Data driven safe vehicle routing analytics: a differential evolution algorithm to reduce CO2 emissions and hazardous risks

Contemporary vehicle routing requires ubiquitous computing and massive data in order to deal with the three aspects of transportation such as operations, planning and safety. Out of the three aspects, safety is the most vital and this study refers safety as the reduction of CO2 emissions and hazardous risks. Hence, this paper presents a data driven multi-objective differential evolution (MODE) algorithm to solve the safe capacitated vehicle routing problems (CVRP) by minimizing the greenhouse gas emissions and hazardous risk. The proposed data driven MODE is tested using benchmark instances associated with real time data which have predefined load for each of the vehicle travelling on a specific route and the total capacity summed up from the customers cannot exceed the stated load. Pareto fronts are generated as the solution to this multi-objective problem. Computational results proved the viability of the data driven MODE algorithm to solve the multi-objective safe CVRP with a certain trade-off to achieve an efficient solution. Overall the study suggests 5% increment in cost function is essential to reduce the risk factors. The major contributions of this paper are to develop a multi-objective model for a safe vehicle routing and propose a multi-objective differential evolution (MODE) algorithm that can handle structured and unstructured data to solve the safe capacitated vehicle routing problem

Implementation of lean manufacturing and lean audit system in an auto parts manufacturing industry – an industrial case study

Lean manufacturing is one of the innovative manufacturing concepts being applied in many industries to avoid the wastages of resources and improving the quality of products and help the company to become more effective and productive. It also focuses on continuous improvements with the total involvement of all employees with optimum utilization of man power and machine by reducing idle time and reducing lead time with help of lean tools like value stream mapping and kaizen. This paper presents a case study carried out in a foundry division of an auto parts manufacturing industry, where lean tools are implemented for the defect reduction and productivity improvement. In this paper, a conceptualized implementation of total productive maintenance practices of lean tools: Kaizen or continuous improvement and value stream map in an auto parts industry are presented. The result shows improved performance in terms of average core rejections, sand leakage and air lock problem

An improved design for cellular manufacturing system associating scheduling decisions

This paper presents a model for the design of Cellular Manufacturing System (CMS) to evolve simultaneously structural design decisions of Cell Formation (CF) and operational issue decisions of optimal schedule. This integrated decision approach is important for designing a better performing cell. The model allows machine duplication and incorporates cross-flow for scheduling flexibility. The cross-flow is the term introduced to mean the inter-cell movement of parts from parent cell to identical machines in other cells though machines are available in the parent cell. This cross-flow facilitates routing flexibility and paves way for reduced schedule length thereby optimizing resources leading to minimized operational cost. A non-linear integer mathematical programming model is formulated with the objective function of minimizing operating cost which is the sum of Machine Utility Cost (MUC) and inter-cell costs. The MUC is a new cost parameter based on machine utility and it integrates CF, scheduling, and machine duplication decisions. The proposed model belongs to the class of NP-hard problems. A hybrid heuristic (HH) that has “Simulated Annealing Algorithm (SAA) embedded with Genetic Algorithm (GA)” is proposed. A comparison with the mathematical solution reveals that the proposed HH is capable of providing solutions closer to optimal in a computationally efficient manner. The model is validated by studying the effect of integrated decisions, machine duplications, and association of scheduling and cross-flow. The model validation reveals that the proposed CMS model evolves CF, scheduling, and machine duplication decisions with minimum operating cost. Thus, it can be inferred that the proposed model gives optimal integrated decisions for designing an effectively and efficiently performing cells and thus evolves improved CMS design decisions

PCB Drill Path Optimization by Combinatorial Cuckoo Search Algorithm

Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB), the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process

Identification and evaluation of criteria of agile manufacturing using dematel: a case from an indian metal fabrication industry

In metal fabrication industry, assembling department plays the major role since it involves risks in assembling the components. Hence, it is always difficult for the manufacturers to identify the criteria of agile manufacturing in assembling department that effects the assembly of the fabricated metal components. Agile manufacturing is one of the innovative method of manufacturing, which focus on the customer satisfaction and also maintaining the quality and cost of the product. Metal fabrication industries generally struggle to find right criteria for better agile manufacturing process. This study focuses on the selection of suitable criteria for agile manufacturing, which requires an in-depth analysis depending on the influence they possess on the agile manufacturing. The objective of this paper is to analyze and identify the most influencing criteria for the metal manufacturing industry based on the customers’ and industrial expert’s perspective. Here we have selected ten different criteria based on the literatures available on the agile manufacturing. The criteria are segregated and ranked according to the nature and influence they possess on other criteria using decision making trial and evaluation laboratory (DEMATEL) methodology. This study also helps the metal fabrication industry to identify the most influencing criteria to implement on agile manufacturing and to have high efficiency on the production. The results show that the customer satisfaction seems to be the primary criteria that will have more influence in metal fabrication industry

Analyze the factors influencing human-robot interaction using MCDM method

Robots play a key role in medical equipment manufacturing industry by safeguarding human workers from hazardous environment and risky jobs. Human robot interaction (HRI) is one of the robotic features that are enhanced in industrial robots. They mimic human behavior while arriving at a decision, contributing to the proficiency of the product. Tasks involving human cognitive skills and flexibility in the workers are combined with robots to obtain high-level accuracy, repeatability, and speed. Further, more challenges are to be met for achieving an effective human-robot interaction. In this paper, risk factors affecting the interaction between both robot and humans are discussed, and a contextual case is performed in a top south Indian medical equipment manufacturing industry. Industrial experts' inputs and relevant literature are considered to recognize the risk factors. Multi-Criteria decision-making method (MCDM) like DEMATEL (Decision Making Trial and Evaluation Laboratory) is used to analyze the risk factors influencing HRI in the assembly section. The paper's findings show that automation level and reliability of the robot are the most influential factor in the assembly section and need more attention to control and reduce the risk factor for the effective assembly

Quantifying the phosphorylation timescales of receptor–ligand complexes: a Markovian matrix-analytic approach

Cells interact with the extracellular environment by means of receptor molecules on their surface. Receptors can bind different ligands, leading to the formation of receptor–ligand complexes. For a subset of receptors, called receptor tyrosine kinases, binding to ligand enables sequential phosphorylation of intra-cellular residues, which initiates a signalling cascade that regulates cellular function and fate. Most mathematical modelling approaches employed to analyse receptor signalling are deterministic, especially when studying scenarios of high ligand concentration or large receptor numbers. There exist, however, biological scenarios where low copy numbers of ligands and/or receptors need to be considered, or where signalling by a few bound receptor–ligand complexes is enough to initiate a cellular response. Under these conditions stochastic approaches are appropriate, and in fact, different attempts have been made in the literature to measure the timescales of receptor signalling initiation in receptor–ligand systems. However, these approaches have made use of numerical simulations or approximations, such as moment-closure techniques. In this paper, we study, from an analytical perspective, the stochastic times to reach a given signalling threshold for two receptor–ligand models. We identify this time as an extinction time for a conveniently defined auxiliary absorbing continuous time Markov process, since receptor–ligand association/dissociation events can be analysed in terms of quasi-birth-and-death processes. We implement algorithmic techniques to compute the different order moments of this time, as well as the steady-state probability distribution of the system. A novel feature of the approach introduced here is that it allows one to quantify the role played by each kinetic rate in the timescales of signal initiation, and in the steady-state probability distribution of the system. Finally, we illustrate our approach by carrying out numerical studies for the vascular endothelial growth factor and one of its receptors, the vascular endothelial growth factor receptor of human endothelial cells

Adaptive Analytical Approach to Lean and Green Operations

Recent problems faced by industrial players commonly relates to global warming and depletion of resources. This situation highlights the importance of improvement solutions for industrial operations and environmental performances. Based on interviews and literature studies, manpower, machine, material, money and environment are known as the foundation resources to fulfil the facility's operation. The most critical and common challenge that is being faced by the industrialists is to perform continuous improvement effectively. The needs to develop a systematic framework to assist and guide the industrialist to achieve lean and green is growing rapidly. In this paper, a novel development of an adaptive analytic model for lean and green operation and processing is presented. The development of lean and green index will act as a benchmarking tool for the industrialist. This work uses the analytic hierarchy process to obtain experts opinion in determining the priority of the lean and green components and indicators. The application of backpropagation optimisation method will further enhance the lean and green model in guiding the industrialist for continuous improvement. An actual industry case study (combine heat and power plant) will be presented with the proposed lean and green model. The model is expected to enhance processing plant performance in a systematic lean and green manner