Operations Management Problems Research Articles

Math Programming based Reinforcement Learningfor Multi-Echelon Inventory Management

Reinforcement Learning has lead to considerable break-throughs in diverse areas such as robotics, games and many others. But the application to RL in complex real-world decision making problems remains limited. Many problems in Operations Management (inventory and revenue management, for example) are characterized by large action spaces and stochastic system dynamics. These characteristics make the problem considerably harder to solve for existing RL methods that rely on enumeration techniques to solve per step action problems. To resolve these issues, we develop Programmable Actor Reinforcement Learning (PARL), a policy iteration method that uses techniques from integer programming and sample average approximation. Analytically, we show that the for a given critic, the learned policy in each iteration converges to the optimal policy as the underlying samples of the uncertainty go to infinity. Practically, we show that a properly selected discretization of the underlying uncertain distribution can yield near optimal actor policy even with very few samples from the underlying uncertainty. We then apply our algorithm to real-world inventory management problems with complex supply chain structures and show that PARL outperforms state-of-the-art RL and inventory optimization methods in these settings. We find that PARL outperforms commonly used base stock heuristic by 51.3% and RL based methods by up to 9.58% on average across different supply chain environments.

Human resource management of small and medium-sized enterprises from the perspective of corporate governance

In the economic environment, the development process of small and medium-sized enterprises has many challenges, how to meet the challenges correctly and seize the opportunity has become a common problem faced by small and medium-sized enterprises. In this regard, enterprise human resources management has played an important role in the future development of small and medium-sized enterprises. This paper analyzes the challenges faced in the process of human resource management from the perspective of corporate governance. This paper focuses on the problems in personnel recruitment, employee training and performance management from the perspective of small and medium-sized corporate governance, and puts forward its own suggestions for improvement, namely, optimizing the organizational structure, strengthening the importance of the human resources department, formulating recruitment plans to standardize the recruitment process, enhancing the targeted and diverse training of employees, optimizing performance appraisal methods, etc., with the aim of allowing small and medium-sized enterprises to better achieve their own strategic objectives in the future economic development, while improving the efficiency of employees and achieving a win-win situation for enterprises and employees.

Perspective of Local Government on the Performance Assessment of District Sports and Leisure Centers

This paper addresses the problem of performance management of operate-transfer (OT) project finance for public sports and leisure centers (SLC) from the perspective of local government. This study contributes to use an evolutionary theory of competitive advantage and mixed-methods, including a modified Delphi method to develop the efficiency-based performance model (EPM) under OT project finance for the public SLCs. The total-factor framework disaggregating the efficiency into an innovative output surplus target ratio (OSTR) provides local governments with a contracted period to manage the SLCs through further specific improvement advice. This study further proposed the four-quadrant matrix formulated by long-term efficiency and short-term profitability to identify the benchmark and improvement directions. The empirical results indicated that there are fifteen SLCs located in the benchmark quadrant. This study provides policy makers in the local governments with a scientific reference to keep or drop the current operating private enterprise in the next concession period. The most underperforming SLCs could follow this proposed quadrant analysis and OSTR index, utilizing their internal resources to develop more attractive and reasonable-price exercise courses for participant growth.

Effect of Live Chat on Traffic-to-Sales Conversion: Evidence from an Online Marketplace

Over the years, online retailers have increasingly embraced live chat to improve customer service and facilitate transactions. We examine how online sellers’ use of live chat influences their online traffic conversion into sales. Like brick-and-mortar stores, a fundamental task for online stores is to convert traffic into purchases, an important problem in retail service operations management. We argue that live chat can increase online sales conversion by performing the functions of informing and persuading. We explore how the two roles of live chat interact with existing information regarding sellers and their products, such as product descriptions, ratings, price, and reviews, as these types of information also influence consumers’ perceived product quality and help mitigate uncertainties in product fit to their needs. We apply a random-coefficient model in a Bayesian hierarchical framework and test the model using a panel dataset from Taobao, the leading C2C platform in China. Our results suggest that live chat has a positive impact on conversion and that the strength of this positive effect depends on seller and product characteristics. The positive effect is stronger when product information on web pages is less comprehensive (where live chat manifests an informative role), and when the perceived value of the product is higher (where live chat manifests a persuasive role) with either a higher product rating or a lower product price. Our results provide relevant and useful implications for online merchants and platform owners.

How to avoid the 10 biggest problems in performance management

This article describes the 10 most common problems in performance management as it is used in organizations. Each problem is described, why it is a problem is discussed, along with the best ways to fix each. The hope is that this will help organizations design and implement performance management techniques so that they more effectively change employee behaviour.

Dynamic Routing with Tree Based Value Function Approximations

This paper is motivated from a collaboration with a start-up delivering on-demand health care services. In this setting, nurse practitioners need to be dynamically routed to patients' houses as service requests are received. We solve this problem using approximate dynamic programming and machine learning techniques. At every stage, an optimization problem needs to be solved to assign nurse practitioners to patients. We capture the future cost of having nurse practitioners positioned at certain locations around the city by estimating a cost-to-go function. Many previous approaches treat the cost-to-go function as a black box, which requires the enumeration of all possible actions to make a routing decision at each stage. This is an intractable problem in the reoptimization setting. Our approach overcomes this issue by approximating the cost-to-go function using a tree or tree ensemble, and leverages formulation techniques from Mixed Integer Optimization to solve the optimization problem at each stage sufficiently fast. Furthermore, this approach has the advantage of being more accurate relative to separable approximations used in the literature. We also apply this approach to several other online optimization problems in operations management which can be modeled through an underlying optimization formulation that needs to be solved at each stage, and show improvements relative to state-of-the-art methods.

A survey on skill-based routing with applications to service operations management

Service systems often feature multiple classes of customers with different service needs and multiple pools of servers with different skillsets. How to efficiently match customers of different classes with servers of different skillsets is of great importance to the management of these systems. In this survey, we review works on skill-based routing in queues. We first summarize key insights on routing/scheduling policies developed in the literature. We then discuss complications brought by modern service operations management problems, particularly healthcare systems. These complications stimulate a growing body of literature on new modeling and analysis tools. Lastly, we provide additional numerical experiments to highlight the complex nature of a routing problem motivated from hospital patient-flow management, and provide some useful intuition to develop good skill-based routing policies in practice. Our goal is to provide a brief overview of the skill-based routing research landscape and to help generate interesting research ideas.

Redefining quality in terms of value, risk and cost: a literature review

PurposeThe purpose of this paper is to present a literature review demonstrating that quality and its management are increasingly definable as a balancing act between value, risk and cost throughout the value stream, from product/service design to production and delivery, and purchaser decision-making. An original framework is presented showing this interplay across the value stream, referred to as the QVRC framework.Design/methodology/approachContent analysis is combined with bibliometric analytics, displayed via temporal graphs and citation networks. Reviewed literature is transdisciplinary, encompassing marketing, operations/quality and psychology sources. Core quality management methodologies are positioned on the framework illustrating their relative contribution to value, risk and cost management.FindingsThe QVRC framework is developed, and used as a basis for classifying models and methodologies associated with quality management. A set of propositions are developed, which, together with the framework, set an agenda for further research.Research limitations/implicationsNo literature review can capture the richness of discourses on terms as pervasive as value, risk and cost. This paper aims to present a systematic and reliable sampling of such literature.Practical implicationsThe resulting model can be applied to management tools, and to products and services.Originality/valueResearchers, particularly in marketing, have developed models of value, risk and cost in terms of products and services. However, delivering products that provide the appropriate value, risk and cost trade-off is an operations management problem. This is the first paper to combine value, risk and cost across the value stream showing how this interplay extends beyond product.

Anomalies in Probability Estimates for Event Forecasting on Prediction Markets

Innovative forecasting methods using new data sources have been developed to address various problems in operations management, such as demand, sales, and event forecasts. One of the methods for forecasting events consists of prediction markets where participants can take financial positions that may generate returns depending on whether certain events occur or not. Results in experimental psychology and behavioral economics have shown that individuals, including experts, can be subject to judgment bias when making probability estimates for future events. We examine, in this study, whether prediction markets are immune to such bias in estimating event probability. We find that even when there are large numbers of transactions and high volumes of trades, probabilistic fallacies still occur. Moreover, when they occur, they tend to be persistent over a certain period of time, and they tend to happen in situations similar to the ones where individual probabilistic fallacies are reported to occur. Our results have implications for the design of prediction markets and at the same time call for caution when using forecasts generated this way.

Scheduling with day shifts and breaks

AbstractTraditional scheduling assumes that production can continue around the clock unless machines are not available. This is not without question for labor‐intensive industries where operations are performed manually. Motivated by operation management problems arising in the assembly stage of aeroengines and in the paint‐spraying stage of wooden furniture, we consider a scheduling problem with day shifts and breaks. We transform the problem into a new bin‐packing problem and present some theoretical analyses. To meet the needs of practical applications, we propose a binary integer programming (BIP) formulation and a hybrid genetic algorithm (HGA) that is based on five heuristic algorithms and two postoptimization procedures. Computational results indicate that the BIP formulation is suitable for some data sets while the HGA is the most promising in that it saves about 37% of day shifts compared to the best of the five heuristic algorithms in the context of scheduling.

Dynamic Pricing and Inventory Control with Fixed Ordering Cost and Incomplete Demand Information

We consider the periodic review dynamic pricing and inventory control problem with fixed ordering cost. Demand is random and price dependent, and unsatisfied demand is backlogged. With complete demand information, the celebrated (s,S,p) policy is proved to be optimal, where s and S are the reorder point and order-up-to level for ordering strategy, and p, a function of on-hand inventory level, characterizes the pricing strategy. In this paper, we consider incomplete demand information and develop online learning algorithms whose average profit approaches that of the optimal (s,S,p) with a tight O (√T) regret rate. A number of salient features differentiate our work from the existing online learning researches in the OM literature. First, computing the optimal (s,S,p) policy requires solving a dynamic programming (DP) over multiple periods involving unknown quantities, which is different from the majority of learning problems in operations management that only require solving single-period optimization questions. It is hence challenging to establish stability results through DP recursions, which we accomplish by proving uniform convergence of the profit-to-go function. The necessity of analyzing action-dependent state transition over multiple periods resembles the reinforcement learning question, considerably more difficult than existing bandit learning algorithms. Second, the pricing function p is of infinite dimension, and approaching it is much more challenging than approaching a finite number of parameters as seen in existing researches. The demand-price relationship is estimated based on upper confidence bound, but the confidence interval cannot be explicitly calculated due to the complexity of the DP recursion. Finally, due to the multi-period nature of (s,S,p) policies the actual distribution of the randomness in demand plays an important role in determining the optimal pricing strategy p, which is unknown to the learner a priori. In this paper, the demand randomness is approximated by an empirical distribution constructed using dependent samples, and a novel Wasserstein metric based argument is employed to prove convergence of the empirical distribution.

Social network analysis in operations and supply chain management: a review and revised research agenda

PurposeSocial network analysis (SNA) seeks to manage the connections between entities through investigating and understanding behaviours and relationships. This study demonstrates the increasing relevance of social network approaches to solving contemporary and looming operations management (OM) and supply chain management (SCM) problems; including the coordination operations challenges raised by increased connectivity.Design/methodology/approachThe systematic literature review approach adopted here examines 63 papers in OM and SCM published between 2000 and 2019. To-date OM reviews on SNA have focussed on discussing archetypal supply chains, what differentiates this study is the focus on how value was created in other forms of chains and operations.FindingsThis study reveals that current SNA adoption in OM is dominated by a manufacturing style focus on linear, sequential value creation, with a resulting focus only on sequential interdependence. SNA studies on reciprocally co-ordinated value creation (e.g. many service and network operations) are shown to have been neglected and are linked to a new agenda on contemporary management issues.Research limitations/implicationsBeyond encouraging the use of SNA, this study seeks to re-orient SNA approaches towards how contemporary services and networks create value.Originality/valueThrough adopting a unique combination of approaches and frameworks, this study challenges extant work to offer a substantially revised agenda for SNA use in Operations and Supply Chain Management.

A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint

With the growing concern of energy shortage and environment pollution, the energy aware operation management problem has emerged as a hot topic in industrial engineering recently. An integrated model consisting of production scheduling, preventive maintenance (PM) planning, and energy controlling is established for the flow shops with the PM constraint and peak demand constraint. The machine’s on/off and the speed level selection are considered to save the energy consumption in this problem. To minimize the makespan and the total energy consumption simultaneously, a multi-objective algorithm founded on NSGA-II is designed to solve the model effectively. The key decision variables are coded into the chromosome, while the others are obtained heuristically using the proposed decoding method when evaluating the chromosome. Numerical experiments were conducted to validate the effectiveness and efficiency by comparing the proposed algorithm and the traditional rules in manufacturing plant. The impacts of constraints on the Pareto frontier are also shown when analyzing the tradeoff between two objectives, which can be used to explicitly assess the energy consumption.

PUBLIC POLICY PERFORMANCE MANAGEMENT ON SHAPING PUBLIC OPINION: METHODOLOGICAL ASPECTS

The article analyzes the methodological foundations of the study of performance management of public authorities on shaping public opinion. The Author considers various definitions of the concept of “public opinion”, considering that public opinion is an expression of certain morals and ethics of society and the attitude of social communities to the phenomena of public life, and social-psychological communicative phenomenon. Special attention is paid to strategies, concepts and approaches to the problem of public policy performance management.

Storage Location Assignment with Fuzzy PROMETHEE Method in Warehouse Systems with Uncertain Demand

Storage location assignment is an important decision problem in warehouse operations management. Because the main purpose of the storage location assignment system is to create various parameters to facilitate the diagnosis and positioning of the products in the warehouse. In addition, time spent on storage activities is an important factor in the demand cycle. However, uncertainty in product demands causes various problems in assigning products to storage locations. Therefore, it is necessary to research viable and sustainable tools in cases of uncertainty. One of these tools is fuzzy decision making methods, which are frequently used in the literature and give effective results. In this study, it is proposed to rank the products with the Fuzzy PROMETHEE (F-PROMETHEE) method under qualitative criteria for warehouse systems where the demand is uncertain and assign them to the most suitable storage locations according to this rank. The effectiveness of the proposed approach has been tested with a small sample problem. Criteria addressed for this problem are demand, profitability and sensitivity. Solution results have shown that the proposed approach is effective.

Национальные проекты – качественно новый уровень ответственности государства

The article reveals the approaches of the Russian Treasury to solving the problem of operational management of risks arising during the budget cycle, the most important parameters that affect the efficiency of the financial and budgetary system, by introducing information technologies in the activities of Control and Supervisory bodies. We are talking about increasing the degree of automation of control processes and increasing the level of openness of the government in the implementation of national projects, including those related to the creation of capital construction projects. The topic of functioning of the state integrated information system for public finance management ‘Electronic budget’, through which citizens can track the results of the state’s activities on federal and regional projects, is discussed in detail. As an effective tool for tracking control points of national (federal, regional) projects, it is proposed to introduce a unique identifier for the project result. It is important to create risk profiles for each individual project, for which the Russian Treasury, together with Rosfinmonitoring, has developed a mechanism for information interaction that allows identifying unscrupulous performers of state contracts. We are talking about the need for a risk-based approach to control, in which control and supervisory authorities can remotely analyze the object of control and coordinate their control and supervisory activities with other authorized bodies. An important condition for the sustainable development of the state is the solution of the problem of monitoring the achievement of national goals with minimal costs, without excessive pressure on the controlled environment.

Parallel or Sequential? Platforms' Search-Pattern Preference: The Role of Assortment

Problem Definition: Many digital platforms provide a search environment for consumers to evaluate sellers' products. We investigate a strategic platform's preference in search pattern (parallel pattern or sequential pattern) to keep in check consumers' search behavior and sellers' price and assortment reactions. Academic/Practical Relevance: Although both parallel and sequential patterns are prevalent in the online shopping environment, few studies have considered the platform's preferences in these search patterns, and implications in relevant operations management problems---sellers' assortment decisions in our paper. Methodology: We use the multinomial logit choice model to analytically explore the platform's preference in search pattern in anticipation that a specific pattern will affect the interactions between consumers and sellers. Consumers optimally choose the number of sellers to visit and the amount of product attributes to evaluate, and sellers optimally choose their prices and assortment levels, with all decisions being affected by which pattern the platform selects. Results: In our benchmark model with exogenous assortment level, our results show that the platform prefers parallel (sequential) pattern when the search cost is small (large) or when the assortment level is high (low). However, when the assortment level is a decision, the platform's preference will be altered qualitatively; that is, the platform prefers parallel (sequential) pattern when the search cost is large (small). We have identified several novel effects that are built off the fundamental difference between parallel and sequential patterns and use them to explain the platform's search-pattern preference. Interestingly, our paper shows that the platform can strategically use operational means (assortment prevention effect) and marketing means (pricing prevention effect) to manipulate consumers' search to maximize its profit. Managerial Implications: Our analytical predictions are consistent with several interesting observations in practice and shed some light on how a strategic platform designs its search environment and monetizes assortment management service.

Technical Note—Stochastic Optimization with Decisions Truncated by Positively Dependent Random Variables

In many operations management problems, the decisions are truncated by random variables. Take a dual sourcing inventory management problem as an example: the suppliers may have random capacities, and the actual received quantity from ordering is truncated by this random capacity. Often the random capacities of different suppliers may be dependent. An interesting challenge is that due to the truncation, the optimization problem may not be convex. In “Stochastic Optimization with Decisions Truncated by Positively Dependent Random Variables”, X. Chen and X. Gao propose a transformation technique to convert the original nonconvex minimization problem to an equivalent convex one. They demonstrate the application of their method using an inventory substitution problem with dependent random supply capacities and a two-part fee cost structure. In addition, their method can also incorporate the decision maker’s risk attitude.

Exploring challenges of boundary-crossing performance dialogues in hybrids

Literature on performance management in hybrid organisations remains lacking, even though they increasingly are providing public services. This study created new knowledge on performance management in hybrid organisations by answering a question that addressed what kinds of challenges are in hybrid organisation’s boundary-crossing performance dialogues. Boundary-crossing performance dialogues here refer to performance management discussions between representatives of public and private sector organisations forming the hybrid. To answer the proposed question, a case study approach based on interviews and documents was chosen. Using inductive content analysis, the study identified several challenges that occurred during the boundary-crossing dialogues. This contributes to existing research on performance management in hybrid organisations. For practitioners, the results provide insights for tackling the specific problems of performance management in hybrid organisations.

An Integrated Heuristic for Machine Sequencing With Specific Reference to the Permutation Flow-Shop Scheduling Problem

This article presents an integrated heuristic for the permutation flow-shop scheduling problem with the makespan criterion as one of the most widely studied classical sequencing problems in operations management. The proposed method consists of two phases: arranging the jobs in a priority order and then constructing a sequence by a job insertion principle. A fuzzy greedy evaluation function is employed to prioritize the jobs for incorporating into the construction phase of the proposed heuristic. It is shown that the developed method gives a significantly improved performance for a wide range of standard problems of varying sizes.