Yossi Bukchin

A branch-and-bound based solution approach for the mixed-model assembly line-balancing problem for minimizing stations and task duplication costs

A common assumption in the literature on mixed-model assembly line balancing is that a task that is common to multiple models must be assigned to a single station. In this paper, we relax this restriction, and allow a common task to be assigned to different stations for different models. We seek to minimize the sum of costs of the stations and the task duplication. We develop an optimal solution procedure based on a backtracking branch-and-bound algorithm and evaluate its performance via a large set of experiments. A branch-and-bound based heuristic is then developed for solving large-scale problems. The heuristic solutions are compared with a lower bound and experiments show that the heuristic provides much better solutions than those obtained by traditional approaches. � 2005 Elsevier B.V. All rights reserved.

Diversity Maximization Approach for Multiobjective Optimization

One of the most common approaches for multiobjective optimization is to generate the whole or partial efficient frontier and then decide about the preferred solution in a higher-level decision-making process. In this paper, a new method for generating the efficient frontier for multiobjective problems is developed, called the diversity maximization approach (DMA). This approach is capable of solving mixed-integer and combinatorial problems. The DMA finds Pareto optimal solutions by maximizing a proposed diversity measure and guarantees generating the complete set of efficient points. Given a subset of the efficient frontier, DMA finds the next Pareto optimal solution which, combined with the existing ones, yields the most diversified subset of efficient points. This solution is defined as the most diverse solution. In fact, it aims to maximize the distance between the new efficient point and the closest point in the given subset of the efficient frontier. The proposed approach can be applied to any problem that can be solved for the single-objective case. We can use the DMA by solving directly a modified version of the mixed-integer programming (MIP) formulation of the multiobjective problem. In this case, the Pareto optimal solutions are found sequentially in an iterative way. Consequently, as we terminate the procedure before completion, a partial efficient frontier is available. The diversity measure assures that in every stage of the procedure, the partial efficient frontier is well diversified. This partial efficient frontier can be perceived as a filtered set of the complete efficient frontier and can be used by the decision maker in case the complete efficient frontier contains too many points. An additional way of using DMA is by incorporating it in a problem oriented branch-and-bound algorithm. Detailed examples of both approaches are given.

Decentralization Cost in Scheduling: A Game-Theoretic Approach

Decentralized organizations may incur inefficiencies because of scheduling issues associated with competition among decision makers (DMs) for limited resources. We analyze the decentralization cost (DC), i.e., the ratio between the Nash equilibrium cost and the cost attained at the centralized optimum. Solution properties of a dispatching-sequencing model are derived and subsequently used to develop bounds on the DC for an arbitrary number of jobs and DMs. A scheduling-based coordinating mechanism is then provided, ensuring that the centralized solution is obtained at equilibrium.

Optimizing a dynamic order-picking process

This research studies the problem of batching orders in a dynamic, finite-horizon environment to minimize order tardiness and overtime costs of the pickers. The problem introduces the following trade-off: at every period, the picker has to decide whether to go on a tour and pick the accumulated orders, or to wait for more orders to arrive. By waiting, the picker risks higher tardiness of existing orders on the account of lower tardiness of future orders. We use a Markov decision process (MDP) based approach to set an optimal decision making policy. In order to evaluate the potential improvement of the proposed approach in practice, we compare the optimal policy with two naive heuristics: (1) “Go on tour immediately after an order arrives”, and, (2) “Wait as long as the current orders can be picked and supplied on time”. The optimal policy shows a considerable improvement over the naive heuristics, in the range of 7–99%, where the specific values depend on the picking process parameters. We have found that one measure, the slack percentage of the picking process, associated with the difference between the promised lead time and the single item picking time, predicts quite accurately the cost reduction generated by the optimal policy. Since relatively small-scale problems could be solved by the optimal algorithm, a heuristic was developed, based on the structure and properties of the optimal solutions. Numerical results show that the proposed heuristic, MDP-H, outperforms the naive heuristics in all experiments. As compared to the optimal solution, MDP-H provides close to optimal results for a slack of up to 40%.

Design and operation of dynamic assembly lines using work-sharing

Dynamic line balancing (DLB) deals with dynamic shifting of assembly tasks (shared tasks) between consecutive stations during the operation of the assembly line. In traditional assembly lines, work-sharing is not allowed. Consequently, the cycle time is determined by the amount of work performed in the most loaded station. The DLB approach enables achieving a shorter cycle time and a higher throughput rate, as compared to the traditional lines. In the implementation of the DLB approach the following has to be determined: (1) the identity of the shared tasks, and (2) the operational task allocation rule. In some cases, the data regarding the percentage of cycles each shared task will be performed in a station is also required. We first analyse a case in which an initial task assignment to stations (line balance solution) and the identity of the shared tasks is known and given. Conditions for the balanceability (the ability of the DLB to achieve an equivalent performance to a perfect balanced line in the steady state) are developed, as well as the formulation for minimizing the cycle time. Next, the sharing costs are considered and approaches for minimizing the shared time and sharing cost are presented, based on the new concept of bottleneck segment. The operational aspect is also addressed, while examining the effect of several state-dependent and state-independent operating rules for task allocation on the cycle time and the system work in process.

A space allocation algorithm for assembly line components

A component stockout during the assembly process is one of the most undesirable events that can occur since the resulting line stoppage is associated with extremely high costs. In this paper we address the problem of allocating space to components along the line, subject to practical constraints. The objective is to maximize the line fill-rate; namely, the probability of no line stoppages due to a lack of components between consecutive replenishments. A model for calculating the line fill-rate is presented. This model is then incorporated into a design algorithm that determines the space allocation. A large experiment for moderate-sized and large-scale problems is performed to evaluate the algorithm performance. Our experiment indicates this performance to be excellent, producing optimal solutions on moderate-sized problems in most cases and solutions that are better than three intuitive rules for most of the large-scale problems considered.

Robust eco-design: A new application for air quality engineering

The method of robust design has long been used for the design of systems that are insensitive to noises. In this paper it is demonstrated how this approach can be used to obtain a robust eco-design (ecological design). In a case study, robust design principles are applied to the design of a factory smokestack, using the Gaussian Plume Model (GPM). The GPM is a well-known model for describing pollutant dispersal from a point source, subject to various atmospheric conditions. In this research, the mean-square-error (MSE) of the accumulated and the maximum pollution values around a given target are defined as the performance measures and used to adjust the design parameters. Both analytical and numerical approaches are used to evaluate the MSE measures over the design space. It is demonstrated how to use the non-linearity in the GPM to reach a low MSE value that produces a cheaper design configuration. The differences between the manufacturer viewpoint and the environmentalist viewpoint with respect to the considered eco-design problem are discussed and analyzed.

Assembly system facility design

This paper addresses the design of an assembly system facility consisting of multiple assembly lines of different shapes. In such a design problem there are two conflicting objectives: (i) to minimize the overall area of the facility; and (ii) to maximize the efficiency of the material handling transportation system. We first address the optimization problem of objective (ii) when replacing objective (i) with a constraint on the facility area. We propose a mixed-integer linear program to determine the layout of a facility with given dimensions and with given assembly line areas and shapes (that cannot be changed due to technological considerations). In the layout model, the physical placement of each line within the facility is a decision variable. The objective function of the layout model is to minimize the distances traveled by material flow. Our performance analysis provides an indication of the maximal problem size that can be solved in a reasonable amount of time and we examine the effect of the problem parameters on the solution run time. This layout model is then incorporated into an efficiency frontier approach for facility design to address both objectives. Examples are presented to illustrate the use of the proposed facility design model.

Minimising throughput loss in assembly lines due to absenteeism and turnover via work-sharing

In this paper we analyse the loss of throughput rate of assembly line caused by slow pace of substitute workers (replacing absentees) having no prior experience in the required tasks. We proposed work-sharing mechanisms that improve the balance of the workload during the learning period. The proposed mechanisms add to the experienced neighbouring workers some of the workload of the inexperienced worker substituting an absentee. We call this workload ‘shared work’. After the performance of the substitute workers improves due to learning, the shared work is re-assigned to them (relieving their experienced neighbours). We provide analytic expressions for the line throughput rate, which is determined by sets of bottleneck workstations. These sets of consecutive workstations consist of the inexperienced workers replacing the absentees and the experienced workers assisting them during the learning periods. The decision variables of this model are: (1) the amount of shared work, and (2) the time in which the shared work is re-assigned to the substitute worker. Unique optimal values of these two variables are found via numerical study, for buffered and non-buffered lines. Experiments show that the proposed work-sharing mechanisms can significantly improve the line’s throughput, compared to the original system without work-sharing.

Evaluation of Telerobotic Interface Components for Teaching Robot Operation

Remote learning has been an increasingly growing field in the last two decades. The Internet development, as well as the increase in PCs capabilities and bandwidth capacity, has made remote learning through the internet a convenient learning preference, leading to a variety of new interfaces and methods. In this work, we consider a remote learning interface, developed in a Computer Integrated Manufacturing (CIM) Laboratory, and evaluate the contribution of different interface components to the overall performance and learning ability of end users. The evaluated components are the control method of the robotic arm and the use of a three-dimensional simulation tool before and during the execution of a robotic task. An experiment is designed and executed, comparing alternative interface designs for remote learning of robotic operation. A teleoperation task was given to 120 engineering students through five semesters. The number of steps required for completing the task, the number of errors during the execution, and the improvement rate during the execution were measured and analyzed. The results provide guidelines for a better design of an interface for remote learning of robotic operation. The main contribution of this paper is in the introduction of a new teaching tool for laboratories and the supplied guidelines for an efficient design of such tools.