Showing papers in "OR Spectrum in 2005"
TL;DR: A new Markov process model is developed for machines with both quality and operational failures, and important differences between types of quality failures are identified, and extensions to larger systems are proposed.
Abstract: During the past three decades, the success of the Toyota Production System has spurred much research in manufacturing systems engineering. Productivity and quality have been extensively studied, but there is little research in their intersection. The goal of this paper is to analyze how production system design, quality, and productivity are inter-related in small production systems. We develop a new Markov process model for machines with both quality and operational failures, and we identify important differences between types of quality failures. We also develop models for two-machine systems, with infinite buffers, buffers of size zero, and finite buffers. We calculate total production rate, effective production rate (ie, the production rate of good parts), and yield. Numerical studies using these models show that when the first machine has quality failures and the inspection occurs only at the second machine, there are cases in which the effective production rate increases as buffer sizes increase, and there are cases in which the effective production rate decreases for larger buffers. We propose extensions to larger systems.
146 citations
TL;DR: This work develops easily implementable heuristic algorithms and identifies both the absolute and asymptotic worst-case performance ratios of these heuristics and shows that most of these algorithms are optimal for the dispatching problem.
Abstract: We consider a container terminal discharging and uploading containers to and from ships. The discharged containers are stored at prespecified storage locations in the terminal yard. Containers are moved between the ship area and the yard using a fleet of vehicles, each of which can carry one container at a time. The problem is to dispatch vehicles to the containers so as to minimize the total time it takes to serve a ship, which is the total time it takes to discharge all containers from the ship and upload new containers onto the ship. We develop easily implementable heuristic algorithms and identify both the absolute and asymptotic worst-case performance ratios of these heuristics. In simple settings, most of these algorithms are optimal, while in more general settings, we show, through numerical experiments, that these algorithms obtain near-optimal results for the dispatching problem.
129 citations
TL;DR: A Grouping Genetic Algorithm (GGA) is proposed for solving the Pickup and Delivery Problem with Time Windows which features a group-oriented genetic encoding in which each gene represents a group of requests instead of a single request.
Abstract: The Pickup and Delivery Problem with Time Windows (PDPTW) is a generalization of the well studied Vehicle Routing Problem with Time Windows (VRPTW). Since it models several typical planning situations in operational transportation logistics and public transit, the PDPTW has attracted growing interest in recent years. This paper proposes a Grouping Genetic Algorithm (GGA) for solving the PDPTW which features a group-oriented genetic encoding in which each gene represents a group of requests instead of a single request. The GGA is subject to a comparative test on the basis of two publicly available benchmark problem sets that comprise 9 and 56 PDPTW instances, respectively. The results show that the proposed GGA is competitive.
125 citations
TL;DR: This work investigates the importance of a good workload based order acceptance method in over-demanded job shop environments, and presents sophisticated methods that consider technological restrictions, such as precedence relations, and release and due dates of orders.
Abstract: In practice, order acceptance and production planning are often functionally separated As a result, order acceptance decisions are made without considering the actual workload in the production system, or by only regarding the aggregate workload We investigate the importance of a good workload based order acceptance method in over-demanded job shop environments, and study approaches that integrate order acceptance and resource capacity loading We present sophisticated methods that consider technological restrictions, such as precedence relations, and release and due dates of orders We use a simulation model of a generic job shop to compare these methods with straightforward methods, which consider capacity restrictions at an aggregate level and ignore precedence relations We compare the performance of the approaches based on criteria such as capacity utilisation The simulation results show that the sophisticated approaches significantly outperform the straightforward approaches in case of tight due dates (little slack) In that case, improvements of up to 30% in utilisation rate can be achieved In case of much slack, a sophisticated order acceptance method is less important
93 citations
TL;DR: This work considers the multiple-depot multiple-vehicle-type scheduling problem (MDVSP) which arises in public transport bus companies and aims to assign buses to cover a given set of timetabled trips with consideration of practical requirements, such as multiple depots and vehicle types as well as depot capacities.
Abstract: We consider the multiple-depot multiple-vehicle-type scheduling problem (MDVSP) which arises in public transport bus companies and aims to assign buses to cover a given set of timetabled trips with consideration of practical requirements, such as multiple depots and vehicle types as well as depot capacities. An optimal schedule is characterized by minimal fleet size and minimal operational costs including costs for empty movements and waiting time. It is well-known that the MDVSP is NP-hard. Although progress has recently been made in solving large practical MDVSP to optimality with time-space network models, current optimization technology sets limits to the model size that can be solved. In order to approach very large practical instances we propose a two-phase method which produces close to optimal solutions. This modeling approach enables us to solve real-world problem instances with thousands of scheduled trips by direct application of standard optimization software. Furthermore, we introduce the concept of depot groups for the case that a bus may return in the evening into another depot than where it started in the morning.
90 citations
TL;DR: A comparison between a Production Control Strategy (PCS) from each approach, and a comparison of the performance of several pull-type production control strategies in addressing the Service Level vs. WIP trade-off in an environment with low variability and a light-to-medium demand load are presented.
Abstract: In order to overcome the disadvantages of Kanban Control Strategy (KCS) in non-repetitive manufacturing environments, two research approaches have been followed in the literature in past two decades. The first approach has been concerned with developing new, or combining existing, pull-type production control strategies in order to maximise the benefits of pull control while increasing the ability of a production system to satisfy demand. The second approach has focused on how best to combine Just-In-Time (JIT) and Material-Requirements-Planning (MRP) philosophies in order to maximise the benefits of pull control in non-repetitive manufacturing environments. This paper provides a review of the research activities in these two approaches, presents a comparison between a Production Control Strategy (PCS) from each approach, and presents a comparison of the performance of several pull-type production control strategies in addressing the Service Level vs. WIP trade-off in an environment with low variability and a light-to-medium demand load.
62 citations
TL;DR: An approximate decomposition technique that is based on repeated application of the solution of a simpler single-stage problem is suggested that is compared to exact results in a numerical study.
Abstract: A multi-stage assembly network is considered. A number of end items should be delivered at a certain time. Otherwise a delay cost is incurred. End items and components that are delivered before they are needed will cause holding costs. All operation times are independent stochastic variables. The objective is to choose starting times for different operations in order to minimize the total expected costs. We suggest an approximate decomposition technique that is based on repeated application of the solution of a simpler single-stage problem. The performance of our approximate technique is compared to exact results in a numerical study.
56 citations
TL;DR: A solution to a class of problems where flexible machines take different parts to process from distinct dedicated input buffers and deposit produced parts into distinct dedicated output buffers with finite capacity.
Abstract: This paper presents an approximate analytical method for the performance evaluation of a production line with finite buffer capacity, multiple failure modes and multiple part types. This paper presents a solution to a class of problems where flexible machines take different parts to process from distinct dedicated input buffers and deposit produced parts into distinct dedicated output buffers with finite capacity. This paper considers the case of two part types processed on the line, but the method can be extended to the case of n part types. Also, the solution is developed for deterministic processing times of the machines which are all identical and are assumed to be scaled to unity. The approach however is amenable of extension to the case of inhomogeneous deterministic processing times. The proposed method is based on the approximate evaluation of the performance of the k-machine line by the evaluation of 2(k-1) two-machine lines. An algorithm inspired by the DDX algorithm has been developed and the validation of the method has been carried out by means of testing and comparison with simulation.
52 citations
TL;DR: An efficient approximation method to determine performance characteristics such as the throughput and mean sojourn times is developed, based on decomposition into two-station subsystems, the parameters of which are determined by iteration.
Abstract: In this paper we study multi-server tandem queues with finite buffers and blocking after service. The service times are generally distributed. We develop an efficient approximation method to determine performance characteristics such as the throughput and mean sojourn times. The method is based on decomposition into two-station subsystems, the parameters of which are determined by iteration. For the analysis of the subsystems we developed a spectral expansion method. Comparison with simulation shows that the approximation method produces accurate results. So it is useful for the design and analysis of production lines.
47 citations
TL;DR: This paper suggests useful propositions to develop efficient heuristic algorithms for solving the joint replenishment problem and develops two algorithms using these propositions.
Abstract: In many practical situations quantity discounts on basic purchase price exist, and taking advantage of these can result in substantial savings. Quantity discounts have been considered in many production and inventory models. But unlike other research areas, there have been no studies to quantity discounts in the joint replenishment problem. The purpose of this paper is to develop efficient algorithms for solving this problem. Firstly, we suggest useful propositions to develop efficient heuristic algorithms. Secondly, we develop two algorithms using these propositions. Numerical examples are shown to illustrate the procedures of these algorithms. Extensive computational experiments are performed to analyze the effectiveness of the heuristics.
46 citations
TL;DR: In this article, the authors present a methodology that a manufacturer can utilize to make its production and sourcing decisions, i.e., to decide how much to produce, when to produce and where to produce.
Abstract: We study a stochastic multiperiod production planning and sourcing problem of a manufacturer with a number of plants and/or subcontractors. Each source, i.e. each plant and subcontractor, has a different production cost, capacity, and lead time. The manufacturer has to meet the demand for different products according to the service level requirements set by its customers. The demand for each product in each period is random. We present a methodology that a manufacturer can utilize to make its production and sourcing decisions, i.e., to decide how much to produce, when to produce, where to produce, how much inventory to carry, etc. This methodology is based on a mathematical programming approach. The randomness in demand and related probabilistic service level constraints are integrated in a deterministic mathematical program by adding a number of additional linear constraints. Using a rolling horizon approach that solves the deterministic equivalent problem based on the available data at each time period yields an approximate solution to the original dynamic problem. We show that this approach yields the same result as the base stock policy for a single plant with stationary demand. For a system with dual sources, we show that the results obtained from solving the deterministic equivalent model on a rolling horizon gives similar results to a threshold subcontracting policy.
TL;DR: The method leads to determining the distribution function of the time required to complete a product in this system (called the manufacturing lead time) through solving a system of linear differential equations with non-constant coefficients, which is obtained from a related continuous-time Markov process.
Abstract: In this paper, we consider acyclic networks of queues as a model to support the design of a dynamic production system. Each service station in the network represents a manufacturing or assembly operation. Only one type of product is produced by the system, but there exist several distinct production processes for manufacturing this product, each one corresponding with a directed path in the network of queues. In each network node, the number of servers in the corresponding service station is either one or infinity. The service time in each station is either exponentially distributed or belongs to a special class of Coxian distribution. Only in the source node, the service system may be modeled by an $M/G/\infty $
queue. The transport times between every pair of service stations are independent random variables with exponential distributions. In method proposed in this paper, the network of queues is transformed into an equivalent stochastic network. Next, we develop a method for approximating the distribution function of the length of the shortest path of the transformed stochastic network, from the source to the sink node. Hence, the method leads to determining the distribution function of the time required to complete a product in this system (called the manufacturing lead time). This is done through solving a system of linear differential equations with non-constant coefficients, which is obtained from a related continuous-time Markov process. The results are verified by simulation.
TL;DR: A new heuristic algorithm for the pallet loading problem, the problem of packing the maximum number of identical rectangular boxes onto a rectangular pallet, is presented, based on a tabu search algorithm based on new types of moves.
Abstract: This paper presents a new heuristic algorithm for the pallet loading problem, the problem of packing the maximum number of identical rectangular boxes onto a rectangular pallet. The problem arises in distribution and logistics and has many practical applications. We have developed a tabu search algorithm based on new types of moves. Instead of moving individual boxes, we propose moving blocks, sets of boxes with the same orientation. We have tested our algorithm on the whole sets Cover I and Cover II, usually taken as a reference for this problem, and we obtain excellent results in very short computing times.
TL;DR: A flow line model consisting of machines with Cox-2-distributed processing times and limited buffer capacities and a two-machine subsystem is analyzed exactly and a larger flow lines are evaluated through a decomposition into a set of coupled two- machine lines.
Abstract: We describe a flow line model consisting of machines with Cox-2-distributed processing times and limited buffer capacities. A two-machine subsystem is analyzed exactly and a larger flow lines are evaluated through a decomposition into a set of coupled two-machine lines. Our results are compared to those given by Buzacott, Liu and Shantikumar for their “Stopped Arrival Queue Modell”.
TL;DR: In this article, an empirical law for calculating the lean level of buffering as a function of machine efficiency, line efficiency, the number of machines in the system, and CV up and CV down is introduced.
Abstract: In this paper, lean buffering (i.e., the smallest level of buffering necessary and sufficient to ensure the desired production rate of a manufacturing system) is analyzed for the case of serial lines with machines having Weibull, gamma, and log-normal distributions of up- and downtime. The results obtained show that: (1) the lean level of buffering is not very sensitive to the type of up- and downtime distributions and depends mainly on their coefficients of variation, CV up and CV down; (2) the lean level of buffering is more sensitive to CV down than to CV up but the difference in sensitivities is not too large (typically, within 20%). Based on these observations, an empirical law for calculating the lean level of buffering as a function of machine efficiency, line efficiency, the number of machines in the system, and CV up and CV down is introduced. It leads to a reduction of lean buffering by a factor of up to 4, as compared with that calculated using the exponential assumption. It is conjectured that this empirical law holds for any unimodal distribution of up- and downtime, provided that CV up and CV down are less than 1.
TL;DR: Using some branch and bound techniques, a new interactive method is developed for MOLFP that drastically reduces the computational effort needed, while providing guidance for the decision maker in the choice of his/her preferred solutions.
Abstract: Multiple objective linear fractional programming (MOLFP) is an important field of research. Using some branch and bound techniques, we have developed a new interactive method for MOLFP that drastically reduces the computational effort needed, while providing guidance for the decision maker in the choice of his/her preferred solutions. The basic idea of the computation phase of the algorithm is to optimize one of the fractional objective functions while constraining the others. Several linear programming problems, organized in a tree structure, are generated as the search evolves. The whole idea is simple and it results in a fast and very intuitive approach to exploring the non-dominated set of solutions in MOLFP, and eventually to finding the preferred solution.
TL;DR: This paper considers the notion of epsilon-efficient solutions and proposes several new methods for their generation and supporting theoretical results are established and a numerical example is provided.
Abstract: It is a common characteristic of many multiple objective programming problems that the efficient solution set can only be identified in approximation: as this set often contains an infinite number of points, only a discrete representation can be computed, and due to numerical difficulties, each of these points itself might, in general, be only approximate to some efficient point. From among the various approximation concepts, this paper considers the notion of epsilon-efficient solutions and proposes several new methods for their generation. Supporting theoretical results are established and a numerical example is provided.
TL;DR: A heuristic is presented that minimizes the relevant costs by making near-optimal production and inventory control decisions while target customer service levels are satisfied.
Abstract: This paper investigates a multi-product multi-machine production-inventory system, characterized by job shop routings and stochastic demand interarrival times, set-up times and processing times. The inventory points and the production system are controlled integrally by a centralized decision maker. We present a heuristic that minimizes the relevant costs by making near-optimal production and inventory control decisions while target customer service levels are satisfied. The heuristic is tested in an extensive simulation study and the results are discussed.
TL;DR: A slightly aggregated system model is developed and a special near-product-form solution is proposed that provides excellent approximations of relevant performance measures of closed loop two-echelon repairable item systems with repair facilities both at a number of local service centers and at a central location.
Abstract: In this paper we consider closed loop two-echelon repairable item systems with repair facilities both at a number of local service centers (called bases) and at a central location (the depot). The goal of the system is to maintain a number of production facilities (one at each base) in optimal operational condition. Each production facility consists of a number of identical machines which may fail incidentally. Each repair facility may be considered to be a multi-server station, while any transport from the depot to the bases is modeled as an ample server. At all bases as well as at the depot, ready-for-use spare parts (machines) are kept in stock. Once a machine in the production cell of a certain base fails, it is replaced by a ready-for-use machine from that base's stock, if available. The failed machine is either repaired at the base or repaired at the central repair facility. In the case of local repair, the machine is added to the local spare parts stock as a ready-for-use machine after repair. If a repair at the depot is needed, the base orders a machine from the central spare parts stock to replenish its local stock, while the failed machine is added to the central stock after repair. Orders are satisfied on a first-come-first-served basis while any requirement that cannot be satisfied immediately either at the bases or at the depot is backlogged. In case of a backlog at a certain base, that base's production cell performs worse. To determine the steady state probabilities of the system, we develop a slightly aggregated system model and propose a special near-product-form solution that provides excellent approximations of relevant performance measures. The depot repair shop is modeled as a server with state-dependent service rates, of which the parameters follow from an application of Norton's theorem for Closed Queuing Networks. A special adaptation to a general Multi-Class MDA algorithm is proposed, on which the approximations are based. All relevant performance measures can be calculated with errors which are generally less than one percent, when compared to simulation results.
TL;DR: Network Topology Dependencies are a class of externalities in the maintenance cost structure of infrastructure networks with applications to many network industries, including natural gas and water distribution pipelines and it is shown that they may be included to infrastructure maintenance decisions, if optimal maintenance is formulated as a Rhys-Balinski selection problem.
Abstract: Network Topology Dependencies (NTD) are a class of externalities in the maintenance cost structure of infrastructure networks with applications to many network industries, including natural gas and water distribution pipelines. It is shown that the above externalities may be included to infrastructure maintenance decisions, if optimal maintenance is formulated as a Rhys-Balinski selection problem. A unique contribution is that this risk management problem is analyzed from the point of view of integrating quantitative analysis to organizational and inter-organizational decision processes. Hence, the importance of various procedural requirements is established in addition to computational efficiency and numerical accuracy. In particular, the benefits of sensitivity analysis facilitation and of avoiding manipulability are stressed. The proposed solution process achieves all four requirements. Special attention is paid to the role of submodularity and antitone differences in sensitivity analysis.
TL;DR: The considered problem is described as a stochastic programming problem, which can easily cope with problems with arbitrary utility functions, multiple risky assets and many periods, and the derived model can help the investor to find robust consumption-investment decisions.
Abstract: This paper examines the modelling and solution method of complex multiperiod optimal consumption and investment problems with several kinds of constraints. Our work differs from previous results in several ways: typical market imperfections such as short sale constraints, proportional transaction costs are considered simultaneously; the MGARCH model is adopted to provide a satisfactory description of the time-varying behavior of stock returns; the CVaR constraint is introduced to control the wealth loss risk while maximizing the expected utility; it is assumed that the investor wants to maximize the expected utility of both his intermediate consumptions and his terminal wealth; most importantly, the considered problem is described as a stochastic programming problem, which can easily cope with problems with arbitrary utility functions, multiple risky assets and many periods. The derived model can help the investor to find robust consumption-investment decisions. The procedure to solve the resulting nonlinear stochastic optimization problem is discussed in detail. Numerical results show the suitability and promise of our methodology.
TL;DR: A general purpose analytical approximation method for the performance evaluation of a multi-stage, serial, echelon kanban control system to decompose the original system into a set of nested subsystems.
Abstract: We develop a general purpose analytical approximation method for the performance evaluation of a multi-stage, serial, echelon kanban control system. The basic principle of the method is to decompose the original system into a set of nested subsystems, each subsystem being associated with a particular echelon of stages. Each subsystem is analyzed in isolation using a product-form approximation technique. An iterative procedure is used to determine the unknown parameters of each subsystem. Numerical results show that the method is fairly accurate.
TL;DR: This model for the lot sizing problem with sequence dependent setup costs is limited to the case where production state between two consecutive periods is conserved only if the available capacity of the preceding period exceeds the minimum batch quantity.
Abstract: Fleischmann and Meyr (1997) develop a model for the lot sizing problem with sequence dependent setup costs. In this note we show that this model is limited to the case where production state between two consecutive periods is conserved only if the available capacity of the preceding period exceeds the minimum batch quantity. We generalize the model by modification.
TL;DR: The choice of the order quantities for a group of end items, each facing random demand in a period of interest, are considered, and a number of managerial insights are provided.
Abstract: This paper was motivated by a practical situation in the fast food industry. We consider the choice of the order quantities for a group of end items, each facing random demand in a period of interest. There is the additional option of having a limited number of units of unfinished stock that can be customized to produce any of the end items once demands materialize. Solution procedures are developed, the results of a large set of examples are presented, and a number of managerial insights are provided.
TL;DR: The approach has been developed for the specific planning situation at Ford Service Organisation in Germany and has shown to significantly improve the planning process with respect to quality of schedules, time-to-plan, and flexibility.
Abstract: We describe the concept and implementation of a decision support system for professional course scheduling which allows the planner to generate, evaluate, and compare different schedules obtained by different runs based on variations of the objective function and different strategies of blocking, i.e. pre-assigning certain subsets of courses. The core of the system is a population-based improvement heuristic, i.e. a specific genetic algorithm. We have implemented the decision support system as web-based service, i.e. a distributed client server system with a thin client, where communication between server and client is via the Internet. The approach has been developed for the specific planning situation at Ford Service Organisation in Germany and has shown to significantly improve the planning process with respect to quality of schedules, time-to-plan, and flexibility.
TL;DR: The idea is to exploit recent technological devices to move in reasonable times pieces from a machine to a common buffer area of the system and vice versa in such a way machines can avoid their blocking since they can send pieces to the shared buffer area.
Abstract: The paper addresses the problem of fully using buffer spaces in manufacturing flow lines. The idea is to exploit recent technological devices to move in reasonable times pieces from a machine to a common buffer area of the system and vice versa. In such a way machines can avoid their blocking since they can send pieces to the shared buffer area. The introduction of the buffer area shared by all machines of the system leads to an increase of production rate as demonstrated by simulation experiments. Also, a preliminary economic evaluation on a real case has been carried out to estimate the profitability of the system comparing the increase of production rate, obtained with the new system architecture, with the related additional cost.
TL;DR: Third degree stochastic dominance is usually defined by stating two separate conditions, but it is shown that the second condition is superfluous, because it is already implied by the first one.
Abstract: Third degree stochastic dominance is usually defined by stating two separate conditions. It is shown in this note that the second condition is superfluous, because it is already implied by the first one.
TL;DR: The paper presents sufficient conditions for the existence of stable profit sharing schemes using linear programming techniques, as stability is necessary to sustain the long term cooperation of investors.
Abstract: This paper examines profit sharing in cooperative investments where investors bundle their capital endowments to meet the capital requirements of long term investment projects. Furthermore, investors may reinvest intertemporal gains from existing projects into new projects. Focus is on stable allocation schemes as stability is necessary to sustain the long term cooperation of investors. The paper presents sufficient conditions for the existence of stable profit sharing schemes using linear programming techniques.
TL;DR: This paper introduces a simple econometric methodology for studying market conduct in prices and variety between rival brands of consumer goods markets and identifies Nash behavior in pricing and collusive behavior in variety among the two leading brands in the market.
Abstract: Decisions on product variety are a central part of the (strategic) marketing planning process of many consumer goods manufacturers. However, we have only limited information about the effects of product variety on competitive market conduct and profitability. In this paper, we introduce a simple econometric methodology for studying market conduct in prices and variety between rival brands of consumer goods markets. Our study follows the recent trend in empirical industrial organization, it is fully structural and starts from the specification of demand and supply functions. We introduce a number of different game theoretic regimes and characterize the equilibrium of each of these games. The equilibrium of each game is considered to be unique. On the basis of non-nested model selection, we can identify the form of competitive market conduct that is most suitable for the underlying data. Our empirical study identifies Nash behavior in pricing and collusive behavior in variety among the two leading brands in the market. The estimated parameters offer theoretically founded insight into the competitive rules in the market and the impact of prices and variety on profits.
TL;DR: The results show the significant impact of the ordering sequence on the average marginal revenue and that the GA is an effective and efficient method to search for a good sequence and can improve the profit margin of the MTO firm and satisfaction of its customers.
Abstract: This paper proposes a Genetic Algorithm (GA) in searching for a near-optimal sequence of jobs in a make-to-order (MTO) production system in order to maximize the average marginal revenue earned per bid in the bidding model that allows contingent orders. Even though the complexity of the sequencing problem is NP-hard by nature, it is found to be a key determinant in improving the capacity allocation and the expected tardiness cost for an arriving order. The model incorporates operational constraints and marketing policies to effectively reflect the interests of customers. A simulation study was conducted to analyze the relative performance of the proposed system in a finite horizon. The results show the significant impact of the ordering sequence on the average marginal revenue and that the GA is an effective and efficient method to search for a good sequence and can improve the profit margin of the MTO firm and satisfaction of its customers.