Scheduling optimization of design stream line for production research and development projects

In a development project, efficient design stream line scheduling is difficult and important owing to large design imprecision and the differences in the skills and skill levels of employees. The relative skill levels of employees are denoted as fuzzy numbers. Multiple execution modes are generated by scheduling different employees for design tasks. An optimization model of a design stream line scheduling problem is proposed with the constraints of multiple executive modes, multi-skilled employees and precedence. The model considers the parallel design of multiple projects, different skills of employees, flexible multi-skilled employees and resource constraints. The objective function is to minimize the duration and tardiness of the project. Moreover, a two-dimensional particle swarm algorithm is used to find the optimal solution. To illustrate the validity of the proposed method, a case is examined in this article, and the results support the feasibility and effectiveness of the proposed model and algorithm.     

An improved robust buffer allocation method for the project scheduling problem

Unpredictable uncertainties cause delays and additional costs for projects. Often, when using traditional approaches, the optimizing procedure of the baseline project plan fails and leads to delays. In this study, a two-stage multi-objective buffer allocation approach is applied for robust project scheduling. In the first stage, some decisions are made on buffer sizes and allocation to the project activities. A set of Pareto-optimal robust schedules is designed using the meta-heuristic non-dominated sorting genetic algorithm (NSGA-II) based on the decisions made in the buffer allocation step. In the second stage, the Pareto solutions are evaluated in terms of the deviation from the initial start time and due dates. The proposed approach was implemented on a real dam construction project. The outcomes indicated that the obtained buffered schedule reduces the cost of disruptions by 17.7% compared with the baseline plan, with an increase of about 0.3% in the project completion time.     

An Optimization Based Heuristic for Scheduling Parallel Project Networks with Constrained Renewable Resources

In this paper we address the scheduling problem for the simultaneous management of multiple resource constrained project networks in parallel. This situation is common in the construction of a housing development where the building contractor is concerned with the simultaneous completion of a number of individual, identical construction projects. If only one project were managed, a resource constrained critical path method could be utilized. In this paper we present a model for the simultaneous planning of many individual project networks. We formulate the problem as an integer program that is similar to models found in the inventory/production scheduling literature. A Dantzig-Wolfe decomposition is used to obtain solutions to the linear programming relaxation of the problem. The algorithm selects a subset of these solutions to form a final schedule. Computational results are presented. Extensions of the algorithm, including non-identical projects, alternative objective functions and implementation on a rolling horizon are also discussed.     

A multi-objective variable-fidelity optimization method for genetic algorithms

A novel variable-fidelity optimization (VFO) scheme is presented for multi-objective genetic algorithms. The technique uses a low- and high-fidelity version of the objective function with a Kriging scaling model to interpolate between them. The Kriging model is constructed online through a fixed updating schedule. Results for three standard genetic algorithm test cases and a two-objective stiffened panel optimization problem are presented. For the stiffened panel problem, statistical analysis of four performance metrics are used to compare the Pareto fronts between the VFO method, full high-fidelity optimizer runs, and Pareto fronts developed by enumeration. The fixed updating approach is shown to reduce the number of high-fidelity calls significantly while approximating the Pareto front in an efficient manner.     

Multi-objective optimization by a new hybridized method: applications to random mechanical systems

In this article two linear problems with random Gaussian loading are transformed into multi-objective optimization problems. The first problem is the design of a pillar geometry with respect to a compressive random load process. The second problem is the design of a truss structure with respect to a vertical random load process for several frequency bands. A new algorithm, motivated by the Pincus representation formula hybridized with the Nelder–Mead algorithm, is proposed to solve the two multi-objective optimization problems. To generate the Pareto curve, the normal boundary intersection method is used to produce a series of constrained single-objective optimizations. The second problem, depending on the frequency band of excitation, can have as Pareto curve a single point, a standard Pareto curve, or a discontinuous Pareto curve, a fact that has been reported here for the first time in the literature, to the best of the authors’ knowledge.     

软件开发项目多技能人力资源均衡调度模型

为了在软件开发项目中同时解决项目活动调度问题和多技能人员指派问题,实现资源均衡分配,本文建立了一个多技能人力资源均衡调度的混合整数规划模型,并以一个软件开发项目为背景开展了算例研究,采用Lingo11软件环境下改良后的分支定界法进行优化求解。优化之后,项目资源强度方差和员工工作量方差大幅度下降。研究结果表明,本文创建的资源均衡调度模型可以显著提高项目周期内员工需求量和员工工作量分配的均衡水平。     

Comparison of multi-objective evolutionary approaches for task scheduling in distributed computing systems

Parallel and distributed systems play an important part in the improvement of high performance computing. In these type of systems task scheduling is a key issue in achieving high performance of the system. In general, task scheduling problems have been shown to be NP-hard. As deterministic techniques consume much time in solving the problem, several heuristic methods are attempted in obtaining optimal solutions. This paper presents an application of Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and a Non-dominated Sorting Particle Swarm Optimization Algorithm (NSPSO) to schedule independent tasks in a distributed system comprising of heterogeneous processors. The problem is formulated as a multi-objective optimization problem, aiming to obtain schedules achieving minimum makespan and flowtime. The applied algorithms generate Pareto set of global optimal solutions for the considered multi-objective scheduling problem. The algorithms are validated against a set of benchmark instances and the performance of the algorithms evaluated using standard metrics. Experimental results and performance measures infer that NSGA-II produces quality schedules compared to NSPSO.     

Optimizing time–cost trade-offs in product development projects with a multi-objective evolutionary algorithm

Time–cost trade-offs arise when organizations seek the fastest product development (PD) process subject to a predefined budget, or the lowest-cost PD process within a given project deadline. Most of the engineering and project management literature has addressed this trade-off problem solely in terms of crashing—options to trade cost for time at the individual activity level—and using acyclical networks. Previously (Meier et al. in IEEE Trans Eng Manag 62(2):237–255, 2015), we presented a rich model of the iterative (cyclical) PD process that accounts for crashing, overlapping, and stochastic activity durations and iterations. In this paper, we (1) propose an optimization strategy for the model based on a multi-objective evolutionary algorithm, called ε-MOEA, which identifies the Pareto set of best time–cost trade-off solutions, and (2) demonstrate the approach using an automotive case study. We find that, in addition to crashing, activity overlapping, process architecture, and work policy provide further managerial levers for addressing the time–cost trade-off problem. In particular, managerial work policies guide process cost and duration into particular subsets of the Pareto-optimal solutions. No work policy appeared to be superior to the others in both the cost and duration dimensions; instead, a time–cost trade-off arises due to the choice of work policy. We conclude that it is essential for managers to consider all of the key factors in combination when planning and executing PD projects.     

Resource Allocation of a Multi-Resource Project for Variable Resource Availabilities

An algorithm for resource allocation of multi-resource projects is described for the assumption of unlimited resource availabilities. The basis of the allocation is a binary enumeration of all feasible activity combinations, from which the daily optimum is obtained. Optimality is defined by the minimal 'schedule deviation,' which measures the weighted deviation squares between future resource loads and currently assigned resource levels. The algorithm simplifies both scheduling and project control, since the next periods schedule depends only on the current schedule status. Appropriate changes in levels for all resources over the project duration are consistent with the needs of project managers in practice.     

Pareto layer: Its formulation and search by way of evolutionary multi-objective optimization

This article examines multi-objective problems where a solution (product) is related to a cluster of performance vectors within a multi-objective space. Here the origin of such a cluster is not uncertainty, as is typical, but rather the range of performances attainable by the product. It is shown that, in such cases, comparison of a solution to other solutions should be based on its best performance vectors, which are extracted from the cluster. The result of solving the introduced problem is a set of Pareto optimal solutions and their representation in the objective space, which is referred to here as the Pareto layer. The authors claim that the introduced Pareto layer is a previously unattended novel representation. In order to search for these optimal solutions, an evolutionary multi-objective algorithm is suggested. The article also treats the selection of a solution from the obtained optimal set.     

A hybrid evolutionary graph-based multi-objective algorithm for layout optimization of truss structures

In this paper a new graph-based evolutionary algorithm, gM-PAES, is proposed in order to solve the complex problem of truss layout multi-objective optimization. In this algorithm a graph-based genotype is employed as a modified version of Memetic Pareto Archive Evolution Strategy (M-PAES), a well-known hybrid multi-objective optimization algorithm, and consequently, new graph-based crossover and mutation operators perform as the solution generation tools in this algorithm. The genetic operators are designed in a way that helps the multi-objective optimizer to cover all parts of the true Pareto front in this specific problem. In the optimization process of the proposed algorithm, the local search part of gM-PAES is controlled adaptively in order to reduce the required computational effort and enhance its performance. In the last part of the paper, four numeric examples are presented to demonstrate the performance of the proposed algorithm. Results show that the proposed algorithm has great ability in producing a set of solutions which cover all parts of the true Pareto front.     

考虑老年人满意度的多目标居家养老护理员路径优化

居家养老服务是适应我国国情的养老模式,在推进养老服务发展的同时,必须提高养老服务质量,合理优化服务资源。以居家养老服务的实际需求为关注点,首先在考虑居家养老服务中心运营成本的基础上,构建基于前景理论的符合老人心理特征的服务满意度函数,建立最大化满意度和最小化成本的多目标养老护理员调度模型;然后利用改进的遗传算法求Pareto最优解集;最后通过算例分析得出老人对时间窗的敏感性越低时服务满意度越高,而当服务满意度提高到一定的程度时需要更多的成本来支撑,此外与传统遗传算法相比,运营成本相同下服务满意度提高了约10%。不仅为管理者提供了有效的权衡决策,同时提升了老年人的服务满意度。     

Classifying Risks on Information Technology Projects

ABSTRACT

Through an examination of the literature and a review of industrial case studies, a qualitative model is developed of the risks associated with implementing information technology (IT) projects. When this approach was applied to case studies drawn from academia and from companies in the manufacturing and service industries, potential high risk factors were identified and classified. Ongoing research seeks to develop an intelligent risk analyzer that incorporates this qualitative model and industry best practices to give IT project managers significant decision support in the area of risk management by enabling them to match system development strategies with project complexity.     

带调整时间的多目标流水车间调度的优化算法

为高效地求解带调整时间的多目标流水车间调度问题,提出了一种多目标混合遗传算法,此算法依据基于Pareto优于关系的个体排序数和密度值计算适应度,保持解的多样性,并采用非劣解并行局部搜索策略,提高算法的搜索效率.此外,引入精英策略保证算法的收敛性,在进化过程中通过淘汰掉个别最差个体,进一步加快解的收敛速度.仿真结果表明,新算法能够有效地解决带调整时间的多目标流水车间调度问题.     

I2QFD: A blackboard-based multiagent system for supporting concurrent engineering projects

Although Concurrent Engineering has been accredited as a superior approach to product development, its intrinsic complexity in coordinating the multidisciplinary project team is further augmented when team members are remotely distributed. Assuming the teamwork is supported by an Intranet, this paper proposes the framework of a blackboard-based multiagent system, called I 2 QFD, to facilitate the communication and coordination of the design projects. Configured within a hierarchical structure, this system is defined with IDEF0 and high-level Petri nets to depict the operations of Intelligent Agencies and their affiliated agents. Based on temporal logic, the operation of the project team with I 2 QFD is simulated to manifest the dynamics of the design process as well as to generate the project's schedule that ensures the intended concurrency.     

Evaluation of the influence of dominance rules for the assembly line design problem under consideration of product design alternatives

The current competitive situation increases the importance of realistically estimating product costs during the early phases of product and assembly line planning projects. In this article, several multi-objective algorithms using difference dominance rules are proposed to solve the problem associated with the selection of the most effective combination of product and assembly lines. The list of developed algorithms includes variants of ant colony algorithms, evolutionary algorithms and imperialist competitive algorithms. The performance of each algorithm and dominance rule is analysed by five multi-objective quality indicators and fifty problem instances. The algorithms and dominance rules are ranked using a non-parametric statistical test.     

A Pareto firefly algorithm for multi-objective disassembly line balancing problems with hazard evaluation

The safety hazards existing in the process of disassembling waste products pose potential harms to the physical and mental health of the workers. In this article, these hazards involved in the disassembly operations are evaluated and taken into consideration in a disassembly line balancing problem. A multi-objective mathematical model is constructed to minimise the number of workstations, maximise the smoothing rate and minimise the average maximum hazard involved in the disassembly line. Subsequently, a Pareto firefly algorithm is proposed to solve the problem. The random key encoding method based on the smallest position rule is used to adapt the firefly algorithm to tackle the discrete optimisation problem of the disassembly line balancing. To avoid the search being trapped in a local optimum, a random perturbation strategy based on a swap operation is performed on the non-inferior solutions. The validity of the proposed algorithm is tested by comparing with two other algorithms in the existing literature using a 25-task phone disassembly case. Finally, the proposed algorithm is applied to solve a refrigerator disassembly line problem based on the field investigation and a comparison of the proposed Pareto firefly algorithm with another multi-objective firefly algorithm in the existing literature is performed to further identify the superior performance of the proposed Pareto firefly algorithm, and eight Pareto optimal solutions are obtained for decision makers to make a decision.     

Bi-objective partial flexible job shop scheduling problem: NSGA-II,NRGA, MOGA and PAES approaches

This paper deals with a problem of partial flexible job shop with the objective of minimising makespan and minimising total operation costs. This problem is a kind of flexible job shop problem that is known to be NP-hard. Hence four multi-objective, Pareto-based, meta-heuristic optimisation methods, namely non-dominated sorting genetic algorithm (NSGA-II), non-dominated ranked genetic algorithm (NRGA), multi-objective genetic algorithm (MOGA) and Pareto archive evolutionary strategy (PAES) are proposed to solve the problem with the aim of finding approximations of optimal Pareto front. A new solution representation is introduced with the aim of solving the addressed problem. For the purpose of performance evaluation of our proposed algorithms, we generate some instances and use some benchmarks which have been applied in the literature. Also a comprehensive computational and statistical analysis is conducted in order to analyse the performance of the applied algorithms in five metrics including non-dominated solution, diversification, mean ideal distance, quality metric and data envelopment analysis are presented. Data envelopment analysis is a well-known method for efficiently evaluating the effectiveness of multi-criteria decision making. In this study we proposed this method of assessment of the non-dominated solutions. The results indicate that in general NRGA and PAES have had a better performance in comparison with the other two algorithms.     

Robust scheduling for multi-objective flexible job-shop problems with flexible workdays

This study determines a robust schedule for a flexible job-shop scheduling problem with flexible workdays. The performance criteria considered in this study are tardiness, overtime and robustness. Furthermore, the problem is addressed in a Pareto manner, and a set of Pareto-optimal solutions is determined for this purpose. In consideration of all the aforementioned features, a goal-guided neighbourhood function is proposed based on efficient problem-dependent move-filtering methods. Two metaheuristic algorithms, named goal-guided multi-objective tabu search and goal-guided multi-objective hybrid search, are proposed in this work based on this neighbourhood function. The effectiveness of these approaches is demonstrated via empirical studies.     

Quantification of Project Execution Complexity and its Effect on Performance of Infrastructure Development Projects

Growing complexity is one of the main reasons behind the failure of many projects. Thus, identifying and measuring complexity is crucial to the success of any project. Various schedule complexity indices have been proposed in the past, but a comprehensive complexity measurement tool encompassing factors of schedule along with other important project constraints, such as time and scope, is still missing. First, a theoretical framework consisting of 46 complexity factors has been developed through literature review followed by its refinement through a three-round Delphi study. The complexity factors are categorized into three categories, namely, Schedule complexity, Scope complexity and Cost/Resource complexity. The Project Execution Complexity Index (PECI) tool is proposed, followed by development of a questionnaire in order to compute PECI values and validated using three infrastructure development projects during the planning phase. Finally, in the execution phase, the performance of the projects is measured through earned value analysis. PECI values are then mapped with project performance indices (i.e., schedule/cost performance index) in order to assess the effect of project complexity on the performance of projects. The results showed that projects with higher complexity tend to have larger cost or schedule overruns. These case projects are also ranked according to the degree of their complexity on the complexity scale. This ranking can help decision makers to decide which projects should be included in the portfolio and which projects need prioritizing through assigning resources more efficiently. The proposed PECI tool may also help project managers to better anticipate prospective difficulties during the initial stages of the project lifecycle.