Minimizing tardiness on a single processor with sequence-dependent setup times: a simulated annealing approach

In today's fast-paced Just-In-Time and mass customization manufacturing in a sequence-dependent setup environment, the challenge of making production schedules to meet due-date requirements is becoming a more complex problem. Unfortunately, much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. This paper considers the problem of minimizing tardiness, a common measure of due-date performance, in a sequence-dependent setup environment. Simulated annealing was used to solve the sequencing problem, and its performance was compared with random search. Our experimental results show that the algorithm can find a good solution fairly quickly, and thus can rework schedules frequently to react to variations in the schedule. The algorithm is invaluable for ‘on-line’ production scheduling and ‘last-minute’ changes to production schedule. The results of this research also suggest ways in which more complex and realistic job shop environments, such as multiple machines with a higher number of jobs in the sequence, and other scheduling objectives can be modeled. This research also investigates computational aspects of simulated annealing in solving complex scheduling problems.     

Efficient composite heuristics for total flowtime minimization in permutation flow shops

In this paper, permutation flow shops with total flowtime minimization are considered. General flowtime computing (GFC) is presented to accelerate flowtime computation. A newly generated schedule is divided into an unchanged subsequence and a changed part. GFC computes total flowtime of a schedule by inheriting temporal parameters from its parent in the unchanged part and computes only those of the changed part. Iterative methods and LR (developed by Liu J, Reeves, CR. Constructive and composite heuristic solutions to theP∥Σ_Ci$P\parallel \Sigma C_i$ scheduling problem, European Journal of Operational Research 2001; 132:439–52) are evaluated and compared as solution improvement phase and index development phase. Three composite heuristics are proposed in this paper by integrating forward pair-wise exchange-restart (FPE-R) and FPE with an effective iterative method. Computational results show that the proposed three outperform the best existing three composite heuristics in effectiveness and two of them are much faster than the existing ones.     

Tactical production and distribution planning with dependency issues on the production process

Tactical production-distribution planning models have attracted a great deal of attention in the past decades. In these models, production and distribution decisions are considered simultaneously such that the combined plans are more advantageous than the plans resolved in a hierarchical planning process. We consider a two-stage production process, where in the first stage raw materials are transformed into continuous resources that feed the discrete production of end products in the second stage. Moreover, the setup times and costs of resources depend on the sequence in which they are processed in the first stage. The minimum scheduling unit is the product family which consists of products sharing common resources and manufacturing processes. Based on different mathematical modelling approaches to the production in the first stage, we develop a sequence-oriented formulation and a product-oriented formulation, and propose decomposition-based heuristics to solve this problem efficiently. By considering these dependencies arising in practical production processes, our model can be applied to various industrial cases, such as the beverage industry or the steel industry. Computation tests on instances from an industrial application are provided at the end of the paper.     

Scheduling to minimize stringer utilization for continuous annealing operations

In this paper we present a mathematical program and heuristic algorithms to schedule coils for the production operations in a copper (or steel) coil manufacturing industry. The processing facility uses continuous operations for processing (e.g., galvanizing and annealing) while the handling unit is a discrete coil. The ends of coils are “stitched” or welded together to enable continuous processing, and the joint is later sheared off to obtain the processed coils. Processing constraints impose restrictions on the compatibility between a pair of coils that are overcome by introducing a dummy coil called stringer, which is very expensive to a mill. This paper deals with modeling the sequencing/scheduling problem of coils on parallel non-identical machines to minimize stringer utilization. Both computational and practical experiences show the efficiency and effectiveness of the solution approaches. Implementing these methods in an actual coil annealing facility resulted in 65% reduction in stringer utilization.     

等待时间受限的置换流水车间调度启发式算法

针对等待时间受限的置换流水车间调度问题,分析了其可行解与流水车间调度最优解的关系,给出了计算最大完工时间的有向图,证明了等待时间受限的置换流水车间调度问题的可逆性,并以此为基础提出了一种启发式算法.算法首先根据等待时间受限约束与无等待(no-wait)约束的相似特征,生成初始工件序列集;然后利用问题可逆性给出了复杂度为O(n2m)的插入优化机制,进一步优化初始解.数据实验的结果验证了启发式算法的可行性和有效性.     

An estimation of distribution algorithm for lot-streaming flow shop problems with setup times

Lot-streaming flow shops have important applications in different industries including textile, plastic, chemical, semiconductor and many others. This paper considers an n-job m-machine lot-streaming flow shop scheduling problem with sequence-dependent setup times under both the idling and no-idling production cases. The objective is to minimize the maximum completion time or makespan. To solve this important practical problem, a novel estimation of distribution algorithm (EDA) is proposed with a job permutation based representation. In the proposed EDA, an efficient initialization scheme based on the NEH heuristic is presented to construct an initial population with a certain level of quality and diversity. An estimation of a probabilistic model is constructed to direct the algorithm search towards good solutions by taking into account both job permutation and similar blocks of jobs. A simple but effective local search is added to enhance the intensification capability. A diversity controlling mechanism is applied to maintain the diversity of the population. In addition, a speed-up method is presented to reduce the computational effort needed for the local search technique and the NEH-based heuristics. A comparative evaluation is carried out with the best performing algorithms from the literature. The results show that the proposed EDA is very effective in comparison after comprehensive computational and statistical analyses.     

Evaluation of heuristics for scheduling in a flowshop: a case study

The problem of finding the best permutation schedule for a flowshop has engaged the attention of researchers for almost four decades. In view of its NP-completeness, the problem is not amenable to the development of efficient optimizing algorithms. A number of heuristics have been proposed, most of which have been evaluated by using randomly generated problems for a single measure of performance. However, real-life problems often have more than one objective. This paper discusses a live flowshop problem that has the twin objectives of minimizing the production run-time as well as the total flowtime of jobs. Five heuristics are evaluated in this study and some interesting findings are reported.     

The Incremental Group Testing Model for Gap Closing in Sequencing Long Molecules

In this paper, we propose the incremental group testing model for the gap closing problem, which assumes that we can tell the difference between the outcome of testing a subset S, and the outcome of testing S {x}. We also give improvements over currently best results in literature for some other models.     

Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next‐Generation Sequencing Data

Next‐generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored with Listeria monocytogenes as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain. These frequency data were discretized into seven ordinal outcome categories and used for supervised machine learning and model selection from five ensemble algorithms. There was no significant difference in accuracy between the models, and support vector machine with linear kernel was chosen for further inference (accuracy of 89% [95% CI: 68%, 97%]). The virulence genes FAM002725, FAM002728, FAM002729, InlF, InlJ, Inlk, IisY, IisD, IisX, IisH, IisB, lmo2026, and FAM003296 were important predictors of higher frequency of illness. InlF was uniquely truncated in the sequence type 121 strains. Most important risk predictor genes occurred at highest prevalence among strains from ready‐to‐eat, dairy, and composite foods. We foresee that the findings and approaches described offer the potential for rethinking the current approaches in MRA.     

Value‐Driven Creation of Functionality in Software Projects: Optimal Sequencing and Reuse

We study the problem of optimally sequencing the creation of elements in a software project to optimize a time‐weighted value objective. As elements are created, certain parts of the system (referred to as “groups”) become functional and provide value, even though the entire system has not been completed. The main tradeoff in the sequencing problem arises from elements that belong to multiple groups. On the one hand, creating groups with common elements early in the project reduces the effort required to build later functionality that uses these elements. On the other hand, the early creation of such groups can delay the release of some critical functionality. We formulate the element sequencing problem and propose a heuristic to solve it. This heuristic is compared against a lower bound developed for the problem. Next, we study a more general version of the element sequencing problem in which an element requires some effort to be made reusable. When a reusable element is used in another group, some more effort is needed to specialize the element to work as desired in that group. We study reuse decisions under a weighted completion time objective (i.e., the sum of the completion time of each group weighted by its value is minimized), and show how these decisions differ from those under a traditional makespan objective (i.e., only the final completion time of the project is minimized). A variety of analytical and numerical results are presented. The model is also implemented on data obtained from a real software project. A key finding of this work is that the optimal effort on reuse is never increased (typically lowered) when a weighted completion time objective is used. This finding has implications for managing reuse in projects in which user value influences the order in which functionality is created.     

New high performing heuristics for minimizing makespan in permutation flowshops

The well-known NEH heuristic from Nawaz, Enscore and Ham proposed in 1983 has been recognized as the highest performing method for the permutation flowshop scheduling problem under the makespan minimization criterion. This performance lead is maintained even today when compared against contemporary and more complex heuristics as shown in recent studies. In this paper we show five new methods that outperform NEH as supported by careful statistical analyses using the well-known instances of Taillard. The proposed methods try to counter the excessive greediness of NEH by carrying out re-insertions of already inserted jobs at some points in the construction of the solution. The five proposed heuristics range from extensions that are slightly slower than NEH in most tested instances to more comprehensive methods based on local search that yield excellent results at the expense of some added computational time. Additionally, NEH has been profusely used in the flowshop scheduling literature as a seed sequence in high performing metaheuristics. We demonstrate that using some of our proposed heuristics as seeds yields better final results in comparison.     

A Parametric Approach for a Nonlinear Discrete Location Problem

A discrete location problem is formulated for the design of a postal service network. The cost objective of this problem includes a nonlinear concave component. A parametric integer programming algorithm is developed to find an approximate solution to the problem. The algorithm reduces the problem into a sequence of p-median problems and deals with the nonlinear cost by a node-replacement scheme. Preliminary computational results are presented.     

Barriers and drivers for process innovation in the petrochemical industry: A case study

Process innovation and energy efficiency improvement are among the key options to reduce greenhouse gas emissions in petrochemicals production. This case study presents some of the main drivers and barriers to activities aimed at improving existing processes and developing new processes in the petrochemical industry. Interviews, statistics and related literature were used. The results suggest that as far as strategic scenario planning is concerned, feedstock economics of new processes is of strategic importance, while energy efficiency is much less important. This explains why firms see new processes as strategic innovations, although they are often less energy efficient than existing processes.     

中小呼叫中心月度排班优化模型与算法

国内中小呼叫中心制定坐席人员月度排班表时,通常考虑劳动法规合同约束和体现企业自身用工管理诉求。构建坐席人员月度排班优化问题的二次整数规划模型。鉴于问题模型难解性,依据调研企业需求和模型逻辑结构分析,把问题分解成三个子问题。通过构建整数规划模型和提出启发式算法来求出子问题解,从而生成排班问题优化解。问题实例计算表明,模型算法能够有效控制人力成本和兼顾员工同班次管理目标。与周排班方法比较,该方法能够充分体现月度排班人力灵活性来实现人力优化配置。     

Optimal investment in setup reduction for finite horizon periodic review inventory system under deterministic time varying demand

This paper examines the impact of setup reduction on a finite horizon, periodic review inventory system, under deterministic time varying demand. A total relevant cost function is developed for such systems. Using this, the impact of setup reduction is examined under various forms of setup reduction cost functions that have been suggested in the literature. The operating characteristics and optimization of the various scenarios are discussed. Our analysis shows that the effects of setup reduction in a periodic review system are similar to those in a reorder point system. Our results are likely to help practitioners who use similar periodic review systems towards decreasing total inventory related costs by investing in setup reduction.     

A generalized procedure for minimizing makespan in a CNC environment

This paper presents the problem of scheduling N jobs, each manufactured in a number of settings, in order, on a CNC machine equipped with an automatic tool changer. A generalized procedure to minimize makespan in a CNC environment has been developed and tested on real industrial problems, considering possible constraints. It is worthy of mention that the procedure suggested finds an optimal solution of the problem discussed by Sule in a short and simplified manner. The step-by-step analysis shows the simplicity and flexibility of the proposed approach.     

An Evolutionary Algorithm for Sequencing Production on a Paced Assembly Line

A new sequencing method for mixed‐model assembly lines is developed and tested. This method, called the Evolutionary Production Sequencer (EPS) is designed to maximize production on an assembly line. The performance of EPS is evaluated using three measures: minimum cycle time necessary to achieve 100% completion without rework, percent of items completed without rework for a given cycle time, and sequence “smoothness.” The first two of these measures are based on a simulated production system. Characteristics of the system, such as assembly line station length, assembly time and cycle time, are varied to better gauge the performance of EPS. More fundamental variation is studied by modeling two production systems. In one set of tests, the system consists of an assembly line in isolation (i.e., a single‐level system). In another set of tests, the production system consists of the assembly line and the fabrication system supplying components to the line (i.e., a two‐level system). Sequence smoothness is measured by the mean absolute deviation (MAD) between actual component usage and the ideal usage at each point in the production sequence. The performance of EPS is compared to those of well‐known assembly line sequencing techniques developed by Miltenburg (1989), Okamura and Yamashina (1979), and Yano and Rachamadugu (1991). EPS performed very well under all test conditions when the criterion of success was either minimum cycle time necessary to achieve 100% production without rework or percent of items completed without rework for a given cycle time. When MAD was the criterion of success, EPS was found inferior to the Miltenburg heuristic but better than the other two production‐oriented techniques.     

Relations between environmental problems,psychology and health among shift-workers in the Norwegian process industry

Abstract

A group of 89 male shift-workers, mean age 38 years, was examined for health complaints, subjective experience of their work environment, psychological defence strategies, and immunological factors. They worked in a process industry, controlling a complex chemical process partly by instruments and partly by direct checking of mechanical devices such as valves. Shifts were eight hours long and changed every second week.

The main problem areas identified were problems with the management, colleagues and superiors, feelings of being under-valued, time pressure, fear of making mistakes, and lack of influence and communication. The main health complaints were sleep disturbance and gastro-intestinal problems. Shift workers also complained about allergies, breathing difficulties, tension, anxiety and depression. There were no pathological levels of plasma immunoglobulins (Ig) or Ig complements. However, levels did correlate with experience of work-related problems and with measures of defence mechanisms. Up to 25% of the variance in die immunological indicators was explained by a combination of defence mechanism scores and those of perceived health and work problems. This suggests that immunoglobulins may be an indicator of work-related health risks (‘stress’), even if the level of explained variance is too low to use immunoglobulins alone as a ‘marker’. The same variables, and immunoglobulins, also related significantly to perceived health. This may be a particularly important indicator, and when combined with immunology and psychological variables, also as a risk factor for organic disease.     

Natural Hazards and Motivation for Mitigation Behavior: People Cannot Predict the Affect Evoked by a Severe Flood

Past research indicates that personal flood experience is an important factor in motivating mitigation behavior. It is not fully clear, however, why such experience is so important. This study tested the hypothesis that people without flooding experience underestimate the negative affect evoked by such an event. People who were affected by a severe recent flood disaster were compared with people who were not affected, but who also lived in flood-prone areas. Face-to-face interviews with open and closed questions were conducted ( n = 201). Results suggest that people without flood experience envisaged the consequences of a flood differently from people who had actually experienced severe losses due to a flood. People who were not affected strongly underestimated the negative affect associated with a flood. Based on the results, it can be concluded that risk communication must not focus solely on technical aspects; in order to trigger motivation for mitigation behavior, successful communication must also help people to envisage the negative emotional consequences of natural disasters.     

Optimum common cycle for scheduling a single-machine multiproduct system with a budgetary constraint

This paper deals with the single machine multi-product lot size scheduling problem, and has two objectives. The first objective is to minimize the maximum aggregate inventory for the common cycle (CC) scheduling policy. A simple and easy-to-apply rule has been developed which determines the optimal production sequence that achieves this objective. The second objective is to find an optimal common cycle for minimizing the average production and inventory costs per unit time, subject to a given budgetary constraint. A method has been presented that achieves this objective