In manufacturing systems, there often exists a bottleneck machine whose capacity is equal to or less than the market demand. Any idle or waste time at the bottleneck machine directly impacts the output of the entire plant because it results in a loss of throughput. In order to maximize the capacity utilization by less setup losses at the bottleneck machine, the parts are often produced in batches. Traditionally, most batch sizing decisions are made based on the economic order quantity model where setup and inventory holding costs are considered. This paper presents an alternative method to determine batch size at a bottleneck machine. We present a new objective function and cost factors for batch sizing and investigate queuing and throughput models. A linear search algorithm is introduced to find the optimal throughput rate and batch size at the same time. Numerical examples are examined to see how the batching algorithm works.

The widespread outbreak of the novel coronavirus disease COVID-19 has posed an enormous threat to global public health. A different set of policy interventions has been implemented to mitigate the spread in most countries. While the use of personal protective equipment and social distancing has been specifically emphasized, South Korea has deployed massive testing and contact-tracing program from the early stage of the outbreak. This study aims at investigating the effectiveness of testing and contact-tracing to counter the spread of infectious diseases. Based on the SEICR (susceptible-exposed-infectious-confirmed-recovered) model, an agent-based simulation model is developed to represent the behavior of disease spreading with the consideration of testing and contact-tracing in place. Designed experiments are conducted to verify the effects of testing and contact tracing on the peak number of infections. It has been observed that testing combined with contact tracing may lower the peak infections to a great extent, and it can thus be avoided for the hospital bed capacity to be overwhelmed by infected patients. It is implied that an adequate capability of testing and contact-tracing may enable us to become better prepared for an impending risk of infectious diseases.

This study considers a two-stage supply chain (SC) consisting of a single supplier and a manufacturer. When the manufacturer introduces a new product to the market, both the manufacturer and supplier should install production capacity in advance. Since capacity building often takes a long time, the demand is uncertain at the time of capacity decision making. The supplier often makes a conservative decision on capacity building to avoid possible capital risks due to excess capacity, which leads to the so-called double marginalization problem. Various risk-sharing supply contracts have recently been studied in academia to overcome the double marginalization problem. However, most existing studies ignore the bargaining power of each SC member and capacity investment. This study aims to fill the research gap by including capacity investment and bargaining power in the supply contract process. A capacity cost-sharing (CCS) contract is introduced in which the manufacturer shares the supplier’s capacity investment risk. We investigate how to set the contract parameters in the CCS contract to coordinate the supply chain. It is found that the wholesale price and manufacturer’s CCS ratio are negatively proportional to each other, and the manufacturer’s expected profit increases as the CCS ratio (wholesale price) increases (decreases) in the coordinated CCS contract. We show that there exists a CCS contract leading to a coordinated supply chain for a specific range of bargaining power. We also present a new CCS contract for a supply chain with a risk-averse supplier. A numerical illustration is provided to clarify how the contract parameters are determined and to examine the effect of the contract parameters on SC performance. Managerial implications and possible future work are discussed.