International Journal of Supply and Operations Management
Volume:10 Issue: 4, Autumn 2023

Energy efficiency and process integration play a vital role in minimizing fossil fuel consumption and electricity demand within industrial processes. Therefore, experts have prioritized research on enhancing and promoting the thermal energy efficiency of this sector, with a specific emphasis on energy recovery and sustainability goals. Pinch analysis (PA) and exergy analysis (ExA) have been employed separately or in conjunction to optimize energy recovery and minimize the work potential losses (exergy loss). This paper demonstrates the effectiveness of a developed algorithm that handle the impact of ∆Tmin on energy and exergy targets in an automatic manner through a set of scripts. The scripts manipulate input data and intermediate data through loops in order to quantify and determine different energetic and exergetic quantities. The developed algorithm is testified using a literature case study in order to prove its validity. For δTmin in range [0,10] and step s =2, the algorithm performs the calculations for each δTmin in range ∆Tmin. The obtained results include the pinch analysis parameters such as the global pinch point temperature [Tpinch] as well as the minimum heating and cooling requirements ([Uhot] and [Ucool]). For the scripts devoted to the exergy concept, the algorithm determines all the exergy targets (rejection, requirement and avoidable losses). As a result for δTmin in ∆Tmin, the process external utilities Uhot and Ucool increased simultaneously from 6.85 and 4.39 MW to 12.2 and 9.75 MW with increment of δTmin, which means that the energy recovery and avoidable exergy losses reduced with respect to δTmin. For the exergy requirement and rejection targets, they increased simultaneously from 2.6602 and 1.3231 MW to 6.711 and 2.88 MW with δTmin increment, indicating the opportunity to design a system to recover work through turbine expansion. In addition to the originality of the interconnected scripts, the obtained results are in accordance with those in the literature, indicating the applicability of the developed algorithm

Emergency Departments (EDs) in hospitals typically aim to deliver accurate and rapid treatment to patients. The scheduling of patients in EDs is a challenging task that depends not only on the triage process but also on the availability of both human (staff) and material resources. In this paper, a real case study is conducted to tackle the issues coming from crowding and long waiting times for patients processing at the largest hospital in the region of Sfax (Tunisia). An integer programming formulation is proposed to minimize total patient waiting times (PWT) in EDs subject to procedural and staff availability constraints. Due to the large scale of the treated problem, a Genetic Algorithm (GA) is developed as a solution method. The efficiency of the presented approach is evaluated based on diverse sets of theoretically and randomly generated instances in a first way and on the actual data obtained from the real case study hospital in a second way. Results show significant improvements compared to the First Come First Served (FCFS) real case study’s rule. The decrease in patient waiting time ranges between 18.84 % to 27.45%.

Currently, the performance of outbound logistics processes is an important element for companies that would like to increase the level of customer satisfaction and improve the visibility of the supply chain, thus guaranteeing the quality and safety of products. On the other hand, the concept of smart logistics has been proposed as a technological solution that aims to improve performance, security and traceability in the logistics of companies. A crucial element to achieve this goal is to benefit from the emergence of the Internet of Things (IOT) and related technologies. Indeed, IOT streamlines the logistics process and improves its efficiency. However, to track and trace a product’s life cycle, its physical state, associated activities, and involved objects, a large amount of heterogeneous data will be generated from various sources, especially from sensors and RFID (Radio Frequency Identification) tags. Moreover, the observations produced by these sources are made available with heterogeneous vocabularies and data formats. This heterogeneity creates interoperability problems and prevents the adoption of generic solutions on a global scale which make it difficult to reuse data for other purposes and share them among different stakeholders. To address this challenge, we propose in this work an approach based on semantic modeling, using the semantic web and ontologies, to improve the interoperability and knowledge sharing of different phenomena in the outbound logistics domain. In this sense, we have designed and developed an OLP-IOT ontological approach adapted to logistics data and offering semantic enrichment of IOT data. This approach allows the sharing of sensor observations, the identification of products and logistics objects involved as well as the contextualization of data and the reuse of processed knowledge and information. The ontology was developed using the Neon methodology, which emphasizes reuse and modularization. This explicit knowledge is then used to develop a reasoning system to guide the logistic expert for an incremental and semi-automatic construction of a software solution to an instantaneous problem.

This paper presents a simple and easy-to-use methodology for designing the menu in Food and Beverage (F&B) enterprises over a period of time, considering that certain elements of the problem are subject to uncertainty. The methodology considers both nutritional and financial aspects and allows the decision makers to explore the effect of the uncertainty on the final solutions, according to their perception of risk.The proposed methodology is based on multi-objective mixed integer programming and in particular the Almost Robust Optimization (ARO) approach introduced by Baron et al. (2019). In contrast to conventional Robust Optimization techniques, the ARO approach is more flexible and offers the decision makers the possibility to express their attitude towards risk through appropriate parameters and obtain a series of solutions corresponding to different levels of risk.The proposed model is applied in a case study concerning F&B enterprises from the island of Crete, Greece, using real data that was collected in collaboration with nutritionists and managers employed in F&B enterprises.Decision-makers in F&B enterprises may use the proposed model as a decision support tool to incorporate the inherent uncertainty into the decision-making process. Through appropriate parameters, they may select optimal diets that are feasible for most realizations of the uncertain parameters, without incurring significant increases in cost. The model is flexible and produces a series of alternative solutions based on the decision makers’ preferences and perception of risk.

Increasing uncertainties in the supply chains have caused more attentions to the supply chain risk management approaches. Because of the inherent turbulences in the international transactions, these uncertainties in the global context are more important. On the other hand, due to competitive pressures, businesses has been prepared themselves to operate in a global context to take advantage of the international markets. In addition, supplier selection is a challenge for purchasing managers by having more uncertainties in supply from the foreign supplier (exchange rate risk, extended lead times, regional risks). On the other hand, lower price procurement and having more diversified suppliers are the benefits that a company could obtain from global supply chains. In this paper a scenario based supply chain model for global purchasing of substitutable products is introduced and as a solution method a simulation-optimization approach is proposed. The model is applied on the modified data adopted from a case study and sensitivity analyzes (on the risk attitude of retailers, product substitutability and exchange rate) are presented for different amounts of parameters.

The importance of applying disruptive technologies to improve the efficiency of different processes within the agri-food chain for sustainable development is increasing day by day. In the current scenario, agri-food chains face disruptions caused by the consequences of COVID-19 or the War in Ukraine, resulting in reduced quality, availability, transparency, trust, and security of different food products within the distribution chain. This paper aims to map the convergence between the use of Blockchain technology for sustainable development and agri-food chains. The specific objectives are pointing to key co-occurrence networks and clusters, mapping the emerging thematic axes from the literature, showcasing key authors and journals, and organizing the collected data based on economic, social, and environmental (three pillars of sustainability). The research design is organized using Systematic Review Methodology. The originality of this review includes the verification of data performance through bibliometric and the organization and analysis of the identified articles based on the dimensions of sustainability. The findings show that the adoption and use of Blockchain technology improve supply chain sustainability performance and point to a developing trend in the area under study. There is a high concentration of theoretical contributions, with the environmental dimension being less addressed. A detailed analysis of the findings is presented to provide a comprehensive and up-to-date view of agri-food chains, Blockchain, and sustainable development. Furthermore, this work offers research opportunities to develop new research based on Blockchain and sustainable development.

The circular economy is one of the most important issues in the optimal use of resources all around the world. The combination of circular economy and supply chain creates a new concept called circular supply chain, which seeks to increase the efficiency of the supply chain by making the best use of resources. In this research, the main purpose is to apply a hybrid Multi-Criteria Decision-Making (MCDM) method to evaluate the effective factors in implementing the circular supply chain. First, the effective factors in the field of the circular supply chain are identified, and in the next step, the weight of the factors is obtained by implementing the Analytic Hierarchy Process (AHP) method. Next, the intensity of the effect of each factor is calculated. Moreover, the correlation between the factors affecting the circular supply chain and the effectiveness of the factors is analyzed using the Decision Making Trial and Evaluation Laboratory (DEMATEL) method. Finally, using the Simple Additive Weighting (SAW) method, the most important factors in the implementation of the circular supply chain are identified. The core results of this research show that the quality of final products is the most important factor in implementing a circular supply chain. Moreover, applying the circular economy approach leads to the zero-waste goal, which can increase the efficiency of supply chains.