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In this study, two mathematical models have been developed for assigning emergency vehicles, namely ambulances, to geographical areas. The first model, which is based on the assignment problem, the ambulance transfer (moving ambulances) between locations has not been considered. As ambulance transfer can improve system efficiency by decreasing the response time as well as operational cost, we consider this in the second model, which is based on the transportation problem. Both models assume that the demand of all geographical locations must be met. The major contributions of this study are: ambulance transfer between locations, day split into several time slots, and demand distribution of the geographical zone. To the best of our knowledge the first two have not been studied before. These extensions allow us to have a more realistic model of the real-world operation. Although, in previous studies, maximizing coverage has been the main objective of the goal, here, minimizing operating costs is a function of the main objective, because we have assumed that the demand of all geographical areas must be met., International Journal of Innovation in Engineering (IJIE), 2021

Supply chain is one of the main pillars of manufacturing and industrial companies whose smartness can help business to be intelligent. To this end, the use of innovative technologies to make it smart is always a concern. The smart supply chain utilizes innovative tools to enhance quality, improve performance and facilitate the decision-making process. Internet of things (IoT) is one of the key components of the IT infrastructure for the development of smart supply chains that have high potential for creating sustainability in systems. Furthermore, IoT is one of the most important sources of big data generation. Big data and strategies for data analysis as a deep and powerful solution for optimizing decisions and increasing productivity are growing rapidly. For this reason, this paper attempts to examine informative supply chain development strategies by investigating the supply chain in FMCG industries as a special case and to provide a complete analytical framework for building a sustainable smart supply chain using IoT-based big data analytics. The proposed framework is based on the IoT implementation methodology, with emphasis on the use of input big data and expert reviews. Given the nature of the FMCG industry, this can lead to better production decisions.

In this supply chain, blood and blood products are investigated as a product from donor to patient. Specific characteristics of blood supply chain such as perishability and existence of uncertainty in the structure of this chain have caused problems for planning in this regard. However, this point should be considered that the importance of this supply chain cannot be compared to perishable products. Life and death issue of this product is the main difference between blood and other perishable products. Therefore, a comprehensive model was presented in this study to locate blood bank components within a network and to determine the allocation of these components considering blood donation centers, blood testing and processing laboratories, distribution centers or blood banks, and demand centers. Since designing supply chain and locating issues at large dimensions are NP-hard, the suggested models at small sizes were compared using the exact method (GAMS), non-dominated genetic sorting algorithm, and multi-objective imperialist competitive algorithm. The results were compared with GAMS. This shows normal performance of the proposed algorithms and led to using these two algorithms at average and large sizes for research questions. Also, by analyzing sensitivity on important parameters, important managerial findings are suggested for similar conditions.

Every year, natural disasters such as earthquake, flood, hurricane and etc. impose immense financial and humane losses on governments owing to their unpredictable character and arise of emergency situations and consequently the reduction of the abilities due to serious damages to infrastructures, increases demand for logistic services and supplies. First, in this study the necessity of paying attention to locating procedures in emergency situations is pointed out and an outline for the studied case of disaster relief supply chain was discussed and the problem was validated at small scale. On the other hand, to solve this kind of problems involving three objective functions and complicated time calculation, meta-heuristic methods which yield almost optimum solutions in less time are applied. The EC method and NSGA II algorithm are among the evolutionary multi-objective optimization algorithms applied in this case. In this study the aforementioned algorithm is used for solving problems at large scale.

With the increase in pollutants, the need to use electric vehicles (EVs) in various urban logistics activities is an increasingly important issue. Currently, there are issues with the efficiency of transport companies in recognizing the effects of uncertain factors in daily logistics operations. Thus, this research proposes a novel fuzzy two-echelon vehicle routing problem involving heterogeneous fleet EVs and internal combustion vehicles (ICVs). The first echelon is recyclable wastes collected from waste pickup points and transported to the primary centers by EVs. The second echelon is transporting recyclable wastes to recycling centers by ICVs. In the proposed models, fuzzy numbers are used to express the rate and energy consumption depending on the amount of load, vehicle speed, and recyclable waste. In addition, a penalty cost of the time windows is considered in both echelons. The models are solved by CPLEX and two meta-heuristic algorithms, gray wolf optimizer (GWO) and tabu search (TS), based on different instance sizes. The results show the efficiency of the proposed algorithms.

Scheduling the supply chain in an integrated manner is a quite fundamental subject in supply chain management. To consider due-dates with production and distribution times at the same time could lead to reduced costs and, thereby, increased profitability. Integrating the problems dealing with due-date assignment, production/distribution times, and routing, while incorporating environmental considerations, could not only increase the cohesion among different decision-making levels and reduce costs in the long run, it would also contribute to the improvement of environmental conditions and benefit the world population. In this study, the integrated supply chain scheduling problem features assignment of due dates, batch delivery, assignment to multiple heterogeneous vehicles based on their capacity, and delivery of customer orders in time-windows. The objective is to minimize distribution cost, fixed and variable fuel costs, the carbon emitted by the vehicles, total delivery tardiness, and customer dissatisfaction. In this model, the customers are categorized into five clusters based on the length of their association, how recently they began interacting, how often they interact, and the monetary value of their interaction with the organization. The members of these clusters are called core customers, potential customers, new customers, lost customers, and resource-consumption customers. A mixed integer non-linear programming model is introduced for this problem which is solved using three multi-objective metaheuristic algorithms: Multi-Objective Particle Swarm Optimization, Non-dominated Sorting Genetic Algorithm II, and Multi Objective Ant Colony Optimization. A number of performance criteria and statistical tests are used to evaluate the algorithms.

© 2017 Elsevier Ltd Many organizations and institutions are implementing accurate and practical tools to accelerate decision-making process. In this regard, hospitals and healthcare centers are not exceptions, in particular, because they directly impact the health and well-being of the community. When it comes to disease diagnosis, practitioners may have different opinions, which lead to different decisions and actions. On the other hand, the amount of available information, even in a case of a typical disease is so vast that rapid and accurate decision-making may be difficult. For example, practitioners may prescribe several expensive tests in order to diagnose a heart disease whereas many of those tests might not even be required. Accordingly, a Clinical Decision Support System (CDSS) can be very helpful here. In particular, such a CDSS can be developed as an expert system for those patients who have a high likelihood of developing heart diseases. This study develops an expert system based on Fuzzy Analytic Hierarchy Process (AHP) and Fuzzy Inference System in order to evaluate the condition of patients who are being examined for heart diseases. The Fuzzy AHP is used to calculate weights for different criteria that impact developing heart diseases, and the Fuzzy Inference System is used to assess and evaluate the likelihood of developing heart diseases in a patient. The developed system has been implemented in a hospital in Tehran. The outcomes show efficiency and accuracy of the developed approach.

A multi-skilled project scheduling problem (MSPSP) has generally been presented to schedule information technology projects in deterministic conditions. The contribution of this model is to consider the resources, called 'staff members'. These members are regarded as valuable, renewable, and discrete resources with different multiple skills. The different skills of staff members, as well as the project network's activity requirement of different skills, cause this problem to become a special type of multi-mode resource-constrained project scheduling problem (MM-RCPSP), with a huge number of modes. Taking into account the importance of this issue and the few studies performed on this problem, a novel mathematical model for the MSPSP is presented. Since the complexity of this problem is NP-hard, an efficient scatter search (SS) algorithm is developed to solve such a difficult problem. This proposed SS is capable of generating optimised solutions in small sizes, and the excellent solutions in large sizes are compared with the solutions reported by a proposed Tabu search (TS) algorithm. 'n Veeldoel projekskeduleringsvraagstuk (MSPSP) word voorgehou vir skedulering onder deterministiese toestande van inligtingstegnologieprojekte. Die model word aangewend vir hulpbronne genaamd "personeellede". Die bronne is waardevol, hernubaar, diskreet en beskik oor uiteenlopende vaardighede. Die eienskappe van die vraagstuk berus by uiteenlopendheid. Vir die voorafgaande omstandighede word 'n nuwe model geskep vir die hantering van eienaardighede. Aangesien die problem NP-moeilik is, word 'n doeltreffende spreisoek-algoritme gebruik met groot sukses.

Manpower scheduling is an intricate problem in production and service environments with the purpose of generating fair schedules that consider employers’ objectives and employees’ preferences as much as possible. However, sometimes, vagueness of information related to employers’ objectives and employees’ preferences leads to the fuzzy nature of the problem. This paper presents a multi-objective manpower scheduling model regarding the lack of clarity on the target values of employers’ objectives and employees’ preferences. Hence, a fuzzy goal programming model is developed for the presented model. Afterwards, two fuzzy solution approaches are used to convert the fuzzy goal programming model to two single-objective models. Finally, the results obtained by both single-objective models are compared with each other to select the solution that has the greatest degree of the satisfaction level of employers’ objectives and employees’ preferences.

Manpower scheduling is a complicated problem to solve that strives to satisfy employers’ objectives and employees’ preferences as much as possible by generating fairly desirable schedules. But sometimes, objectives and preferences may not be determined precisely. This problem causes manpower scheduling takes the fuzzy nature. This paper presents a new fuzzy multi-objective mathematical model for a multi-skilled manpower scheduling problem considering imprecise target values of employers’ objectives and employees’ preferences. Hence, a fuzzy goal programming model is developed for the presented mathematical model and two fuzzy solution approaches are used to convert the fuzzy goal programming model to two single-objective models. Since the complexity of a manpower scheduling problem is NP-hard, the single-objective models are solved by two meta-heuristics, namely particle swarm optimization and elite tabu search. Eventually, the performance of the proposed algorithms is verified and the results are compared with each other to select the best schedules.

Article history: Received July 20, 2011 Received in Revised form October, 12, 2011 Accepted 15 October 2011 Available online 20 October 2011 A multi-skilled project scheduling problem (MSPSP) has been generally presented to schedule a project with staff members as resources. Each activity in project network requires different skills and also staff members have different skills, too. This causes the MSPSP becomes a special type of a multi-mode resource-constrained project scheduling problem (MM-RCPSP) with a huge number of modes. Given the importance of this issue, in this paper, a mixed integer linear programming for the MSPSP is presented. Due to the complexity of the problem, a metaheuristic algorithm is proposed in order to find near optimal solutions. To validate performance of the algorithm, results are compared against exact solutions solved by the LINGO solver. The results are promising and show that optimal or near-optimal solutions are derived for small instances and good solutions for larger instances in reasonable time. © 2012 Growing Science Ltd. All rights reserved.

A multiskilled project portfolio scheduling problem (MSPPSP) is an extension of a multiobjective multimode resource-constrained project portfolio scheduling problem that is generally propounded to schedule a set of projects performed by human skills in an organization. The main idea of the MSPPSP is to consider resources that are called staff members to perform projects’ activities in different required skills. Since the required staff members have various skills, different combinations of skills are applied to accomplish the project. These definitions cause to encounter a huge number of modes while performing activities of a project. In this paper, a novel goal programming model for the multiobjective MSPPSP with precedence constraints that aim at finding a minimum deviation from the expected time to complete each project and assignment of resources is presented. To solve such a hard problem, an efficient metaheuristic algorithm based on differential evolution (DE) is developed. To evaluate the efficiency of the proposed DE algorithm, the results are compared to the results of the tabu search algorithm and the optimal results. The comparison confirms the effectiveness of the DE algorithm. Finally, regarding the size of organizations in terms of staff members, the maximum number of the determined structure projects, which is performable with minimum delay from aspiration times, is examined.

This paper presents a new solution framework to locate the workstations in the tandem automated guided vehicle (AGV) systems. So far, the research has focused on minimizing the total flow or minimizing the total AGV transitions in each zone. In this paper, we focus on minimizing total cumulative flow among workstations. This objective allocates workstations to an AGV route such that total waiting time of workstations to be supplied by the AGV is minimized. We develop a property which simplifies the available mathematical formulation of the problem. We also develop a heuristic algorithm for the problem. Computational results show that our heuristic could yield very high-quality solutions and in many cases optimal solutions. © 2010 Springer-Verlag London Limited.

A tandem automated guided vehicle (AGV) system deals with grouping workstations into some non-overlapping areas and assigning to each area exactly one AGV. This paper presents a new non-linear integer mathematical model to group n machines into N loops to minimize both inter-loop and intra-loop flow simultaneously based on balanced-loops strategy and inter-machine flows taken from ideas of cellular manufacturing systems. Due to computational difficulties of exact methods in solving the proposed model, a simulated annealing (SA) algorithm is proposed. A number of test problems are generated at random and solved by the proposed SA in order to show the efficiency of the algorithm. Finally, the results are reported by both the Lingo software and the proposed SA algorithm.

This paper presents a novel bi-objective manpower scheduling problem that minimizes the penalty incurred by the employees' assignment at lower skill levels than their real skills and maximizes the employees' utility by assigning them at desired skill levels in some shifts/days. Employees are classified in two specialist groups and three skill levels in each specialization. In addition, the presented model executes some essential work regulations. This paper also proposes a solution procedure based on the utility of objective values. Applying this procedure, an effective point is obtained for the given problem. This is the point where both objective functions have the highest utility simultaneously.