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.     

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.     

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.     

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 hybrid genetic algorithm with dominance properties for single machine scheduling with dependent penalties

In this paper, a hybrid genetic algorithm is developed to solve the single machine scheduling problem with the objective to minimize the weighted sum of earliness and tardiness costs. First, dominance properties of (the conditions on) the optimal schedule are developed based on the switching of two adjacent jobs i and j. These dominance properties are only necessary conditions and not sufficient conditions for any given schedule to be optimal. Therefore, these dominance properties are further embedded in the genetic algorithm and we call it genetic algorithm with dominance properties (GADP). This GADP is a hybrid genetic algorithm. The initial populations of schedules in the genetic algorithm are generated using these dominance properties. GA can further improve the performance of these initial solutions after the evolving procedures. The performances of hybrid genetic algorithm (GADP) have been compared with simple genetic algorithm (SGA) using benchmark instances. It is shown that this hybrid genetic algorithm (GADP) performs very well when compared with DP or SGA alone.     

A genetic algorithm for robust schedules in a one-machine environment with ready times and due dates

Computing a schedule for a single machine problem is often difficult, but when the data are uncertain, the problem is much more complicated. In this paper, we modify a genetic algorithm to compute robust schedules when release dates are subject to small variations. Two types of robustness are distinguished: quality robustness or robustness in the objective function space and solution robustness or robustness in the solution space. We show that the modified genetic algorithm can find solutions that are robust with respect to both types of robustness. Moreover, the risk associated with a specific solution can be easily evaluated. The modified genetic algorithm is applied to a just-in-time scheduling problem, a common problem in many industries.     

基于改进混合遗传算法安排生产调度

研究了某工厂生产调度问题,建立了数学模型.针对这一实际问题,通过引入小生境技术、最优保存策略、近优淘汰策略、自适应调整交叉概率和变异概率,设计了用于求解多个最优顺序的混合遗传算法,用所设计的混合遗传算法对该模型进行了计算,获得了许多最优顺序,这就使得生产调度安排灵活机动,便于智能调度,同时生产量比原来大幅度提高.这表明使用混合遗传算法安排生产调度是非常有效的.     

A multi-objective genetic algorithm for robust flight scheduling using simulation

Traditional methods of developing flight schedules generally do not take into consideration disruptions that may arise during actual operations. Potential irregularities in airline operations such as equipment failure are not adequately considered during the planning stage of a flight schedule. As such, flight schedules cannot be met as planned and their performance is compromised, which may eventually lead to huge losses in revenue for airlines. In this paper, we seek to improve the robustness of a flight schedule by re-timing its departure times. The problem is modeled as a multi-objective optimization problem, and a multi-objective genetic algorithm (MOGA) is developed to solve the problem. To evaluate flight schedules, SIMAIR 2.0, a simulation model which simulates airline operations under operational irregularities, has been employed. The simulation results indicate that we are able to develop schedules with better operation costs and on-time performance through the application of MOGA.     

车间作业调度的可行化判定及不可行解的纠正

生产调度过程中出现不可行解是调度研究经常遇到的问题之一.提出了对JSP调度方案进行可行化判定和纠正不可行解的可行算子,算子包括了基于有向图拓扑排序原理对车间作业调度方案进行可行判定的方法和将不可行解纠正为可行解的算法.证明了该纠正算法总能成功,并对算子的功能进行了拓展使之还可应用于不完备调度.最后讨论了可行算子的特点、时间效率和应用前景.     

New solution methods for single machine bicriteria scheduling problem: Minimization of average flowtime and number of tardy jobs

We consider the bicriteria scheduling problem of minimizing the number of tardy jobs and average flowtime on a single machine. This problem, which is known to be NP-hard, is important in practice, as the former criterion conveys the customer’s position, and the latter reflects the manufacturer’s perspective in the supply chain. We propose four new heuristics to solve this multiobjective scheduling problem. Two of these heuristics are constructive algorithms based on beam search methodology. The other two are metaheuristic approaches using a genetic algorithm and tabu-search. Our computational experiments indicate that the proposed beam search heuristics find efficient schedules optimally in most cases and perform better than the existing heuristics in the literature.     

Bridging the gap between self schedules and feasible schedules in staff scheduling

Every company that has employees working on irregular schedules must deal with the difficult and time consuming problem of creating feasible schedules for the employees. We introduce an algorithm that takes a partial schedule created by requests from employees and creates feasible schedule where most of the employee’s requests are unchanged, while still making sure that rules and regulations are not violated. The algorithm is based on independent modules, which can be executed in any order, and each module tries to emulate some action taken by a staff manager. Our goal is to create a transparent and fair system that creates feasible schedules of high quality, but also a system where the employees can get an explanation and justification for every change that the algorithm makes to the employee requests. By emulating the actions of staff managers, the algorithm is easily understood by staff managers and, using detailed logs of any action, make any decision easy to explain to the employees. We will present the algorithm and show results from four real world companies and institutions. The results show that a simple module based heuristic can get good results and create fair and feasible schedules that encourage employees to participate in the self-scheduling process.     

兼顾成本与时间保证率的泊位及岸桥协同调度优化

针对集装箱班轮根据船期表按计划到离港的运行规律以及港口企业追求低运营成本的需求,本文以集装箱班轮按计划离港保证率最大和码头作业成本最低为目标,构建了泊位及岸桥协同调度多目标优化模型;设计了叠加式局部搜索算法,将其嵌入到带精英策略的非支配排序遗传算法中,经过相互交叉反馈运算,得到Pareto非劣解;采用“性价比”的概念和量化方法,选择出对港口和船公司的利益偏向最小的实施方案,解决了在Pareto解集中寻优的问题。最后,以大连港集装箱码头的生产实际为例,验证了上述优化模型及算法的合理性和有效性。     

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。     

Optimisation of fault-tolerant fabric-cutting schedules using genetic algorithms and fuzzy set theory

In apparel industry, manufacturers developed standard allowed minutes (SAMs) databases on various manufacturing operations in order to facilitate better scheduling, while effective production schedules ensure smoothness of downstream operations. As apparel manufacturing environment is fuzzy and dynamic, rigid production schedules based on SAMs become futile in the presence of any uncertainty. In this paper, a fuzzification scheme is proposed to fuzzify the static standard time so as to incorporate some uncertainties, in terms of both job-specific and human related factors, into the fabric-cutting scheduling problem. A genetic optimisation procedure is also proposed to search for fault-tolerant schedules using genetic algorithms, such that makespan and scheduling uncertainties are minimised. Two sets of real production data were collected to validate the proposed method. Experimental results indicate that the genetically optimised fault-tolerant schedules not only improve the operation performance but also minimise the scheduling risks.     

Maintenance scheduling of rolling stock using a genetic algorithm

We have developed a Genetic algorithm (GA) for the optimisation of maintenance overhaul scheduling of rolling stock (trains) at the Hong Kong Mass Transit Railway Corporation (MTRC). The problem is one of combinatorial optimisation. Genetic algorithms (GAs) belong to the class of heuristic optimisation techniques that utilise randomisation as well as directed smart search to seek the global optima. The workshop at MTRC does have difficulties in establishing good schedules for the overhaul maintenance of the rolling stock. Currently, an experienced scheduler at MTRC performs this task manually. In this paper, we study the problem in a scientific manner and propose ways in which the task can be automated with the help of an algorithm embedded in a computer program. The algorithm enables the scheduler to establish the annual maintenance schedule of the trains in an efficient manner; the objective being to satisfy the maintenance requirements of various units of the trains as closely as possible to their due dates since there is a cost associated with undertaking the maintenance tasks either `too early’ or ‘too late’. The genetic algorithm developed is found to be very effective for solving this intractable problem. Computational results indicate that the genetic algorithm consistently provides significantly better schedules than those established manually at MTRC. More over, we provide evidence that the algorithm delivers close to optimal solutions for randomly generated problems with known optimal solutions. We also propose a local search method to reconfigure the trains in order to improve the schedule and to balance the work load of the overhaul maintenance section of the workshop throughout the planning horizon. We demonstrate that the reconfiguration of trains improves the schedule and reduces cost significantly.     

Rotating schedules

In many public services and in some industries, work schedules for employees very often consist of a cycle spreading over a few weeks and in which shift work and holidays alternate. Various constraints relate to the length of work and holiday segments in the cycle and the construction of suitable schedules is in itself a complex combinatorial problem. This paper presents an effective ILP based algorithm for the construction of such schedules. Computational results are given. Difficulties which may arise in the implementation of such schedules are also discussed.     

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.     

Scheduling Classes on a College Campus

We consider the problem of scheduling a set of classes to classrooms with the objective of minimizing the number of classrooms used. The major constraint that we must obey is that no two classes can be assigned to the same classroom at the same time on the same day of the week. We present an algorithm that produces a nearly optimal schedule for an arbitrary set of classes. The algorithm's first stage produces a packing of classes using a combination of a greedy algorithm and a non-bipartite matching and the second stage consists of a bipartite matching.First we show that for one variant of the problem our algorithm produces schedules that require a number of classrooms that is always within a small additive constant of optimal. Then we show that for an interesting variant of the problem the same algorithm produces schedules that require a small constant factor more classrooms than optimal. Finally, we report on experimental results of our algorithm using actual data and also show how to create schedules with other desirable characteristics.     

Scheduling a non-professional indoor football league: a tabu search based approach

This paper deals with a real-life scheduling problem of a non-professional indoor football league. The goal is to develop a schedule for a time-relaxed, double round-robin tournament which avoids close successions of games involving the same team in a limited period of time. This scheduling problem is interesting, because games are not planned in rounds. Instead, each team provides time slots in which they can play a home game, and time slots in which they cannot play at all. We present an integer programming formulation and a heuristic based on tabu search. The core component of this algorithm consists of solving a transportation problem, which schedules (or reschedules) all home games of a team. Our heuristic generates schedules with a quality comparable to those found with IP solvers, however with considerably less computational effort. These schedules were approved by the league organizers, and used in practice for the seasons 2009–2010 till 2016–2017.