Sensitivity results considering rescheduling by AND/OR graphs

Closely connected with scheduling are also rescheduling considerations which can obviously be addressed by sensitivity analysis. We discuss this question by using AND/OR graphs and show how sensitivity analysis can be performed in this context. We show that AND/OR graphs can be used to model job shop problems and alternative process scheduling, hence sensitivity analysis can be performed in these contexts as well.     

Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm

This paper addresses the problem of finding robust and stable solutions for the flexible job shop scheduling problem with random machine breakdowns. A number of bi-objective measures combining the robustness and stability of the predicted schedule are defined and compared while using the same rescheduling method. Consequently, a two-stage Hybrid Genetic Algorithm (HGA) is proposed to generate the predictive schedule. The first stage optimizes the primary objective, minimizing makespan in this work, where all the data is considered to be deterministic with no expected disruptions. The second stage optimizes the bi-objective function and integrates machines assignments and operations sequencing with the expected machine breakdown in the decoding space. An experimental study and Analysis of Variance (ANOVA) is conducted to study the effect of different proposed measures on the performance of the obtained results. Results indicate that different measures have different significant effects on the relative performance of the proposed method. Furthermore, the effectiveness of the current proposed method is compared against three other methods; two are taken from literature and the third is a combination of the former two methods.     

A genetic algorithm based heuristic for two machine no-wait flowshop scheduling problems with class setup times that minimizes maximum lateness

Machine scheduling problem has been extensively studied by researchers for many decades in view of its numerous applications on solving practical problems. Due to the complexity of this class of scheduling problems, various approximation solution approaches have been presented in the literature. In this paper, we present a genetic algorithm (GA) based heuristic approach to solve the problem of two machine no-wait flowshop scheduling problems that the setup time on the machines is class dependent, and the objective is to minimize the maximum lateness of the jobs processed. This class of machine scheduling problems has many practical applications in manufacturing industry, such as metal refinery operations, food processing industry and chemical products production processes, in which no interruption between subsequent processes is allowed and the products can be grouped into families. Extensive computation experiments have been conducted to evaluate the effectiveness of the proposed algorithm. Results show the proposed methodology is suitable to be adopted for the development of an efficient scheduling plan for this class of problems in real life application.     

Modeling the effect of patient choice on the performance of open access scheduling

Open access scheduling, introduced in recent years, is a revolutionary concept for improving healthcare access and reducing patient no-shows. In this paper, a Markov chain model is presented to capture appointment scheduling in open access clinics when considering patient choice of appointments. Due to the curse of dimensionality, it is impossible to solve the steady-state distribution of the Markov chain model for a typical-size open access primary care clinic. Therefore, an approximate approach is proposed to efficiently estimate the performance of a provider capacity policy based on the Markov chain model. This approach can accurately estimate the performance of a practical capacity policy in a significantly shorter time. Using this approach, the impact of patient choice on the performance of provider capacity policies is investigated.     

A tabu search approach for cell scheduling problem with makespan criterion

Scheduling problem in a cellular manufacturing system is treated as the group scheduling problem, assuming that intercellular moves can be eliminated by duplicating machines. However, in a typical CMS, duplicating bottleneck machines may be costly and infeasible. This fact limits the applicability of group scheduling. Scheduling problem in the presence of bottleneck machines is termed as cell scheduling. A mixed-integer linear programming model is proposed for the attempted cell scheduling problem and a nested application of tabu search approach is investigated in this paper to solve the problem heuristically. The effectiveness of the proposed nested tabu search (NTS) algorithm is evaluated on 16 problems selected from the literature. Comparison of the results of NTS with SVS-algorithm reveals the effectiveness and efficiency of the proposed algorithm.     

考虑工艺流程的船体分段多堆场调度问题研究

为了有效解决在船舶建造过程中物流拥堵的问题,综合考虑分段建造工艺流程、分段大小和堆场布局,以最小分段移动度为目标,提出基于工艺流程的多堆场船体分段调度方法,并针对调度过程中出现的阻挡分段提出堆位调整算法。对上海某造船厂分段物流进行的实际验证表明,本方法可获得较优化的调度方案,可有效提高作业效率。     

A local search algorithm for the optimization of the stochastic economic lot scheduling problem

This paper describes a model for the stochastic economic lot scheduling problem (SELSP) and a Local Search heuristic to find close to optimal solutions to this model. The SELSP considers multiple products, which have to be scheduled on a single facility with limited capacity and significant setup times and costs. The demand is modeled as a stationary compound renewal process. The objective is to find a schedule that minimizes the long-run average costs for setups and inventories while satisfying a given fill rate. We use a cyclic scheduling approach in which the individual cycle time of each product is a multiple of some basic period (fundamental cycle).For the deterministic version of the SELSP, efficient heuristics have been developed which guarantee the feasibility of the solution by adding an additional constraint to the problem. In our case this is not sufficient, because for the calculation of the average inventory levels and fill rates we need to develop a schedule with detailed timing of the lots. We present an efficient heuristic for this scheduling problem, which can also be used to check the feasibility of the solution. Thereby, the most time-consuming step (the calculation of average inventory levels and fill rates) is only performed for a limited set of candidates.The algorithm was tested on deterministic benchmark problems from literature and on a large set of stochastic instances. We report on the performance of the heuristic in both cases and try to identify the main factors influencing the objective.     

A production rate control policy for stochastic repair and remanufacturing systems

We consider the control of a manufacturing system responding to planned demand at the end of the expected life of each individual piece of equipment and unplanned demand triggered by a major equipment failure. The difficulty of controlling this type of production system resides in the variable nature of the remanufacturing process. In practice, remanufacturing operations for planned demand can be executed at different rates, referring to different component replacement and repair strategies. We formulate this problem as a multi-level control problem and propose a suboptimal control policy. The proposed control policy is described by inventory thresholds triggering the use of different execution modes. Determination of the control policy parameters is based on parameter optimization of analytical cost expressions. A numerical example based on a real case is presented. Our analysis demonstrates that the use of the proposed control approach can lead to a significant reduction in the total average cost, as compared to current practices.     

PATENT LENGTH AND THE TIMING OF INNOVATIVE ACTIVITY

The standard result in patent policy, as demonstrated by Gilbert and Shapiro (1990), is that infinitely lived but very narrow patents are optimal as deadweight losses are minimised and spread through time but inventors can still recover their R&D expenditures. By extending their innovative environment to include timing as an important choice, we demonstrate that a finitely lived, but broader, patent can be socially desirable. This is because a patent breadth is a better instrument than length to encourage socially optimal timing. Thus, patents need not be infinitely long in order to encourage a greater number of inventions     

Selecting make-to-stock and postponement policies for different products in a chemical plant: A case study using discrete event simulation

We present a guideline for selecting the inventory management policy for a specialty chemical plant that produces products with limited shelf life, by producing multiple grades of bulk chemical and packaging them into a large variety of bottles. The policy for each product was selected based on a cost-benefit analysis, using the multiple objectives of maximizing the on time order fulfillment, minimizing the production (cleaning and shelf life expiration) and inventory costs. To generate realistic cost estimates, we developed a discrete event simulation model that included demand variability, planning and scheduling policies, and operational details. We evaluated three different policies: make-to-stock (MTS), postponement, and combined MTS/postponement. We also evaluated different storage media and optimized the number of storage units for products selected for postponement. A combined MTS/postponement policy, in which postponement is applied to products with low to medium demand and high expiration provided lowest costs without significantly impacting the customer on time order fulfillment. For all other products, a MTS policy is applied to maximize order fulfillment on time, reduce cleaning costs and to reduce number of storage units.     

Scheduling a capacitated single machine with time deteriorating job values

We address the scheduling problem with the following characteristics: (i) there is a single machine available, (ii) the machine has limited capacity, and (iii) job value deteriorates with time. The problem is motivated from several real world situations, such as, downloading process of web pages, and scheduling of multiplexes. Since the problem is NP-hard, we propose new heuristics based on a multiplicative piece-wise metric as an approximation of the slope of job value deterioration. Computational results show that the proposed heuristics perform better than other existing heuristics for similar types of problems.     

A co-ordination model combining incentive scheme and co-ordination policy for a single-vendor-multi-buyer supply chain

Incentive scheme and co-ordination policy are substantial components for the co-ordination of a decentralized supply chain system, but there is little work combining the two components as a single decision model (Li and Wang, 2007). This paper proposes a model which incorporates both incentive and co-ordination issues into a single co-ordination model for a single-vendor multi-buyer supply chain. In the proposed model, the co-ordination is achieved by synchronizing ordering and production cycles while price discount, which is based on the buyers’ order intervals, is used as the incentive to motivate buyers to participate in the co-ordination. While the vendor is benefited from the co-ordination by synchronized cycles, the proposed price discount scheme can guarantee that a buyer’s total relevant cost of co-ordination will be reduced when compared with independent optimization. More importantly, the proposed price discount scheme does not require any cost information from the buyers.     

A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search

The job-shop scheduling problem is one of the most arduous combinatorial optimization problems. Flexible job-shop problem is an extension of the job-shop problem that allows an operation to be processed by any machine from a given set along different routes. This paper present a new approach based on a hybridization of the particle swarm and local search algorithm to solve the multi-objective flexible job-shop scheduling problem. The particle swarm optimization is a highly efficient and a new evolutionary computation technique inspired by birds’ flight and communication behaviors. The multi-objective particle swarm algorithm is applied to the flexible job-shop scheduling problem based on priority. Also the presented approach will be evaluated for their efficiency against the results reported for similar algorithms (weighted summation of objectives and Pareto approaches). The results indicate that the proposed algorithm satisfactorily captures the multi-objective flexible job-shop problem and competes well with similar approaches.     

Constraint programming for project-driven manufacturing

Project-driven manufacturing, based on the make-to-order or the build-to-order principle and predominant in small and medium-size enterprises (SMEs), calls for an efficient solution of large combinatorial problems, especially in such areas as task scheduling or resource management. This paper addresses the problem of finding a computationally effective approach to scheduling a new project subject to constraints imposed by a multi-project environment. A constraint programming (CP) modeling framework is discussed in the context of an efficient decomposition of the constraint satisfaction problem (CSP) and the evaluation of strategies for pruning the search tree. The proposed approach is illustrated through examples of its application to the evaluation of a new production order.     

Scheduling multiple robots in a no-wait re-entrant robotic flowshop

No-wait re-entrant robotic flowshops are widely used in the electronic industry, such as PCB and semiconductor manufacturing. In such an industry, cyclic production policy is often used due to large lot size and simplicity of implementation. This paper addresses cyclic scheduling of a no-wait re-entrant robotic flowshop with multiple robots for material handling. We formulate the problem and propose a polynomial algorithm to find the minimum number of robots for all feasible cycle times. Consequently, the minimum cycle time for any given number of robots can be obtained with the proposed algorithm. The algorithm runs in O(N⁵) time in the worst case, where N is the number of machines in the robotic flowshop.     

A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems—A case study for solar cell industry

This paper studies a solar cell industry scheduling problem which is similar to the traditional hybrid flow shop scheduling (HFS). In a typical HFS with parallel machines problem, the allocation of machine resources for each order should be scheduled in advance and then the optimal multiprocessor task scheduling in each stage could be determined. However, the challenge in solar cell manufacturing is the number of machines can be dynamically adjusted to complete the job within the shortest possible time. Therefore, the paper addresses a multi-stage HFS scheduling problem with characteristics of parallel processing, dedicated machines, sequence-independent setup time, and sequence-dependent setup time. The objective is to schedule the job production sequence, number of sublots, and dynamically allocate sublots to parallel machines such that the makespan time is minimized. The problem is formulated as a mixed integer linear programming (MILP) model. A hybrid approach based on the variable neighborhood search and particle swarm optimization (VNPSO) is developed to obtain the near-optimal solution. Preliminary computational study indicates that the developed VNPSO not only provides good quality solutions within a reasonable amount of time but also outperforms the classic branch and bound method and the current industry heuristic practiced by the case company.     

A discounted cash flow approach to the base stock inventory model

A widespread approach to inventory modelling is to associate costs with measures of system performance and determine the control policy which minimises the long run average cost per unit time. This type of approach ignores the impact of a control policy on the timing of the cash flows associated with payments to suppliers and revenue streams from customers. The approach in this paper is to concentrate on cash flows and determine the control policy which maximises the expected net present value of the cash flows associated with a demand, valued at the time when that demand occurs. There is a Poisson demand process, a fixed lead time, unsatisfied demand is backordered and the system is controlled using a base stock policy. A solution procedure is given and a comparison is made with an equivalent simple interest model and with the standard cost model with linear holding and shortage costs.     

The intentional use of chapter XI: Lingering versus immediate filing

This paper focuses on the timing, i.e. delay or accelerate, of the declaration of Chapter XI prior to financial insolvency. Chapter XI can be declared by firms who believe that the firm can emerge from bankruptcy protection as a re-vitalized organization. The timing of its selection is proposed to be a function of the level of environmental adversity and organizational attributes, especially slack resources. Recent developments in the bankruptcy code and its usage by firms, suggest the increasing use of managerial prerogative in the selection of Chapter XI prior to financial insolvency. Chapter XI protection can allow firms the necessary time for effective reorganization if managerial intervention is exercised. As a result of a more precise timing of selection and a proactive reorganization, there will be a higher probability of re-emerging from bankruptcy as a revitalized organization.     

跳频通信中一种结合干扰预处理的平方定时同步算法

为了提高跳频通信中定时同步算法的准确性,设计了一种结合干扰预处理的跳频信号定时同步算法。首先对解跳后信号在时域完成基于能量的自适应门限的干扰检测,并根据检测结果将受干扰信号进行置零预处理;其次对预处理后的信号采用平方法进行定时误差估计;最后根据估计结果内插完成定时恢复。结果表明,提出的改进算法选取长度为200跳的符号,在干扰相对带宽为30%、干信比为10 dB、信噪比为5 dB、时钟稳定度为1×10^-6的条件下,进行干扰预处理后定时,归一化定时误差的方差为0.000 75,满足跳频系统解调性能损失小于0.5 dB的要求。设计方法提高了干扰条件下平方法定时的准确性,在跳频通信抗干扰应用中有一定的参考价值。     

Economic lot and delivery scheduling problem for multi-stage supply chains

The economic lot and delivery scheduling problem for a multi-stage supply chain comprising multiple items is studied in this paper. It is required to develop a synchronized replenishment strategy, and specify the sequence of production and the replenishment cycle time that achieves synchronization through the supply chain at minimum cost. The problem is presented in a novel formulation based on the quadratic assignment representation. The common cycle time and the integer multipliers policies are adopted to accomplish the desired synchronization. The two policies are represented by nonlinear models handled through a hybrid algorithm. The algorithm combines linearization, outer approximation and Benders decomposition techniques. Results of the two policies demonstrate that a cost reduction up to16.3% can be attained by employing the integer multipliers policy instead of the common cycle time. Computational experiments show the efficiency of the new formulation and solution algorithm by reaching the optimal solution for large problem instances in short time.