Real Case Problem Research Articles

A Multi Objective Time-Cost-Quality Optimization Model fot Project Scheduling and Human Resource Assignment Considering Manpower Competency

Today, information and knowledge are so intertwined with human life that the present age has been called the information era. In this situation, the role of human in the organization and the way of looking at him/her will have a significant contribution to the success or failure of the organization. The role of human resources is becoming more and more prominent and it is now mentioned as the only factor in gaining a sustainable competitive advantage. Furthermore, due to dramatic developments in the field of human resource management, competency-based human resource planning has found a special place. In this study, a project scheduling problem considering multi-skilled resource and manpower competency is investigated. To do this end, an integrated multi-objective optimization model of project scheduling and human resource assignment including total completion time and execution cost minimization and project quality maximization is presented. To solve problem exactly, a mixed-integer programming model is developed and then solved by e-constraint method in GAMS software. As the proposed model is NP-hard, a non-dominated sorting genetic (NSGA-II) algorithm is developed to solve the large scale problem. Furthermore, in order to validate the proposed algorithm, its results compare with exact method for instance problem in small scale. Results indicate that the proposed algorithm is outperformed and it can be used for real case problems.

Science, Business, and Policy: A long-term reflection on multidisciplinary work-based learning in a master’s track for societal integration of Science

A strong theoretical approach with a specific focus on disciplinary research characterizes the common science master’s education in the Netherlands. However, a work-based learning (WBL) approach may as well be expedient and suitable for science education at master’s level. In this paper, a case study is presented of a WBL-program designed for an academic setting: the one year Science, Business and Policy (SBP) master’s track, offered at the Faculty of Science and Engineering of the University of Groningen. The paper describes the design and curriculum of the track, including its underlying theoretical framework, courses, multidisciplinary projects and work placements. Based on the SBP-track’s design we identified six possible indicators of a successful elaboration of an academic WBL-program: the SBP-track 1) is designed in response to the Bologna process; 2) is offered fully within the curriculum of a master’s program of a research university; 3) requires a sufficient academic level and disciplinary knowledge at entrée; 4) follows an educational project approach; 5) focuses on the integration and implementation of knowledge, and; 6) applies learning objectives that are specifically formulated to match the WBL educational method. A directed content analysis of SBP work placements revealed an increase in the number of SBP-students between 2003 and 2019, with an overrepresentation of life science students, as well as a large variety of real-case problems addressed for both business and policy organisations diverse in sector, size and region. Students’ grades showed a positive correlation between the initial theoretical preparation and the report made during the work placement. In conclusion, the societal interpretation of the Bologna process has been implemented successfully with SBP, by combining academic learning with gaining professional experience using a WBL-approach.
 Received: 03 December 2020Accepted: 09 April 2021

Conventional and Improved Inclusion-Exclusion Derivations of Symbolic Expressions for the Reliability of a Multi-State Network

This paper deals with an emergent variant of the classical problem of computing the probability of the union of n events, or equivalently the expectation of the disjunction (ORing) of n indicator variables for these events, i.e., the probability of this disjunction being equal to one. The variant considered herein deals with multi-valued variables, in which the required probability stands for the reliability of a multi-state delivery network (MSDN), whose binary system success is a two-valued function expressed in terms of multi-valued component successes. The paper discusses a simple method for handling the afore-mentioned problem in terms of a standard example MSDN, whose success is known in minimal form as the disjunction of prime implicants or minimal paths of the pertinent network. This method utilizes the multi-state inclusion-exclusion (MS-IE) principle associated with a multi-state generalization of the idempotency property of the ANDing operation. The method discussed is illustrated with a detailed symbolic example of a real-case study, and it produces a more precise version of the same numerical value that was obtained earlier. The example demonstrates the notorious shortcomings and the extreme inefficiency that the MS-IE method suffers, but, on the positive side, it reveals the way to alternative methods, in which such a shortcoming is (partially) mitigated. A prominent and well known example of these methods is the construction of a multi-state probability-ready expression (MS-PRE). Another candidate method would be to apply the MS-IE principle to the union of fewer (factored or composite) paths that is converted (at minimal cost) to PRE form. A third candidate method, employed herein, is a novel method for combining the MS-PRE and MS-IE concepts together. It confines the use of MS-PRE to ‘shellable’ disjointing of ORed terms, and then applies MS-IE to the resulting partially orthogonalized disjunctive form. This new method makes the most of both MS-PRE and MS-IE, and bypasses the troubles caused by either of them. The method is illustrated successfully in terms of the same real-case problem used with the conventional MS-IE.

A Blood Supply Chain Network with Backup Facilities Considering Blood Groups and Expiration Date: A Real-world Application

The purpose of this paper is to design a green Blood Supply Chain (BSC) network regarding expiration date and backup facilities. The proposed model is a bi-objective Mixed Integer Programming (MIP) one. The two objective functions are to minimize the total cost and the detrimental environmental impacts of shipping between facilities and generated wastes in the network. A Goal Programming (GP) approach is used to convert the multi-objective model into a single one. Moreover, to meet the demand, blood groups and plasma expiration date are also investigated. Since it has been proven that plasma of the people who have fully recovered from COVID-19, can help other patients to recover from this insidious disease; therefore, the proposed BSC network can supply the needs of this particular category of patients as well. To examine the feasibility of the proposed model, some random examples with different dimensions are generated and solved using the CPLEX solver of GAMS software. Furthermore, a real-case problem in Esfahan (Iran) was investigated to illustrate the applicability of the proposed model, and the sensitivity analysis was performed as well. Results approved the applicability of the proposed model in a real situation.

Profile Monitoring for Com Poisson Responses

In some real case problems, the relationship between a response variable and one or more explanatory variables called as profile should be monitored over time instead of the quality characteristic itself. Profile monitoring is used in such instances. Many researches have been done in the area of profile monitoring but in most of them it is assumed that the response variable follows normal distribution. In recent years Yeh et al. (2009) proposed 5 T2 based methods for monitoring logistic profiles in which response variable is binary and Amiri et al. (2011) evaluate two of the best T2 methods for Poisson response profiles monitoring in Phase I. In this paper we will obtain the form of the Com Poisson equation and parametric estimation.

Solving Mixed-Model Assembly Lines Using a Hybrid of Ant Colony Optimization and Greedy Algorithm

The assembly line balancing problem deals with the assignment of tasks to work stations. Mixed-model assembly line problem is a type of assembly line balancing problem at which two or more models of the same product are assembled sequentially at the same line. To achieve optimality and efficiency of solving this problem, tasks at each work station have to be well balanced satisfying all constraints. This paper deals with the mixed-model assembly line balancing problem (MALBP) in which the objective is to minimize the cycle time for a given number of work stations. The problem is solved by using a hybrid of an ant colony optimization and a greedy algorithm (Ant-Greedy). MATLAB Software is used to perform the proposed method. Then, the proposed method is applied to a real case problem found in the literature for the assembly line of automatic changeover in the Electronic Industries Company in Iraq. The results of the proposed method are compared with the performance of the Merging Shortest and Longest Operation (MMSLO) method. The comparison shows that the Ant-Greedy optimization method is more efficient, where the efficiency increased from 93.53% for MMSLO method to 97.26% for the Ant-Greedy method.

Utilization of grey madm methodology in technology attractiveness assessment: a case study in upstream industry

Technology assessment help managers to accomplish an overall evaluation of technologic options and to identify investment priorities. Making such priority has become of great importance owing to ever-increasing costs of technological research and development and resource scarcity. Technology Attractiveness Assessment, as a primary step of Technology assessment process, has been considered in this paper. Based on the multi-criteria decision-making approach and because of the inherent uncertainty in the preference information on attributes, a Grey-MADM based methodology has been utilized in this paper to assess the technology attractiveness and rank the upstream industry technological options. Its application to a real case problem has been described step by step. The results of the case study showed that "Nano Coating for Drilling Tools", "Petroleum Systems Model Building" and "Integrated Asset Modelling" are the most attractive upstream technologies.

Location and Routing Planning Considering Electric Vehicles with Restricted Distance in Agriculture

Electric vehicles (EVs) are anticipated to play a critical role in the green transportation of the future. Logistics companies have started several projects operating with EVs in road transportation. However, routing decisions for EVs must take limited driving ranges into account. Previous related research on electric vehicle location routing problems (EVLRP) has investigated intra route facilities that support the energy supply network. Contrarily, this paper studies a new type of EVLRP with a restricted distance, where EVs are used for route planning in reverse flow logistics. The model is formulated from a real case problem in agriculture that combines both locating multiple depots and determining routing paths with a limited distance constraint. An adaptive large neighborhood search (ALNS) algorithm has been extended into four combinations and is proposed here for solving the problem. The computational results indicate that the ALNS algorithm can obtain quality solutions in short processing time when compared with software using exact methods. Furthermore, the proposed ALNS algorithm is applied to a case study problem to provide suitable locations and vehicle routes with a minimized total cost.

A multi-cut L-shaped method for resilient and responsive supply chain network design

We present a stochastic optimisation model that can be used to design a resilient supply chain operating under random disruptions. The model aims to determine sourcing and network design decisions that minimise the expected total cost while ensuring that the minimum customer service level is achieved. The proposed model incorporates several resilience strategies including multiple sourcing, multiple transport routes, considering backup suppliers, adding extra production capacities, as well as lateral transshipment and direct shipment. A multi-cut L-shaped solution approach is developed to solve the proposed model. Data from a real case problem in the paint industry is utilised to test the model and solution approach. Important managerial insights are obtained from the case study. Our analyses focus on (1) exploring the relationship between supply chain cost and customer service level, (2) examining the impacts of different types of disruptions on the total cost, (3) evaluating the utility of resilience strategies, (4) investigating the benefits of the proposed solution approach to solve problems of different sizes and (5) benchmarking the performance of the proposed stochastic programming approach.

Simulation Modeling of Dual Warehouse System

Warehouse space allocation is a critical issue to be addressed in many firms, especially firms that have various types of products. Further, most of the owned warehouse has a limited capacity which leads to the usage of outsourcing policy for warehouse storage. In this case, a rented warehouse that has more flexibility in terms of capacity is used to store the products. Therefore, an optimal decision of warehouse configuration is required. The previous study proposed a two-stage stochastic optimization model for warehouse configuration and inventory policy for deteriorating items. Unfortunately, direct implementation of their proposed method in real case problems will incur high cost and takes up a lot of time. In this research, a simulation model is developed to monitor the real condition in the company as well as to ensure that the warehouse configuration is suitable to be implemented. A textile and apparel company is used as the study case. The result shows that simulation model can be used to monitor the real condition as well as to obtain the net profit when facing different demand possibility.
 Keywords: inventory control; real-time monitoring; system simulation; warehouse configuration; warehouse space allocation

Solving Assembly Production Line Balancing Problem Using Greedy Heuristic Method

This paper focuses on assembly line balancing (ALB) problem which the objective is to maximize the assembly line efficiency. The problem solved using a greedy heuristic method. MATLAB Software is used to perform the proposed greedy heuristic method. Then, the proposed method is applied to a previous real life case problem that is found in literature for the cookers assembly line in the Light Industrial Company in Iraq. The results of the proposed method is compared with the performance of the company without applying any assembly line balancing method. The result of this paper is also compared with the results obtained from a previous work in the literature which is based on Shortest Operation Time (SOT) using the software named Quantitative Methods, Production and Operations Management (POM-QM). The outcomes of the research have shown that the greedy heuristic method is more efficient, where the efficiency increased from 78.24% before applying assembly line balancing and 81.64% for the previous work based on SOT method to 85.53% when applying the greedy heuristic method. The research has recommended that the decision maker in the company should follow the most appropriate method to achieve a high efficiency operations.

A new approach for solving fuzzy multi-objective quadratic programming of water resource allocation problem

This paper describes an application of fuzzy multi-objective quadratic model with flexible constraints for optimal allocation of limited available water resources among different water-user sectors. Due to the fact that, water resource allocation problem is one of the practical and essential subjects in real world and many of the parameters may be faced by uncertainty. In this paper, we present α - cut approach for transforming fuzzy multi-objective quadratic programming model with flexible constraints into a crisp form. By using this approach a multi-parametric multi-objective programming model corresponding to α and parameters of flexible constraints is obtained. One of the advantages of this model is that the α - cut level is not determined by the decision makers. Actually, this model itself can calculate the α - cut level. In order to achieve a desired Pareto optimal value of multi-parametric multi-objective model, we use goal programming method for illustration of water resource allocation with sensitivity analysis of lower bound of parameters in flexible constraints. To illustrate the efficiency of the proposed approach, we apply it for a real case problem of water resource allocation.

A comparison study of bank data in fractional calculus

Abstract The present paper investigate the dynamics of the bank data through a competition model with real field data for the year 2004–2014. Initially, we formulate a competition model for the bank data and then use different fractional approaches to simulate the model with the real data for many fractional order parameters α. Then, we present a novel approach for each fractional model and provide a graphical illustration with real data. We show that all these fractional approaches have good resemblance to each other and can be used to model such real data case. We prove in general that the results of the fractional approaches utilized here are good for modeling purpose but also we prove the results of the fractional Atangana–Baleanu operator is more accurate and flexible and can be used confidently to modeled such real case problem.

Proceso de enseñanza para el aprendizaje de análisis de probabilidad en base a su aplicación en casos de la vida real

El presente trabajo persigue facilitar la enseñanza y comprensión de la probabilidad en base a ejercicios aplicados a la vida real. Los estudiantes que cursan la asignatura de probabilidad y estadística no comprenden la utilidad de la aplicabilidad de la estadística en la vida real, por lo que este trabajo de investigación permitirá determinar a través de problemas de casos reales la utilidad que esta puede conllevar. Se hará uso de una base de datos proporcionado por la INEC con 400 registros de donde se buscará la probabilidad en diferentes casos en relación a estos datos reales, junto con la implementación de RStudio como herramienta de resolución. Los ejercicios que componer esta investigación demuestran lo importante que puede llegar a ser aplicar la probabilidad. Se prevé que en base a lo desarrollado a continuación pueda existir una mejor comprensión respecto al tema.

An improved decision support system for ABC inventory classification

In this study, an Improved Decision Support System (IDSS) is developed to help the decision makers in their inventory classification decisions. For the first time in this paper, the novel IDSS for ABC classification is developed. Certain new algorithms regarding the manufacturing company’s features are applied in a framework of IDSS. The IDSS is developed as modular structure and provided the integrated modules of “Data-Base” and “ABC Analysis”. In the developed IDSS, the appropriate ABC classification models are considered among Annual Dollar Usage (ADU), Analytic Hierarchy Process (AHP), Scoring (SCR), Fuzzy C-means Algorithm (FCM), and Analytic Network Process (ANP). Some issues and applicability of the IDSS are illustrated with real case problems in the paper. The proposed IDSS software is considerably decreased the time for the inventory classification. In the meantime it could be easily used in various sectors. Therefore, the proposed IDSS significantly contributed to obtaining more accurate and quickly modifiable ABC classification in real cases. Furthermore, the user friendly software can be updated readily according to recent developments in the market.

A Benders decomposition approach for a real case supply chain network design with capacity acquisition and transporter planning: wheat distribution network

This paper considers a real case problem of supply chain network design inspired from a wheat distribution network in Iran. It generates a network with capacity acquisition and fleet management. The problem first is formulated as a mixed integer linear programming model. Then, a logic-based Benders decomposition algorithm is appropriately developed as the solution methodology. In the presented algorithm, the problem is decomposed into two models of master and subproblem. The master problem is improved by means of the preprocessing and valid inequalities. Moreover, three Benders cuts, one optimality and two feasibility cuts, are developed for the algorithm. The general and relative performance of the model and algorithm is experimentally evaluated. The wheat distribution system of Iran is considered here as the case study of this research. The model is developed based on Iran’s wheat distribution system. All the results show that the algorithm significantly outperforms the mathematical model of the case study. For example, the algorithm solves 95% of the tested instances to optimality, yet the model solves 29%.

A Hybrid Greedy Algorithm and Simulated Annealing for Single Container Loading Problem: A Case Study

A single container loading problem is a problem to effectively load boxes in a three-dimensional container. There are many researchers in this problem try to find the best solution to solve the problem with feasible computation time and to develop some models to solve real case problem. Heuristics are the most method used to solve this problem since the problem is an NP-hard. In this paper, we introduce a hybrid greedy algorithm and simulate annealing algorithm to solve a real container loading problem in one flexible packaging company in Indonesia. Validation is used to show that the method can be applied practically. We use seven real cases to check the validity and performance of the model. The proposed method outperformed the solution developed by the company in all seven cases with feasible computational time.

Multi-Level Planning and Scheduling for Parallel PCB Assembly Lines Using Hybrid Spider Monkey Optimization Approach

Printed circuit board (PCB) assembly lines are significant to produce a variety of electronic products. PCB manufacturing industries tend to move towards automated and complex manufacturing system due to an increase in the customer demand for more sophisticated products. PCB assembly process involves highly interrelated levels of planning and scheduling problems. Therefore, the current research investigates multi-level planning and scheduling of PCB assembly lines, which include line assignment to the PCB models, component allocations to machines, and component placement sequencing by machines on PCB boards. A mixed integer-programming model is developed to integrate the planning and scheduling problem of parallel PCB assembly lines to maximize the net profit. A hybrid spider monkey optimization (HSMO) algorithm is proposed to solve the multi-level planning and scheduling problem. The performance of the proposed HSMO algorithm is compared to artificial bee colonial (ABC), genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithms. Moreover, the proposed HSMO algorithm is validated against ABC, GA, PSO, and SA algorithms with the case problem taken from well-reputed PCB manufacturing industry in China. The computation experiments indicate that the proposed HSMO algorithm can achieve good near-optimal solutions when compared with the other-mentioned four algorithms based on performance and efficiency for benchmark problems and real case problem.

Network-level Optimization of Bus Stop Placement in Urban Areas

Bus stops provide accessibility to public transit service, whereas they also influence the efficiency of mobility due to the extra dwell time. The decision of bus stop locations is a tradeoff between access coverage and mobility. This paper formulates a biobjective optimization model of bus stop placement problem at the network level. Two objectives are pertinent to the improvement of mobility: minimization of total dwell time at stops and minimization of total number of bus stops. Access coverage is constrained to ensure a certain level of accessibility. The issue of stop congestion and its effect on road traffic flow is also considered in the model. The model is applied to a case study of the bus network in urban areas of Yancheng, China. The results show that the proposed model can be efficiently solved by CPLEX to obtain Pareto optimal solutions for real-case problems.

Robust cooperative maximal covering location problem: a case study of the locating Tele-Taxi stations in Tabriz, Iran

Today, maximising the number of steady customers and consistency in meeting their needs are two important factors affecting the profitability of companies. Therefore, researchers are trying to model, study and analyse factors affecting the businesses. To this end and with regard to covering problem applications, this problem is inclusively studied in the operations research (OR) literature. This article proposes a novel maximal covering location problem (MCLP) in which different factors (capacity of facilities, budget, number of available facilities in different types) have been considered. To provide a real case problem, locating Tele-Taxi stations in Tabriz, Iran have been studied. Moreover, to analyse the effects of uncertainty in the rate of demand in each demand point and capacity of facilities as undeniable facts of real-world, the robust counterpart of the proposed model is developed. Furthermore, a sensitivity analysis on the constant perturbation for uncertain parameters has been conducted.