NETWORK DESIGN FOR THE TEMPORAL AND SPATIAL COLLABORATION WITH SERVICE CLASS IN DELIVERY SERVICES

The COVID-19 pandemic has significantly impacted e-commerce and the delivery service sector. As lockdowns and social distancing measures were put in place to slow the spread of the virus, many brick-and-mortar stores were forced to close, leading to an increase in online shopping. This situation led to a surge in demand for delivery services as more people turned to the internet to purchase goods. However, this increase in demand also created several challenges for delivery companies. They experienced delays in delivering packages due to increased volume, limited staff, and disruptions to supply chains. It led to more competition and increased pressure on delivery companies to improve their services and delivery times. To overcome such competition, collaboration among small and medium-sized delivery companies can be a good way to compete with larger delivery companies. By working together, small and medium-sized companies can combine their resources and expertise to offer more extensive coverage and competitive prices than they could individually. This can help them to gain market share and expand their customer base. This study proposes a network design model for collaboration with service class in delivery services considering multi-time horizon. The problem to be considered is deciding which company is dedicated to delivering certain types of items, such as regular or refrigerated items, in designated regions in each time horizon. During the agreed-upon timeframe, the companies operate, using each other's infrastructure (such as vehicles and facilities) and sharing delivery centers for the coalition's benefit to improve efficiency and reduce costs. We also propose a multi-objective, nonlinear programming model that maximizes the incremental profit of participating companies and a linearization methodology to solve it. The max-sum criterion and Shapley value allocation methods are applied to find the best solution and ensure a fair distribution of profits among the collaborating group. The efficiency of the suggested model is shown through a numerical illustration

AN EXTENSION OF THE FAILURE MODE EFFECTS AND CRITICALITY ANALYSIS WITH FUZZY ANALYTICAL HIERARCHY PROCESS METHOD TO ASSESS THE EMERGENCY SAFETY BARRIERS

The emergency safety barrier is one of the reactive technical safety barriers in industrial facilities. Degrade of emergency safety barriers can lead to a major accident with serious consequences for people, property and the environment. In this context, the purpose of this article is to present a proposed methodology to identify these deficiencies, thus ensuring the effectiveness of the emergency safety barriers. This paper presents an integrated approach that uses fuzzy set theory, extension of failure modes, effects and criticality analysis and the fuzzy analytic hierarchy process method to deal with uncertainty in decision-making related to the prioritization of risk factors. These risk factors are the prioritization of corrective actions associated with the most critical disturbance modes to improve the reliability of emergency safety barriers. In addition, a Liquefied Petroleum Gas production facility was selected as a case study to assess the emergency safety barriers. The results show that the proposed methodology provides the possibility to evaluate the fire-fighting systems. In addition, the fuzzy analytical approach method is the most reliable and accurate. Therefore, some corrective actions are suggested to reduce the failure criticality of the emergency safety barriers and help practitioners prioritize the improvement of the emergency safety barriers of the Liquefied Petroleum Gas storage facility. This paper has an important role in the dysfunctional analysis of the emergency safety barriers related to the others effects of the release of LPG, such as the effects of domino scenarios

HUMAN MACHINE INTERFACE DESIGN OF INDUSTRIAL AUTOMATED MACHINE USING SIMATIC SCADA SYSTEM

In an industrial world full of technological advancements where competitiveness is the essential objective, automation has become a fundamental necessity to achieve higher productivity with less chance of error in a limited time. Additionally, regular monitoring of processes is required to improve system performance and ensure employee safety. In this paper, a method for realizing an automated weighing and bagging machine is proposed, and special emphasis is placed on the weighing system. A simulated process prototype based on a SCADA (Supervisory Control and Data Acquisition) system and PLC (Programmable Logic Controller) of weighing and bag packing machines is designed to fill and close bags with the product. Operators can monitor the process and control outputs through the HMI (Human Machine Interface) screen

EFFECTS OF DRIVER PERSONAL VARIABLES ON PREFERRED VEHICLE INTERIOR COMPONENTS SETTING

This study identified and characterized the relationship between driver personal variables and preferred vehicle interior components setting. A two-phase modeling approach was employed to characterize the temporal, logical process involved in the driver selection of a preferred vehicle interior components setting. The modified Bayesian multivariate adaptive regression splines (BMARS) modeling method was employed to identify nonlinear and interactive relationships. Forty-two male and forty-four female drivers with a wide range of ages, stature, and BMI participated in the data collection. A highly adjustable vehicle mock-up was used to empirically obtain each participant’s preferred vehicle interior components setting. The study results indicated substantial non-anthropometric variability in the driver-selected seat horizontal positions and identified various interpretable nonlinearities and interactions. The study findings improve the understanding of the relationship between driver personal variables and preferred vehicle interior configuration and further inform the vehicle interior package design for driver accommodation

MULTI-OBJECTIVE ROBUST PRODUCTION PLANNING CONSIDERING WORKFORCE EFFICIENCY WITH A METAHEURISTIC SOLUTION APPROACH

Timely delivery of products to customers is one of the main factors of customer satisfaction and a key to the survival of a manufacturing system. Therefore, decreasing wasted time in manufacturing processes significantly affects production delivery time, which can be achieved through the maximization of workforce efficiency. This issue becomes more complicated when the parameters of the production system are under uncertainty. This paper presents a bi-objective scenario-based robust production planning model considering maximizing workforce efficiency and minimizing costs where the backorder, demand, and costs are uncertain. Also, backorder, raw materials purchasing, inventory control, and manufacturing time capacity are considered. A case study in a faucet manufacturing plant is considered to solve the model. Furthermore, the ε-constraint method, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and the Pareto Envelope-based Selection Algorithm II (PESA-II) are employed to solve the model. Also, the Taguchi method is used to tune the parameters of these algorithms. To compare these algorithms, five indicators are defined. The results show that the SPEA2 is the most time-consuming algorithm and the NSGA-II is the fastest, while their objective function values are nearly the same

GREEN VEHICLE ROUTING MODEL VIA LINEAR FRACTIONAL PROGRAMMING: A RETAIL CASE STUDY FOR MARMARA REGION, TÜRKİYE

The Vehicle Routing Problem (VRP) is a crucial subject in the discipline of logistics and transportation management since it connects the distribution centers to the arrival points of services and goods in the most efficient way possible. Considering the VRP in the framework of operational research, the optimization quality of the VRP mainly depends on an efficient model built for the network and the successful choice for the objective of the model. The optimization problem, which involves the objective function as the ratio of two functions, is described as Fractional Programming (FP), which has attracted noteworthy attention in the previous five decades because of its usefulness in modeling several decision processes in operations research, management science, and economics. In this research, we have studied a Linear Fractional Vehicle Routing Problem (LFVRP) regarding the contribution of the mathematical modeling of VRP to both the optimization literature and the commercial market, which is incontrovertible. For this purpose, we propose an iterative method for LFVRP that aims to optimize the delivery of goods and services while minimizing the rate of total cost/load without any variable transformation technique proposed for the first time. By this methodology of iterative optimization, unlike the literature, the mathematical complexity of the model will be facilitated as a Linear Programming Problem (LPP) and, meanwhile, provide the capability of considering multiple objectives (both cost and load) as one. Furthermore, by this advantage, it has been able to express a green-based approach by presenting an objective that minimizes fuel consumption which constructs transportation expenses, and hence it lowers its carbon footprint for our world while keeping the aim of maximum load. In order to illustrate the effectiveness of our approach, we have built a real-life model with real data in the retail sector in Türkiye and provided a comparative analysis

A METHODOLOGY FOR THE BIDDERS EVALUATION AND SELECTION IN THE PUBLIC PROCUREMENT PROCESS BASED ON HETEROGENEOUS INFORMATION AND ADAPTIVE CONSENSUS APPROACHES

The public procurement problem is a special problem of supplier selection that requires strict adherence to the principles of non-discrimination, free competition, and transparency in the contract awarding procedures. It is a very complex multi-criteria problem, which requires the engagement of several decision-makers (experts). The public procurement problem requires the usage of different types of conflicting criteria, the combination of different models (methods and techniques) of decision-making, as well as the modeling of different forms of uncertainty, inaccuracy, and subjectivity of decision-makers, which can represent a rather complex, difficult, and lengthy decision-making process. Therefore, the paper proposes a methodology for improving the tender process that focuses on heterogeneous preference structures of information (preference ordering, utility values, fuzzy (additive) preference relations, multiplicative preference relations, and linguistic preference relations) and an adaptive consensus approach for subjectively determining the weight of criteria and evaluation and selection of alternative bids. The Simple Additive Weighting (SAW) method is used for the final ranking of bidders. The proposed methodology enables obtaining a more objective and measurable value during subjective decision-making as well as minimizing the risk of unscrupulous, incompetent, and irresponsible decision-making, which is shown in the given example

A NOVEL TYPE OF FLEXIBLE SOFT ANALYTIC NETWORK PROCESS TO SOLVE THE MULTIPLE-ATTRIBUTE DECISION-MAKING PROBLEM

  Research and development of scientific and technological products have been changing with each passing day in this new millennium. Decisions related to the production of technical products are the key to affecting the sustainable development and market share of enterprises. However, the decision-making related to the production of technology products contains many different evaluation criteria as well as qualitative and quantitative evaluation attributes. Moreover, the correlation between criteria must be considered so it can be treated as a complex multiple-attribute decision-making (MADM) problem. Moreover, performing a multi-attribute decision evaluation often encounters incomplete or missing information provided by experts, which will lead to difficulties in the solution process. In view of the incomplete or missing information of the assessment data, the traditional analytic network process (ANP) method and decision-making trial and evaluation laboratory ANP (DANP) method will delete the incomplete information during the process of assessment and decision-making, and this will bring about non-objective assessment results. In order to solve the above problems, this study proposes a novel type of flexible soft ANP (SANP) method to solve the MADM problems and uses a practical example of smartphone text entry to prove the effectiveness and suitability of the proposed SANP method

DYNAMIC SIMULATION ANALYSIS FOR VARIOUS NUMBERS OF ORDERS IN AN INTEGRATED CAR-MANUFACTURING WAREHOUSE

The order-picking process in a warehouse is critical in managing customer orders, especially in retail stores. It is expensive because fulfilling online orders takes up to 70% of all warehouse activities. Procedures in order picking, including different route selection schemes, can significantly increase yield and reduce costs. The research shows that a suitable routing method can reduce the travel time of the order picker to fulfill the order. However, the number of orders may vary. This paper presented a dynamic simulation analysis based on a real scenario of a various number of orders in an integrated car manufacturing warehouse. The simulation reduced the travel time of the voters by about 44.89%. This simulation model helps to visualize the potential reduction in customer waiting times, leading to increased customer satisfaction

A MODIFIED CLASS OF COMPOSITE DESIGNS FOR THE RESPONSE MODEL APPROACH WITH NOISE FACTORS

A class of composite designs involves factorial, axial, and center points. Factorial points are with a variance-optimal design for a first-order or interaction model, and axial points provide information about the existence of curvature. The center points allow for efficient estimation of the pure quadratic terms. From these properties, a class of composite designs is recommended if resources are readily available and a high degree of precision of parameter estimate is expected and evolves from their use in sequential experimentation. However, there are often cost constraints imposed on experiments. Previous studies show that resolution, orthogonal quadratic effect property, and saturated or near-saturated design reduce the number of experiments. This study extends the response model approach with noise factors to composite designs satisfying these properties. These modified composite designs are further discussed and examined in terms of scaled prediction error variance and extended scaled prediction variance, which provides a good distribution of the prediction variance of the response. Based on these criteria, the best performance design is suggested according to the number of control and noise factors. As a result, we show that the modified designs showing robustness to noise factors and stability of predictive variance are a class of modified small composite designs and modified augmented-pair designs