Mobility-On-Demand Service In Mass Transit: Hypercommute options

Digitization, increasing automation and new business models like shared mobility have revolutionized transportation and mobility. Ridesharing companies like Uber and Lyft provide technological platforms and support to connect drivers and riders on the basis of demand-response services. Although the most improvements in on-demand applications have been experimented in private transit services, there is no any implementation in public transportation to connect public transit services and passengers each other. Ondemand is still vague. However, providing on-demand services in public transportation is complicated because of the big capacity problem in mass transit, its application in public transit services can enable flexible mobility for riders and provide personalized mobility experience. This paper presents the concept of mobility-on-demand service and its application in public transit services with an technological innovation of FM/LM pilot project represented by HyperCommute. The paper starts with introduction, then the business model of mobility-on-demand service is described and the most used algorithms are explained, then an illustrative example of HyperCommute mobility-on-demand service is given. Also, the applicability of mobility-on-demand service in Istanbul is discussed. The paper ends up with conclusion and future directions

Stream processing data decision model for higher environmental performance and resilience in sustainable logistics infrastructure

Purpose: As the global freight transport network has experienced high vulnerability and threats from both natural and man-made disasters, as a result, a huge amount of data is generated in freight transport system in form of continuous streams; it is becoming increasingly important to develop sustainable and resilient transport system to recover from any unforeseen circumstances quickly and efficiently. The aim of this paper is to develop a stream processing data driven decision-making model for higher environmental performance and resilience in sustainable logistics infrastructure by using fifteen dimensions with three interrelated domains. Design/methodology/approach: A causal and hierarchical stream processing data driven decision-making model to evaluate the impact of different attributes and their interrelationships and to measure the level of environmental performance and resilience capacity of sustainable logistics infrastructure are proposed. This work uses fuzzy cognitive maps (FCMs) and fuzzy analytic hierarchy process (FAHP) techniques. A real-life case under a disruptive event scenario is further conducted. Findings: The result shows which attributes have a greater impact on the level of environmental performance and resilience capacity in sustainable logistics infrastructure. Originality/value: In this paper, causal and hierarchical stream processing data decision and control system model was proposed by identified three domains and fifteen dimensions to assess the level of environmental performance and resilience in sustainable logistics infrastructure. The proposed model gives researchers and practitioners insights about sustainability trade-offs for a resilient and sustainable global transport supply chain system by enabling to model interdependencies among the decision attributes under a fuzzy environment and streaming data

Sustainability impact of digitization in logistics

Today, most enterprises are undergoing a digitization process with the fourth industrial revolution, named industry 4.0. The focus of the digital transformation lies mainly on production, therefore the terms such as "Factory of the Future" or "Smart Factory" are used similar with this concept. However, there are many reasons for considering the impact of digitalization in logistics and the importance of supply chain for industry 4.0. The key promises of this concept are enabling real-time full-transparency from suppliers to customers, small lot sizes, multiple product variants, connected processes and decentralized, autonomous management. These benefits cannot be achieved by production alone, but only along the entire supply chain. Moreover, logistics should gain a greater vision to fulfill the requirements of industry 4.0 as sustainably as possible in terms of using appropriate technologies and enhancing vertical and horizontal integration among the supply chain partners. In this respect, this study highlights the benefits of the digitization of logistics process and examines the sustainability impact of digitization in logistics. The study is pursued as a single case study within the FMCG companies and their transport service providers in Turkey and it is based on a qualitative method and on connected semi-structured interviews

Artificial neural networks-based route selection model for multimodal freight transport network during global pandemic

The global pandemic caused major disruptions in all supply chains. Road transport has been particularly affected by the challenges posed by the COVID-19 pandemic. The selection of an efficient and effective route in multimodal freight transport networks is a crucial part of transport planning to combat the challenges and sustain supply chain continuity in the face of the global pandemic. This study introduces a novel optimal route selection model based on integrated fuzzy logic approach and artificial neural networks. The proposed model attempts to identify the optimal route from a range of feasible route options by measuring the performance of each route according to transport variables including, time, cost, and reliability. This model provides a systematic method for route selection, enabling transportation planners to make smart decisions. A case study is conducted to exhibit the proposed model's applicability to real pandemic conditions. According to the findings of the study, the proposed model can accurately and effectively identify the best route and provides transportation planners with a viable option to increase the efficiency of multimodal transport networks. In conclusion, by proposing an innovative and efficient strategy for route selection in complex transport systems, our research significantly advances the field of transportation management

Minimizing losses at red meat supply chain with circular and central slaughterhouse model

Purpose: The purpose of this paper is to find solutions to improve the red meat sector in an emerging economy, Turkey, from the circular economy point of view, and taking sustainability approach. The need for circular management within the red meat sector in Turkey is emphasized by using Grey method. As theoretical contribution of this study, the investigation of the causes of losses at the slaughter stages of the red meat supply chain leads to proposals for sustainable and circular solutions. Design/methodology/approach: Grey method is used to predict the number of slaughtered cattle and the amount of bone and blood waste in the slaughtering process between 2018 and 2020. Findings: It is revealed that according to Grey prediction calculations, although the amount of slaughtered cattle, bone and blood waste seem have decreased between 2018 and 2020, there are still significant losses in Turkish red meat sector. For bone waste, this is expected to be 56,581,200 kg in 2018, 48,235,840 kg in 2019 and 41,121,380 kg in 2020. For blood waste, it is expected to be 24,754,275 kg in 2018, 21,103,180 kg in 2019 and 17,990,604 kg in 2020. Social implications: The proposed model in the study will contribute on sector revitalization, increase in product safety, quality and hygiene, development in the management of training and education centers for farmers/labors and increase in employment. Originality/value: This paper represents policymakers with a proposal for triple bottom line (TBL) based circular and central slaughterhouse model, based on TBL, which brings social, economic and environmental benefits for the red meat sector in Turkey

A novel stochastic fuzzy decision model for agile and sustainable global manufacturing outsourcing partner selection in footwear industry

Purpose – The decision-making to outsource and select the most suitable global manufacturing outsourcing partner (MOP) is complex and uncertain due to multiple conflicting qualitative and quantitative criteria as well as multiple alternatives. Vagueness and variability exist in ratings of criteria and alternatives by group of decision-makers (DMs). The paper provides a novel Stochastic Fuzzy (SF) method for evaluation and selection of agile and sustainable global MOP in uncertain and volatile business environment. Design/methodology/approach – Four main selection criteria for global MOP selection were identified such as economic, agile, environmental and social criteria. Total 16 sub-criteria were selected. To consider the vagueness and variability in ratings by group of DMs, SF method using t-distribution or z-distribution was adopted. The criteria weights were determined using the Stochastic Fuzzy-CRiteria Importance Through Intercriteria Correlation (SF-CRITIC), while MOP selection was carried out using Stochastic FuzzyVIseKriterijumskaOptimizacija I KompromisnoResenje (SF-VIKOR) in the case study of footwear industry. Sensitivity analysis was performed to test the robustness of the proposed model. A comparative analysis of SFVIKOR and VIKOR was made. Findings – The worker’s wages and welfare, product price, product quality, green manufacturing process and collaboration with partners are the most important criteria for MOP selection. The MOP3 was found to be the best agile and sustainable global MOP for the footwear company. In sensitivity analysis, significance level is found to have important role in MOP ranking. Hence, the study concluded that integrated SF-CRITIC and SF-VIKOR is an improved method for MOP selection problem. Research limitations/implications – In a group decision making, ambiguity, impreciseness and variability are found in relative ratings. Fuzzy variant Multi-Criteria Decision-Making methods cover impreciseness in ratings but not the variability. On the other hand, deterministic models do not cover either. Hence, the stochastic method based on the probability theory combining fuzzy theory is proposed to deal with decision-making problems in imprecise and uncertain environments. Most notably, the proposed model has novelty as it captures and reveals both the stochastic perspective and the fuzziness perspective in rating by group of DMs. Practical implications – The proposed multi-criteria group decision-making model contributes to the sustainable and agile footwear supply chain management and will help the policymakers in selecting the best global MOP. Originality/value – To the best of the authors’ knowledge, SF method has not been used to select MOP in the existing literature. For the first time, integrated SF-CRITIC and SF-VIKOR method were applied to select the best agile and sustainable MOP under uncertainty. Unlike other studies, this study considered agile criteria along with triple bottom line sustainable criteria for MOP selection. The novel method of SF assessment contributes to the literature and put forward the managerial implication for improving agility and sustainability of global manufacturing outsourcing in footwear industry

Investigating enablers to improve transparency in sustainable food supply chain using F-BWM

Food Supply Chains (FSC) are complex and dynamic in behavior and prone to increasing risks of unsustainability. Consumers increasingly demand food quality, safety, and sustainability, which are fast becoming issues of great importance in FSC. Lack of real-time information sharing and connectivity among stakeholders make these issues tougher to mitigate. Supply chain transparency (SCT) is thus an essential attribute to manage these supply chain complexities and enhance the sustainability of FSC. The paper identifies and analyses key enablers for SCT in FSC. Several technical, as well as sustainability-related enablers, contribute to the implementation of SCT. The identified enablers are analyzed using Fuzzy-best worst methodology (F-BWM), which determine the most critical factors using the decision maker’s opinion. Extending BWM with fuzzy logic incorporates the vagueness of human-behaviour into decision making approach. The results of this research provides decision makers with the priority of enablers to the decision maker. Enhancing these enablers in will help improve the transparency for better management of FSC. The article expands upon the practical as well as theoretical implications of SCT on sustainability in FSC. It addresses the requirement of including sustainability in the decision-making process. The results demonstrate the effectiveness of the F-BWM for the decision making process. The study is conducted by considering downstream supply chain activities in Indian context. It is one of the first studies that analyzes SCT enablers using F-BWM method in Indian context. The study contributes towards improving the environmental, economical, and social sustainability of FSC

Optimal number of remanufacturing in a circular economy platform

In reducing waste and protecting natural resources benefits in a circular economy platform, performing remanufacturing tasks are complex, as it may be associated with costs such as investment, setup and disposal cost. Thus, many studies those aims to find the optimal number of remanufacturing has been investigated whether it is an infinite or a constant number of remanufacturing via trial-and-error method. During the investigation, the disposal rate is assumed as a fixed value for each unique case, which needs further focus. The current study aims to propose a novel decision model to figure out an optimal number of remanufacturing regarding to the various ratio of used units returned for recovery. The proposed model was extended in context of remanufacturing opportunities of PVC products. The obtained findings are useful for companies in managing remanufacturing processes by knowing optimal remanufacturing times, and results in enhanced economic–ecological–social gains in the circular economy