Genetic subgradient method for solving location–allocation problems

Facility location–allocation (FLA) problem is a very important subject in today's business. It is an important part of a company's global logistic system. Various FLA problems have been considered in operations research (OR) under somehow stringent conditions. Restrictive conditions are placed to reduce the size of the search space, however, they also make the model inappropriate for the real-business world. In this paper, we consider a class of FLA problems that can assume more realistic conditions in real-life applications. A hybrid method of genetic algorithm and subgradient technique is used to solve the problem efficiently.     

Stochastic skill-based manpower allocation in a cellular manufacturing system

In this paper, stochastic skill-based manpower allocation problem is addressed, where operation times and customer demand are uncertain. A four-phased hierarchical methodology is developed. Egilmez and Süer's [1] stochastic general manpower allocation problem is extended such that each worker's individual performance is considered for a more accurate manpower allocation to manufacturing cells to maximize the production rate. The proposed methodology optimized the manpower levels, product-cell formations and individual worker assignment hierarchically with respect to a specified risk level. Three stochastic nonlinear mathematical models were developed to deal with manpower level determination, cell loading and individual worker assignment phases. In all models, processing times and demand were assumed to be normally distributed. Firstly, alternative configurations were generated. Secondly, IID sampling and statistical analysis were utilized to convert probabilistic demand into probabilistic capacity requirements. Thirdly, stochastic manpower allocation was performed and products were loaded to cells. In the final phase, individual worker assignments were performed. The proposed methodology was illustrated with an example problem drawn from a real manufacturing company. The hierarchical approach allows decision makers to perform manpower level determination, cell loading and individual worker assignment with respect to the desired risk level. The main contribution of this approach is that each worker's expected and standard deviation of processing time on each operation is considered individually to optimize the manpower assignment to cells and maximize the manufacturing system production rate within a hierarchical robust optimization approach.     

考虑风险规避与质量和服务水平的VMI供应链期权协调策略

针对一个考虑风险规避供应商与质量和服务水平的二级VMI供应链,应用条件风险价值(CVaR)准则刻画供应商的风险规避行为,提出由期权和成本分担构成的组合契约,构建以零售商为主导的Stackelberg博弈模型,探讨供应链协调策略以及风险规避对供应链协调和利润分配的影响.研究表明,供应商的最优生产量随着其风险规避程度的增加而减小,但最优质量和服务水平与风险规避程度无关;当且仅当供应商风险规避程度较低时供应链才能实现协调,且供应商风险规避程度是影响供应链契约设计和利润分配的关键因素.     

Augmented Lagrangian coordination for energy-optimal allocation of smart manufacturing services

The rapid development of information and communication technologies has triggered the proposition and implementation of smart manufacturing paradigms. In this regard, efficient allocation of smart manufacturing services (SMSs) can provide a sustainable manner for promoting cleaner production. Currently, centralized optimization methods have been widely used to complete the optimal allocation of SMSs. However, personalized manufacturing tasks usually belong to diverse production domains. The centralized optimization methods could hardly include related production knowledge of all manufacturing tasks in an individual decision model. Consequently, it is difficult to provide satisfactory SMSs for meeting customer's requirements. In addition, energy consumption is rarely considered in the SMS allocation process which is unfavorable for performing sustainable manufacturing. To address these challenges, augmented Lagrangian coordination (ALC), a novel distributed optimization method is proposed to deal with the energy-optimal SMS allocation problem in this paper. The energy-optimal SMS allocation model is constructed and decomposed into several loose-coupled and distributed elements. Two variants of the ALC method are implemented to formulate the proposed problem and obtain final SMS allocation results. A case study is employed to verify the superiority of the proposed method in dealing with energy-optimal SMS allocation problems by comparing with the centralized optimization method at last.     

基于鲁棒Restless Bandits模型的多水下自主航行器任务分配策略

针对水下监测网络中多自主航行器（AUV）协同信息采集任务分配问题进行了研究。首先，为了同时考虑系统中目标传感器的节点状态与声学信道状态对AUV任务分配问题的影响，构建了水声监测网络系统的综合模型；其次，针对水下存在的多未知干扰因素并考虑了模型产生不精确的情况，基于强化学习理论将多AUV任务分配系统建模为鲁棒无休止赌博机问题（RBP）。最后，提出鲁棒Whittle算法求解所建立的RBP，从而求解得出多AUV的任务分配策略。仿真结果表明，在干扰环境下与未考虑干扰因素的分配策略相比，在系统分别选择1、2、3个目标时，鲁棒AUV分配策略对应的系统累计回报值参数的性能分别提升了5.5%、12.3%和9.6%，验证了所提方法的有效性。     

Noise and verbal or spatial solutions of Rubik's cube

Subjects were required to solve simple problems using a Rubik's cube, posed either spatially (a pictorial representation of the problem) or verbally (written instructions). Results showed that subjects classified on a pretest as spatially able performed better on the Rubik's cube than those classified as verbally able. In addition, verbal performance was better under 95 dBA than 70 dBA and spatial performance better under 70 dB A than under 95 dB A white noise. The results are considered in the light of the contradictory literature on the effects of noise on human memory, with particular reference to the adoption of problem-solving strategies in noise.     

Passivity-based robust control of systems with redundant sensors and actuators

This paper considers passivity-based control of a class of uncertain linear, time-invariant systems having redundant sensors and actuators. The approach consists of robustly 'passifying'(i.e. rendering passive) the system, which is assumed to have affine parametric uncertainties in the system matrix as well as the input and output matrices. The passification is accomplished by finding constant matrices to be placed at the input and output of the system which will appropriately combine the sensor signals and distribute the control signals to the actuators. Sufficient conditions are first obtained for the system to remain passive for all parameter values that lie in a hyper-rectangular region in the parameter space. The conditions take the form of a set of linear matrix inequalities (LMIs). Next, the problem of finding an optimal sensor blending matrix is considered, which will maximize the region in the parameter space in which the system remains robustly passive. The dual problem of optimal control allocation is also considered, and a method is given for combined sensor blending and control allocation for robust passification. A method for reduction of the number of LMIs is investigated, and a numerical example is given for demonstrating the approach.     

A feasible directions method for computing Bayes E-optimal block designs

The Bayes extension of the classical E-optimality design criterion is considered for the two-way analysis of variance model. A specialized variant of Topkis and Veinott's feasible directions algorithm is used to solve a convex programming problem arising under special prior assumptions. Numerical examples are computed.     

Market_based grid resource allocation using new negotiation model

This paper presents a new negotiation model for designing Market- and Behavior-driven Negotiation Agents (MBDNAs) that address computational grid resource allocation problem. To determine the amount of concession for each trading cycle, the MBDNAs are guided by six factors: (1) number of negotiator's trading partners, (2) number of negotiator's competitors, (3) negotiator's time preference, (4) flexibility in negotiator's trading partner's proposal, (5) negotiator's proposal deviation from the average of its trading partners’ proposals, and (6) previous concession behavior of negotiator's trading partner. In our experiments, we compare grid resource consumer (GRC) of type MBDNAs (respectively grid resource owner (GRO) of type MBDNAs) with MDAs (Market Driven Agents) in terms of the following metrics: total tasks complementation and average utility (respectively resource utilization level and average utility). The results show that by taking the proposed factors into account, MBDNAs of both types make a more efficient concession amount than MDAs and are, therefore, considered an appropriate mechanism for grid resource allocation in different grid workloads and market types.     

A decision-theoretic approach to the test allocation problem in partition testing

A partition testing strategy consists of two components: a partitioning scheme which determines the way in which the program's input domain is partitioned into subdomains; and an allocation of test cases which determines the exact number of test cases selected from each subdomain. This paper investigates the problem of determining the test allocation when a particular partitioning scheme has been chosen. We show that this problem can be formulated as a classic problem of decision-making under uncertainty, and analyze several well known criteria to resolve this kind of problem. We present algorithms that solve the test allocation problem based on these criteria, and evaluate these criteria by means of a simulation experiment. We also discuss the applicability and implications of applying these criteria in the context of partition testing.     

Buffer allocation in flow-shop-type production systems with general arrival and service patterns

This study investigates the buffer allocation strategy of a flow-shop-type production system that possesses a given total amount of buffers and finite buffer capacity for each workstation as well as general interarrival and service times in order to optimize such system performances as minimizing work-in-process, cycle time and blocking probability, maximizing throughput, or their combinations. In theory, the buffer allocation problem is in itself a difficult NP-hard combinatorial optimization problem, it is made even more difficult by the fact that the objective function is not obtainable in closed form for interrelating the integer decision variables (i.e., buffer sizes) and the performance measures of the system. Therefore, the purpose of this paper is to present an effective design methodology for buffer allocation in the production system. Our design methodology uses a dynamic programming process along with the embedded approximate analytic procedure for computing system performance measures under a certain allocation strategy. Numerical experiments show that our design methodology can quickly and quite precisely seek out the optimal or sub-optimal allocation strategy for most production system patterns.Scope and purposeBuffer allocation is an important, yet intriguingly difficult issue in physical layout and location planning for production systems with finite floor space. Adequate allocation and placement of available buffers among workstations could help to reduce work-in-process, alleviate production system's congestion and even blocking, and smooth products manufacturing flow. In view of the problem complexity, we focus on flow-shop-type production systems with general arrival and service patterns as well as finite buffer capacity. The flow-shop-type lines, which usually involve with product-based layout, play an important role in mass production type of manufacturing process organization such as transfer line, batch flow line, etc. The purpose of this paper is to present a design methodology with heuristic search and imbedded analytic algorithm of system performances for obtaining the optimal or sub-optimal buffer allocation strategy. Successful use of this design methodology would improve the production efficiency and effectiveness of flow-shop-type production systems.     

The global system-ranking efficiency model and calculating examples with consideration of the nonhomogeneity of decision-making units

Data envelopment analyses have been widely used to evaluate the relative efficiency of decision-making units (DMUs). However, the traditional data envelopment analysis model has not considered the problem of DMUs' nonhomogeneity. If nonhomogeneous DMUs are evaluated under the same production frontier, conclusions may not be precise. For example, some DMUs' input redundancy and output deficit cannot be adjusted as per planning results, which may lead to mistakes in management. This paper loosens the assumption of DMU homogeneity and builds a global system-ranking efficiency model based on existing literature, which divides the problem of DMUs' nonhomogeneity into external nonhomogeneity and internal homogeneity. Data have been collected from 114 listed enterprises in China's solar power industry, and the analysis results indicate that this paper's model is stable and reliable and can be used as a reference for production managers.     

基于纳什均衡博弈的多无人机对地攻击目标分配方法

研究一种新的多无人机对地攻击目标分配问题.该问题中攻击方试图通过无人机击毁防御方的高价值目标,防御方试图通过发射拦截导弹对无人机进行拦截,但攻防双方无法事先观察到对方实际采取的目标分配方案.通过分析防御方的拦截导弹目标分配方案对攻击方收益的影响,将问题构建为一个零和矩阵博弈模型,模型的策略空间随无人机、高价值目标、拦截导弹数量的增加呈爆炸式增长.鉴于此,现有算法难以在有效时间内对其进行求解,提出一种基于两阶段邻域搜索的改进Double Oracle (DO-TSNS)算法.实验结果表明,相较于DO、UWMA和DO-NS算法, DO-TSNS算法能够更有效地求解考虑防御方具有拦截行为的多无人机对地攻击目标分配问题.     

Multi-AUV SOM task allocation algorithm considering initial orientation and ocean current environment

There is an ocean current in the actual underwater working environment. An improved self-organizing neural network task allocation model of multiple autonomous underwater vehicles (AUVs) is proposed for a three-dimensional underwater workspace in the ocean current. Each AUV in the model will be competed, and the shortest path under an ocean current and different azimuths will be selected for task assignment and path planning while guaranteeing the least total consumption. First, the initial position and orientation of each AUV are determined. The velocity and azimuths of the constant ocean current are determined. Then the AUV task assignment problem in the constant ocean current environment is considered. The AUV that has the shortest path is selected for task assignment and path planning. Finally, to prove the effectiveness of the proposed method, simulation results are given.

     

Note on a unified approach to the single-machine scheduling problem with a deterioration effect and convex resource allocation

In many resource allocation problems in physical or economic systems, a linear resource consumption function is commonly considered, and job processing times are assumed to be fixed parameters. However, the former assumption fails to reflect the law of diminishing returns, and the latter may be controlled by changing the allocation of resources to jobs. Motivated by these observations, we provide a unified model for solving single-machine scheduling problems in which each job's processing time is a function of its starting time and convex resource allocation. The objective is to find the optimal sequence of jobs subject to a limited resource consumption. We first show how this unified model can be useful in solving scheduling problems under due date assignment considerations. We analyze the problem with four different due date assignment methods, and our objective function includes costs for earliness, tardiness and due date assignments. We also consider scheduling problems without involving due date assignment decisions. The objective function is to minimize the makespan, total completion time, total absolute variation in completion times, and total absolute variation in waiting times. We show that several existing well-known problems can be reduced to a special case of our unified model and solved in O(nlogn) time.     

Facility location in anticipation of future competition

In this paper, we consider locating a new facility in a competitive environment. A future competitor is expected to enter the market and locate his facility at its best site. The best location for one's own facility is to be found such that the market share captured following the competitor's entry is maximized. The problem is complicated because the best location for the competitor depends on the selected location for one's own facility. The problem is formulated using the gravity model for the estimation of market share. Three heuristic solution procedures are proposed. Computational experiments with these heuristics are presented.     

SIMULATION MODELLING FOR COMPLEX PRODUCTION SYSTEMS

Resource allocation and scheduling are the key areas in the operation of complex industrial systems. One can argue that the systems have grown in complexity over the years mainly due to the increased striving for performance enhancing combined with a greater degree of uncertainty and imprecision in system's external and internal environments. This complexity, always present in real life systems, makes the application of quantitative scheduling and allocation tools as problem solvers questionable in many instances. In this paper simulation technique is proposed as an analysis tool that proved to be an adequate, effective and economically efficient problem solver in the case of scheduling and resource allocation and utilization for a complex manufacturing system. The implementation described in the paper was developed using SLAMSYSTEM modelling environment.     

带有交货期的比例流水车间调度问题的合作博弈

针对具有多个客户订单的比例流水车间调度问题,在考虑有交货期及提前和拖期惩罚下,以客户支出成本为优化指标,在客户通过合作结成联盟的方式下,以联盟内成员进行重新调度所获得的最大成本节省为联盟的价值,建立合作博弈模型.该合作博弈是具有无外部性的平衡博弈,从而有非空核.考虑到客户对提前加工和延迟加工的迫切程度不同,提出基于提前及拖期惩罚的β规则分配方法,该方法能得到带有交货期的比例流水车间调度合作博弈的一个核分配.通过混合差分进化算法求解最优调度顺序,实验结果验证了基于合作博弈模型的调度方法及成本分配方法的有效性.     

Numerical analysis of a time allocation model accounting for choice overload

The cost of time has been suggested as a factor associated with the choice overload problem. This term refers to the discomfort or paralysis experienced by individuals when facing a choice within a large set of alternatives, as it has been evidenced in experiments by behavioural and social psychologists. We introduce a rational model of time allocation to analyse how increasing the number of options of a given product may change consumer's allocation of time and in turn affect her welfare. Under some standard assumptions, the numerical analysis of the model reproduces two key experimental findings, namely choice paralysis – i.e. the choice problem is abandoned if the number of options is too large – and choice dissatisfaction – that is, the apparent paradox that increasing the number of considered options beyond certain limit, in turn choosing better, eventually diminishes welfare. The model analysis provides specific threshold values for the occurrence of both phenomena.     

二级垃圾回收中转设施选址问题的降阶回溯算法

随着我国城市化进程的加快和经济的高速发展,城市中因生产生活所产生的垃圾废料量日益增加,如何有效地建立回收中转设施是当前社会需要解决的问题。对二级垃圾回收设施选址问题进行研究,其实质为组合优化中的NP-hard问题。首先根据实际情况对二级垃圾回收中转设施选址问题进行数学建模,研究该问题的数学性质并给予证明,利用这些性质减小问题规模,降低求解难度;然后设计符合该问题的分配子算法、上下界子算法,基于以上算法提出一种可以在减小问题规模的同时得到精确解的降阶回溯算法;最后通过分析和模拟若干个示例进一步阐述该算法的原理及执行过程,结果表明该算法能通过减小问题规模,降低问题求解的难度。