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College athletics are a multi-billion dollar industry in the United States, but how well do university athletic programs employ their resources? The question is germane in light of increasing costs of higher education and scrutiny of university budgets. This study furnishes a template to weigh the tradeoffs inherent in collegiate sports, which is the main contribution of the paper. A dataset of 117 American college football programs from 2011-2015 is analyzed using DEA and AHP methods to assess the efficiency and perception of these programs. The result is the aforementioned framework measuring the programs' success and answers the question of program efficiency.

An unmanned aerial vehicle (UAV), commonly known as a drone, offers the advantage of speed, flexibility, and ease in delivering goods to customers. They are particularly useful for tasks that are dull, hazardous, or dirty. Whether the use of drone delivery is beneficial to the environment and cost saving is still a topic under debate. Ideally, drones yield lower energy consumption and reduce greenhouse gas emissions, thus reducing the carbon footprint and enhancing environmental sustainability. In this research, we analytically study the impact of UAVs on CO2 emission and cost. We propose a mixed-integer (0–1 linear) green routing model for UAV to exploit the sustainability aspects of the use of UAVs for last-mile parcel deliveries. A genetic algorithm is developed to efficiently solve the complex model, and an extensive experiment is conducted to illustrate and validate the analytical model and the solution algorithm. We find that optimally routing and delivering packages with UAVs would save energy and reduce carbon emissions. The computational results strongly support the notion that using UAVs for last-mile logistics is not only cost effective, but also environmentally friendly.

This study examines the impact of a firm’s polluting activities (measured as environmental inefficiency) on the firm’s bond credit rating. We posit that firms with excessive polluting activities (i.e., a high level of environmental inefficiency) receive low bond ratings because prior research links pollution reduction to better firm performance and outcomes. Using a 29-year panel sample with 4969 firm-year observations (representing 310 unique firms) from 1987 to 2015, we find a significant negative relation between environmental inefficiency and bond ratings. Our results still hold after a battery of robustness checks. In addition, we find that our results are largely driven by firms that are not near a broad bond rating change (i.e., firms without a plus or minus specification in their bond ratings).

Purpose The purpose of this paper is to examine the impact of business volatility on employee performance. Design/methodology/approach The authors use regression analysis to examine the authors’ research question. Findings The results suggest that business volatility has a significant and positive impact on employee performance. Furthermore, the authors find that the relationship between business volatility and employee performance is stronger for larger firms and firms with higher labor intensity. Originality/value The study links and contributes to two streams of literature: employee/labor cost management from the accounting literature and business volatility from the management literature. Whether business volatility affects employee performance remains an interesting question that has not been definitively answered empirically. To the best of the authors’ knowledge, this is the first empirical study that directly examines the relationship between business volatility and employee performance at the firm level.

We develop a two-phase approach to solving the capacitated routing problem (CVRP) with stochastic demand. A nonlinear chance-constrained optimization model is solved to determine delivery quantities, and a tabu search metaheuristic is used to determine vehicle routes. The goal of this research is to assure that a logistics company would satisfy the demands of customers with a high probability, while minimizing the overall transportation cost. We introduce the concept of premium customers, who are guaranteed a higher level of service. We show that our chance-constrained method has some strategic advantages over the CVRP with recourse approach. We examine the possibility of the logistics company charging customers selectively with an additional service fee to assure a high level of service. Moreover, we provide managerial insight on when the best time is to pay for the premium membership. We present computational results on commonly studied small to large-scale CVRP instances. A simulation study is conducted to explore the performance of the proposed chance-constrained approach using the CVRP with recourse. We conclude that our chance-constrained CVRP model could serve a logistics company well when resource costs and service guarantees are of concern.

This study investigates the association between whether firms are near a broad bond rating change (a rating with a plus or minus specification) and their level of irresponsible corporate social responsibility (CSR) activities. Using a 24-year panel sample with 1182 U.S. firms and 11,335 firm-year observations, we find that firms near a broad bond rating change tend to reduce their irresponsible CSR activities more than firms that are not near a broad bond rating change. The results persist through a battery of robustness checks. Furthermore, we find that our results are stronger for high growth firms.

The Financial Accounting Standards Board (FASB) has greatly expanded the use of fair value instruments, which amplifies an increase of unverifiable assets and liabilities that do not have actively traded market prices. Prior research indicates that managers may have strong incentives to use discretion in estimating fair values of such assets and liabilities for his/her self-serving interests, leading to more agency conflicts. This study examines the impact of unverifiable net assets on reading difficulty of annual reports. We predict that annual reports of firms with more unverifiable assets and liabilities are more difficult to read because prior research links agency conflicts to increased reading difficulty. Our results show a significant positive relation between unverifiable net assets and reading difficulty, consistent with the agency theory. Our study has implications for revenue management because prior research links earnings management to annual report readability.

The system provides an automated and adaptive model selection process.The system predicts the stock price direction, rather than the forecasted level.Particle swarm optimization is used to reduce computation time.Denoising is used to deal with stock market volatility. Predicting the direction and movement of stock index prices is difficult, often leading to excessive trading, transaction costs, and missed opportunities. Often traders need a systematic method to not only spot trading opportunities, but to also provide a consistent approach, thereby minimizing trading errors and costs. While mechanical trading systems exist, they are usually designed for a specific stock, stock index, or other financial asset, and are often highly dependent on preselected inputs and model parameters that are expected to continue providing trading information well after the initial training or back-tested model development period. The following research leads to a detailed trading model that provides a more effective and intelligent way for recognizing trading signals and assisting investors with trading decisions by utilizing a system that adapts both the inputs and the prediction model based on the desired output. To illustrate the adaptive approach, multiple inputs and modeling techniques are utilized, including neural networks, particle swarm optimization, and denoising. Simulations with stock indexes illustrate how traders can generate higher returns using the developed adaptive decision support system model. The benefits of adding adaptive and intelligent decision making to forecasts are also discussed.

One of the primary elements of a sustainable manufacturing initiative is that of energy efficiency. Line balancing can be used to design efficient manufacturing systems for paced assembly lines when the operation times are known, but may provide inefficient assignments with variable task times. Thus, we propose the use of unpaced synchronous lines as an alternative to paced lines when there is considerable variability in task times. While a great deal of research has been conducted on the line-balancing problem for paced synchronous production lines as well as for unpaced asynchronous lines, relatively little has focused on the unpaced synchronous configuration, despite its practical relevance. This research addresses this type of production line, with stochastic task completion times, by formulating an appropriate model and developing and evaluating a variety of solution methodologies utilizing extreme value theory as well as simulation. Computational results are presented to gain insight into the design and operation of unpaced synchronous systems.

In a two-sided assembly line, tasks can be executed simultaneously on both sides of the line. One task cannot be started until both of its direct predecessors on the left and right sides are completed. Therefore, the start time of the task is the maximum of the two predecessors’ finish times. In many realistic situations, it is assumed that the task times are independent and normally distributed with known means and variances. However, the maximum of two normal variables is not normally distributed, but can be well approximated by results from extreme value theory. In this paper, we utilise these results to develop a solution methodology to balance two-sided assembly lines with stochastic task times, minimising the line length and the number of stations while guaranteeing all tasks are completed within the cycle time with a given confidence level.

Hazardous materials accidents during road transport have caused catastrophic losses to humans and the environment all over the world in recent years. Especially in an urban environment, heavy traffic congestion and road closures present serious and complex situations during hazmat transportation. The challenge of urban hazmat transportation is to determine the vehicle routes that minimize risk subject to cost considerations and road closures. This paper presents a bi-objective programming model and a heuristic algorithm to optimize the routing of hazardous materials transportation with road closure considerations. A model is formulated that minimizes hazmat risk and transportation cost subject to road closure constraints. A new heuristic algorithm is proposed to solve the bi-objective hazmat vehicle routing and it is applied to a realistic case study involving hazmat transportation in the highly populated metropolitan area of Shanghai, China.

In this paper, we focus on the relationship between operations-based variables (specifically, production speed, scrap rate and maintenance speed) and the manufacturing cost. These variables usually produce opposite influences on the variable cost and the fixed cost. For example, setting the production speed at a high level is beneficial for reducing the variable cost. However, maintaining the high speed incurs considerable fixed costs at the same time. Therefore, an optimization approach is necessary to determine the optimal values of the operational variables for minimizing the average cost. First, a discrete-event simulation procedure is designed for describing the stochastic production environment and for evaluating the settings. Then, an optimization approach based on the ordinal optimization (OO) philosophy and particle swarm optimization (PSO) is used to search in the continuous space of the operational variables. In this process, the optimal computing budget allocation technique is applied so as to fully utilize the computational resource and potentially save the computational time. Finally, numeric computations are conducted for verifying the effectiveness of the proposed algorithm. Sensitivity analysis and discussions are also presented.

The synchronization and coordination of material flows is a key element in the supply chain management. To analyze the effects of coordinated replenishment for components, we consider an assembly system with two component-suppliers, one supply-hub and one manufacturer, under stochastic final product demand. We propose three different strategies: (i) the decentralized replenishment, (ii) the coordinated replenishment without coordinated quantity, and (iii) the coordinated replenishment policy with coordinated quantity for infinite planning horizon. We propose optimal decisions for all strategies. Results show that policy (ii) is always better than policy (i). We further identify the conditions under which the third strategy outperforms the other two. Policy (iii) is better on cost saving and service level, only when it satisfies certain conditions. Numerical studies are conducted to validate the model and to derive managerial implications.

This paper evaluates both the optimal service level and optimal economic payout in service payout models. A service guarantee level is explicitly taken into consideration to obtain the optimal payout. In this study, we consider a generic model to provide insights into the dynamic interaction between the service guarantee and optimal payout levels. Parametric analyses show that when the demand payout coefficient is high, the impact of the payout is positive only if the payout rate is high enough.

The recent volatility in gasoline prices and the economic downturn have made the management of public transportation systems particularly challenging. Accurate forecasts of ridership are necessary for the planning and operation of transit services. In this paper, monthly ridership of the Metropolitan Tulsa Transit Authority is analyzed to identify the relevant factors that influence transit use. Alternative forecasting models are also developed and evaluated based on these factors—using regression analysis (with autoregressive error correction), neural networks, and ARIMA models—to predict transit ridership. It is found that a simple combination of these forecasting methodologies yields greater forecast accuracy than the individual models separately. Finally, a scenario analysis is conducted to assess the impact of transit policies on long-term ridership.

Synchronous production lines are those in which the timing of the job movement between stations is coordinated in such a way that all of the jobs are indexed simultaneously. Unpaced synchronous lines are synchronous lines that advance only when all the stations have completed their tasks. Numerous industrial applications have used unpaced synchronous lines, but little research has been conducted regarding unpaced synchronous production line-balancing problems. This research studies the unpaced synchronous production line-balancing problem with stochastic task-completion times, and develops a tabu-search metaheuristic approach to solve the problem using two methods of analysis: extreme value theory and simulation. Through the analysis of the computational results, we discover that unpaced synchronous production lines are quite efficient relative to paced lines.

Few papers have studied the optimal pricing and ordering with partial lost sales from a two-stage game theoretic perspective. This paper considers a supply chain game with a buyer and a seller through pricing and ordering models with partial lost sales. We evaluate the optimal solutions for the buyer and the seller and provide insights into both the deterministic and stochastic demand models. We also propose search algorithms to obtain the global optimum when convexity and concavity conditions are not satisfied.

Corporate scandals such as those at Tyco, Enron, and WorldCom have caused the decline of public trust in accounting and reporting practices. In response, US passed the Sarbanes Oxley Law (Sarbanes) in 2002, the most important corporate governance law since securities laws in 1930s. Section 302 of Sarbanes mandates corporations to ensure accurate financial disclosure and to take greater financial reporting and control responsibilities. Management is required to make an annual assertion regarding the design and effectiveness of company’s internal controls. Consequently, many internal auditing resources are stretched. The objective of this case study is to develop a multi-criteria decision making aid that can identify the most critical businesses units within a corporation. Using the aid, we can use efficiently and effectively the internal auditing resources. Internal audits determine if the accounting processes and systems are working as intended. It focuses on the reliability of the accounting data and evaluates business through financial, operational, and compliance review. It assesses the risk of asset loss, studies business processes, and identifies opportunities to improve efficiency and effectiveness. This study explores the potential of applying data envelopment analysis (DEA) and analytic hierarchy process (AHP) to determine the business units that need audit. Compared with conventional methods, the proposed combined model incorporates a much wider range of quantitative and qualitative criteria, and provides a more detailed and thorough study. The proposed evaluation framework is comprehensive and flexible and it shows great potential for internal audit prioritization and resource allocation.

In this paper, we examine two-level and three-level supply chains for a midsize US newspaper in the Midwest that delivers to households and businesses in over 200 zip codes that span across various states. We formally define this problem as an integrated newspaper production and distribution supply chain management problem. Using a combination of model formulation, simulation, and an improvement heuristic we address both the efficiency and robustness of the solution by considering the impact of the stochastic nature of various parameters in both production and distribution. A deterministic formulation of the open vehicle routing problem with time windows and zoning constraints is utilized for vehicle scheduling and routing. A generalized network assignment problem is used to generate initial solutions and a tabu search metaheuristic solution approach is applied to improve solutions satisfying zone constraints and various approximate probabilities of satisfying delivery deadlines. However, the production and distribution process involves stochastic phenomena. We use simulation to evaluate the performance of the proposed solution. We estimate the customer service level of delivering to locations before 4:00 am. We also discuss improvement procedures if the service level is unsatisfactory.

This paper presents a tabu search metaheuristic to aid in the coordination and synchronization of the production and delivery of multi-product newspapers to bulk delivery locations. The problem is modeled as an open vehicle routing problem with time windows and zoning constraints. The methodology is applied to the newspaper production and distribution problem in a major metropolitan area. Computational results are presented and show significant improvement relative to an existing newspaper company's operations.

The introduction of multiple, independent production lines has helped many firms to increase their production flexibility, provide for redundancy when equipment breaks down, reduce idle time and labor costs, and achieve many other benefits. This paper introduces and formalizes the multiple U-line balancing problem. Optimal solution methodologies are provided for Type I (minimize the number of stations for a given cycle time), Type II (minimize the cycle time for a given number of stations), and cost-minimization line-balancing problems. A branch-and-bound algorithm is also developed for the situation in which equipment requirements are dependent on the line configuration and the task assignment to stations. Computational results indicate that the greatest benefit of exploiting multiple lines occurs for smaller cycle-time problems that require higher output.

Unanticipated events may take place and disrupt demand and/or production in a supply chain. Conditional on the type, magnitude and duration of disruptions, changes may be called to revise the original production plan. We analyse different disruption scenarios and propose optimal production–inventory models for products facing demand and production disruptions. To lower the cost, we optimise the production run time, purchasing times and order quantity for the manufacturer. Numerical experiments are conducted to examine the influences of disruption time and magnitude on optimal production and purchasing decisions.

Many heuristics have been proposed for the assembly line balancing problem due to its computational complexity and difficulty in identifying an optimal solution. Still, the basic line balancing model fails to consider a number of realistic elements. The implementation of a Just-In-Time manufacturing system generally entails the replacement of traditional straight assembly lines with U-shaped lines. An important issue in the U-line balancing problem is the consideration of task time variability due to human factors or various disruptions. In this paper, we consider the stochastic U-line balancing problem. A hybrid heuristic is presented consisting of an initial feasible solution module and a solution improvement module. To gain insight into its performance, we analyze the heuristic under different scenarios of task time variability. Computational results clearly demonstrate the efficiency and robustness of our algorithm.

The effect of workflow interference is a major concern in facility layout design. Yet, despite the extensive amount of research conducted on the facility layout problem, very little has been done to incorporate interference as part of an overall approach to layout design. This paper examines the impact of workflow interference considerations on facility layout analyses. Linear and nonlinear integer programming formulations of the problem are presented. The structural properties of the resulting formulations, as applied to facility design, are investigated. Finally, a multi-objective approach to facility layout design is presented, incorporating the traditional distance-based objective with that of workflow interference.

In this paper we use a scatter search framework to solve the vehicle routing problem with time windows (VRPTW). Our objective is to achieve effective solutions and to investigate the effects of reference set design parameters pertaining to size, quality and diversity. Both a common arc method and an optimization-based set covering model are used to combine vehicle routing solutions. A reactive tabu search metaheuristic and a tabu search with an advanced recovery feature, together with a set covering procedure are used for solution improvement. Our approach led to a robust solution method, generating solution quality that is competitive with the current best metaheuristics.

The utilization of U-shaped layouts in place of the traditional straight-line configuration has become increasingly popular, with reported benefits of substantial improvements in productivity and quality. This paper examines the U-line balancing problem with stochastic task times. A chance-constrained, piecewise-linear, integer program is formulated to find the optimal solution. Various approaches used to identify a tight lower bound are also presented, as are operable extensions to the basic model. Computational results show that the proposed method is able to solve practical-sized problems.

This paper develops a metaheuristic for the vehicle-routeing and scheduling problem with soft time window constraints. A tabu search solution method is developed, which utilizes a mixed neighbourhood structure and an advanced recovery procedure to generate high-quality solutions. Computational results on test problems from the literature are reported. The metaheuristic achieves solutions that compare favourably with previously reported results.

This paper addresses the integration of purchasing and routing for a propane gas supply chain. The focus is on the wholesale side of the supply network. Rigorous solution methods using both set partitioning and tabu search are developed for obtaining optimal and near-optimal solutions for the purchasing/routing problem. The proposed methods are applied to a real-world propane distribution problem. The results of the study indicate that the potential annual cost savings through the integration of purchasing and routing decisions can range up to millions of dollars for large distributors. The methods can also be used strategically as an aid in reconfiguring the supply network involving depot locations, tanker fleet sizing, and allocation of capacity at supply terminals. The methodology could also be used to provide decision support for distributing other energy products such as heating oil.

In multiple-attribute decision-making, the overall values of alternatives would be interval numbers due to the inherent uncertain property of the problems in the ambiguous decision domain. Bryson and Mobolurin proposed the use of linear programming models to compute attribute weights and overall values of the alternatives in the form of interval numbers. The intervals of the overall values of alternatives are then transformed into points or crisp values for comparisons among the alternatives. However, transforming the overall values of the alternatives from intervals to points may result in information loss. In this paper, statistical distributions, normal distribution and uniform distribution, are placed on the intervals of attribute weights and attribute values of the alternatives. By adopting a simulation method, means together with standard deviations and correlations of the overall values of alternatives are used to conduct the comparisons. The proposed statistical approach simplifies and enriches Br...

Although many authors have noted the importance of minimizing workflow interference in facility layout design, traditional layout research tends to focus on minimizing the distance-based transportation cost. This paper formalizes the concept of workflow interference from a facility layout perspective. A model, formulated as a quartic assignment problem, is developed that explicitly considers the interference of workflow. Optimal and heuristic solution methodologies are developed and evaluated.

Over the past years, considerable advances have been made in computational intelligence. Literally hundreds of papers have been written on applying intelligent technologies and/or algorithms for va...

Radio-frequency identification (RFID), as the key technology of Internet of Things (IoT), has been hailed as a major innovation to solve misplaced inventory and reduce lead-time. Many retailers have been pushing their suppliers to invest this technology. However, its associated costs seem to prohibit its widespread application. This paper analyzes the situation of service level in a retail supply chain, which has resulted from misplaced inventory and lead-time. By newsvendor model, we analyze the difference between with- and without-RFID technologies in service level of centralized and decentralized supply chains, respectively. Then with different service levels, we determine the tag cost thresholds at which RFID technology investment becomes profitable in centralized and decentralized supply chains, respectively. Furthermore, we apply a linear transfer payment coefficient strategy to coordinate with the decentralized supply chain. It is found that whether the adoption of RFID technology improves the service level depends on the cost of RFID tag in the centralized system, but it improves the service level in the decentralized system when only the supplier bears the cost of RFID tag. Moreover, the same cost thresholds of RFID tag with different service levels exist in both the centralized and the decentralized cases.

Cellular manufacturing has received a considerable amount of attention in the research literature as an approach for improving the performance of manufacturing facilities. However, recent studies have shown that cellular layouts are not always superior to the traditional functional machine layout. We propose a model that does not require the machines to be placed in a functional layout or in a cellular arrangement, but allows the material flow requirements to dictate the machine placement. The model is formulated as an aggregation of the quadratic assignment problem and several network flow problems coupled with linear side constraints. A mixed integer program is presented to find the optimal solution for small problems, and heuristics are developed to solve larger problems. Computational results evaluating the quality of the solution methodologies are also presented.

In this paper, we implement a tabu search heuristic, a probabilistic tabu search heuristic, a simulated annealing heuristic, and a hybrid tabu search heuristic to solve facility layout problems with the Quadratic Assignment Problem formulation Classical problems are used as a benchmark for performance comparison. Extensive computational experiments were conducted, and the results were statistically analyzed to observe the behavior of each heuristic and to discriminate the performance of each heuristic. The computational results strongly support the competitiveness of intelligent heuristics for the design of facilities. The hybrid tabu search heuristic, combining the advantages from both tabu search and simulated annealing, can generally outperform their parent algorithms.

This paper describes the application of tabu search, a metaheuristic technique for optimization problems, to assembly line balancing problems. Four different versions of algorithms are developed. They all share the same tabu search strategy except that the first one uses the best improvement with task aggregation, the second one uses best improvement without task aggregation, the third one uses the first improvement with task aggregation, and the last one uses the first improvement without task aggregation. Computational experiments with these different search strategies have been performed for some assembly line problems from the open literature. The results show that tabu search performs extremely well. Except for a few cases, tabu search always finds optimal solutions.

This article develops a reactive tabu search metaheuristic for the vehicle routing and scheduling problem with time window constraints. Reactive tabu search dynamically varies the size of the list of forbidden moves to avoid cycles as well as an overly constrained search path. Intensification and diversification strategies are examined as ways to achieve higher quality solutions. The λ-interchange mechanism of Osman is used as the neighborhood structure for the search process. Computational results on test problems from the literature as well as large-scale real-world problems are reported. The metaheuristic achieves solutions that compare favorably with previously reported results.

The design of the facility layout of a manufacturing system, usually formulated and solved as a quadratic assignment problem (QAP), is of tremendous importance for its effective utilization. In this paper we discuss a new implementation of the tabu search metaheuristic to solve the QAP. Our tabu search implementation includes recency-based and long term memory structure, dynamic tabu list size strategies, and intensification and diversification strategies. The tabu search algorithm converges with a reasonable speed from any random initial solution to very good layouts. Our extensive computational experiments, including statistical analysis and library analysis, strongly support the superiority of our tabu search implementation (we refer to it as (CK)) over existing algorithms in the literature.