter Shewhart control scheme. The proposed model can be utilized to monitor processes where apart from multiple independent assignable causes, affecting both the mean and variance, failures can also occur. Each time an alarm is issued by the control scheme, preventive maintenance actions are initiated, whereas, corrective maintenance actions are required after a failure. The more realistic assumption of imperfect preventive maintenance actions has been considered. The optimal parameter values are selected through a bi-objective optimization problem formulated by the long-run average cost per time unit minimization, and the long-run expected availability maximization, subject to statistical constraints. A real case example is presented to illustrate the application of the model. An extended numerical investigation is utilized to evaluate the superiority of the proposed model.     

Performance analysis of automatic assembly systems with in-line parallel stations

An automatic assembly system—a key tool for mass production—isgenerally composed of a number of workstations and a transportsystem. One common problem associated with automatic assemblysystems is that some assembly operations may have relativelylong cycle times. As a consequence, productivity, which is determinedby the operations with the longest cycle time, can be reducedsignificantly. Therefore a special form of parallel workstationknown as an in-line parallel (tandem-gated) station was developedto improve the performance of an automatic assembly system.In this design, stations working in parallel are installed ina serial structure and perform identical operations. Thus, morethan one assembly may be processed simultaneously—a typeof design especially beneficial when a stage requires a longoperation cycle time. A typical example is a computer assemblysystem which performs long inspection operations to ensure thatthe quality level of each product is up to the acceptance level. In this paper, we describe a simulation study of the performancecharacteristics of this type of system, with a statistical analysisof each decision factor. Phenomena affecting the performanceof in-line parallel stations, identified by the assistance ofthe computer graphical display, will then be discussed. An analyticalmodel based on these phenomena identified is then developed.The results generated by the analytical model developed arevalidated by comparisons with the simulation results. Finally,guidelines for optimization of buffer size are presented.     

Matching product architecture with supply chain design

Product architecture is typically established in the early stages of the product development (PD) cycle. Depending on the type of architecture selected, product design, manufacturing processes, and ultimately supply chain configuration are all significantly affected. Therefore, it is important to integrate product architecture decisions with manufacturing and supply chain decisions during the early stage of the product development. In this paper, we present a multi-objective optimization framework for matching product architecture strategy to supply chain design. In contrast to the existing operations management literature, we incorporate the compatibility between the supply chain partners into our model to ensure the long term viability of the supply chain. Since much of the supplier related information may be very subjective in nature during the early stages of PD, we use fuzzy logic to compute the compatibility index of a supplier. The optimization model is formulated as a weighted goal programming (GP) model with two objectives: minimization of total supply chain costs, and maximization of total supply chain compatibility index. The GP model is solved by using genetic algorithm. We present case examples for two different products to demonstrate the model’s efficacy, and present several managerial implications that evolved from this study.     

A multi-objective mathematical optimization model for process targeting using 100% inspection policy

The selection of the optimal process target is an important problem in production planning and quality control. Such process targeting problems are usually modeled in the literature using a single objective optimization model. In this paper multi-objective optimization is introduced in the process targeting area. The quality characteristic under consideration is normally distributed with unknown mean and known standard deviation, and has two market specification limits. 100% inspection is used as the mean of product quality control. Product satisfies the first specification limit is sold in a primary market at a regular price and products fails the first specification limit and satisfies the second one is sold in a secondary market at a reduced price. The product is reworked if it does not satisfy both specification limits. The developed multi-objective optimization model consists of three objective functions, which are to maximize profit, income and product uniformity using Taguchi quadratic function as a surrogate for product uniformity. An algorithm is proposed to obtain and rank the set of Pareto optimal points. The utility of the model has been demonstrated using a numerical example from the literature with some additional data the new model requires. Sensitivity analysis was conducted and showed that the results of the model are sensitive to changes in process variance. In addition the optimal objectives of the profit function and product uniformity are more sensitive to changes in model parameters than the income function.     

Optimization models for quality and cost of modular software systems

This study presents two optimization models for selecting the best Commercial Off-The-Shelf (COTS) software product among alternatives for each module in the development of modular software systems. The objective function of the models is to maximize quality within a budgetary constraint. The software system consists of several programs, where a specific function of each program can call upon a series of modules. Several alternative COTS products are available for each module. A weight to the modules is given by utilizing the Analytic Hierarchy Process (AHP) based on the access frequencies of the modules. A simplified example is given to demonstrate each optimization model.     

面向可选服务的产品服务系统配置研究

可选服务是制造企业继必选服务后获取差异化服务竞争优势的有效手段,类似于必选服务,可选服务模块的设计与选择对产品功效和企业服务成本均有影响。本文运用模糊测度研究产品模块与可选服务模块之间的相关关系,建立客户满意度最大和企业成本最小的多目标优化模型,求解不同相关关系下面向可选服务的产品服务系统配置方案;以某制造企业矿渣立磨机产品模块与可选服务模块配置为例,验证了方法的有效性。研究结果表明:产品模块与可选服务模块的相关关系会影响产品服务系统配置方案,相关关系越强,客户越在意重视度较高模块的质量和功能,市场成本报价越高,可选服务在方案配置过程中越重要。本文为企业提供面向可选服务的产品服务系统配置方案提供借鉴。     

Assessing the benefits of remanufacturing option under one-way substitution

In this study, we consider a segmented market for a product that can either be manufactured or remanufactured. It is assumed that the remanufactured products can be substituted by the new ones. A steady-state profit model is constructed under certain environmental assumptions on capacity requirements of operations, and revenue and cost schemes. Exact steady-state probabilities of the Markovian model constructed are solved via matrix geometric techniques. An extensive computational study is performed to investigate the conditions under which the utilization of remanufacturing option and the use of one-way substitution policy increase the average expected profit.     

Simulation as a tool for planning of exploration for deep seated base metal deposits

Exploration for massive sulphide ore bodies at great depths in the Skellefte district in northern Sweden is expensive and relies heavily on extensive drilling operations. Different drilling strategies have been studied by mean simulation.The bedrock structure, the targets and the drilling procedure have been described by mathematical models. These models are based on known facts from the field. For some significant geological parameters assumptions about the statistical properties are made. A number of different drilling strategies are designed. Drilling campaigns are simulated in the model and the expected outcomes are calculated and compared.A case study is presented. Quantitative estimates of the optimal drilling pattern are obtained, the influence on the expected outcome when the geological parameters are varied as well as the cost-benefit relation for investments in the project are estimated.     

A large scale machine loading problem in flexible assembly

Flexible manufacturing is characterized by versatile work stations with minimum change over times and a versatile material handling system. The loading problem in flexible manufacturing is to assign tools, material, operations and jobs to work stations in order to minimize the total number of job-to-work station assignments. In this paper, we describe a special case of the general loading problem applied to flexible assembly and develop a discrete optimization model. We then discuss approaches for obtaining good heuristic solutions and present results for a large scale study.     

Optimal investment with a constraint on ruin for a fuzzy discrete-time insurance risk model

In this paper, we consider a mean–variance portfolio optimization problem for a fuzzy discrete-time insurance risk model. The model consists of independent, identically distributed net losses considered within successive time periods, and incorporates investment incomes from a two-asset portfolio. More precisely, in the beginning of each period, the surplus is invested in both a risk-free bond with fixed interest, and a risky stock with fuzzy return rate. Our purpose is to determine the proportion invested in the stock that maximizes the insurer’s expected wealth, while reducing his risks. Therefore, for this fuzzy model, we formulate mean–variance optimization problems that also include constraints on ruin, and we present a method for determining the resulting optimal proportion to be invested in the risky stock. This method is illustrated in a numerical study in which the fuzzy return rate is considered to be an adaptive fuzzy number that generalizes the well-known trapezoidal fuzzy number.     

Airport noise modelling and aircraft scheduling so as to minimize community annoyance

A mathematical model of the annoyance created at an airport by aircraft operations is developed. The model incorporates population distribution considerations around an airport and the annoyance caused by aircraft noise. The objective function of this model corresponds to seeking to minimize total population annoyance created by all aircraft operations in a 24-hour period. Several factors are included in this model as constraint relationships. Aircraft operations by type and time period are upper bounded. Demand for flight services is incorporated by including lower bounds on the number of operations by type of aircraft, runway used and time period. Also upper bounds on the number of operations for each runway are included. The mathematical model as formulated is recognized as corresponding to a nonlinear integer mathematical programming problem.The solution technique selected makes use of a successive linear approximation optimization algorithm. An especially attractive feature of this solution algorithm is that it is capable of obtaining solutions to large problems. For example, it would be feasible to attempt the solution of problems involving several thousand variables and over 500 linear constraints. This suggested solution algorithm was implemented on a computer and computational results obtained for example problems.     

An EMQ model with inspections and random machine failures

In the classical Economic Manufacturing Quantity (EMQ) model, it is assumed that all items produced are of perfect quality and the production facility never breaks down. However, in real production, the product quality is usually a function of the state of the production process which may deteriorate over time and the production facility may fail randomly. In this paper, we study the effect of machine failures on the optimal lot size and on the optimal number of inspections in a production cycle. The formula for the long-run expected average cost per unit time is obtained for a generally distributed time to failure. An optimal production/inspection policy is found by minimising the expected average cost.     

Mean and variance optimization of non–linear systems and worst–case analysis

In this paper, we consider expected value, variance and worst–case optimization of nonlinear models. We present algorithms for computing optimal expected value, and variance policies, based on iterative Taylor expansions. We establish convergence and consider the relative merits of policies based on expected value optimization and worst–case robustness. The latter is a minimax strategy and ensures optimal cover in view of the worst–case scenario(s) while the former is optimal expected performance in a stochastic setting. Both approaches are used with a small macroeconomic model to illustrate relative performance, robustness and trade-offs between the alternative policies.     

Expected number of steps of a random optimization method

In this paper, we give an estimate of the expected number of steps of Matya's random optimization method applied to the constrained nonlinear minimization problem. It is also shown that, in a sense, this random optimization method can be optimized by the uniform distribution, in which case the exact value of the expected number of steps is computed.     

A discrete time optimal control model with uncertainty for dynamic machine allocation problem and its application to manufacturing and construction industries

This paper proposed a discrete time optimal control model in which machine failure time is modeled assuming a Weibull distribution and machine productivity is regarded as a fuzzy variable for dealing with a dynamic machine allocation problem (DMAP) in manufacturing and construction industries. The aim is to maximize total production or construction throughput when uncertainties such as machine breakdowns are taken into account. A failure probability-work time equation is presented to describe the relationship between machine failure probability and mean time to work. To transform the uncertain optimal control model into a deterministic one, the expected value model (EVM) was introduced for forming an equivalent crisp model. The fuzzy variables in the model are also defuzzified by using an expected value operator with an optimistic–pessimistic index. Then a number of lemmas and theorems are presented and proved to formulate the theoretical algorithm so that the crisp model of the DMAP can be solved. Three actual construction and production projects are used as practical application examples. The theoretical algorithm results for the three project examples are compared with a particle swarm optimization approach and a genetic algorithm method, which demonstrates the practicality and efficiency of our optimization method.