The influence of demand variability on the performance of a make-to-stock queue

Variability, in general, has a deteriorating effect on the performance of stochastic inventory systems. In particular, previous results indicate that demand variability causes a performance degradation in terms of inventory related costs when production capacity is unlimited. In order to investigate the effects of demand variability in capacitated production settings, we analyze a make-to-stock queue with general demand arrival times operated according to a basestock policy. We show that when demand inter-arrival distributions are ordered in a stochastic sense, increased arrival time variability indeed leads to an augmentation of optimal base-stock levels and to a corresponding increase in optimal inventory related costs. We quantify these effects through several numerical examplesproduction/inventory; make-to-stock; base-stock; stochastic comparisons; GI/M/1, POLICIES; COSTS; SYSTEMS; LEAD

Adjust or invest : what is the best option to green a supply chain?

Greening a supply chain can be achieved by considering several options. However, companies lack of clear guidelines to assess and compare these options. In this paper, we propose to use multiobjective optimization to assess operational adjustment and technology investment options in terms of cost and carbon emissions. Our study is based on a multiobjective formulation of the economic order quantity model called the sustainable order quantity model. The results show that both options may be effective to lower the impacts of logistics operations. We also provide analytical conditions under which an option outperforms the other one for two classical decision rules, i.e. the carbon cap and the carbon tax cases. The results allow deriving some interesting and potentially impacting practical insight

An Inserted α/β Subdomain Shapes the Catalytic Pocket of Lactobacillus johnsonii Cinnamoyl Esterase

Microbial enzymes produced in the gastrointestinal tract are primarily responsible for the release and biochemical transformation of absorbable bioactive monophenols. In the present work we described the crystal structure of LJ0536, a serine cinnamoyl esterase produced by the probiotic bacterium Lactobacillus johnsonii N6.2.We crystallized LJ0536 in the apo form and in three substrate-bound complexes. The structure showed a canonical α/β fold characteristic of esterases, and the enzyme is dimeric. Two classical serine esterase motifs (GlyXSerXGly) can be recognized from the amino acid sequence, and the structure revealed that the catalytic triad of the enzyme is formed by Ser(106), His(225), and Asp(197), while the other motif is non-functional. In all substrate-bound complexes, the aromatic acyl group of the ester compound was bound in the deepest part of the catalytic pocket. The binding pocket also contained an unoccupied area that could accommodate larger ligands. The structure revealed a prominent inserted α/β subdomain of 54 amino acids, from which multiple contacts to the aromatic acyl groups of the substrates are made. Inserts of this size are seen in other esterases, but the secondary structure topology of this subdomain of LJ0536 is unique to this enzyme and its closest homolog (Est1E) in the Protein Databank.The binding mechanism characterized (involving the inserted α/β subdomain) clearly differentiates LJ0536 from enzymes with similar activity of a fungal origin. The structural features herein described together with the activity profile of LJ0536 suggest that this enzyme should be clustered in a new group of bacterial cinnamoyl esterases

A Periodic Review Inventory Model subject to Shrinkage Type Errors: Impact of RFID

International audienc

Optimising reorder intervals and order-up-to levels in guaranteed service supply chains

We consider the problem of determining the optimal reorder intervals R and order-up-to levels S in a multi-echelon supply chain system where all echelons are assumed to have fixed ordering costs and to operate with a (R, S) policy with stationary nested power-of-two reorder intervals. By using the guaranteed service approach to model the multi-echelon system facing a stochastic demand, we formulate the problem as a deterministic optimisation model in order to simultaneously determine the optimal R and S parameters as well as the guaranteed service times. The model is a non-linear integer programming (NLIP) problem with a non-convex and non-concave objective function including rational and square root terms. Then, we propose a sequential optimisation procedure (SOP) to obtain near-optimal solutions with reasonable computational time. The numerical study demonstrates that for a general acyclic multi-echelon system with randomly generated parameters, the SOP is able to obtain near-optimal solutions of about 0.46% optimality gap in average in a few seconds. Moreover, we propose an improved direct approach using a global optimiser, bounding the decision variables in the NLIP model and considering the SOP solution as an initial solution. Numerical examples illustrate that this reduces significantly the computational time

Optimising reorder intervals and order-up-to levels in guaranteed service supply chains

We consider the problem of determining the optimal reorder intervals R and order-up-to levels S in a multi-echelon supply chain system where all echelons are assumed to have fixed ordering costs and to operate with a (R, S) policy with stationary nested power-of-two reorder intervals. By using the guaranteed service approach to model the multi-echelon system facing a stochastic demand, we formulate the problem as a deterministic optimisation model in order to simultaneously determine the optimal R and S parameters as well as the guaranteed service times. The model is a non-linear integer programming (NLIP) problem with a non-convex and non-concave objective function including rational and square root terms. Then, we propose a sequential optimisation procedure (SOP) to obtain near-optimal solutions with reasonable computational time. The numerical study demonstrates that for a general acyclic multi-echelon system with randomly generated parameters, the SOP is able to obtain near-optimal solutions of about 0.46% optimality gap in average in a few seconds. Moreover, we propose an improved direct approach using a global optimiser, bounding the decision variables in the NLIP model and considering the SOP solution as an initial solution. Numerical examples illustrate that this reduces significantly the computational time

Analysis of order-up-to-level inventory systems with compound Poisson demand

We analyse a single echelon single item inventory system where the demand and the lead time are stochastic. Demand is modelled as a compound Poisson process and the stock is controlled according to a continuous time order-up-to (OUT) level policy. We propose a method for determining the optimal OUT level for cost oriented inventory systems where unfilled demands are backordered. We first establish an analytical characterization of the optimal OUT level. The actual calculation is based on a numerical procedure the accuracy of which can be set as highly as desired. By means of a numerical investigation, we show that the method is very efficient in calculating the optimal OUT level. We compare our results with those obtained using an approximation proposed in the literature and we show that there is a significant difference in accuracy for slow moving items. Our work allows insights to be gained on stock control related issues for both fast and slow moving Stock Keeping Units (SKUs).Stock control Compound Poisson Queuing system Order-up-to-level Slow moving items