Minimizing setups and waste when printing labels of consumer goods

A real-world planning problem of a printing company is presented where different sorts of a consumer goods’ label are printed on a roll of paper with sufficient length. The printer utilizes a printing plate to always print several labels of same size and shape (but possibly different imprint) in parallel on adjacent lanes of the paper. It can be decided which sort is printed on which (lane of a) plate and how long the printer runs using a single plate. A sort can be assigned to several lanes of the same plate, but not to several plates. Designing a plate and installing it on the printer incurs fixed setup costs. If more labels are produced than actually needed, each surplus label is assumed to be “scrap”. Since demand for the different sorts may be heterogeneous and since the number of sorts is usually much higher than the number of lanes, the problem is to build “printing blocks”, i.e., to decide how many and which plates to design and how long to run the printer with a certain plate so that customer demand is satisfied with minimum costs for setups and scrap. This industrial application is modeled as an extension of a so-called job splitting problem which is solved exactly and by various decomposition heuristics, partly basing on dynamic programming. Numerical tests compare both approaches with further straightforward heuristics and demonstrate the benefits of decomposition and dynamic programming for large problem instances.

119 The Potential of Low-fidelity Arthroscopic Simulation Training in Trauma and Orthopaedic Surgery: A Systematic Review of Experimental Studies

Aim In an era of budget tightening and work-hour guideline reductions that have significantly reduced surgical exposure, low-fidelity arthroscopic simulators have an essential role to play in surgical training. The COVID-19 pandemic has only further amplified the need for alternative training models, as 91% of orthopaedic trainees have had elective procedures cancelled. The purpose of this systematic review is to synopsise the limited literature regarding the effectiveness of low-fidelity training models in the instruction of novices, and to formulate recommendations for future studies. Method The Embase, PubMed, Web of Science and Scopus databases were electronically searched. Studies from any year that described the use of orthopaedic, low-fidelity arthroscopic training models in novice populations were included. Questionnaires, case studies and review studies were excluded. Risk of bias assessments were also conducted for all studies. Results 16 studies were identified. Using the PRISMA algorithm, 6 studies were deemed relevant. A cross-study comparison revealed low-fidelity arthroscopic simulators reduced time to completion outcomes (P < 0.05), increased ASSET scores (P < 0.01) and confirmed face validity and transfer of skills (cadaver, live patients). Conclusions Low-fidelity simulator training significantly improves the arthroscopic performance of novices, without the high setup costs and practical constraints of high-fidelity equivalents. Low-fidelity arthroscopic simulators remain a promising training apparatus in an era of reduced surgical exposure (COVID-19). We have identified the need for consistent outcome measures with greater sample sizes across future studies. We recommend the use of standardised pre-intervention teaching (e.g., ABOS curriculum) and further transference, construct, and face validity evaluations to support future result interpretations.

Fuzzy reverse logistics inventory model of smart items with two warehouses of a retailer considering carbon emissions

Running the business smoothly for protecting the environment is a significant challenge, on which industries are trying something to do at their level best. Reverse logistics play an important role in system design by reducing environmental consequences and increasing economic and social impacts. Given the recent fluctuations of the market, the production cost and ordering cost are considered triangular fuzzy numbers in this study. Customers' demand is met at the right time, and there is no shortage of items; thus, attention can be paid to two warehouses of a retailer. The setup costs and deterioration costs of this system are affected by the learning effects, which lead to a decrease in the total cost. Inflation is a significant problem in the market because manufacturing, remanufacturing, and retailers are all affected. This study proposes a reverse logistics system model so that customers can resolve their complaints about defective items and carbon emissions under two warehouses. Numerical results show that the fuzzy model is more economically beneficial than the crisp model, finds that the crisp and fuzzy model saw a difference of 0.34% in total cost. Two numerical examples illustrate this study, and a sensitivity analysis is performed using tables and graphs.

Price Competition and Setup Cost

Few studies analyze the endogenous emergence of price competition in a new product market. This paper analyzes two differentiated products, an existing product and a newly introduced substitutable product, and investigates conditions under which a price competition endogenously emerges in a new product market in the context of a choice between engaging in price competition and holding price leadership. We demonstrate that Bertrand price competition emerges when the setup cost for the new product is high enough. This result implies that government policies reducing setup costs such as subsidies could change the type of competition to price leadership in a new product market.

The capacitated maximal covering location problem with heterogeneous facilities and vehicles and different setup costs: An effective heuristic approach

In this research, a maximal covering location problem (MCLP) with real-world constraints such as multiple types of facilities and vehicles with different setup costs is taken into account. An original mixed integer linear programming (MILP) model is constructed in order to find the optimal solution. Since the problem at hand is shown to be NP-hard, a constructive heuristic method and a meta-heuristic approach based on genetic algorithm (GA) are developed to solve the problem. To find the most effective solution technique, a set of problems of different sizes is randomly generated and solved by the proposed solution methods. Computational results demonstrate that the heuristic method is capable of producing optimal or near-optimal solutions in a rational execution time.