Search

California Utility Firm Implements Innovative Model, Reducing Costs by 4% A California utility firm has successfully implemented a pioneering model to balance electricity demand and supply while minimizing costs. By utilizing direct load control contracts (DLCCs), the firm can reduce energy consumption during peak hours. Researchers developed an integer stochastic dynamic optimization problem that considers monthly and annual constraints, allowing for effective execution of DLCCs. Incorporating a "reduce-to-threshold" policy to flatten energy-consumption curves during high demand, the model was verified using real data from the California Independent System Operator. When implemented, the utility firm achieved an impressive cost reduction of approximately 4%. Sensitivity analysis was conducted to enhance customer experience and improve DLCC contract features. The success of this innovative model highlights the potential of DLCCs and advanced optimization techniques in the energy sector, offering a blueprint for other utility companies seeking to optimize grid stability and reduce costs. Balancing electricity demand and supply is one of the most critical tasks that utility firms perform to maintain grid stability and reduce system cost. Demand-response programs are among the strategies that utilities use to reduce electricity consumption during peak hours and flatten the energy-consumption curve. Direct load control contracts (DLCCs) are a class of incentive-based demand-response programs that allow utilities to assign "calls" to customer groups to reduce their energy usage by a prespecified amount for a given length of time. Given the rapid expansion of such contracts, in this paper, we develop an integer stochastic dynamic optimization problem for executing DLCCs that minimizes total system cost subject to monthly and annual constraints on the number of times and hours customers can be called. We develop a hierarchical approximation approach, which consists of an annual problem and monthly problems, to solve the DLCC implementation problem effectively and in a reasonable amount of time. Motivated by the practice in a large utility firm in California, we incorporate a reduce-to-threshold policy that attempts to flatten energy-consumption curves whenever demand exceeds a given threshold. We verified the quality of our proposed approach on real data from the California Independent System Operator, which is the umbrella organization of the utility firms in California, and measured the quality of our solution against a lower bound. A large utility firm in California implemented our model and informed us that the additional reduction in cost was approximately 4%. Our sensitivity analysis reports the impact of managerial concerns on some policies to enhance customer experience and provides insights for improving the features of DLCC contracts. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2021.0638. [ABSTRACT FROM AUTHOR]

We study direct load control contracts that utilities use to curtail customers' electricity consumption during peak-load periods. These contracts place limits on the number of calls and total number of hours of power reduction per customer per year as well as the duration of each call. The stochastic dynamic program that determines how many customers to call and the timing and duration of each call for each day is an extremely difficult (NP-hard) optimization problem. We design a scenario-based approximation method to generate probabilistic allocation polices in a reasonable amount of time. Our approach consists of three approximations: deterministic approximation of demand, discretization of the expected demand, and aggregation/disaggregation of the resources. We show the relative information error resulting from the deterministic approximation is O (1 / n) , the discretization error is O (1 / n) , and the aggregation/disaggregation error is O (1 / n) , where n represents the length of the horizon. Finally, we show the total relative error is O (1 / n). Our error analysis establishes that our approximation method is near optimal. In addition, our extensive numerical experiments verify the high quality of our approximation approach. The error, conservatively measured, is quite small and has an average and standard deviation of 8.6% and 1.4%, respectively. We apply our solution approach to the data provided by three major utility companies in California. Overall, our study shows our procedure improves the savings in energy-generation cost by 37.7% relative to current practices. This paper was accepted by Chung Piaw Teo, optimization. Funding: The authors thank the UCLA Ziman Center's Howard and Irene Levine Program in Housing and Social Responsibility and the Morrison Family Center for Marketing Studies and Data Analytics for generous funding. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.4493. [ABSTRACT FROM AUTHOR]

We study a new parts-procurement planning problem that is motivated by a global auto manufacturer (GAM) that practices mass customization. Because of the astronomically large number of producible configurations, forecasting their demand is impossible. Instead, firms forecast demand for options that constitute a vehicle. Requirements for many parts (up to 60%) are based on the combinations of options in a fully configured vehicle. The options' forecast, however, does not map into a unique configuration-level forecast. As a result, the options' forecast translates into ranges for many parts' requirements. The combined ranges of a set of parts are not always equal to the sum of the component ranges; they may be less. Determining parts ranges is a large-scale NP-hard problem. Large ranges and inaccurate calculation of these ranges can result in excess inventories, shortages in inventories, and suboptimal flexibility levels. We model and analyze the problem of allocating parts to suppliers and accurately computing the ranges to minimize procurement costs arising because of ranges. The range costs are assumed to be convex increasing. We perform extensive numerical analysis using a large set of randomly generated instances as well as eight industrial instances received from GAM to establish the quality of our approximation framework. Our proposed approach significantly reduces the error in range estimates relative to current industry practice. In addition, the proposed approach for allocations of parts to suppliers reduces joint-parts ranges by an average of 29.87% relative to that of current practice. This paper was accepted by Jeannette Song, operations management. [ABSTRACT FROM AUTHOR]

In "Mass Customization and 'Forecasting Options' Penetration Rates Problem,'" Fattahi et al. study a new problem motivated by a large auto manufacturer that produces an extremely large number of feasible configurations (end products). Because it is impossible to forecast the demand of individual configurations, companies forecast on options level. The current forecasting approach ignores rules for selecting options, and as a result, forecasts are frequently infeasible, which results in excess inventories, shortages, and customer dissatisfaction. An approach is presented that finds the best feasible forecast. The theoretical properties of this approach are analyzed, and its effectiveness is examined on a set of real instances. Auto manufacturers produce a very large number of feasible configurations that make it impossible to forecast the demand of individual configurations. What the companies do forecast is the penetration rate of each option, which is the percentage of cars that include that option. The current forecasting approach ignores rules for selecting options, and as a result, forecast penetration rates are frequently infeasible, which results in excess inventories, shortages, and customer dissatisfaction. The problem of determining the feasibility of the forecast penetration rates and finding the best feasible penetration rates in the case of infeasibility is NP hard. This problem is formulated as finding a point in the convex cone of the feasible configurations that has the minimum Euclidean distance to the forecast penetration rates. We present an approach, similar to a variant of the Frank–Wolfe method, that sequentially constructs the feasible region and stops when it finds the best feasible penetration rates. We analyze the theoretical properties of our approach and provide insights on its convergence rate. We also show the effectiveness of our approach on a set of real instances from a large auto manufacturer. [ABSTRACT FROM AUTHOR]

In "The Weighted Nonnegative Least-Squares Problem with Implicitly Characterized Points," A. Fattahi, S. Dasu, and R. Ahmadi study a new variation of nonnegative least-squares problem when the discrete points are implicitly known and there are an exponentially large number of them. This problem can have applications in manufacturing, machine learning, clustering, pattern recognition, and high-dimensional statistics. They design an effective solution approach with a lower bound and establish the convergence rate of the lower and upper bounds. The nonnegative least-squares (NNLS) problem is defined as finding the Euclidean distance to a convex cone generated by a set of discrete points in R n . In this paper, we study NNLS when the discrete points are implicitly known and there are an exponentially large number of them (e.g., the set of integer feasible solutions of a mixed-integer program). This problem is motivated by a large auto manufacturer that produces mass customized products where the products are configured by choosing a subset of features. In addition, this problem can have applications in other manufacturing settings, machine learning, clustering, pattern recognition, and statistics. The NNLS can be formulated as a convex optimization problem, and there are efficient algorithms in the literature for solving it. However, NNLS with implicit points cannot be solved using the existing methods. Therefore, we first present a surrogate problem that enables us to find the optimal solution to this problem. Second, we design a Frank–Wolfe-based solution approach for solving our surrogate problem. At each iteration k of our proposed methodology, we generate a feasible solution (upper bound) and show that the upper bound converges to the optimal value at O (1 / k) . Third, we develop a lower bound that enables us to estimate the optimality gap at each iteration and show that the lower bound also converges to the optimal value at O (1 / k) . Finally, we test the effectiveness of our approach on a set of simulated and real industrial instances. [ABSTRACT FROM AUTHOR]

We develop a model of repeated microcredit lending to study how group size affects optimal group-lending contracts with joint liability. In the setting being studied, a benevolent lender provides microcredit to a group of borrowers to invest in projects. The outcome of each risky project is not observable by the lender; therefore, if some of the borrowers default on their loan repayments, the lender cannot identify strategic default. The group will be entitled to a subsequent loan if total loan obligation is met. We characterize the optimal contract and determine the optimal size of the borrowers' group endogenously. We find that although joint liability contracts are feasible under a smaller set of parameter values than individual liability contracts, joint liability has positive effects on the borrowers' repayment amount and welfare. Our analysis also suggests that group size should increase with project risk. Furthermore, we analyze the effects of partial joint liability, less severe punishment, and project correlation on the feasibility and characteristics of joint liability contracts. Our results show that, first, although partial joint liability has a negative effect on the borrowers' repayment amount and welfare, it can increase the loan ceiling of joint liability when collusion is not as likely, or when borrowers have high discount factors. Second, less severe punishment does not affect the borrowers' repayment amount or welfare, but decreases the loan ceiling of joint liability. However, these negative effects created by partial joint liability and less severe punishment on the borrowers' repayment amount, borrowers' welfare, and loan ceiling can be offset by forming larger groups. Third, we also found that project correlation allows a higher loan ceiling in larger groups. [ABSTRACT FROM AUTHOR]

We study a microprocessor company selling short-life-cycle products to a set of buyers that includes large consumer electronic goods manufacturers. The seller has a limited capacity for each product and negotiates with each buyer for the price. Our analysis of their sales data reveals that larger purchases do not always result in bigger discounts. Instead, the discount curve is like an 'N.' While existing theories cannot explain this nonmonotonic pattern, we develop an analytical model and show that the nonmonotonicity is rooted in how sellers value capacity when negotiating with a buyer. Large buyers accelerate the selling process and small buyers are helpful in consuming the residual capacity. However, satisfying midsized buyers may be costly because supplying these buyers can make it difficult to utilize the remaining capacity, which is often too much for small buyers but not enough for large buyers. We briefly discuss the implications for capacity rationing and posted pricing as well as potential applications to other industries. This paper was accepted by Serguei Netessine, operations management. [ABSTRACT FROM AUTHOR]

Every year, companies that produce commercial tax preparation software struggle with thousands of state and federal changes to tax laws and forms. Three competitors dominate the market with its short selling season, and release delays slash profits. Tax authorities issue updates August-December, and all changes must be processed and incorporated before year end. Systematic resource allocation and process management are crucial yet problematic due to the volume and complexity of changes, brief production time frame, and feedback loops for bug resolution. A leading tax software provider asked us to propose systematic approaches for managing process flow and staffing development stages with the goal of releasing the new version on time at minimum cost. To that end, we developed deterministic models that partitioned tax forms into development groups and determined staffing levels for each group. Partitioning forms into groups simplified workflow management and staffing decisions. To provide a range of resource configurations, we used two modeling approaches. Numerical experiments showed that our models capture the salient features of the process and that our heuristics perform well. Implementing our models reduced company overtime hours by 31% and total workforce cost by 13%. [ABSTRACT FROM AUTHOR]

This research addresses two common tools for reducing product development lead times: overlapping of development stages and crashing of development times. For the first time in the product development literature, a formal model addresses both tools concurrently, thus facilitating analysis of the interdependencies between overlapping and crashing. The results exhibit the necessity of addressing overlapping and crashing concurrently, and exhibit general characteristics of optimal overlapping/crashing policies. The impact of different evolution/sensitivity constellations on optimal policies is investigated, and comprehensive guidelines for structuring development processes are provided. For the special case of linear costs, an efficient procedure is presented that generates the efficient time-cost trade-off curves and determines the corresponding optimal overlapping/crashing policies. The impact of key parameters and the robustness regarding their estimates is illustrated with a simple two-stage example. [ABSTRACT FROM AUTHOR]

In many theme parks, stores are located within major attractions to sell related merchandise. Sales at such stores form a significant portion of theme park profits. Typically, store sales depend upon visitor flows through the attraction, customer satisfaction with the attraction, and the merchandise at the store. In addition, such stores constitute a unique retail environment, as visitor flows to attractions can be managed and stores are not competitors, but belong to the same parent company. This provides the opportunity to increase store sales by interfacing park operations, which manages visitor flows by setting schedules and capacity of attractions, with the store-level merchandising process, which determines which and how much product to order. Motivated by a study at Universal Hollywood (USH), we develop a flow management model to link park operations with store-level merchandising. This model sets the capacities and schedules of the major attractions to increase visitor flows to high-profit retail areas subject to visitor satisfaction, capacity, scheduling, and flow-balance constraints. In addition, this model serves as an important tool to generate and evaluate various strategies aimed at increasing theme park profitability at USH. [ABSTRACT FROM AUTHOR]

Increasingly shorter product life cycles impel firms to design, develop, and market more products in less time than ever before. Overlapping of design and development stages is commonly regarded as the most promising strategy to reduce product development times. However, overlapping typically requires additional resources and can be costly. Our research addresses the trade-off between product development time and costs and introduces an algorithm to determine an appropriate overlapping strategy under different scenarios. The methodology developed was successfully employed at Rocketdyne Division of Rockwell International. [ABSTRACT FROM AUTHOR]

We examine the problem of parallel imports: unauthorized flows of products across countries, which compete with authorized distribution channels. The traditional economics model of a discriminating monopolist that has different prices for the same good in different markets requires the markets to be separated in some way, usually geographically. The profits from price discrimination can be threatened by parallel imports that allow consumers in the high-priced region some access to the low-priced marketplace. However, as this article shows, there is a very real possibility that parallel imports may actually increase profits. The basic intuition is that parallel importation becomes another channel for the authentic goods and creates a new product version that allows the manufacturer to price discriminate. We propose a two-country, three-stage model to quantitatively study the effects and strategies. In the third stage, and in the higher priced country where parallel fin- ports have entered, we characterize the resulting market seg- mentation. One segment of consumers stays with the authorized version as they place more value on the warranty and services that come with the authorized version. Mother segment switches to parallel imports because a lower price is offered due to lack of country-specific features or warranties. Parallel imports also generate a third and new segment that would not have bought this product before. Unlike counterfeits that are fabricated by imitators, all parallel imports are genuine and sourced from the manufacturer in the lower-priced country through authorized dealers. Therefore, the manufacturer's global sales quantity should increase, but profit may rise or fall depending on the relative sizes and profitability of the segments. A profit-maximizing parallel importer should set price and quantity in the second stage after observing the manufacturer's prices in both countries. There will be a threshold of across-country price gap above which parallel imports would occur. In the first stage, the manufacturer can anticipate the possible occurrence of a parallel import, its price and quantity, and its effect on authorized sales in each country to make a coordinated pricing decision to maximize the global supply chain profit. Under some circumstances the manufacturer should allow parallel imports and under others should prevent them. Through a Stackelberg game we solve for the optimal pricing strategy in each scenario. We then find in one extension that when the number of parallel importers increases, the optimal authorized price gap should narrow, but the prices and quantifies of parallel imports may rise or fall. In another extension, we find that when the manufacturer has other means such as monitoring dealers, differentiating designs, and unbundling warranties-to contain parallel imports, the authorized price gap can widen as a function of the effectiveness of non- pricing controls. In summary, parallel imports may help the manufacturer to extend the global reach of its product and even boost its global profit. If the manufacturer offers a discount version through its authorized dealers, it is running a high risk of confusing customers and tarnishing brand images. Parallel imports may cause similar concerns for the manufacturer, but unauthorized dealers are perceived as further removed from the manufacturer. Therefore, there is less risk of confusing consumers when parallel imports are channeled through unauthorized dealers. Furthermore, they are more nimble in diverting the product whenever their transshipment and marketing costs are small enough not to offset the authorized price gap and the valuation discount. This may explain why some manufacturers fiercely fight parallel imports, while others knowingly use this alternative channel. [ABSTRACT FROM AUTHOR]

Product development has become the focal point of industrial competition and is the cornerstone of long-term survival for most firms. One of the major management challenges in product development is to deal with development risk in the design process. In this paper we provide a strategic guideline as to how the design process should be managed and controlled. We describe how design reviews and engineering resources can be scheduled as the control mechanisms to operationally manage development risk. The methodologies developed are an integral part of a project to fundamentally restructure product design processes at Rocketdyne Division of Rockwell International, which designs and develops liquid-propellant rocket propulsion systems. [ABSTRACT FROM AUTHOR]

The growth of service industries and their impact on the U.S. economy have attracted considerable attention in recent years. While some service sectors, most notably airline and telecommunication industries, have been in the forefront of model development, the industry is rather fragmented, and similar rigor is lacking in most other sectors. This paper describes an application of a model-based approach to some of the short-term ride capacity and visitor flow issues faced by the Six Flags Magic Mountain (SFMM), a major national theme park. Specifically, we consider daily operations at the theme park and focus on the generation and evaluation of alternative strategies for managing ride capacities and visitor flow. Management of demand involves two aspects: (a) understanding customer preferences as revealed by routing behavior, and (b) using the model to evaluate the implications of changes in transition-behavior. A crucial component of the study relates to the empirical data collected. Besides verifying the validity of the models, these data provide several insights for developing schemes to manage the day-to-day operations of the park. The SFMM management was actively involved in various phases of this study and as a result has been introducing the proposed models in a phased manner. [ABSTRACT FROM AUTHOR]

This paper considers the problem of positioning component fixtures on the carriers of computer, numerically controlled dual delivery machines used for populating printed circuit boards with surface mounted technology. This reel positioning problem (RPP) is one of a series of optimization problems that are critical for improving system productivity and realizing the full potential of concurrent operations. We formulate the RPP as a mathematical program and establish its complexity. Since the problem is NP-complete we focus on the development of heuristics. Our solution procedure was prompted by engineering considerations that included concerns for minimizing the changes in the carrier direction and total movement. We also present encouraging results with test problems. The method has been implemented and achieved 7 to 8% reductions in cycle time. [ABSTRACT FROM AUTHOR]

This paper addresses a material staging problem arising from a dual delivery pick-and-place machine that is used for the assembly of printed circuit boards where components are attached using "surface mount technology." The staging problem of such machines involves allocation of component feeders to its two-feeder carriers along with the corresponding sets of vacuum nozzles to dispense the components. The usual managerial objective for the staging problem is minimization of the total time required to populate the circuit boards, i.e., the pick-and-place machine's cycle time. For the dual delivery pick-and-place machines this implies minimization of the workload imbalance between the two carriers and reduction of the number of vacuum nozzle changes required. In this paper, we develop algorithms to optimally solve the single and multiple product staging problem for the above managerial objectives. [ABSTRACT FROM AUTHOR]

The paper presents a formulation of an n-job, m-machine flowshop problem whose objective is to determine a processing sequence of jobs that minimizes total job idleness subject to meeting job deadlines. A mirror image problem is defined with the property that there is a one-to-one correspondence between the feasible schedules of the original problem and the feasible schedules of the mirror image problem. The mirror image problem is a traditional scheduling problem with a regular performance measure, whereas the performance measure in the original problem is not regular. The equivalence of the original problem is investigated. It concerns minimization of total job idleness in a 2-machine flowshop. For this NP-hard problem we study permutation schedules under sufficient conditions of feasibility. We present complexity results, dominance properties, bounding criteria and computational experience with a branch-and-bound procedure. [ABSTRACT FROM AUTHOR]

We consider a situation in which the manufacturing system is equipped with batch and discrete processors. Each batch processor can process a batch (limited number) of jobs simultaneously. Once the process begins, no job can be released from the batch processor until the entire batch is processed. In this paper, we analyze a class of two-machine batching and scheduling problems in which the batch processor plays an important role. Specifically, we consider two performance measures: the makespan and the sum of job completion times. We analyze the complexity of this class of problems, present polynomial procedures for some problems, propose a heuristic, and establish an upper bound on the worst case performance ratio of the heuristic for the NP-complete problem. In addition, we extend our analysis to the case of multiple families and to the case of three-machine batching. [ABSTRACT FROM AUTHOR]

This paper presents an operation partitioning problem (OPP) that arises from the design of an automated assembly system. To reduce the traffic flow of the system, the OPP assigns operations to machines so that the total number of movements of jobs between machines is minimized. This problem has applications in flexible manufacturing and VLSI design. In flexible manufacturing, OPP relates to a part grouping problem in which different parts are grouped into families. In VLSI design, this problem is related to a VLSI design problem in which a large circuit is partitioned into layers of small circuits, in this paper, we develop a simulated annealing heuristic that finds a near-optimal solution. Random problems are generated for examining the effectiveness of this heuristic. [ABSTRACT FROM AUTHOR]

This paper describes a line segmentation problem in a multistage, multimachine production system. The production facility can concurrently produce several types of circuit boards because each production stage consists of multiple machines. The items produced are categorized into families, and items belonging to the same family share the common major setup, while switching over from one family to another requires a major setup. The line segmentation problem determines an allocation of machines at each production stage to families so as to minimize the time to complete all jobs. As a result of segmenting the line, several minilines are formed which are dedicated to the production of items in each family. Forming dedicated minilines and producing the items in a family on the same line captures the benefits of group technology and focused factory. We first formalize the line segmentation problem as a quadratic integer programming problem, and establish its NP-completeness. Since the problem is NP-complete, we propose several heuristics to find a good solution. Lower bounding procedures are developed to show the quality of the feasible solution. We also provide bounds on the performance of the heuristic solutions, and then empirically evaluate their performance. [ABSTRACT FROM AUTHOR]

Firms that build flexibility into their manufacturing system gain a competitive edge from their ability to efficiently produce a mid-variety of products at mid-volumes. In order to realize the competitive edge of flexible systems, manufacturing management has to deal effectively with the greater complexity that flexibility brings about. One of the key factors for the success of a flexible system is the management of the product set flow. If the product flow is poorly managed, products may have long manufacturing lead times, and materials may spend a large amount of time in queues as work in process. In such a situation, most of the competitive potential of the flexible system may be lost. But if the flow of materials through the production/ assembly stations is carefully synchronized, with materials moving smoothly and continuously from one operation to the next, then it is possible to attain short manufacturing lead times and little waiting. We refer to this operating condition as synchronized flow. In this paper, we study how to attain a synchronized flow for product sets with different characteristics regarding process flexibility and consistency. [ABSTRACT FROM AUTHOR]

Consider a plant that has information about the arrival schedule of its "inputs" over a planning cycle. The plant has parallel production lines for processing multiple types of products. However, changeover cost and changeover time are incurred when a line changes from processing one type of products to a different type of products. We present a dynamic line allocation problem that determines an optimal line allocation so that the total relevant cost (changeover and waiting costs) is minimized. In this paper we analyze the complexity of the problem and develop three different heuristics for generating near-optimal allocations. [ABSTRACT FROM AUTHOR]

We consider the NP-hard, one-machine scheduling problem whose objective is to minimize the weighted sum of completion times, subject to meeting deadlines. We propose a new lower bound that dominates the bounds found in the literature. [ABSTRACT FROM AUTHOR]