A computational study of risk-averse parameter effects on a 2-stage supply chain coordination under refund-dependent demand

This paper examines a 2-stage supply chain that features a buyback contract between manufacturer and retailer under uncertain demand and consumer returns policy with partial refund amount. The supply chain is optimized using the utility of profit that includes the mean and variance of profit. The optimal values of buyback price, wholesale price, and retailer's order quantity are determined for the coordination situation of the decentralized supply chain when its members are risk averse. Through a computational study, the impacts of the supply chain members' risk attitudes and refund amount on the optimal decisions are investigated for the uncoordinated supply chain where one of the agents makes off-optimal decision

Task time estimation in a multi-product manually operated workstation

Many manual workstations are designed so that a specific set of tasks to be completed on jobs are performed at the workstation. The subset of individual tasks executed on each job may vary, and a jobs total processing time at the workstation is the sum of those individual task times. Since estimates of the mean and variance of the individual task times are used to make operational and planning decisions, data should be collected on a regular basis to ensure accurate estimates. In this research, we apply a least-squares method and maximum likelihood estimation to estimate the mean and variance of individual task times at a manual workstation from total job-processing time data. Both methods assume that the time to execute individual tasks at a workstation can be treated as independent random variables whose distributions are the same regardless of what other tasks are executed on a job. The maximum likelihood method also assumes that these times are normally distributed. Efficient computational formulas developed for the least-squares method are ideal for use with an automatic data collection system, and both methods provide good estimates of mean task times that are adequate for planning and operational decisions.

Buyback contract in a risk-averse supply chain with a return policy and price dependent demand

This paper examines a two-stage supply chain where a retailer offers a return policy with partial refund to end customers, and a manufacturer offers a buyback contract for all unsold and returned items to the retailer to share the risk. Customer demand is stochastic and price dependent. Key performance measure is the utility of profit, a function of the mean and variance of profit. Supply chain members are risk averse. Optimal buyback price, wholesale price, and retailer's order quantity are determined for the supply chain under coordination. A computational study is conducted to investigate the impacts of the members' risk attitudes, refund amount, and retail price on the optimal decisions. Further analysis shows break-even points among utility values at different retail prices as the risk attitude parameters and refund amount change. These break-even points provide a guideline for the retailers to adjust the price to maximise the utility of profit

An Association Between Pledging Policies and the Financial Performance of Cassava Product Manufacturers

This paper involves a study to investigate the association between pledging policies by the government and financial performance of cassava product manufacturers in Thailand. A polynomial regression model is constructed to describe a key financial performance measure using a set of control variables and pledging policy variables. The control variables are obtained from financial statements of 58 starch manufacturers and 8 ethanol manufacturers that solely use fresh cassava roots as raw material during 2009-2014. Result from the model suggests an appropriate agricultural policy for the cassava product industry in Thailand

Heterogeneity-aware resource allocation in HPC systems

In their march towards exascale performance, HPC systems are becoming increasingly more heterogeneous in an effort to keep power consumption at bay. Exploiting accelerators such as GPUs and MICs together with traditional processors to their fullest requires heterogeneous HPC systems to employ intelligent job dispatchers that go beyond the capabilities of those that have been developed for homogeneous systems. In this paper, we propose three new heterogeneity-aware resource allocation algorithms suitable for building job dispatchers for any HPC system. We use real workload traces extracted from the Eurora HPC system to analyze the performance of our allocators when they are coupled with different schedulers. Our experimental results show that significant improvements can be obtained in job response times and system throughput over solutions developed for homogeneous systems. Our study also helps to characterize the operating conditions in which heterogeneity-aware resource allocation becomes crucial for heterogeneous HPC systems

Constraint Programming-Based Job Dispatching for Modern HPC Applications

HPC systems are increasingly being used for big data analytics and predictive model building that employ many short jobs. In these application scenarios, HPC job dispatchers need to process large numbers of short jobs quickly and make decisions on-line while ensuring high Quality-of-Service (QoS) levels and meet demanding timing requirements. Constraint Programming (CP) is an effective approach for tackling job dispatching problems. Yet, the state-of-the-art CP-based job dispatchers are unable to satisfy the challenges of on-line dispatching and take advantage of job duration predictions. These limitations jeopardize achieving high QoS levels, and consequently impede the adoption of CP-based dispatchers in HPC systems. We propose a class of CP-based dispatchers that are more suitable for HPC systems running modern applications. The new dispatchers are able to reduce the time required for generating on-line dispatching decisions significantly, and are able to make effective use of job duration predictions to decrease waiting times and job slowdowns, especially for workloads dominated by short jobs