A robust optimization model for dynamic market with uncertain production cost

This article presents a robust optimization formulation for dealing with production cost uncertainty in an oligopolistic market scenario. It is not uncommon that players in the market face an equilibrium selling price but uncertain production costs. We show that, based on a nominal problem, the robust optimization formulation can be derived as a variational inequality with control and state variables. This convenient approach may be applied for computing optimal solutions efficiently, which help manufacturers dramatically and rapidly reform production and distribution schedules such that they can compete in the market successfully.     

Integrating design and production planning considerations in multi-bay manufacturing facility layout

This paper develops a new mathematical model that integrates layout design and production planning to prescribe efficient multi-bay manufacturing facilities. The model addresses the need to distribute department replicas throughout the facility and extends the use of product and process requirements as problem parameters in order to increase process routing flexibility. In addition, the model allows for the consideration of practical material handling and production features such as alternate process routings, product flow production patterns, and department replica capacities. Computational results demonstrate that the run time required to solve our test problems is quite acceptable given the long-term nature of facility layout decisions. Moreover, comparative results indicate that department replication can reduce material movement significantly while maintaining the existing production capacity.     

Dynamic visioning for dynamic environments

This article considers the particular challenges to organisations facing major change in turbulent times. It briefly reviews the use of visioning tools, especially scenario planning, to support the process of developing strategies requiring major change, and the communication of these plans to key employees. However, it deduces that written scenarios may not adequately portray the dynamic nature of the change, nor provide managers with a vivid enough picture of the post-change environment. System dynamics has been identified as a powerful tool for bringing such a dynamic view, and case examples are offered which use both the diagramming techniques in a qualitative manner and quantitative models to simulate the possible futures. Such simulators enable managers to pre-experience the changed environment, preparing them better to face the transients of the change implementation and the challenges of managing the post-change situation. Future developments and uses of system dynamics in ‘computer-aided visioning’ are considered.     

New heuristic for the dynamic layout problem

The dynamic layout problem addresses the situation where the traffic among the various units within a facility changes over time. Its objective is to determine a layout for each period in a planning horizon such that the total of the flow and the relocation costs is minimized. The problem is computationally very hard and has begun to receive attention only recently. In this paper, we present a new heuristic scheme, based on the idea of viable layouts, which is easy to operationalize. A limited computational study shows that, depending upon how it is implemented, this scheme can be reasonably fast and can yield results that are competitive with those from other available solution methods.     

A genetic algorithm for cellular manufacturing design and layout

Cellular manufacturing (CM) is an approach that can be used to enhance both flexibility and efficiency in today’s small-to-medium lot production environment. The design of a CM system (CMS) often involves three major decisions: cell formation, group layout, and group schedule. Ideally, these decisions should be addressed simultaneously in order to obtain the best results. However, due to the complexity and NP-complete nature of each decision and the limitations of traditional approaches, most researchers have only addressed these decisions sequentially or independently. In this study, a hierarchical genetic algorithm is developed to simultaneously form manufacturing cells and determine the group layout of a CMS. The intrinsic features of our proposed algorithm include a hierarchical chromosome structure to encode two important cell design decisions, a new selection scheme to dynamically consider two correlated fitness functions, and a group mutation operator to increase the probability of mutation. From the computational analyses, these proposed structure and operators are found to be effective in improving solution quality as well as accelerating convergence.     

Efficient multi-swarm PSO algorithms for dynamic environments

Particle swarm optimization has been successfully applied in many research and application areas because of its effectiveness and easy implementation. In this work we extend one of its variants to address multi-modal dynamic optimization problems, the multi-swarm PSO (mPSO) proposed by Blackwell and Branke. The aim of our proposal is to increase the efficiency of this algorithm. To this end, we propose techniques operating at swarm level: one of which divides each swarm into two groups depending on the quality of the particles for facing the loss of diversity, and the other control the number of active swarms during the run using a fuzzy rule. A detailed experimental analysis shows the robustness of our proposal.     

Methodology for determining the acceptability of system designs in uncertain environments

In practice, managers often wish to ascertain that a particular engineering design of a production system meets their requirements. The future environment of this design is likely to differ from the environment assumed during the design. Therefore it is crucial to find out which variations in that environment may make this design unacceptable (unfeasible). This article proposes a methodology for estimating which uncertain environmental parameters are important (so managers can become pro-active) and which combinations of parameter values (scenarios) make the design unacceptable. The proposed methodology combines simulation, bootstrapping, design of experiments, and linear regression metamodeling. This methodology is illustrated through a simulated manufacturing system, including fourteen uncertain parameters of the input distributions for the various arrival and service times. These parameters are investigated through the simulation of sixteen scenarios, selected through a two-level fractional–factorial statistical design. The resulting simulation Input/Output (I/O) data are analyzed through a first-order polynomial metamodel and bootstrapping. A second experiment with other scenarios gives some outputs that turn out to be unacceptable. In general, polynomials fitted to the simulation’s I/O data can estimate the border line (frontier) between acceptable and unacceptable environments.     

An integrated decision support approach for project investors in risky and uncertain environments

In project investment decisions, it is often assumed that estimated values of project parameters are certain and they would not deviate by the time. However, project parameters normally change during a life cycle of the project. Therefore, an existence of a deviation or gap between forecasted values and actual values is inevitable. Because of the uncertainty of the future, forecasting the true and exact values of project parameters is almost impossible. In this study, an integrated decision support approach based on simulation and fuzzy set theory is proposed for project investors in risky and uncertain environments. This approach determines the risk levels of the projects and helps investors to make investment decisions. In the scope of the study, a flowchart is presented to guide to decision maker in different situations of information uncertainty that belongs to project parameter values. Via this flowchart, the values of project parameters can be chosen depending on how they are determined (deterministic, stochastic or fuzzy) by project analyst. Besides, calculating and analyzing the project risk in all possible situations would be easier. Illustrative examples are given to demonstrate the application of this approach.     

Heuristics for the dynamic facility layout problem with unequal-area departments

The dynamic facility layout problem (DFLP) is the problem of finding positions of departments on the plant floor for multiple periods (material flows between departments change during the planning horizon) such that departments do not overlap, and the sum of the material handling and rearrangement costs is minimized. In this paper, the departments may have unequal-areas and free orientations, and the layout for each period is generated on the continuous plant floor. Because of the complexity of the problem, only small-size problems can be solved in reasonable time using exact techniques. As a result, a boundary search (construction) technique, which places departments along the boundaries of already placed departments, is developed for the DFLP. The solution is improved using a tabu search heuristic. The heuristics were tested on some instances from the DFLP and static facility layout problem (SFLP) literature. The results obtained demonstrate the effectiveness of the heuristics.     

Decentralized robust control design for uncertain delay systems

We consider a class of large-scale uncertain delay systems. The uncertain parameter vector in the system is possibly fast time-varying. It may be nonlinear in the system dynamics. No statistical or fuzzy information of the uncertainty is known. Based on only the possible bound of the uncertain parameter, a decentralized linear robust control is proposed, which renders the system asymptotically stable.     

Integrated cellular manufacturing systems design with production planning and dynamic system reconfiguration

This paper presents and analyzes a comprehensive model for the design of cellular manufacturing systems (CMS). A recurring theme in research is a piecemeal approach when formulating CMS models. In this paper, the proposed model, to the best of the authors’ knowledge, is the most comprehensive one to date with a more integrated approach to CMS design, where production planning and system reconfiguration decisions are incorporated. Such a CMS model has not been proposed before and it features the presence of alternate process routings, operation sequence, duplicate machines, machine capacity and lot splitting. The developed model is a mixed integer non-linear program. Linearization procedures are proposed to convert it into a linearized mixed integer programming formulation. Computational results are presented by solving some numerical examples, extracted from the existing literature, with the linearized formulation.     

Learning and transfer in dynamic decision environments

An important aspect of learning is the ability to transfer knowledge to new contexts. However, in dynamic decision tasks, such as bargaining, firefighting, and process control, where decision makers must make repeated decisions under time pressure and outcome feedback may relate to any of a number of decisions, such transfer has proven elusive. This paper proposes a two-stage connectionist model which hypothesizes that decision makers learn to identify categories of evidence requiring similar decisions as they perform in dynamic environments. The model suggests conditions under which decision makers will be able to use this ability to help them in novel situations. These predictions are compared against those of a one-stage decision model that does not learn evidence categories, as is common in many current theories of repeated decision making. Both models' predictions are then tested against the performance of decision makers in an Internet bargaining task. Both models correctly predict aspects of decision makers' learning under different interventions. The two-stage model provides closer fits to decision maker performance in a new, related bargaining task and accounts for important features of higher-performing decision makers' learning. Although frequently omitted in recent accounts of repeated decision making, the processes of evidence category formation described by the two-stage model appear critical in understanding the extent to which decision makers learn from feedback in dynamic tasks. Faison (Bud) Gibson is an Assistant Professor at College of Business, Eastern Michigan University. He has extensive experience developing and empirically testing models of decision behavior in dynamic decision environments.     

Robust stabilization control design for multivariable nonlinear uncertain systems

The paper develops a new control technique for multivariablenonlinear systems in the presence of uncertainties and externaldisturbances. The proposed design method does not require thatthe uncertainties should satisfy matching conditions; nor doesit require that the nominal system should be stable or prestabilized.The robust-control strategy is established using concepts fromvariable-structure theory and is based on Lyapunov stabilitytheory. The control possesses a quite simple structure whichis related to the given uncertainty bounds.     

Detailed layout planning for irregularly-shaped machines with transportation path design

In order to obtain a competitive level of productivity in a manufacturing system, efficient machine or department arrangements and appropriate transportation path structures are of considerable importance. By defining a production system’s basic structure and material flows, the layout determines its operational performance over the long term. However, most approaches proposed in the literature provide only a block layout, which neglects important operational details. By contrast, in this paper, we introduce approaches to planning layouts at a more detailed level. Hence, this present paper introduces an integrated approach which allows a more detailed layout planning by simultaneously determining machine arrangement and transportation paths. Facilities to be arranged as well as the entire layout may have irregular shapes and sizes. By assigning specific attributes to certain layout subareas, application-dependent barriers within the layout, like existing walls or columns, can be incorporated. We introduce a new mathematical layout model and develop several improvement procedures. An analysis of the computational experiments shows that more elaborate heuristics using variable neighborhoods can generate promising layout configurations.     

Heuristics for urban road network design: Lane layout and signal settings

This paper concerns the urban road network design problem. In urban areas supply has usually been unable to keep pace with increasing demand: the only possibility is often to reorganise the current supply configuration in order to use existing resources efficiently. Thus, in urban areas signal settings and network topology (in particular lane layout) are the two major factors that can be handled by design models. Methods for the combined design of signal settings and topology are proposed in this paper. All the methods proceed in two stages: the first deals with integer variables (topology), while the second deals with continuous variables (signal settings). Some metaheuristics (Hill Climbing, Simulated Annealing, Tabu Search, Genetic Algorithms and Path Relinking) are specified for the topology design stage, and they are used singularly or jointly. The continuous part of the solution, with fixed topology is optimized through an algorithm for asymmetrical deterministic equilibrium assignment. This paper focuses on evaluating performances obtained by all the different algorithms proposed for the topology design stage. The algorithms are compared by applications to real networks, and some conclusions are drawn about their efficiency.     

洗衣机设计中的一个重要指标

本文建立了随机洗衣模型,给出了最佳漂洗次数的估计和刻划洗衣机性能的一个重要指标。     

Maximum thick paths in static and dynamic environments

We consider the problem of finding a large number of disjoint paths for unit disks moving amidst static or dynamic obstacles. The problem is motivated by the capacity estimation problem in air traffic management, in which one must determine how many aircraft can safely move through a domain while avoiding each other and avoiding “no-fly zones” and predicted weather hazards. For the static case we give efficient exact algorithms, based on adapting the “continuous uppermost path” paradigm. As a by-product, we establish a continuous analogue of Menger's Theorem.Next we study the dynamic problem in which the obstacles may move, appear and disappear, and otherwise change with time in a known manner; in addition, the disks are required to enter/exit the domain during prescribed time intervals. Deciding the existence of just one path, even for a 0-radius disk, moving with bounded speed is NP-hard, as shown by Canny and Reif [J. Canny, J.H. Reif, New lower bound techniques for robot motion planning problems, in: Proc. 28th Annu. IEEE Sympos. Found. Comput. Sci., 1987, pp. 49–60]. Moreover, we observe that determining the existence of a given number of paths is hard even if the obstacles are static, and only the entry/exit time intervals are specified for the disks. This motivates studying “dual” approximations, compromising on the radius of the disks and on the maximum speed of motion.Our main result is a pseudopolynomial-time dual-approximation algorithm. If K unit disks, each moving with speed at most 1, can be routed through an environment, our algorithm finds (at least) K paths for disks of radius somewhat smaller than 1 moving with speed somewhat larger than 1.     

A labeling method for dynamic driver-task assignment with uncertain task durations

The problem of assigning drivers to cover tasks with service time windows and uncertain task durations is formulated as a dynamic stochastic decision model. We develop an adaptive labeling solution procedure that can incorporate various practical constraints and work rules. Experiments are conducted to evaluate the procedure's performance and compare the stochastic and deterministic formulations.