Heuristics for integrated job assignment and scheduling in the multi-plant remanufacturing system

We consider a multi-plant remanufacturing system where decisions have to be made on the choice of plant to perform the remanufacturing and the remanufacturing options. Each plant is in different geographical locations and differs in technological capability, labour cost, distance from customers, taxes and duties. There are three options of remanufacture: replacement, repair and recondition. Furthermore, the probability that each remanufacture job needs to be reworked depends on the remanufacturing option selected. We show the interdependencies among the plant selection, remanufacturing option and job scheduling when subject to resource constraints, which motivate the integrated solution proposed in this paper. The solution method is composed of the linear physical programming and the multi-level encoding genetic algorithm (GA). By performing a case study, we illustrate the use of the model and we present the resulting managerial insights. The results show that the proposed integrated approach performs better compared with the regular GA in terms of makespan.     

Multi-product continuous plant scheduling: combination of decomposition,genetic algorithm,and constructive heuristic

We propose a polylithic method for medium-term scheduling of a large-scale industrial plant operating in a continuous mode. The method combines a decomposition approach, a genetic algorithm (GA) and a constructive MILP-based heuristic. In the decomposition, decisions are made at two levels, using the rolling horizon approach. At the upper level, a reduced set of products and the time period is chosen to be considered in the lower level. At the lower level, a short-term scheduling MILP-model with event-based representation is used. A heuristic solution to the lower level problem is found using a constructive Moving Window heuristic guided by a genetic algorithm. The GA is applied for finding efficient utilisation of critical units in the lower level problem. For solving the one unit scheduling problem, a parallel dynamic programming algorithm is proposed. Implementation of the dynamic programming algorithm for a graphics processing unit (GPU) is incorporated in the GA for improving its performance. The experimental study of the proposed method on a real case of a large-scale plant shows a significant improvement of the solution quality and the solving time comparing to the pure decomposition algorithm proposed in the earlier study, and confirmed suitability of the proposed approach for the real-life production scheduling. In particular, the reduction of the number of changeovers and their duration in the obtained solution as well as the CPU time of solving the problem was about 60% using the new approach.     

A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty

Humanitarian relief logistics is one of the most important elements of a relief operation in disaster management. The present work develops a multi-objective robust stochastic programming approach for disaster relief logistics under uncertainty. In our approach, not only demands but also supplies and the cost of procurement and transportation are considered as the uncertain parameters. Furthermore, the model considers uncertainty for the locations where those demands might arise and the possibility that some of the pre-positioned supplies in the relief distribution center or supplier might be partially destroyed by the disaster. Our multi-objective model attempts to minimize the sum of the expected value and the variance of the total cost of the relief chain while penalizing the solution’s infeasibility due to parameter uncertainty; at the same time the model aims to maximize the affected areas’ satisfaction levels through minimizing the sum of the maximum shortages in the affected areas. Considering the global evaluation of two objectives, a compromise programming model is formulated and solved to obtain a non-dominating compromise solution. We present a case study of our robust stochastic optimization approach for disaster planning for earthquake scenarios in a region of Iran. Our findings show that the proposed model can help in making decisions on both facility location and resource allocation in cases of disaster relief efforts.     

Genetic algorithms and Monte Carlo simulation for optimal plant design

We present an approach to the optimal plant design (choice of system layout and components) under conflicting safety and economic constraints, based upon the coupling of a Monte Carlo evaluation of plant operation with a Genetic Algorithms-maximization procedure. The Monte Carlo simulation model provides a flexible tool, which enables one to describe relevant aspects of plant design and operation, such as standby modes and deteriorating repairs, not easily captured by analytical models. The effects of deteriorating repairs are described by means of a modified Brown–Proschan model of imperfect repair which accounts for the possibility of an increased proneness to failure of a component after a repair. The transitions of a component from standby to active, and vice versa, are simulated using a multiplicative correlation model. The genetic algorithms procedure is demanded to optimize a profit function which accounts for the plant safety and economic performance and which is evaluated, for each possible design, by the above Monte Carlo simulation.In order to avoid an overwhelming use of computer time, for each potential solution proposed by the genetic algorithm, we perform only few hundreds Monte Carlo histories and, then, exploit the fact that during the genetic algorithm population evolution, the fit chromosomes appear repeatedly many times, so that the results for the solutions of interest (i.e. the best ones) attain statistical significance.     

Enhanced joint pricing and lotsizing problem in a two-echelon supply chain with logit demand function

In retail supply chains, ordering and pricing policies for the retailer and shipment plan for the supplier are the most important decisions. These policies are often conducted either individually or sequentially with poor overall performance for the whole supply chain resulting to extra inventory and other deficiencies. In this paper, an integrated marketing-inventory model in a two-echelon supply chain model is developed involving discount promotion, customer behaviour more realistically and operations aspects to determine optimal ordering, shipping and pricing quantities simultaneously. An efficient analytical solution procedure and a Particle Swarm Optimisation solution algorithm are also developed. Finally, a number of numerical tests are conducted to approve the interesting theoretical results of the analytical approach.     

Correcting erroneous crash locations in transportation safety analysis

Errors and inconsistencies in crash location records are realistic problems that often compromise accuracy of safety model outcomes. This paper proposes a new safety analysis framework where an optimal network design module that estimates the most-probable site for each crash is combined with the standard regression analysis. An effective solution algorithm based on Lagrangian relaxation is developed for the network design model, and an iterative computation approach is proposed for location estimation and statistical regression. The proposed models are implemented with empirical data and numerical results are presented.     

和声搜索算法优化快速消费品生产配送协调调度

研究具有生产准备环节的快速消费品生产配送问题,考虑工厂和配送中心的库存限制,工厂产能限制和劳动力限制,建立一个多周期、多工厂、多产品、多配送中心、多客户的混合整数线性规划模型,旨在最小化准备成本、生产成本、库存成本和配送成本。通过设计一种遗传和声搜索算法对模型进行求解。最后给出一个算例说明所提模型和算法的可行性和有效性。     

A heuristic algorithm for master production scheduling problem with controllable processing times and scenario-based demands

Master production scheduling (MPS) is widely used by manufacturing industries in order to handle the production scheduling decisions in the production planning hierarchy. The classical approach to MPS assumes infinite capacity, fixed (i.e. non-controllable) processing times and a single pre-determined scenario for the demand forecasts. However, the deterministic optimisation approaches are sometimes not suitable for addressing the real-world problems with high uncertainty and flexibility. Accordingly, in this paper, we propose a new practical model for designing an optimal MPS for the environments in which processing times may be controllable by allocating resources such as facilities, energy or manpower. Due to the NP-hardness of our model, an efficient heuristic algorithm using local search technique and theory of constraints is developed and analysed. The computational results especially for large-sized test problems show that the average optimality gap of proposed algorithm is four times lower than that of exact solution using GAMS while it consumes also significantly smaller run times. Also, the analysis of computational results confirms that considering the controllable processing times may improve the solution space and help to more efficiently utilise the available resources. According to the model structure and performance of the algorithm, it may be proposed for solving large and complex real-world problems particularly the machining and steel industries.     

Supply chain network design with unreliable suppliers: a Lagrangian relaxation-based approach

This paper deals with the integrated facility location and supplier selection decisions for the design of supply chain network with reliable and unreliable suppliers. Two problems are addressed: (1) facility location/supplier selection; and (2) facility location/supplier reliability. We first consider the facility location and supplier selections problem where all the suppliers are reliable. The decisions concern the selection of suppliers, the location of distribution centres (DCs), the allocation of suppliers to DCs and the allocation of retailers to DCs. The objective is to minimise fixed DCs location costs, inventory and safety stock costs at the DCs and ordering costs and transportation costs across the network. The introduction of inventory costs and safety stock costs leads to a non-linear NP-hard optimisation problem. To solve this problem, a Lagrangian relaxation-based approach is developed. For the second problem, a two-period decision model is proposed in which selected suppliers are reliable in the first period and can fail in the second period. The corresponding facility location/supplier reliability problem is formulated as a non-linear stochastic programming problem. A Monte Carlo optimisation approach combining the sample average approximation scheme and the Lagrangian relaxation-based approach is proposed. Computational results are presented to evaluate the efficiency of the proposed approaches.     

Acquisition of new technology information for maintenance and replacement policies

In this paper, we propose the first model that considers the option to acquire information on the profitability of a new technology that is not yet available on the market for asset maintenance and replacement decisions. We consider the uncertainty of future asset characteristics by incorporating information acquisition decisions into a non-stationary Markov decision process framework. Using this framework, we optimise asset maintenance and replacement decisions as well as the optimal timing of new technology adoption. Through mathematical analyses, the monotone properties and convexity of the value function and optimal policy are deduced. Deeper numerical analyses highlight the importance of considering the acquisition of information on future technology when formulating a maintenance and replacement policy for the asset. We also deduce a non-intuitive result: an increase in the arrival probability of new technology does not necessarily make the acquisition of additional information more attractive.     

Adaptive modelling and long-range prediction of mobile fading channels

A key element for many fading-compensation techniques is a (long-range) prediction tool for the fading channel. A linear approach, usually used to model the time evolution of the fading process, does not perform well for long-range prediction applications. An adaptive fading channel prediction algorithm using a sum-sinusoidalbased state-space approach is proposed. This algorithm utilises an improved adaptive Kalman estimator, comprising an acquisition mode and a tracking algorithm. Furthermore, for the sake of a lower computational complexity, an enhanced linear predictor for channel fading is proposed, including a multi-step AR predictor and the respective tracking algorithm. Comparing the two methods in our simulations show that the proposed Kalman-based algorithm can significantly outperform the linear method, for both stationary and nonstationary fading processes, and especially for long-range predictions. The performance and the self-recovering structure, as well as the reasonable computational complexity, makes the algorithm appealing for practical applications.     

Designing experiments for robust-optimization problems: the Vs-optimality criterion

We suggest an experimentation strategy for the robust design of empirically fitted models. The suggested approach is used to design experiments that minimize the variance of the optimal robust solution. The new design-of-experiment optimality criterion, termed V_s-optimal, prioritizes the estimation of a model's coefficients, such that the variance of the optimal solution is minimized by the performed experiments. It is discussed how the proposed criterion is related to known optimality criteria. We present an analytical formulation of the suggested approach for linear models and a numerical procedure for higher-order or nonpolynomial models. In comparison with conventional robust-design methods, our approach provides more information on the robust solution by numerically generating its multidimensional distribution. Moreover, in a case study, the proposed approach results in a better robust solution in comparison with these standard methods.     

Integrated aggregate supply chain planning using memetic algorithm – A performance analysis case study

This paper aims to examine how a complex supply chain yields cost reduction benefits through the global integration of production and distribution decisions. The research is motivated by a complex real world supply chain planning problem facing a large automotive company. A mixed-integer nonlinear production-distribution planning model is solved using a customised memetic algorithm. The performance and effectiveness of the developed model and solution approach in achieving the global optimisation is investigated through experiments comparing the numerical results from the proposed integrated approach with those of a typical non-integrated (hierarchical) production–distribution optimisation.     

A batching and scheduling algorithm for the diffusion area in semiconductor manufacturing

This paper proposes an efficient heuristic algorithm for solving a complex batching and scheduling problem in a diffusion area of a semiconductor plant. Diffusion is frequently the bottleneck in the plant and also one of the most complex areas in terms of number of machines, constraints to satisfy and the large number of lots to manage. The purpose of this study is to investigate an approach to group lots in batches and to schedule these batches on machines. The problem is modelled and solved using a disjunctive graph representation. A constructive algorithm is proposed and improvement procedures based on iterative sampling and Simulated Annealing are developed. Computational experiments, carried out on actual industrial problem instances, show the ability of the iterative sampling algorithms to significantly improve the initial solution, and that Simulated Annealing enhances the results. Furthermore, our algorithm compares favourably to an algorithm reported in the literature for a simplified version of our problem. The constructive algorithm has been embedded in software and is currently being used in a semiconductor plant.     

A capacitated facility location problem with constrained backlogging probabilities

One of the assumptions of the capacitated facility location problem (CFLP) is that demand is known and fixed. Most often, this is not the case when managers take some strategic decisions such as locating facilities and assigning demand points to those facilities. In this paper we consider demand as stochastic and we model each of the facilities as an independent queue. Stochastic models of manufacturing systems and deterministic location models are put together in order to obtain a formula for the backlogging probability at a potential facility location. Several solution techniques have been proposed to solve the CFLP. One of the most recently proposed heuristics, a reactive greedy adaptive search procedure, is implemented in order to solve the model formulated. We present some computational experiments in order to evaluate the heuristics’ performance and to illustrate the use of this new formulation for the CFLP. The paper finishes with a simple simulation exercise.     

基于压缩感知的复合材料板Lamb波场重构及损伤成像

为了解决全波场数据获取耗时的问题,引入压缩感知算法对波场进行稀疏表示。然而受限于导波在复合材料中复杂的传播特性,压缩感知在复合材料板中的应用成为难题。为此通过考虑导波在复合材料板中传播的各向异性波数特性,构建计算频带内不同角度下的波数库用于波场重构。同时在损伤分析阶段,提出了一种无需基准信号的复合材料损伤散射波场分离技术,精准实现入射波场的去除,提升损伤定位的精度。单损伤的实验和仿真结果表明,所提方法能够在低于90%的奈奎斯特采样点情况下实现波场的重构,且损伤定位误差均小于最小波长的2/3。双损伤的实验结果表明,所提方法可有效定位双损伤,相关成果可为复合材料损伤检测的实际运用提供理论和方法基础。      

基于成本/收益权衡的多元需求FIP选址研究

需求种类分析对选址决策至关重要,考虑了消费者需求种类为多元化,且各类需求间有影响的网络上截流(FIP)选址问题,引入路径上需求损失函数,建立了收益最大和总的建设成本最小的双目标的多元需求FIP(mDFIP)的新模型。所建立的模型为NP-hard问题,设计了多目标演化算法求解模型,最后通过具体的算例与基于ε约束法的分支定界法进行比较,结果表明在求解效率上多目标演化算法具有很大优势。     

An improved co-evolutionary algorithm for green manufacturing by integration of recovery option selection and disassembly planning for end-of-life products

There is a strong need for recovery decision-making for end-of-life (EOL) products to satisfy sustainable manufacturing requirements. This paper develops and tests a profit maximisation model by simultaneously integrating recovery option selection and disassembly planning. The proposed model considers the quality of EOL components. This paper utilises an integrated method of multi-target reverse recursion and partial topological sorting to generate a feasible EOL solution that also reduces the complexity of genetic constraints handling. In order to determine recovery options, disassembly level and disassembly sequence simultaneously, this paper develops an improved co-evolutionary algorithm (ICA) to search for an optimal EOL solution. The proposed algorithm adopts the evolutionary mechanism of localised interaction and endosymbiotic competition. Further, an advanced local search operator is introduced to improve convergence performance, and a global disturbance strategy is also suggested to prevent premature convergence. Finally, this paper conducts a series of computational experiments under various scenarios to validate the meta-heuristic integrated decision-making model proposed and the superiority of the developed ICA. The results show that the proposed approach offers a strong and flexible decision support tool for intelligent recovery management in a ubiquitous information environment. We discuss the theoretical and practical contributions of this paper and implications for future research.     

Joint cell loading and scheduling approach to cellular manufacturing systems

A hierarchical multi-objective heuristic algorithm and pricing mechanism are developed to first determine the cell loading decisions, and then lot sizes for each item and to obtain a sequence of items comprising the group technology families to be processed at each manufacturing cell that minimise the setup, inventory holding, overtime and tardiness costs simultaneously. The linkage between the different levels is achieved using the proposed pricing mechanism through a set of dual variables associated with the resource and inventory balance constraints, and the feasibility status feedback information is passed between the levels to ensure internally consistent decisions. The computational results indicate that the proposed algorithm is very efficient in finding a compromise solution for a set of randomly generated problems compared with a set of competing algorithms.     

Designing a supply chain network for deteriorating inventory under preservation effort and trade credits

When designing a perishable goods supply chain network under trade credit arrangements, distribution companies must contend with deteriorating inventory and product preservation efforts to maximise profits. Key decisions include locating distribution centres (DCs), assigning retail stores to DCs, joint replenishment cycle time and investing in preservation technology. This paper addresses these factors from the position that as preservation effort increases, preservation technology cost increases and deterioration rate decreases. An algorithm based on piecewise nonlinear optimisation is provided for solving supply chain network design problems efficiently. In contrast to other studies that have used the approximation approach, the proposed approach solves the original problem accurately and efficiently. Numerical studies are conducted to demonstrate the solutions procedures and determine the effects of the parameters on decisions and profits. The results of this study and the proposed modelling approach are useful references for managerial decisions in designing a supply chain network the context of trade credit and inventory deterioration.