The effect of worker learning and forgetting on storage reassignment decisions in order picking systems

Order picking is a time-intensive and costly logistics activity as it involves a high amount of manual work. Prior research has mostly neglected the influence of human factors on the efficiency of order picking systems. This paper develops a mathematical model that investigates the impact of learning and forgetting of a heterogeneous workforce on order picking time and, consequently, on storage assignment decisions. In particular, the paper investigates when to change a storage assignment and when to keep it if learning and forgetting occur among the members of an order picking workforce. The results show that learning and forgetting should be considered in order to achieve a proper planning of storage assignment strategies.     

A hierarchical procedure for multi-skilled sales force spatial planning

Marketing and sales comprise an important part of a company's core processes. We study this core component in a very large corporation in the process industry. This company has thousands of employees worldwide involved in sales and marketing activities. The optimal utilization of this large workforce is rigorously analyzed in this paper. We develop a hierarchical procedure including a sequence of mathematical models to address workload determination and sales force planning within the company. In particular, our sequence of models determines: (a) the optimal number of visits to each of the existing customers with the goal of retaining them; (b) the optimal number of employees with different kinds of skills for satisfying the workload obtained using model (a) including the time to hunt for new customers; and (c) the optimal way of providing the workforce calculated in part (b) (i.e., optimal strategies for hiring, relocation or dismissal of current workforce). Moreover, we face some nonlinear constrained integer programming problems in our sequence of mathematical models which are not easily solved by commercial software. We develop our own efficient methods for solving such problems optimally.     

PROCASE: a case-based process planning system for machining of rotational parts

This paper introduces a case-based process planning system PROCASE which generates new process routines through learning from existing process routines. In contrast to traditional rule-based systems, the process planning knowledge of the PROCASE is represented in terms of cases instead of production rules. The planning basically comprises case retrieving and case adaptation rather than chaining applicable rules together to form process plans. The advantages are, first, the system is cheaper to build as it saves the expense of knowledge acquisition. Second, the system is able to advance its knowledge automatically through planning practice. Third, it is robust, because the reasoning is not based on pattern matching but similarity comparison. PROCASE has three modules: the retriever, the adapter and the simulator. It is supported by a feature-based representation scheme which naturally serves as the case indices for case retrieving and adaptation. The retriever uses a similarity metric to retrieve an old case which is the most similar case, among all old ones, to the new case. The adapter is then activated to adapt the process plan of the retrieved case to fit the needs for the new case. The simulator is used to verify the feasibility of the adapted plan. PROCASE is implemented on a Silicon Graphics IRIS workstation using C++ . An example is given to demonstrate how the process routine is generated by the system proposed by the authors.     

Simulation-based workforce assignment in a multi-organizational social network for alliance-based software development

The development of alliance-based software requires the collaboration of many stakeholders. These different stakeholders across multiple organizations form a complex social network. The goal of this paper is to develop a novel modeling framework, which will help task managers devise optimal workforce assignments considering both short-term and long-term aspects of the software development process. The proposed framework is composed of an assignment module and a prediction module. For a given task, the assignment module first selects a candidate workforce mix. Based on the candidate workforce mix, the prediction module then predicts the short-term performance (productivity) as well as the long-term performance (workforce training and robustness of the organization) of the organization. Then, the assignment module selects another candidate mix, and this iteration continues until an optimal workforce mix is found. The prediction module and the assignment module are based on an agent-based simulation method and a multi-objective optimization model, respectively. The proposed modeling framework is illustrated with a software enhancement request process in Kuali, an alliance-based open source software development project involving 12 organizations. The constructed framework is executed with varying parameters to demonstrate its use and benefit in the software enhancement process.     

Scheduling mobile collaborating workforce for multiple urgent events

Despite the advancement of wireless technologies that allows collaboration at different places, under emergencies, professionals are often still required to arrive at the scene to carry out critical tasks. Under many practical constraints, how to schedule mobile collaborating workforce for urgent event requirements becomes a challenging problem. In this paper, we study the optimal mobile workforce assignment problems for multiple events and propose an efficient algorithm to find an optimal workforce arrangement with respect to quick response under qualification and location constraints. A practical example is given to illustrate how our method works. We also study the exception case where there are not enough qualified users. We allow a user to take on multiple qualified tasks previously assigned to different users. But each person is restricted within one event location so as to reduce traffic transfer between different places for the quick response purpose. We analyze the computational complexity of the problem of finding an optimal assignment of mobile workforce under such restraints and solve it by means of integer linear programming.     

Simulation and graphical animation of advanced manufacturing systems

Automated production systems, known as Flexible Manufacturing Systems (FMS), offer a promising solution for the low productivity of mid-volume, mid-variety batch production. These systems consist of integrated machine modules and material handling equipment under computer control.This paper describes a general purpose discrete-event Flexible Manufacturing Systems Simulator (FMSSIM). The package is programmed in FORTRAN and is capable of simulating different configurations, material handling systems, and topologies including bidirectional tracks. The simulator checks blockage of routes due to interference of carts and simulates random failures and repairs of the various components in the system. The simulator provides the user with a wide range of priority rules to select from, and enables the user to define his own rules if required. The simulator produces reports on various vital system performance statistics and also displays the movement of parts through the system on a refreshed CRT. This simulator is a modular, user-oriented package which allows the designer to evaluate a wide range of systems with varied design parameters and select an efficient flexible manufacturing system. Some general trends in the behaviour of the flexible manufacturing systems are identified and guidelines for their design and operation are suggested.     

A resilience optimization approach for workforce-inventory control dynamics under uncertainty

The presence of uncertainties in manufacturing systems and supply chains can cause undesirable behavior. Failure to account for these in the design phase can further impair the capability of systems to respond to changes effectively. In this work, we consider a dynamic workforce-inventory control problem wherein inventory planning, production releases, and workforce hiring decisions need to be made. The objective is to develop planning rules to achieve important requirements related to dynamic transient behavior when system parameters are imprecisely known. To this end, we propose a resilience optimization model for the problem and develop a novel local search procedure that combines the strengths of recent developments in robust optimization technology and small signal stability analysis of dynamic systems. A numerical case study of the problem demonstrates significant improvements of the proposed solution in controlling fluctuations and high variability found in the system’s inventory, work-in-process, and workforce levels. Overall, the proposed model is shown to be computationally efficient and effective in hedging against model uncertainties.     

轮手一体机器人能量次优重构规划方法

模块化机器人的重构规划中,由于各模块的目标分配与其轨迹规划之间的耦合关系导致组合爆炸问题.本文提出一种基于简化模型的能量次优规划方法,将重构规划问题转化为最优控制问题,实现目标分配与轨迹规划的解耦.通过求解由Hamilton-Jacobi-Bellman（HJB）方程描述的最优控制问题,得到简化模型的值函数和最优轨迹.各模块的运动目标由值函数的吸引域决定.通过在最优轨迹附近的次优区域内搜索得到实际运动轨迹,提高了搜索效率.仿真实验结果表明,该方法能够选择合适的模块组合,并能在障碍物环境中生成满足机器人动力学约束的运动轨迹.     

A CAD system for distributed networks design

This paper presents an interactive computer package which aids the distributed network designers in choosing the optimum capacity for different links. This package gives the designer a tool to experiment different assignment strategies and compare their results. Four assignment strategies are implemented in this package: Minimum Average Time delay, Min-Max, Equal Capacities and Proportional Capacities Assignment strategies. The designer can also use it to investigate the effect of different routing policies on the values of optimum capacity. Reliability of the network can be examined by choosing routes which avoid broken links. The package is designed to be flexible and extendable so any module can be replaced or new modules can be added without change in other modules of the package.     

A hybrid constraint programming approach to a wood procurement problem with bucking decisions

In this article, we present a wood procurement problem that arises in Eastern Canada. We solve a multi-period wood supply planning problem, while taking into account bucking decisions. Furthermore, we present a new form of flexibility which allows the harvesting capacity to change from one time period to another. We study the impact of such flexibility upon the harvesting cost. We assess the performance of the problem by comparing it with a variant where the harvesting capacity is fixed during sites’ harvesting. To address this problem, we develop a hybrid approach based on both constraint and mathematical programming. In the first phase, we propose a constraint programming model dealing with forest sites harvesting and bucking problems. The result of this model is used as part of an initial solution for the whole problem formulated as a mixed integer model. We test the two versions of the problem on a set of different demand instances and we compare their results.     

Job Shop型制造系统任务分配和能力规划问题的并行优化

在排队理论基础上,建立了Job Shop型制造系统任务分配和能力规划并行优化问题的数学模型,提出了解决这一问题的遗传算法,数值实验表明这一算法是有效的.     

Assigning multiple activities to work shifts

In some companies such as large retail stores, the employees perform different activities (e.g., cashier or clerk in a specific department) to respond to a customer demand for each activity that varies over the planning horizon and must be fulfilled as soon as possible. For a given time period, this demand translates into an ideal number of employees required for the corresponding activity. During a work shift, an employee can be assigned to several activities that are interruptible at any time and subject to operational constraints (required skills, minimum and maximum assignment durations). Given work shifts already assigned to the employees, the multi-activity assignment problem (MAAP) consists of assigning activities to the shifts such that the activity demands are satisfied as best as possible over the planning horizon. In this paper, we propose three integer programming models for the MAAP and develop various heuristics based on mathematical programming techniques. Computational results obtained on randomly generated MAAP instances show that a heuristic column generation method embedded into a rolling horizon procedure provides the best results in general.     

制造物联环境下智能仓库货位分配模型

仓库货位合理规划与分配是减少产品存取时间、提高仓库作业效率的关键。在构建基于制造物联技术的智能仓库环境下,针对多品种智能仓库的货位分配问题,建立了考虑多规则约束的多目标智能仓库货位分配模型,提出一种模型求解的改进遗传算法。实验表明：模型和算法能找到有效的仓库货位分配方案,验证了其有效性。     

Optimized planning of frequency hopping in cellular networks

We consider a generalization of the classical frequency assignment problem. The generalization arises when frequency hopping is used in a cellular network. The planning problem concerns assigning lists of frequencies to blocks of transceivers, such that the total interference is minimized. This problem is considerably more difficult than the classical frequency assignment problem, because of the large number of possible frequency lists. We provide the technical background that motivates our study, and present a mathematical model which includes the classical frequency assignment problem as a special case. We describe a simulated annealing algorithm. The algorithm explores the solution space by solving an integer program in each iteration. We report computational results for real-life and synthesized networks.     

Multi-choice multi-objective mathematical programming model for integrated production planning: a case study

This article develops a multi-choice multi-objective linear programming model in order to solve an integrated production planning problem of a steel plant. The aim of the integrated production planning problem is to integrate the planning sub-functions into a single planning operation. The sub-functions are formulated by considering the capacity of different units of the plant, cost of raw materials from various territories, demands of customers in different geographical locations, time constraint for delivery the products, production cost and production rate at different stages of production process. Departure cost is also considered in the formulation of mathematical programming model. Some of the parameters are decided from a set of possible choices, therefore such parameters are considered as multi-choice type. Multi-choice mathematical programming problem cannot be solved directly. Therefore an equivalent multi-objective mathematical programming model is established in order to find the optimal solution of the problem. Computation of the mathematical programming model is performed with the practical production data of a plant to study the methodology.     

Workforce planning and deployment for a hospital reservation call center with abandonment cost and multiple tasks

We consider a hospital reservation call center where operators handle multiple tasks. We take into account operator labor costs, caller waiting costs, and abandonment costs for lost calls. Instead of relying on the traditional method of queueing theory for call center management, we present a method that obtains expected caller waiting time and expected abandonment rate directly by introducing the inbound-load parameter. We develop a framework that combines workforce planning and operator deployment for a mixed call center in a single mathematical programming model. This paper also demonstrates how the proposed methodology can be applied in practice, with a case study based on actual operational data. Our primary conclusion is that the method presented in this research can significantly reduce both expected total cost (by 55.1% in our case study) and abandonment rate (from 15% to 2.1% in our case study). In addition, we demonstrate, using a sensitivity analysis, that our methodology will be more effective in an environment where the unit penalty cost of an abandoned call is relatively high among competitive hospitals.     

A modeling technique for loading and scheduling problems in FMS

In recent years, due to highly competitive market conditions, it has become necessary for manufacturing systems to have quick response times and high flexibility. Flexible manufacturing systems (FMS's) have gained attention in response to this challenge. FMS has the ability to produce a variety of parts using the same system. However this flexibility comes at the price, which is the development of efficient and effective methods for integrated production planning, and control.In this paper, we analyze the production planning problem in flexible manufacturing systems. We address the problems of part loading, tool loading, and part scheduling. We assume that there is a set of tools with known life and a set of machines that can produce a variety of parts. A batch of various part types is routed through this system with the assumption that the processing time and cost vary with the assignment of parts to different machines and assignment of various tool sets to machines. We developed a mathematical model to select machines and assign operations and the required tools to machines in order to minimize the summation of maximum completion time, material handling time, and total processing time.We first integrate and formulate loading, and routing, two of the most important FMS planning problems, as a 0–1 mixed integer programming problem. We then take the output from the integrated planning model and generate a detailed operations schedule. The results reported in this paper demonstrate the model efficiency and examine the performance of the system with respect to measures such as production rate and utilization.     

Efficient computation of optimal assignments for distributed tasks

We consider the problem of finding an optimal assignment of the modules of a program to processors in a distributed system. A module incurs an execution cost that may be different for each processor assignment, and modules that are not assigned to the same processor but that communicate with one another incur a communication cost. An optimal assignment minimizes the sum of the module execution costs and the intermodule communication costs. This problem is known to be NP-complete for more than three processors. Using a branch-and-bound-with-underestimates algorithm to reduce the size of the search tree, we evaluate its average time and space complexity for two underestimating functions through simulation. The more complex of the two functions, called the minimum independent assignment cost underestimate (MIACU), performs extremely well over a wide range of values of program model parameters such as the number of modules, the number of processors, and the ratio of average module execution cost to average intermodule communication cost. By reordering the list of modules to allow a subset of modules that do not communicate with one another to be assigned last, further improvements using MIACU are possible.     

Belief function combination and conflict management

Within the framework of evidence theory, data fusion consists in obtaining a single belief function by the combination of several belief functions resulting from distinct information sources. The most popular rule of combination, called Dempster's rule of combination (or the orthogonal sum), has several interesting mathematical properties such as commutativity or associativity. However, combining belief functions with this operator implies normalizing the results by scaling them proportionally to the conflicting mass in order to keep some basic properties. Although this normalization seems logical, several authors have criticized it and some have proposed other solutions. In particular, Dempster's combination operator is a poor solution for the management of the conflict between the various information sources at the normalization step. Conflict management is a major problem especially during the fusion of many information sources. Indeed, the conflict increases with the number of information sources. That is why a strategy for re-assigning the conflicting mass is essential. In this paper, we define a formalism to describe a family of combination operators. So, we propose to develop a generic framework in order to unify several classical rules of combination. We also propose other combination rules allowing an arbitrary or adapted assignment of the conflicting mass to subsets.     

On the complexity of short-term production planning and the near-optimality of a sequential assignment problem heuristic approach

In semiconductor manufacturing, the process of short-term production planning requires setting clear and yet challenging and doable goals to each operation and toolset in the process flow per each product type. We demonstrate the complexity of this problem using an experimental study performed with proficient workforce, and then show how the problem can be decomposed, aggregated, and solved using sequential recurrent linear programming assignment problems. We also refer to the improvements that the proposed algorithm has achieved in practice when applied to multiple semiconductor production facilities, and discuss its efficiency and uniqueness as a fast heuristic relative to other proposed methods.