A time-varying lot sizes approach for the economic lot scheduling problem with returns

We consider the economic lot scheduling problem with returns by assuming that each item is returned by a constant rate of demand. The goal is to find production frequencies, production sequences, production times, as well as idle times for several items subject to returns at a single facility. We propose a heu ristic algorithm based on a time-varying (TV) lot sizes approach. The problem is decomposed into two distinct portions: in the first, we find a combinatorial part (production frequencies and sequences) and in the second, we determine a continuous part (production and idle times) in a specific production sequence. We report computational results that show that, in many cases, the proposed TV lot sizes approach with consideration of returns yields a relatively minor error.     

A project scheduling approach to production planning with feeding precedence relations

In Manufacturing-to-Order or Engineering-to-Order systems producing complex and highly customised items, each item has its own characteristics that are often tailored for a specific customer. Project scheduling approaches are suitable for production planning in such environments. However, when we consider the production of complex items, the distinct production operations are often aggregated into activities representing whole production phases. In such cases, the planning and scheduling problem works on the aggregate activities, considering that, in most cases, such activities also have to be manually executed. Moreover, simple finish-to-start precedence relations no longer correctly represent the real production process, but overlapping among activities should be allowed. In this paper, a project scheduling approach is proposed for production planning in Manufacturing-to-Order systems. The Variable Intensity formulation is used to allow the effort committed to the execution of activities to vary over time. Feeding precedences are developed to model generalised precedence relations when the execution mode of activities is not known a priori. Two mathematical formulations of these precedence relations are proposed. The formulations are applied both to randomly generated instances and to an industrial system producing machining centres and are compared in terms of computational efficiency.     

API-based two-dimensional dispatching decision-making approach for semiconductor wafer fabrication with operation due date–related objectives

This paper presents an adjacent pairwise interchanges (API)-based two-dimensional dispatching decision-making approach for semiconductor wafer fabrication with operation due date-related objectives. Each time when a machine becomes idle, the proposed dispatcher chooses a target processing job from the competing jobs and assigns it a start time. Giving the operation due date information of each competing job, we formulate this dispatcher as the mean absolute deviation problem to keep the jobs finished around their operation due dates in a proactive way. Dominance properties of this problem are established using proof by APIs. Then, a heuristic comprised of job selection within candidate set, movement of job cluster and local search is designed to solve this problem more efficiently. Numerical experiments validate the efficiency of the proposed heuristic in a single-machine environment as well as in a simulated wafer fab abstracted from practice. In comparison with four most referenced due date-related dispatching rules, the simulation study reveals the benefits brought by the two-dimensional dispatching decision with different due date tightness taken into account.     

一种透析机自动排台优化算法的实现与应用

针对目前血液透析排台模式复杂化、排台程序固定化等问题,设计了一种透析机自动排台优化算法,以固定时间间隔为单位自动更新患者治疗排台信息,并在此基础上设计了排台管理功能,将排台算法和管理功能相结合,建立血液透析自动排台管理系统。该系统的建立降低了排台的复杂性,提高了透析中心的工作效率,同时也进一步提高了血液透析中心的信息化建设水平。     

Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools

Several grammar-based genetic programming algorithms have been proposed in the literature to automatically generate heuristics for hard optimization problems. These approaches specify the algorithmic building blocks and the way in which they can be combined in a grammar; the best heuristic for the problem being tackled is found by an evolutionary algorithm that searches in the algorithm design space defined by the grammar.In this work, we propose a novel representation of the grammar by a sequence of categorical, integer, and real-valued parameters. We then use a tool for automatic algorithm configuration to search for the best algorithm for the problem at hand. Our experimental evaluation on the one-dimensional bin packing problem and the permutation flowshop problem with weighted tardiness objective shows that the proposed approach produces better algorithms than grammatical evolution, a well-established variant of grammar-based genetic programming. The reasons behind such improvement lie both in the representation proposed and in the method used to search the algorithm design space.     

An effective hybrid teaching–learning-based optimization algorithm for permutation flow shop scheduling problem

Permutation flow shop scheduling (PFSP) is among the most studied scheduling settings. In this paper, a hybrid Teaching–Learning-Based Optimization algorithm (HTLBO), which combines a novel teaching–learning-based optimization algorithm for solution evolution and a variable neighborhood search (VNS) for fast solution improvement, is proposed for PFSP to determine the job sequence with minimization of makespan criterion and minimization of maximum lateness criterion, respectively. To convert the individual to the job permutation, a largest order value (LOV) rule is utilized. Furthermore, a simulated annealing (SA) is adopted as the local search method of VNS after the shaking procedure. Experimental comparisons over public PFSP test instances with other competitive algorithms show the effectiveness of the proposed algorithm. For the DMU problems, 19 new upper bounds are obtained for the instances with makespan criterion and 88 new upper bounds are obtained for the instances with maximum lateness criterion.     

Capacity planning for packaging industry

This paper presents a capacity planning system (CPS) to generate a feasible production schedule, improve production efficiency, and avoid overcapacity for the packaging industry. CPS applies the concept of workload leveling and finite capacity planning to assign orders to production lines by considering several production characteristics such as drying time, quantity splitting owing to the cutting pattern of the product type, and the variability of machine capacity threshold. CPS consists of five modules, namely, order treatment module (OTM), order priority module (OPM), lot release module (LRM), workload accumulation module (WAM), and workload balance module (WBM). The experimental design is used to evaluate the effectiveness and efficiency of the proposed CPS with five factors (number of orders, order size, order size variance, order priority, and balance policy) with various levels and three response variables, namely, machine workload balance, order due date deviation, and lateness. Moreover, this result extends into finding the best settings of order priority and balance policy to generate the best favorable responses under the given three environment factors.     

A model to determine the optimal critical level and the monitoring intervals in condition-based maintenance

In condition-based maintenance, a common practice is to record a condition reading at a regular interval, and once the reading is higher than a pre-set critical level, the item monitored is declared faulty and repair or replacement may be initiated. However, surprisingly both in practice and theory, little attention has been paid to whether or not the critical level and the monitoring interval are set in a cost effective way. This paper reports on the development of a model that can be used to determine the optimal critical level and interval in condition-based maintenance in terms of a criterion of interest. The model is established on the basis of the random coefficient growth model where the coefficients of the regression growth model are assumed to follow known distribution functions. A simple example is given in the paper to illustrate the modelling ideas.     

Improving branch divergence performance on GPGPU with a new PDOM stack and multi-level warp scheduling

General-purpose graphics processing unit (GPGPU) plays an important role in massive parallel computing nowadays. A GPGPU core typically holds thousands of threads, where hardware threads are organized into warps. With the single instruction multiple thread (SIMT) pipeline, GPGPU can achieve high performance. But threads taking different branches in the same warp violate SIMD style and cause branch divergence. To support this, a hardware stack is used to sequentially execute all branches. Hence branch divergence leads to performance degradation. This article represents the PDOM (post dominator) stack as a binary tree, and each leaf corresponds to a branch target. We propose a new PDOM stack called PDOM-ASI, which can schedule all the tree leaves. The new stack can hide more long operation latencies with more schedulable warps without the problem of warp over-subdivision. Besides, a multi-level warp scheduling policy is proposed, which lets part of the warps run ahead and creates more opportunities to hide the latencies. The simulation results show that our policies achieve 10.5% performance improvements over baseline policies with only 1.33% hardware area overhead.     

Cycle time-oriented mid-term production planning for semiconductor wafer fabrication

Wafers are produced in an environment with uncertain demand and failure-prone machines. Production planners have to react to changes of both machine availability and target output, and revise plans appropriately. The scientific community mostly proposes WIP-oriented mid-term production planning to solve this problem. In such approaches, production is planned by defining targets for throughput rates and buffer levels of selected operations. In industrial practice, however, cycle time-oriented planning is often preferred over WIP-oriented planning. We therefore propose a new linear programming formulation, which facilitates cycle time-oriented mid-term production planning in wafer fabrication. This approach plans production by defining release quantities and target cycle times up to selected operations. It allows a seamless integration with the subordinate scheduling level. Here, least slack first scheduling translates target cycle times into lot priorities. We evaluate our new methodology in a comprehensive simulation study. The results suggest that cycle time-oriented mid-term production planning can both increase service level and reduce cycle time compared to WIP-oriented planning. Further, it requires less modelling effort and generates plans, which are easier to comprehend by human planners.