Integration of production planning and scheduling using an expert system and a genetic algorithm

In the traditional approaches, processes of planning and scheduling are done sequentially, where the process plan is determined before the actual scheduling is performed. This simple approach ignores the relationship between the scheduling and planning. Practical scheduling systems need to be able to react to significant real-time events within an acceptable response time and revise schedules appropriately. Therefore, the author proposes a new methodology with artificial intelligence to support production planning and scheduling in supply net. In this approach, the production planning problem is first solved, and then the scheduling problem is considered with the constraint of the solution. The approach is implemented as a combination of expert system and genetic algorithm. The research indicates that the new system yields better results in real-life supply net than using a traditional method. The results of experiments provide that the proposed genetic algorithm produces schedules with makespan that is average 21% better than the methods based on dispatching rules.     

A new lot sizing and scheduling heuristic for multi-site biopharmaceutical production

Biopharmaceutical manufacturing requires high investments and long-term production planning. For large biopharmaceutical companies, planning typically involves multiple products and several production facilities. Production is usually done in batches with a substantial set-up cost and time for switching between products. The goal is to satisfy demand while minimising manufacturing, set-up and inventory costs. The resulting production planning problem is thus a variant of the capacitated lot-sizing and scheduling problem, and a complex combinatorial optimisation problem. Inspired by genetic algorithm approaches to job shop scheduling, this paper proposes a tailored construction heuristic that schedules demands of multiple products sequentially across several facilities to build a multi-year production plan (solution). The sequence in which the construction heuristic schedules the different demands is optimised by a genetic algorithm. We demonstrate the effectiveness of the approach on a biopharmaceutical lot sizing problem and compare it with a mathematical programming model from the literature. We show that the genetic algorithm can outperform the mathematical programming model for certain scenarios because the discretisation of time in mathematical programming artificially restricts the solution space.     

A Common Error in Production Scheduling

Many production scheduling systems use schedules of planned start/finish times for jobs up to a given planning horizon which includes the period between rescheduling operations. The integrity and usefulness of such schedules depends on the accuracy of the estimated time data which is available on the operations to be performed.There is a widespread belief that "on the average" if errors in the estimated time data are not biased then they will balance out and the schedules will give a reasonable plan for the period between rescheduling operations. This paper uses the elementary theory of the simple random walk to show that this assumption is not valid and that scheduling systems based upon it will not make sufficient allowance for the relatively highly probable events of permanent under-schedule or over-schedule conditions.     

Minimizing energy consumption and makespan in a two-machine flowshop scheduling problem

Energy consumption has become a key concern for manufacturing sector because of negative environmental impact of operations. We develop constructive heuristics and multi-objective genetic algorithms (MOGA) for a two-machine sequence-dependent permutation flowshop problem to address the trade-off between energy consumption as a measure of sustainability and makespan as a measure of service level. We leverage the variable speed of operations to develop energy-efficient schedules that minimize total energy consumption and makespan. As minimization of energy consumption and minimization of makespan are conflicting objectives, the solutions to this problem constitute a Pareto frontier. We compare the performance of constructive heuristics and MOGAs with CPLEX and random search in a wide range of problem instances. The results show that MOGAs hybridized with constructive heuristics outperform regular MOGA and heuristics alone in terms of quality and cardinality of Pareto frontier. We provide production planners with new and scalable solution techniques that will enable them to make informed decisions considering energy consumption together with service objectives in shop floor scheduling.     

A hybrid genetic algorithm for the job shop scheduling problem

This paper presents a hybrid genetic algorithm for the job shop scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.     

蔬果类商品网上直销包装作业工序优化模型研究

针对蔬果类商品网上直销模式下,其标准销售单元包装作业问题规模大、商品品类多、订单个性化强、生产配送周期多等特点,基于批量流水作业生产、JIT准时制生产及周期调度的思想,研究该类商品标准销售单元包装作业的生产调度问题,建立蔬果类商品网上直销包装作业优化模型,并设计改进的“模拟增压——退火算法”对其进行求解,以便制定出合理的包装作业计划,有效衔接采摘和订单分拣作业以及后续装车作业,缩短包装时间,保证蔬菜的新鲜性。最后,通过应用实例验证模型和算法的有效性,结果表明,本文周期调度方法得到的调度方案比一般的非周期调度方法大大节约了包装作业成本,为蔬果类商品网上直销企业生成包装作业计划提供了理论指导。     

Cane Railway Scheduling via Constraint Logic Programming: Labelling Order and Constraints in a Real-Life Application

In Australia, cane transport is the largest unit cost in the manufacturing of raw sugar, making up around 35% of the total manufacturing costs. Producing efficient schedules for the cane railways can result in significant cost savings. This paper presents a study using Constraint Logic Programming (CLP) to solve the cane transport scheduling problem. Tailored heuristic labelling order and constraints strategies are proposed and encouraging results of application to several test problems and one real-life case are presented. The preliminary results demonstrate that CLP can be used as an effective tool for solving the cane transport scheduling problem, with a potential decrease in development costs of the scheduling system. It can also be used as an efficient tool for rescheduling tasks which the existing cane transport scheduling system cannot perform well.     

Comparison and hybridization of crossover operators for the nurse scheduling problem

In this paper, we present a hybrid genetic algorithm for the well-known nurse scheduling problem (NSP). The NSP involves the construction of roster schedules for nursing staff in order to maximize the quality of the roster schedule subject to various hard constraints. In the literature, several genetic algorithms have been proposed to solve the NSP under various assumptions. The contribution of this paper is twofold. First, we extensively compare the various crossover operators and test them on a standard dataset in a solitary approach. Second, we propose several options to hybridize the various crossover operators.     

A performance analysis of dispatching rules and a heuristic in static flowshops with missing operations of jobs

An experimental investigation of the performance of dispatching rules and a heuristic for scheduling in static flowshops with missing operations is undertaken in this study. The measure of performance is the minimization of total flow time of jobs. Permutation schedules are generated by using the heuristic for scheduling. General schedules, which can be permutation or non-permutation schedules, are obtained by using dispatching rules. Four dispatching rules, including a new dispatching rule, are considered. Two types of flowshops are studied: one with no missing operations of jobs and another with missing operations of jobs. In the latter type of flowshops, jobs with varying number of missing operations are considered. An extensive investigation of the performance of the dispatching rules and the heuristic is carried out. It is observed that the heuristic minimizes total flow time of jobs more than dispatching rules up to a certain level of missing operations of jobs in flowshops, after which dispatching rules perform better. The performance of the heuristic and the dispatching rules in terms of minimizing the makespan as a secondary measure is also reported.     

Minimizing flow time in a flow-line manufacturing cell with family setup times

This paper considers the problem of scheduling part families and jobs within each part family in a flowline manufacturing cell with independent family setup times where parts (jobs) in each family are processed together. The objective is to minimize total flow time. A branch-and-bound algorithm capable of solving moderate sized problems is developed. Several heuristic algorithms are proposed and empirically evaluated as to their effectiveness and efficiency in finding optimal permutation schedules. These results show that several heuristic algorithms generate solutions that are better than those generated by an existing genetic algorithm.     

预制构件流水车间订单接受与调度的集成优化

针对预制构件生产管理过程中订单工期紧和生产能力不足的问题,在充分考虑中断和不可中断工序,串行和并行工序等复杂工况特点的基础上,以最大化净利润为目标,建立了一种订单接受与调度集成优化模型。鉴于问题的NP难性和模型的高度非线性,通过集成问题性质、构造启发式、邻域搜索和破坏-构造机制,提出了一种混合加速迭代贪婪搜索框架。其中,在调度构造阶段,为提高算法求解质量和搜索效率,设计了两种融合订单插入操作性质的加速构造策略。计算结果显示,与混合遗传禁忌搜索算法,遗传算法以及禁忌搜索算法相比,本文所提算法具有更好的求解质量和搜索效率。同时验证了所提出的加速构造策略能够有效减少算法运行时间。该研究有望显著提高预制生产企业净利润和客户满意度。     

Solution strategies for multi-stage wafer probing scheduling problem with reentry

The multi-stage wafer probing scheduling problem (M-WPSP) with reentry is a practical variation of the parallel-machine scheduling problem. Since the M-WPSP involves multiple product families, to be processed on multiple stages, with various job due dates, ready times, reentry, serial and batch operations, sequential-dependent setup time, it is more difficult to solve than the classical parallel-machine scheduling problems. In this paper, we consider two strategies to solve the M-WPSP with reentry, where the total machine workload must be minimized. These two strategies incorporate a global planning mechanism, in advance, to determine the required stage due date of job at each process stage to prevent the due date problems occurring at the final stage. The sequential strategy schedules the jobs at the required stages according to the sequence of manufacturing process. The parallel strategy is designed specifically for the reentrant characteristic. To evaluate the efficiency of the proposed strategies, a set of test problems involving four critical factors, the product family ratio, the temperature-change consideration, the tightness of due dates, and the ready time, are designed to test the quality of solutions under two levels of workload.     

A hybrid multi-objective evolutionary algorithm based on NSGA-II for practical scheduling with release times in steel plants

The hot metal is produced from the blast furnaces in the iron plant and should be processed as soon as possible in the subsequent steel plant for energy saving. Therefore, the release times of hot metal have an influence on the scheduling of a steel plant. In this paper, the scheduling problem with release times for steel plants is studied. The production objectives and constraints related to the release times are clarified, and a new multi-objective scheduling model is built. For the solving of the multi-objective optimization, a hybrid multi-objective evolutionary algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. In the hybrid multi-objective algorithm, an efficient decoding heuristic (DH) and a non-dominated solution construction method (NSCM) are proposed based on the problem-specific characteristics. During the evolutionary process, individuals with different solutions may have a same chromosome because the NSCM constructs non-dominated solutions just based on the solution found by DH. Therefore, three operations in the original NSGA-II process are modified to avoid identical chromosomes in the evolutionary operations. Computational tests show that the proposed hybrid algorithm based on NSGA-II is feasible and effective for the multi-objective scheduling with release times.     

Project Scheduling with Multiple Modes: A Genetic Algorithm

In this paper we consider the resource-constrained project scheduling problem with multiple execution modes for each activity and makespan minimization as objective. We present a new genetic algorithm approach to solve this problem. The genetic encoding is based on a precedence feasible list of activities and a mode assignment. After defining the related crossover, mutation, and selection operators, we describe a local search extension which is employed to improve the schedules found by the basic genetic algorithm. Finally, we present the results of our thorough computational study. We determine the best among several different variants of our genetic algorithm and compare it to four other heuristics that have recently been proposed in the literature. The results that have been obtained using a standard set of instances show that the new genetic algorithm outperforms the other heuristic procedures with regard to a lower average deviation from the optimal makespan.     

A genetic algorithm for the resource constrained multi-project scheduling problem

This paper presents a genetic algorithm for the resource constrained multi-project scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a heuristic that builds parameterized active schedules based on priorities, delay times, and release dates defined by the genetic algorithm. The approach is tested on a set of randomly generated problems. The computational results validate the effectiveness of the proposed algorithm.     

A graph based model for survivability applications

Many problems found in standard security and survivability applications can be transformed into graph and scheduling problems, thereby opening up the problems to a wealth of potential solutions or knowledge of limitations, infeasibility, scalability or intractability. This paper introduces a model to aid in the design, analysis, or operations of applications with security and survivability concerns. Specifically, a five step model is presented that transforms such applications into a parameterized graph model that, together with model abstraction and representations, can be the basis for solutions derived from graph and scheduling algorithms. A reverse transformation translates the solutions back to the application domain. The model is demonstrated using migratory agent security and fault-tolerant agreement and their transformation into chain constrained and group scheduling problems, respectively.     

A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem

This paper presents a biased random-key genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. Active schedules are constructed using a priority-rule heuristic in which the priorities of the activities are defined by the genetic algorithm. A forward-backward improvement procedure is applied to all solutions. The chromosomes supplied by the genetic algorithm are adjusted to reflect the solutions obtained by the improvement procedure. The heuristic is tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.     

广义优先关系约束下Max-npv项目调度问题及其遗传算法

以往Max-npv项目调度问题的研究都假定活动之间的关系为单一结束-开始类型,现实中活动之间关系复杂多变,因此,将广义优先关系引入Max-npv项目调度问题中,构建了广义优先关系约束下的Max-npv项目调度模型。针对该优化模型设计了一种双层遗传算法,外层遗传算法负责任务执行模式的优化,内层遗传算法负责任务调度的优化。在内层遗传算法中,采用任务开始时间之差作为新的编码方式,大大简化了交叉变异算子,针对网络图中的环状结构设计了修复算子,确保了编码的有效性。通过一个算例对算法进行了测试,实验结果验证了算法的有效性。     

Scheduling at coal handling facilities using Simulated Annealing

The main thrust of this study is the operational scheduling of the continuous coal handling and blending processes when considering multiple, and sometimes conflicting, objectives. A widely applicable generic goal programming model is proposed. Furthermore, assumptions regarding the certainty of demand during different periods are challenged, endeavoring to provide more robust schedules in a largely stochastic environment. As the study aims to provide scheduling solutions to any coal handling facility, the Simulated Annealing metaheuristic is proposed to ensure that acceptably good solutions for large instances of the generic model can be found in reasonable computational time. The generic approach and its suggested application will be valuable not only in the coal handling environment, but also in the continuous product manufacturing/blending or continuous material handling environment.     

Block appointment systems for outpatient clinics with multiple doctors

Studies of appointment systems have to some extent led to a wide acceptance of individual or block appointment schemes in private practice and outpatient clinics. Most of the studies assume there is one punctual doctor in a clinic, which is often not the case in reality. Motivated by observations of actual clinic operations, we develop a block appointment system for clinic operations with multiple random arriving doctors. Through extensive simulation studies, we identify properties shared by the best appointment schedules. With these properties we can design a scheme based on simulation search that provides the optimal schedule for a given scheduling environment in an acceptable computation time. A simple (suboptimal) appointment rule is also proposed.