Maximizing production rate and workload smoothing in assembly lines using particle swarm optimization

Particle swarm optimization (PSO) one of the latest developed population heuristics has rarely been applied in production and operations management (POM) optimization problems. A possible reason for this absence is that, PSO was introduced as global optimizer over continuous spaces, while a large set of POM problems are of combinatorial nature with discrete decision variables. PSO evolves floating-point vectors (called particles) and thus, its application to POM problems whose solutions are usually presented by permutations of integers is not straightforward. This paper presents a novel method based on PSO for the simple assembly line balancing problem (SALBP), a well-known NP-hard POM problem. Two criteria are simultaneously considered for optimization: to maximize the production rate of the line (equivalently to minimize the cycle time), and to maximize the workload smoothing (i.e. to distribute the workload evenly as possible to the workstations of the assembly line). Emphasis is given on seeking a set of diverse Pareto optimal solutions for the bi-criteria SALBP. Extensive experiments carried out on multiple test-beds problems taken from the open literature are reported and discussed. Comparisons between the proposed PSO algorithm and two existing multi-objective population heuristics show a quite promising higher performance for the proposed approach.     

粒子群算法和灰狼算法的融合

为了解决传统粒子群算法(PSO)容易“早熟”、陷入局部最优以及灰狼算法(GWO)收敛速度慢的问题。首先，采用GWO算法的个体极值更新策略来实现个体包围式向最优值趋近，融入PSO算法的速度更新策略来实现群体向最优值的趋近，并且在原始粒子群算法基础上加入线性惯性权重递减来提高算法的收敛速度，从而提出了一种基于灰狼算法和改进的粒子群算法(IPSO)的融合优化算法(GW-IPSO);其次，通过6个经典算例进行仿真试验，将融合算法与PSO算法、IPSD算法、灰狼和粒子群结合算法(GW-PSO)进行对比；最后，应用融合算法对二级直线倒立摆的控制器设计进行参数寻优。结果表明：针对6个标准测试函数，混合算法的30次试验结果平均值更接近最优值，且标准差几乎都是最小的；应用在倒立摆控制问题上，系统在5 s左右进入稳定状态。融合后的GW-IPSO算法能够在一定程度上避免早熟和陷入局部极值的问题发生，并且能够很好地应用于控制器设计过程中参数寻优问题。     

A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times,sequence-dependent setup times and learning effect

This paper addresses multi-objective (MO) optimization of a single-model assembly line balancing problem (ALBP) where the operation times of tasks are unknown variables and the only known information is the lower and upper bounds for operation time of each task. Three objectives are simultaneously considered as follows: (1) minimizing the cycle time, (2) minimizing the total equipment cost, and (3) minimizing the smoothness index. In order to reflect the real industrial settings adequately, it is assumed that the task time is dependent on worker(s) (or machine(s)) learning for the same or similar activity and sequence-dependent setup time exists between tasks. Finding an optimal solution for this complicated problem especially for large-sized problems in reasonable computational time is cumbersome. Therefore, we propose a new solution method based on the combination of particle swarm optimization (PSO) algorithm with variable neighborhood search (VNS) to solve the problem. The performance of the proposed hybrid algorithm is examined over several test problems in terms of solution quality and running time. Comparison with an existing multi-objective evolutionary computation method in the literature shows the superior efficiency of our proposed PSO/VNS algorithm.     

Particle swarm optimization: A study of particle displacement for solving continuous and combinatorial optimization problems

This article addresses the particle swarm optimization (PSO) method. It is a recent proposed algorithm by Kennedy and Eberhart [1995. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks (Perth, Australia), vol. IV, IEEE Service Center, Piscataway, NJ, pp. 1942–1948]. This optimization method is motivated by social behaviour of organisms such as bird flocking and fish schooling. PSO algorithm is not only a tool for optimization, but also a tool for representing socio-cognition of human and artificial agents, based on principles of social behaviour. Some scientists suggest that knowledge is optimized by social interaction and thinking is not only private but also interpersonal. PSO as an optimization tool, provides a population-based search procedure in which individuals called particles change their position (state) with time. In a PSO system, particles fly in a multidimensional search space. During flight, each particle adjusts its position according to its own experience, and according to the experience of neighbours, making use of the best position encountered by itself and its neighbours. In this paper, we propose firstly, an extension of the PSO system that integrates a new displacement of the particles (the balance between the intensification process and the diversification process) and we highlight a relation between the coefficients of update of each dimension velocity between the classical PSO algorithm and the extension. Secondly, we propose an adaptation of this extension of PSO algorithm to solve combinatorial optimization problem with precedence constraints in general and resource-constrained project scheduling problem in particular. The numerical experiments are done on the main continuous functions and on the resource-constrained project scheduling problem (RCPSP) instances provided by the psplib. The results obtained are encouraging and push us into accepting than both PSO algorithm and extensions proposed based on the new particles displacement are a promising direction for research.     

Particle swarm optimization with cocktail decoding method for hybrid flow shop scheduling problems with multiprocessor tasks

This paper addresses the problem of multiprocessor task-scheduling in a hybrid flow shop (HFS) problem to minimize the makespan. Due to the complex nature of an HFS problem, it is decomposed into the following two sequential decision problems: determining the job permutation in stage 1, followed by a decoding method to assign jobs into each machine in subsequent stages when designing a heuristic algorithm. The decoding method plays a pivotal role for improving the solution quality of any algorithm for the HFS problem. However, the majority of existing algorithms ignores the problem and is only concerned with the first decision problem. This study emphasizes the importance of the decoding method via a small test, and searches for a number of solid decoding methods that can be incorporated into the cocktail decoding method. Then, this study develops a particle swarm optimization (PSO) algorithm that can be combined with the cocktail decoding method. In the PSO, a variety of job sequences are generated using the PSO procedure in stage 1, and the cocktail decoding method is used to assign the jobs to machines in sequential stages. Moreover, a modified lower bound is introduced. Computational results show that the proposed lower bound is competitive, and with the help of the cocktail decoding method, the proposed PSO, and even the adoption of a standard PSO framework, significantly outperforms the majority of existing algorithms in terms of quality of solutions, especially for large problems.     

A binary solution method for the multi-product newsboy problem with budget constraint

Multi-product newsboy problem (MPNP) with budget constraint is a classical inventory control/management problem. However, solution methods for MPNP under general demand distributions are limited in the current literature. In this paper, by analyzing properties of the optimal solution to the MPNP with a budget constraint, we develop a solution algorithm for the constrained MPNP. The proposed algorithm is binary in nature, and is applicable to general types of demand distribution functions, discrete as well as continuous. For continuous demand distribution function, our approach can obtain the optimal or near optimal solution to the constrained MPNP with polynomial computation complexity of the o(n) order. On the other hand, for discrete demand distribution functions, it can effectively provide good approximate solution. Numerical experiments are presented to show the performance of our method.     

New Industrial Product Design and Evaluation Using Multiattribute Value Analysis

Increasingly, the design of successful new industrial products is related to careful market assessment. Traditionally, managers and researchers have studied their markets by examining a small number of product attributes that are common across a range of informed respondents. In many ways, these techniques fail to meet the challenges posed by today's often heterogeneous, highly competitive, fast moving industrial markets. Ralph Keeney and Gary Lilien introduce us to a technique they call multiattribute value analysis, both describing the procedure and describing a comprehensive example. Their approach introduces considerable flexibility to the process of market assessment. Technically, it permits the evaluation of many more attributes, value tradeoffs, and synergies among attributes than do more traditional methods. In addition, it permits nonlinear evaluation functions that may be idiosyncratic to the individual. Practically, their approach, illustrated with a detailed case application, is shown to have significant potential for aiding product design decisions.     

Progress and Challenges in Product Line Pricing

In recent years, there has been a substantial increase in research on product line pricing. Modelers in multiple disciplines have offered methods for the optimal design/selection and pricing of the products in new or modified product lines. Behavioral scientists have contributed insights on how consumers' perceptions of product line prices, attributes, and quality levels influence their evaluation of the alternative choices. Significantly, the work of both modelers and behavioral scientists is distributed across three types of product line contexts: price‐quality product lines, multi‐attribute product lines, and product lines that include a core product plus options. This paper reviews this literature, and assesses its usefulness for managers. One observation is that, while scholars have developed approaches to optimization that offer increased scope and tractability, the applicability of these models is constrained by the narrow specification of profit functions, and the limited consideration of competitive and other dynamic forces. A second conclusion is that the managerial usefulness of the behavioral science research on perceptions and product‐line choice has been limited by a dependence on attribute‐based estimation of utilities, uncertainty about possible interaction effects, and an excessive focus on the cannibalization aspects of product line pricing. Based on the review, a research agenda is identified for enhancing the applicability of research on consumer perceptions and choice to product line pricing decisions, and for building more complete product line price optimization models.     

CASE STUDY: ECONOMIC VIABILITY OF COMPONENT MANAGEMENT FOR A NEW DESIGN

This paper explores the viability of standardization of design and manufacturing techniques to expedite product development and control design proliferation using an example of a leading transport refrigeration unit manufacturer. As an incremental approach to implementing standardization in a product development environment, a conceptual framework for component management decision support system is presented to build a case for its technical feasibility. The primary objective of this research case study is to provide an economic justification for implementing the proposed system. A three level decision making hierarchy is proposed with economic optimization for levels 1 and 2 representing standardization of system modules and capacity decisions for a product line respectively and thermodynamic optimization for level 3 representing control systems to keep the system dynamically balanced in changing environments. Other potential applications amenable to classification are identified.     

流水工序调度与生产效率的关系模型分析

提出一种基于粒子群算法的流水工序调度任务优化模型。利用流水工序调度任务的特点得到流水工序时间约束条件,利用粒子群算法的原理建立流水工序调度任务优化模型,利用粒子群算法对模型进行求解。仿真实验表明,利用该算法能够得到流水工序调度问题的最优解,提高生产效率。     

MERCHANT TRANSMISSION INVESTMENT*

We examine the performance attributes of a merchant transmission investment framework that relies on ‘market driven’ investment to increase transmission network capacity needed to support competitive wholesale markets for electricity. Under a stringent set of assumptions, the merchant investment model has a remarkable set of attributes that appears to solve the natural monopoly problem and the associated need for regulating electric transmission companies. We expand the merchant model to incorporate several attributes of wholesale power markets and transmission networks that the merchant model ignores. These include market power in wholesale electricity markets, lumpiness in transmission investment opportunities, stochastic attributes of transmission networks and associated property rights definition issues, strategic behavior by potential merchant transmission investors and issues related to the coordination of transmission system operators and merchant transmission owners. Incorporating these more realistic attributes of transmission networks and the behavior of transmission owners and system operators leads to the conclusion that several potentially significant inefficiencies may result from reliance on the merchant transmission investment framework. Accordingly, it is inappropriate for policymakers to assume that they can avoid dealing with the many challenges associated with stimulating efficient levels of investment in electric transmission networks by adopting the merchant model.     

Product price and performance level in one market or two separated markets under various cost structures and functions

Market characteristics, including intrinsic demand and customer sensitivity on price and product performance level, are distinct at different markets. Comparisons of various product development strategies in one market or two geographically separated markets are conducted for three classes of products: development intensive products (DIPs) with constant unit cost, marginal cost-intensive products (MIPs) with constant fixed cost, and marginal and development intensive products (MDIPs) with non-constant unit cost and fixed cost. Results show that larger demand size, less customer sensitivity on price and/or more sensitivity on performance level lead to more profit, a higher sale price and a not-lower product performance. The customer reservation or the saturation performance level should be generally adopted though the optimal performance level does exist occasionally. Unit cost and/or fixed cost must increase in performance at an increasing rate for the existence of one optimal performance level. Due to the impact of demand size, one high-end (low-end) MDIP or DIP could be introduced into one low-end (high-end) market at a different price if the demand size is significantly large in the low-end market. For DIPs, offering one niche high-end product is not worse than offering the low-end product into two markets. For MIPs with negligible fixed cost, the product line strategy is not worse than the standard product development strategy. Additionally, the product cost reduction approach adopted in one product line has significant effects on the best product development strategy and sequence.     

A methodology for optimal product positioning with engineering constraints consideration

Due to the complex mapping relationship between product attributes and engineering characteristics, as well as the correlations between engineering characteristics and the engineering constraints on a product, a new product following a probabilistic rule of multidimensional scaling may not be in an optimal position in product engineering space. In this paper, a new methodology for optimal product positioning by considering engineering constraints is proposed. In the proposed methodology, perceptual mapping and house of quality are introduced to link the consumer perceptual space, and product engineering space. The degree of overall customer satisfaction is considered in the rule of consumer choice probability. Based on this, an optimal product positioning model can be established. Genetic algorithms are introduced to solve the problem of the optimization model due to its non-linear characteristics. By applying genetic algorithms, the optimal value settings of a new product's engineering characteristics can be obtained. An example of optimal positioning and determination of value settings of engineering characteristics of packing machines is used to illustrate the proposed methodology.     

How accurate is the integrated vendor–buyer continuous model?

The classic single-item, deterministic-demand, integrated vendor–buyer model is revisited. The decision problem may also be viewed as that of one centralized firm, dealing with a two-level EOQ-like supply chain with finite production rates. It is sought to optimize the production lot-size and the integral number of its shipments, in equal batches, from the vendor to the buyer. While the problem calls for discrete optimization techniques, we introduced a continuous model where one can take derivatives with respect to both variables. In this short paper, we establish a 98.5% lower bound on the accuracy of this continuous model.     

基于Adaboost-DPSO-SVM模型的供应链金融信用风险评价研究

数字化赋能供应链金融创新是推动我国产业链、供应链持续稳定优化升级的重要内容,在全球金融风险急剧增加的背景下,供应链金融与大数据、区块链、物联网、人工智能等技术相融合,成为解决我国中小企业融资授信问题的有效方式之一。本文在对供应链金融信用风险评价指标进行特征选择的基础上,采用一种动态变异的粒子群算法(DPSO)和AdaBoost算法对SVM进行协同优化和集成,建立了Adaboost-DPSO-SVM模型,并将该模型应用于我国新能源汽车行业供应链金融信用风险评价中,实验结果表明所建立的模型相对其他评价模型具有更好的分类识别性能。     

Scheduling a single batch processing machine with arbitrary job sizes and incompatible job families

Motivated by a bottleneck operation in a multi-layer ceramic capacitor production line, we study a scheduling problem of batch processing machine in which a number of jobs are processed simultaneously as a batch. The performance measures considered include makespan, total completion time, and total weighted completion time. We first present a new simple integer programming formulation for the problem, and using this formulation, one can easily find optimal solutions for small problems. However, since the problem is NP-hard and the size of a real problem is very large, we propose a number of heuristic algorithms and design a hybrid genetic algorithm to solve practical big-size problems in a reasonable computational time. To verify performance of the algorithms, we compare them with lower bounds for the problems. From the results of these computational experiments the heuristic algorithms including the genetic algorithm show different performances for the three problems.     

Context effects in the evaluation of business-to-business brand alliances

Brand alliances in the business-to-business domain are becoming increasingly popular. This study investigates the impact of context-related effects on the formation of evaluation perceptions in B2B brand alliances. Assimilation and contrast effects represent the conceptual framework. Employing an experimental design we test the influence of two contextual factors on a range of product attributes: quality perceptions of the known brand ally (valence), and amount of information provided for the brand alliance. Using data collected from a cross section sample of large B2B UK companies we report assimilation effects across different product attributes of the alliance. Positive valence of the known brand ally results in higher evaluations of the brand alliance. In terms of provision of information, we find that, (a) unlike consumer markets, in the B2B domain higher evaluations are the result of detailed information, and (b) the impact of detailed information is significant only for tangible product attributes. Further, we find that valence of the known brand ally and amount of information provided are independent factors. The results add significantly to the knowledge on context effects in B2B brand alliances and lead to a number of managerial recommendations on partner selection and marketing communication of new brand alliances.     

大数据驱动下的新一代高速公路 智慧诱导技术

为了解决高速公路出行路径选择问题,基于图论模糊算法,提出了大数据下的智慧诱导技术。首先对高速路网在路径诱导的实时性、线路规划的精准度和算法的适用性方面进行优化;其次通过利用贪心算法和整体寻优算法,对经典路径诱导算法进行研究和比选;最后针对高速路网提出基于大数据动态规划的路径诱导技术,采用大数据、内存计算、图计算和AI结合的方式来实现大数据的动态实时路径诱导。研究结果表明,智慧诱导技术可主动为有不同诉求目标的出行者提供实时最优的方案选择,解决复杂路网下动态路径的合理诱导问题。所提出的方法可实现大数据驱动下的智慧诱导,对进一步提升公路智能化和精细化管理水平具有借鉴意义。     

An improved algorithm and solution on an integrated production-inventory model in a three-layer supply chain

Ben-Daya et al. (2010) established a joint economic lot-sizing problem (JELP) for a three-layer supply chain with one supplier, one manufacturer, and multiple retailers, and then proposed a heuristic algorithm to obtain the integral values of four discrete variables in the JELP. In this paper, we first complement some shortcomings in Ben-Daya et al. (2010), and then propose a simpler improved alternative algorithm to obtain the four integral decision variables. The proposed algorithm provides not only less CPU time but also less total cost to operate than the algorithm by Ben-Daya et al. (2010). Furthermore, our proposed algorithm can solve certain problems, which cannot be solved by theirs. Finally, the solution obtained by the proposed algorithm is indeed a global optimal solution in each of all instances tested.     

Towards a differentiated modeling of origin effects in hedonic analysis: An application to auction prices of specialty coffee

Origin matters. This has been shown by numerous studies using either discrete choice or hedonic approaches to derive implicit prices for origin as a product attribute. In most of the hedonic studies, intercept dummies were introduced for specific regional origins and statistically significant coefficients of those variables were seen as an indication of either a superior or inferior reputation, compared to products from other origins. We argue that hedonic pricing models of this type may be too simple to detect the true origin effects if assessments of a product’s sensory quality are available and interact with prior beliefs about reputation. Based on a supply-and-demand framework to explain auction prices, a reduced-form hedonic pricing model is suggested that includes intercept- as well as slope-dummy effects of the regional origin. Because reputation and the objective product quality are particularly important for markets of differentiated, high-quality foods and beverages, we analyze electronic auction markets for specialty coffees. The findings for the Cup of Excellence data reveal that it is important to distinguish reputation, sensory quality, and their interaction as determinants of coffee auction prices, as well as varying origin impacts across market segments.