Showing papers in "OR Spectrum in 2009"

A framework for collaborative planning and state-of-the-art

Abstract: Inter-organizational supply chain management incurs the challenge to align the activities of all members which contribute to the value creation of a product or service offered to customers. In general, a supply chain faces the “problem” of information asymmetry, members having their own objectives and constraints which may be in conflict with those of the other members. Still, activities have to be aligned in such a way that the supply chain as a whole stays or becomes competitive while each member wins by cooperating. A number of collaborative planning schemes have been put forward in the last two decades with different assumptions and different areas of application. This paper intends to provide a framework and an overview on the state-of-the-art of collaborative planning. The criteria of the framework will allow us to position existing concepts and to identify areas where more research is needed. The focus of the literature reviewed here will be on model-based decision support at the operational planning level.

Delivery strategies for blood products supplies

Abstract: We introduce a problem faced by the blood bank of the Austrian Red Cross for Eastern Austria: how to cost-effectively organize the delivery of blood products to Austrian hospitals? We investigate the potential value of switching from the current vendee-managed inventory set up to a vendor-managed inventory system. We present solution approaches based on integer programming and variable neighborhood search and evaluate their performance.

Simulation and optimization of supply chains: alternative or complementary approaches?

Abstract: Discrete-event simulation and (mixed-integer) linear programming are widely used for supply chain planning. We present a general framework to support the operational decisions for supply chain networks using a combination of an optimi- zation model and discrete-event simulation. The simulation model includes nonlinear and stochastic elements, whereas the optimization model represents a simplified ver- sion. Based on initial simulation runs cost parameters, production, and transportation times are estimated for the optimization model. The solution of the optimization model is translated into decision rules for the discrete-event simulation. This procedure is applied iteratively until the difference between subsequent solutions is small enough. This method is applied successfully to several test examples and is shown to deliver competitive results much faster compared to conventional mixed-integer models in a stochastic environment. It provides the possibility to model and solve more real- istic problems (incorporating dynamism and uncertainty) in an acceptable way. The limitations of this approach are given as well.

An integrated approach to the vehicle routing and container loading problems

Abstract: Real-world distribution problems raise some practical considerations that usually are not considered in a realistic way in more theoretical studies. One of these considerations is related to the vehicle capacity, not only in terms of cubic meters or weight capacity but also in terms of the cargo physical arrangements. In a distribution scene, two combinatorial optimization problems, the vehicle routing problem with time windows and the container loading problem, are inherently related to each other. This work presents a framework to integrate these two problems using two different resolution methods. The first one treats the problem in a sequential approach, while the second uses a hierarchical approach. To test the quality and efficiency of the proposed approaches, some test problems were created based on the well-known Solomon, Bischoff and Ratcliff test problems. The results of the integrated approaches are presented and compared with results of the vehicle routing problem with time windows and the container loading problem applied separately.

Multi-criteria location planning for public facilities in tsunami-prone coastal areas

Abstract: We present a model for multi-objective decision analysis with respect to the location of public facilities as schools in areas near to coasts, taking risks of inundation by tsunamis into account. A mathematical programming formulation with three objective functions is given. The first objective function is a weighted mean of a minisum and a maximum coverage criterion. The second objective function expresses risk by possible tsunami events; for quantifying this risk, a statistical model for tsunami occurrences by Kaistrenko and Pinegina is applied. The third criterion represents costs. For the solution of the multi-objective optimization problem, we propose a heuristic approach based on the NSGA-II algorithm and compare it with a decomposition technique where the region under consideration is partitioned into smaller sub-regions, and the problem is solved for each separate subregion either exactly or heuristically. Both approaches are tested on two real-life instances from southern Sri Lanka.

Lagrangean relaxation for the capacitated hub location problem with single assignment

Abstract: This article considers the capacitated hub location problem with single assignment. We propose a Lagrangean relaxation to obtain tight upper and lower bounds. The Lagrangean function that we formulate exploits the structure of the problem and can be decomposed into smaller subproblems that can be solved efficiently. In addition, we present some simple reduction tests, based on the Lagrangean relaxation bounds that allows us to reduce considerably the size of the formulation and thus, to reduce the computational effort. Computational experiments have been performed with both benchmark instances from literature and with some new larger instances. The obtained results are impressive. For all tested instances (ranging from 10 to 200 nodes), we obtain or improve the best known solution and the obtained duality gaps, between our upper and lower bounds, never exceed 3.4%.

A strategic and tactical model for closed-loop supply chains

Abstract: In this paper, a strategic location-allocation model is developed for the simultaneous design of forward and reverse supply chains. Strategic decisions such as network design are accounted for together with tactical decisions, namely, production, storage and distribution planning. The integration between strategic and tactical decisions is achieved by considering two interconnected time scales: a macro and a micro time. At macro level, the supply chain is designed in order to account for the existing demands and returns, whose satisfaction is planned simultaneously at the micro level where tactical decisions are taken. A Mixed Integer Linear Programming formulation is obtained which is solved to optimality using standard Branch & Bound techniques. Finally, the model accuracy and applicability is illustrated through the resolution of a case study.

Customer segmentation, allocation planning and order promising in make-to-stock production

Abstract: Modern advanced planning systems offer the technical prerequisites for an allocation of “available-to-promise” (ATP) quantities—i.e. not yet reserved stock and planned production quantities—to different customer segments and for a real time promising of incoming customer orders (ATP consumption) respecting allocated quota. The basic idea of ATP allocation is to increase revenues by means of customer segmentation, as it has successfully been practiced in the airline industry. However, as far as manufacturing industries and make-to-stock production are concerned, it is unclear, whether, when, why and how much benefits actually arise. Using practical data of the lighting industry as an example, this paper reveals such potential benefits. Furthermore, it shows how the current practice of rule-based allocation and consumption can be improved by means of up-to-date demand information and changed customer segmentation. Deterministic linear programming models for ATP allocation and ATP consumption are proposed. Their application is tested in simulation runs using the lighting data. The results are compared with conventional real time order promising with(out) customer segmentation and with batch assignment of customer orders. This research shows that—also in make-to-stock manufacturing industries—customer segmentation can indeed improve profits substantially if customer heterogeneity is high enough and reliable information about ATP supply and customer demand is available. Surprisingly, the choice of an appropriate number of priority classes appears more important than the selection of the ATP consumption policy or the clustering method to be applied.

A branch and bound algorithm for the strip packing problem

Abstract: We propose a new branch and bound algorithm for the two dimensional strip packing problem, in which a given set of rectangular pieces have to be packed into a strip of given width and infinite length so as to minimize the required height of the packing. We develop lower bounds based on integer formulations of relaxations of the problem as well as new bounds based on geometric considerations, and reduce the tree search with some dominance criteria. An extensive computational study shows the relative efficiency of the bounds and the good performance of the exact algorithm.

Development and simulation analysis of real-time yard crane control systems for seaport container transshipment terminals

Abstract: As more and more container terminals open up all over the world, terminal operators are discovering that they must increase quay crane work rates to remain competitive. In this paper, we develop a real-time yard crane control system and show that a terminal’s long-run average quay crane rate depends on the portion of this system that dispatches yard cranes in the storage area in real time. Several real-time yard crane dispatching systems are evaluated by a fully-integrated, discrete event simulation model of a pure transshipment terminal that is designed to reproduce the multi-objective, stochastic, real-time environment at an RTGC-based, multiple-berth facility. Results indicate that yard cranes should prioritize the retrieval of containers from the stacks, rather than the storage of containers into stacks. Also, the yard crane dispatching system should not only consider the trucks already waiting for service in the yard, but also the trucks that are heading towards the yard. The experiments provide the first direct connection in the literature between real-time yard crane control systems and long-run performance at a seaport container terminal. We also make a qualitative comparison between rule-based and look-ahead yard crane dispatching schemes, and discuss deadlocking issues in detail.

Modeling and optimizing of strategic and tactical production planning in the automotive industry under uncertainty

Abstract: This work considers the strategic flexibility and capacity planning under uncertain demands in production networks of automobile manufacturers. We present a deterministic and a stochastic model, which extend existing approaches, especially by an anticipation scheme for tactical workforce planning. This scheme is compared to an extended formulation of the deterministic model, which incorporates workforce planning via detailed shift models. The stochastic model is efficiently solved by an accelerated decomposition approach. The solution approach is integrated into a decision support system, which calculates minimum-cost product allocations and capacity plans. Our numerical results show that, in spite of the considerably increased complexity, our approach can efficiently handle hundreds of scenarios. Finally, we present an industrial case study.

Models and algorithms for solving combined vehicle and crew scheduling problems with rest constraints : an application to road feeder service planning in air cargo transportation

Abstract: We present a mathematical model and two algorithms for solving a complex combined vehicle and crew scheduling problem. The problem arises in the area of road feeder service (RFS) for air cargo transportation where cargo airlines engage specifically equipped RFS-carriers to serve so-called lines, i.e. regular weekly patterns of trips starting and ending at the central hub, respectively. The complexity of the problem stems from the time windows, the rest regulations for drivers and the highly heterogenous requirements with respect to the fleet. The model can be applied to different planning scenarios at the RFS-carrier as well as the airline. The model and method has been incorporated into a decision support system called block.buster where sequences of single trips are combined to feasible blocks starting and ending at the hub and then combined to feasible vehicle round trips.

Revenue management and demand fulfillment: matching applications, models, and software

Abstract: Recent years have seen great revenue management successes, notably in the airline, hotel, and car rental businesses Currently, an increasing number of industries, including manufacturers and retailers, are exploring ways to adopt similar concepts Software companies are taking an active role in promoting the broadening range of applications Additionally technological advances, including smart shelves and radio frequency identification (RFID), are removing many of the barriers to extended revenue management The rapid developments in supply chain planning and revenue management software solutions, scientific models, and industry applications have created a complex picture, which is not yet well understood It is not evident which scientific models fit which industry applications and which aspects are still missing The relation between available software solutions and applications as well as scientific models appears equally unclear The goal of this paper is to help overcome this confusion To this end, we structure and review three dimensions, namely applications, models, and software Subsequently, we relate these dimensions to each other and highlight commonalities and discrepancies This comparison also provides a basis for identifying future research needs

A heuristic for the dynamic multi-level capacitated lotsizing problem with linked lotsizes for general product structures

Abstract: In this paper, a new model formulation for the dynamic multi-level capacitated lotsizing problem with linked lotsizes is introduced. Linked lotsizes means that the model formulation correctly accounts for setup carryovers between adjacent periods if production of a product is continued in the next period. This model formulation is a good compromise between the big-bucket and small-bucket model formulation in that it inherits the stability of a big-bucket model and at least partially includes the precise description of setup operations provided by a small-bucket model. A Lagrangean heuristic is developed and tested in a numerical experiment with a set of invented data and a data set taken from industry. The solutions found show a good quality.

Inventory reservation and real-time order promising in a Make-to-Stock system

Abstract: In this paper we consider a Make-to-Stock order fulfillment system facing random demand with random due date preferences from two classes of customers. We develop an integrated approach for reserving inventory in anticipation of future order arrivals from high priority customers and for order promising in real-time. Our research exhibits three distinct features: (1) we explicitly model uncertain due date preferences of the customers; (2) we consider multiple receipts in the planning horizon that can be utilized to fulfill customer orders; and (3) we choose to utilize a service level measure for reserving inventory rather than estimating short- and long-term implications of order promising with a penalty cost function. We propose an algorithm that exploits the time structure in order arrivals and time-phased material receipts to determine inventory reservations for high priority orders. Numerical experiments are conducted to investigate the performance and the benefits of the inventory reservation and order promising approach under varying system parameters.

Vendor-managed inventory and the effect of channel power

Abstract: We analyze decentralized supply chains that follow general continuous review (Q, R) inventory policies subject to vendor-managed inventory agreements where the supplier chooses the order quantity Q, and the retailer chooses the reorder point R. Within the VMI scenario, we explore the effect of divisions of channel power on supply chain and individual agent performance by examining different game theoretic models. Optimal policies and analytical results, including existence and uniqueness proofs for equilibrium solutions under VMI, are derived. Numerical results are provided to compare the effectiveness of VMI and to analyze different channel power relationships under a variety of environmental conditions. We find that VMI can result in considerable supply chain savings over traditional relationships and that the relative division of channel power can significantly effect the performance of VMI. Interestingly, we find that the greatest system benefits from VMI arise in asymmetric channel power relationships, but that individual agents lack the incentive to assume a leadership role.

Strategic network planning for an international automotive manufacturer

Abstract: This article addresses the strategic network planning for international automotive manufacturers, in particular of premium cars. The focus is on the product to plant allocation and capacity expansion decisions for a given network design with fixed plant locations. A mixed integer program minimizing the net present value (NPV) of all capital expenditures and operational cost while incorporating flexibility of a network by a specific allocation structure (“chain”) is formulated. Computational illustrations on the influence of flexible allocation structures on the NPV are demonstrated considering changes in demand, exchange rates and total available capacity.

On a branch-and-bound approach for a Huff-like Stackelberg location problem

Abstract: Modelling the location decision of two competing firms that intend to build a new facility in a planar market can be done by a Huff-like Stackelberg location problem. In a Huff-like model, the market share captured by a firm is given by a gravity model determined by distance calculations to facilities. In a Stackelberg model, the leader is the firm that locates first and takes into account the actions of the competing chain (follower) locating a new facility after the leader. The follower problem is known to be a hard global optimisation problem. The leader problem is even harder, since the leader has to decide on location given the optimal action of the follower. So far, in literature only heuristic approaches have been tested to solve the leader problem. Our research question is to solve the leader problem rigorously in the sense of having a guarantee on the reached accuracy. To answer this question, we develop a branch-and-bound approach. Essentially, the bounding is based on the zero sum concept: what is gain for one chain is loss for the other. We also discuss several ways of creating bounds for the underlying (follower) sub-problems, and show their performance for numerical cases.

Setting safety stocks in multi-stage inventory systems under rolling horizon mathematical programming models

Abstract: This paper considers the problem of determining safety stocks in multi-item multi-stage inventory systems that face demand uncertainties. Safety stocks are necessary to make the supply chain, which is driven by forecasts of customer orders, responsive to (demand) uncertainties and to achieve predefined target service levels. Although there exists a large body of literature on determining safety stock levels, this literature does not provide an effective methodology that can address complex multi-constrained supply chains. In this paper, the problem of determining safety stocks is addressed by a simulation based approach, where the simulation studies are based on solving the supply chain planning problem (formulated as a mathematical programming model) in a rolling horizon setting. To demonstrate the utility of the proposed approach, an application of the approach at Organon, a worldwide operating biopharmaceutical company, will be discussed.

The use of bucket brigades in zone order picking systems

Abstract: As the transactions through electronic commerce and TV home shopping increase, the warehouses often receive a large amount of small orders to be picked within tight time windows One of the important warehousing activities is order picking, the process of retrieving a number of items from warehouse storage to meet a number of independent customer orders This paper examines a new order picking method, bucket brigade order picking (BB picking) Bucket brigade is a way of coordinating workers who progressively perform a set of operations on a flow line In the BB picking system, a worker performs operations on an order until the next worker downstream takes it over; then goes back to the previous worker upstream to take over a new order We discuss distinct characteristics in order picking systems when bucket brigades are applied We identify some efficiency losses under the BB picking and present a new BB picking protocol to improve the performance of order picking systems The new BB picking is compared with the existing BB picking and zone picking through simulation experiments

Value chain management for commodities: a case study from the chemical industry

Abstract: We present a planning model for chemical commodities related to an industry case. Commodities are standard chemicals characterized by sales and supply volatility in volume and value. Increasing and volatile prices of crude oil-dependent raw materials require coordination of sales and supply decisions by volume and value throughout the value chain to ensure profitability. Contract and spot demand differentiation with volatile and uncertain spot prices, spot sales quantity flexibility, spot sales priceȁ3quantity functions and variable raw material consumption rates in production are problem specifics to be considered. Existing chemical industry planning models are limited to production and distribution decisions to minimize costs or makespan. Demand-oriented models focus on uncertainty in demand quantities not in prices.We develop an integrated model to optimize profit by coordinating sales quantity, price and supply decisions throughout the value chain. A two-phase optimization approach supports robust planning ensuring minimum profitability even in case of worst-case spot sales price scenarios. Model evaluations with industry case data demonstrate the impact of elasticities, variable raw material consumption rates and price uncertainties on planned profit and volumes.

Managing product availability in an assemble-to-order supply chain with multiple customer segments

Abstract: In this article, we propose a novel availability management process called Available-to-Sell (ATS) that incorporates demand shaping and profitable demand response to drive better supply chain efficiency. The proposed process aims at finding marketable product alternatives in a quest to maintain a financially viable and profitable product portfolio, and to avoid costly inventory overages and shortages. The process is directly supported by a mathematical optimization model that enables on demand up-selling, alternative-selling and down-selling to better integrate the supply chain horizontally, connecting the interaction of customers, business partners and sales teams to procurement and manufacturing capabilities of a firm. We outline the business requirements for incorporating such a process into supply chain operations, and highlight the advantages of ATS through simulations with realistic production data in a computer manufacturing environment. The models featured in this paper have contributed to substantial business improvements in industry-size supply chains, including over $100M of inventory reduction in IBM’s server computer supply chain.

An integrated approach to inventory and flexible capacity management subject to fixed costs and non-stationary stochastic demand

Abstract: In a manufacturing system with flexible capacity, inventory management can be coupled with capacity management in order to handle fluctuations in demand more effectively. Typical examples include the effective use of temporary workforce and overtime production. In this paper, we discuss an integrated model for inventory and flexible capacity management under non-stationary stochastic demand with the possibility of positive fixed costs, both for initiating production and for using contingent capacity. We analyze the characteristics of the optimal policies for the integrated problem. We also evaluate the value of utilizing flexible capacity under different settings, which enable us to develop managerial insights.

A shelf-space-dependent wholesale price when manufacturer and retailer brands compete

Abstract: We propose a game-theoretic model involving the manufacturer of a national brand and a retailer selling her private label along with the national brand. The retailer can use either a differentiation strategy or an imitation strategy for offering her store brand. We consider two cases: the benchmark case, where both players have symmetric information and play a Nash game, and the incentive case, where the national brand’s manufacturer, acting as a leader, offers an incentive to the retailer in order to benefit from a larger proportion of the shelf space, which ultimately increases her own profit. By comparing both situations, we attempt to derive the conditions under which it is profitable for the manufacturer to implement such an incentive strategy and investigate if the results are idiosyncratic to the PL concept. These conditions are fourfold, and include the private label’s image, the price competition between the national brand and the private label, the transfer price level and the shelf-space allocated to the national brand in the benchmark case.

Pricing and lot-sizing decisions in a two-echelon system with transportation costs

Abstract: We consider a single-buyer single-supplier system. The market demand is sensitive to the selling price set by the buyer. Both the buyer and the supplier operate with unit product costs, inventory holding costs, and order placement costs. In addition, the buyer is responsible for the freight cost. We formulate a model for determining the optimal lot-sizing and pricing decisions. Existing models for the problem do not consider the transportation costs with price sensitive market demand, and determine the optimal decisions through an exhaustive search. We propose an approximate solution procedure, and report the computational results on the effectiveness of the proposed procedure.

Maximal covering location problem with price decision for revenue maximization in a competitive environment

Abstract: In this article, we extend the classical maximal covering model in a competitive environment by including a price decision. We formulate a revenue maximization model and propose two procedures to solve it. By a careful examination of the relationships between the maximal covering problems for different prices, we reveal interesting properties of the deduced revenue maximization model, leading to a full enumeration solution approach. With the help of two more properties we develop a second, more intelligent solution procedure. Computational experiments show promising results for a small, medium and large case study.

k-Shortest routing of trains on shunting yards

Abstract: We consider the problem of designing algorithmic support for k-best routing decisions in train shunting scheduling. A study at the Netherlands Railways revealed that planners like to interact with the solution process of finding suitable routes. Two types of interaction were required: the possibility of assigning specific tracks to a route and of preventing the assignment of specific tracks to a route. The paper develops insights in the structure of the cost matrix in this k-best optimization problem. These dominance results are used in a two stage k-shortest path algorithm to support this task of the shunting planners. The solution approach determines the optimal sequence of the tracks that manually have been added to the route and determines the k shortest paths in this network. The approach is implemented in a prototype of a support system for shunting planners. The required calculation times for practical instances of the problem with varying numbers of alternative solutions (k ≤ 8) and intermediate tracks (m ≤ 5) are between 0.1 and 1.4 s. These calculation times are acceptable to provide adequate support to the planners of these shunting yards.

Branching strategies to improve regularity of crew schedules in ex-urban public transit

Abstract: We discuss timetables in ex-urban bus traffic that consist of many trips serviced every day together with some exceptions that do not repeat daily. Traditional optimization methods for vehicle and crew scheduling in such cases usually produce schedules that contain irregularities which are not desirable especially from the point of view of the bus drivers. We propose a solution method which improves regularity while partially integrating the vehicle and crew scheduling problems. The approach includes two phases: first we solve the LP relaxation of a set covering formulation, using column generation together with Lagrangean relaxation techniques. In a second phase, we generate integer solutions using a new combination of local branching and various versions of follow-on branching. Numerical tests with artificial and real instances show that regularity can be improved significantly with no or just a minor increase of costs.

Supplier managed inventory in the OEM supply chain : the impact of relationship types on total costs and cost distribution

Abstract: We investigate the impact of four variants of supplier managed inventory on total costs and cost distribution in a capital goods supply chain consisting of a parts supplier who delivers parts to an original equipment manufacturer’s assembly plant. The four supplier managed inventory variants differ in the components of inventory costs that the supplier has to carry. The performance of the supplier managed inventory relationships is benchmarked with the situation where the assembly plant manages the inventories. Interesting managerial insights follow from this comparison.

Models for the schedule optimization problem at a public transit terminal

Abstract: This work deals with the proposal of some models for the schedule optimization problem for public transit networks. In particular, we consider the case of a transit terminal where passengers are supposed to split among different lines of a service, or even change mode of transportation in case of intermodal systems. Starting from a given schedule for the transit lines arriving at the terminal, the aim is to decide the optimal schedule for the output lines, in such a way to balance the operative costs of the service and the passenger waiting time at the transit terminal. We propose two different models for this problem, which present strong similarities with some well known combinatorial optimization models. Computational results are also presented, showing the suitability of the models to solve real case studies.