A graph theory approach to subcontracting, machine duplication and intercell moves in cellular manufacturing

We address in this paper the problem of finding an optimal strategy for dealing with bottleneck machines and bottleneck parts in the cell formation process in group technology. Three types of economic decisions are considered: subcontracting, machine duplication and intercell moves. The problem is formulated as a minimum weighted node covering problem in a hypergraph, and we show that it can be solved in polynomial time by finding a maximum weighted stable set in a bipartite graph. We extend this result to cellular manufacturing systems in which the sequence of operations of each part is known in advance.     

Determination of nickel in standard rocks and glasses by photon activation analysis with 30-MeV bremsstrahlung

The determination of nickel in various silicate rocks and glasses by photon activation analysis with a linear electron accelerator is described. Simultaneous irradiation of the sample and comparative standards produces the ⁵⁸Ni(γ, n)⁵⁷Ni reaction, and a post-irradiation chemical separation is used in conjunction with Ge(Li) γ-spectrometry. Nickel abundances for ten standard silicate rocks and two elementally doped glasses are presented and compared with the data previously published. The method is quite simple and gives good reproducible results for nickel down to sub-p.p.m. levels.     

Green chemistry and sustainability metrics in the pharmaceutical manufacturing sector

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Supplier quality improvement: The value of information under uncertainty

We consider supplier development decisions for prime manufacturers with extensive supply bases producing complex, highly engineered products. We propose a novel modelling approach to support supply chain managers decide the optimal level of investment to improve quality performance under uncertainty. We develop a Poisson–Gamma model within a Bayesian framework, representing both the epistemic and aleatory uncertainties in non-conformance rates. Estimates are obtained to value a supplier quality improvement activity and assess if it is worth gaining more information to reduce epistemic uncertainty. The theoretical properties of our model provide new insights about the relationship between the degree of epistemic uncertainty, the effectiveness of development programmes, and the levels of investment. We find that the optimal level of investment does not have a monotonic relationship with the rate of effectiveness. If investment is deferred until epistemic uncertainty is removed then the expected optimal investment monotonically decreases as prior variance increases but only if the prior mean is above a critical threshold. We develop methods to facilitate practical application of the model to industrial decisions by a) enabling use of the model with typical data available to major companies and b) developing computationally efficient approximations that can be implemented easily. Application to a real industry context illustrates the use of the model to support practical planning decisions to learn more about supplier quality and to invest in improving supplier capability.     

An evaluation of semidefinite programming based approaches for discrete lot-sizing problems

The present work is intended as a first step towards applying semidefinite programming models and tools to discrete lot-sizing problems including sequence-dependent changeover costs and times. Such problems can be formulated as quadratically constrained quadratic binary programs. We investigate several semidefinite relaxations by combining known reformulation techniques recently proposed for generic quadratic binary problems with problem-specific strengthening procedures developed for lot-sizing problems. Our computational results show that the semidefinite relaxations consistently provide lower bounds of significantly improved quality as compared with those provided by the best previously published linear relaxations. In particular, the gap between the semidefinite relaxation and the optimal integer solution value can be closed for a significant proportion of the small-size instances, thus avoiding to resort to a tree search procedure. The reported computation times are significant. However improvements in SDP technology can still be expected in the future, making SDP based approaches to discrete lot-sizing more competitive.     

Resilient and agile engineering solutions to address societal challenges such as coronavirus pandemic

The world is witnessing tumultuous times as major economic powers including the US, UK, Russia, India, and most of Europe continue to be in a state of lockdown. The worst-hit sectors due to this lockdown are sales, production (manufacturing), transport (aerospace and automotive) and tourism. Lockdowns became necessary as a preventive measure to avoid the spread of the contagious and infectious “Coronavirus Disease 2019” (COVID-19). This newly identified disease is caused by a new strain of the virus being referred to as Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS CoV-2; formerly called 2019-nCoV). We review the current medical and manufacturing response to COVID-19, including advances in instrumentation, sensing, use of lasers, fumigation chambers and development of novel tools such as lab-on-the-chip using combinatorial additive and subtractive manufacturing techniques and use of molecular modelling and molecular docking in drug and vaccine discovery. We also offer perspectives on future considerations on climate change, outsourced versus indigenous manufacturing, automation, and antimicrobial resistance. Overall, this paper attempts to identify key areas where manufacturing can be employed to address societal challenges such as COVID-19.     

A Self‐contained Two‐electrode Nanosensor for Electrochemical Analysis in Aqueous Microenvironments

We describe the development, fabrication, and characterization of a novel two‐electrode nanosensor contained within the tip of a needle‐like probe. This sensor consists of two, vertically aligned, carbon structures which function as individual electrodes. One of the carbon structures was modified by silver electrodeposition and chlorination to enable it to function as a pseudo‐reference electrode. Performance of this pseudo‐reference electrode was found to be comparable to that of commercially available Ag/AgCl reference electrodes. The unmodified carbon structure was employed as a working electrode versus the silver‐plated carbon structure to form a two‐electrode sensor capable of characterizing redox‐active analytes. The nanosensor was demonstrated to be capable of electrochemically characterizing the redox behavior of para‐aminophenol (PAP) in both bulk solutions and microenvironments. PAP was also measured in cell lysate to show that the nanosensor can detect small concentrations of analyte in heterogenous environments. As the working and reference electrodes are contained within a single nanoprobe, there was no requirement to position external electrodes within the electrochemical cell enabling analysis within very small domains. Due to the low‐cost manufacturing process, this nanoprobe has the potential to become a unique and widely accessible tool for the electrochemical characterization of microenvironments.     

Optimal and heuristic lead-time quotation for an integrated steel mill with a minimum batch size

This paper presents a model of lead-time policies for a production system, such as an integrated steel mill, in which the bottleneck process requires a minimum batch size. An accurate understanding of internal lead-time quotations is necessary for making good customer delivery-date promises, which must take into account processing time, queueing time and time for arrival of the requisite volume of orders to complete the minimum batch size requirement. The problem is modeled as a stochastic dynamic program with a large state space. A computational study demonstrates that lead time for an arriving order should generally be a decreasing function of the amount of that product already on order (and waiting for minimum batch size to accumulate), which leads to a very fast and accurate heuristic. The computational study also provides insights into the relationship between lead-time quotation, arrival rate, and the sensitivity of customers to the length of delivery promises.     

An economic evaluation model of supply chain flexibility

With the advent of the supply chain management concepts, business communities have been realizing that being competitive as a single company is no longer adequate; instead, competitiveness requires consideration of all channels in the supply chain. Despite its importance, the availability of the literature addressing supply chain flexibility is still limited to date. Although relationships between various types of flexibilities have been established, the degree to which one type of flexibility affects the other types and the system performance remains to be investigated. In particular, there is a lack of rigorous analytical models elucidating the relationships between the degree of flexibility in a system and the system level of performance. In this paper, a supply chain flexibility model is developed comprising labor flexibility, machine flexibility, routing flexibility, and information technology, with total system flexibility measured by an economic index. Outputs from the model can assist in making suitable production decisions to produce multiple products under an uncertain environment. Example solutions are given. This paper can help economic evaluation when supply chain flexibility and the factors affecting flexibility are to be improved. It can also assist in making supply chain flexibility-promotion decisions.     

Optimal batch sizing in a multi-stage production system with rework consideration

In a production system, rework process plays an important role in eliminating waste and effectively controlling the cost of manufacturing. Determining the optimal batch size in a system that allows for rework is, therefore, a worthwhile objective to minimize the inventory cost of work-in-processes and the finished goods. In this paper, models for the optimum batch quantity in a multi-stage system with rework process have been developed for two different operational policies. Policy 1 deals with the rework within the same cycle with no shortage and policy 2 deals with the rework done after N cycles, incurring shortages in each cycle. The major components that play a role in minimizing this cost of the system are manufacturing setups, work-in-processes, storage of finished goods, rework processing, waiting-time, and penalty costs to discourage the generation of defectives. The mathematical structure of this rework processing model falls under a nonlinear convex programming problems for which a closed-form solution has been proposed and results are demonstrated through numerical examples, followed by sensitivity analyses of different important parameters. It is concluded that the total cost in policy 2 tends to be smaller than that in policy 1 at lower proportion of defectives if the in-process carrying cost is low. Policy 2 may be preferred when the work-in-process carrying cost is low and the penalty cost is negligible.