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Hazardous material (Hazmat) transportation is an inseparable section of the industry, despite its major financial and health risks. In order to optimize Hazmat transportation, a multi-objective Hazmat routing model with time windows is employed where the risk and distance are minimized. Due to the uncertainty of Hazmat transportation risk, a Z-number fuzzy approach is used to estimate the risk, in which the probability of occurrence and the severity is considered in the context of Z-information. The severity of the event includes the affected population and depends on the amount of transported Hazmat and the number of individuals affected by the explosion. To tackle the proposed model, the present paper utilizes a multi-objective hybrid genetic algorithm, the validity of which is tested by Solomon's problems. Furthermore, the optimization of a case study concerning the Hazmat distribution in Iran is analyzed using the suggested approach to assess the efficiency of the proposed fuzzy problem in real-world applications. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to provide an integrated production-routing model in a three-echelon supply chain containing a two-layer transportation system to minimize the total costs of production, transportation, inventory holding and expired drugs treatment. In the proposed problem, some specifications such as multisite manufacturing, simultaneous pickup and delivery and uncertainty in parameters are considered. Design/methodology/approach: At first, a mathematical model has been proposed for the problem. Then, one possibilistic model and one robust possibilistic model equivalent to the initial model are provided regarding the uncertain nature of the model parameters and the inaccessibility of their probability function. Finally, the performance of the proposed model is evaluated using the real data collected from a pharmaceutical production center in Iran. The results reveal the proper performance of the proposed models. Findings: The results obtained from applying the proposed model to a real-life production center indicated that the number of expired drugs has decreased because of using this model, also the costs of the system were reduced owing to integrating simultaneous drug pickup and delivery operations. Moreover, regarding the results of simulations, the robust possibilistic model had the best performance among the proposed models. Originality/value: This research considers a two-layer vehicle routing in a production-routing problem with inventory planning. Moreover, multisite manufacturing, simultaneous pickup of the expired drugs and delivery of the drugs to the distribution centers are considered. Providing a robust possibilistic model for tackling the uncertainty in demand, costs, production capacity and drug expiration costs is considered as another remarkable feature of the proposed model. [ABSTRACT FROM AUTHOR]

• Monte Carlo simulation has been employed to assess the long-term outage data. • The CCDFs are converted into algebraic functions using the high-accuracy regression. • Outage costs are calculated through the estimated outage duration and designed CDFs. • The annual outage duration of distribution feeders is predicted using Monte Carlo. This paper introduces an effective evaluation method for customer outage costs and durations based on defining customer damage functions (CDFs) and processing the long-term interruption data. The proposed approach sorts and processes interruption data, utilizing the Monte Carlo technique to assess outage durations. Then, by weighting sector customer damage functions in each load point, composite customer damage functions (CCDF) are defined. The determined CCDFs are transformed into algebraic functions through curve fitting for any substation. Then, using the estimated outage duration and designed CCDF, the customer outage costs are calculated at any load center. The methodology introduced enhances the predicting accuracy of outage duration and cost by reducing the influence of rare lengthy outages and amplifying that of recurring events. Furthermore, this study defines fixed CCDFs independent of outage durations and could be employed for future time intervals of the proposed load points. Moreover, sorting data based on outage durations improves the accuracy of Monte Carlo outage estimation. The proposed method validation is evaluated on the sub-transmission system of Sistan and Baluchestan, Iran. [ABSTRACT FROM AUTHOR]

This paper presents a mixed integer non-linear programming (MINLP) model for a bi-objective and multi-depot vehicle routing problem with time windows. The main goals of the paper are minimization of total cost and equitable distribution of commodities between vehicles. Two types of vehicle including delivery and installation vehicles are utilized in the network regarding customers' needs. Satisfying all demands of the customers is not obligatory and unmet demands are permitted which leads to extra cost. A presented model is applied for real life case study in different provinces of Iran. To tackle the small-size problems, the augmented E-constraint method is utilized by linearization of the model. Because of the NP-hard nature of the problem, as the size of the problem increases, so does the complexity. As such, we develop multi-objective simulated annealing (MOSA meta-heuristic) algorithm for large scale problems. Then, several numerical experiments and sensitivity analyses are conducted to validate the presented model and the solution method, which indicate the efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

In view of the significance of transportation management and logistics in the economic concept and raising the productivity of production systems, well-timed procurement of perishable materials and goods is determined as a pivotal prerequisite for economic and environmental development. Since the perishable goods produced must be made delivered to consumers as early as possible on account of the limited lifespan, thus, the vulnerability of these products is extremely high, owing to the high cost of transportation as well as the environmental impacts. So that solves this problem, this study represents a problem of dynamic green vehicle routing of perishable products in green traffic conditions that optimizes the total cost for a dynamic transportation network and minimizes environmental influences, and increases customer satisfaction. The introduced model is implemented in light of time windows as a trustworthy solution for monitoring the dynamic logistics process and attaining instantaneous information on the basis of the green traffic situation and travel duration, which is commonly known by the Logit function. Assuming the three-objective programming model, we consider a new improved algorithm developed for a novel augmented ε-constraint heuristic approach. Furthermore, robust optimization has been conducted for the established problem to tackle with uncertainties. Uncertainties are included demand and economic parameters. Eventually, to validate the proposed model, a case study was carried out at Kaleh Amol Dairy Company in Iran. The conclusions of sensitivity analysis by implementing the model in the real world indicate that the model and approach presented in various uncertainty scenarios have high flexibility. [ABSTRACT FROM AUTHOR]

The Integrated Solid Waste Management (ISWM) is a recent effective tool to manage with the growing challenge of Municipal Solid Waste (MSW). The ISWM integrates all the system components (i.e. transfer, treatment, recycling and disposal of wastes) to enhance the sustainable waste management whilst reducing operational costs. In this paper, we investigate an integrated framework of the Fleet Size and Mix Vehicle Routing Problem (VRP) to optimize the cost-effective ISWM system. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is developed to concurrently minimize the transportation cost in the entire waste management system and total deviation from the fair load allocation to transfer stations. A complete ISWM system with all interdependent facilities and multiple technologies, is developed to address a tri-echelon Fleet Size and Mix VRP with a heterogeneous fleet of vehicles under multiple technologies and waste compatibility constraints. The model was solved for both the Preemptive and Non-Preemptive conditions using Lexicographic and Goal Programming optimization approaches. The model was tested on a case of ISWM in the Southern part of Tehran, Iran. [ABSTRACT FROM AUTHOR]

This paper proposes a new Vehicle Routing Problem (VRP) with fair Carrier Collaboration (CC) which split multi-pickup and delivery services are considered for serving customers. This study has focused on VRP and serving customers with several commodity requirements from different geographically scattered suppliers subject to constraints on the vehicle capacity. A Mixed Integer Programming (MIP) model to maximize the total profit and the fair sharing of profit among the carriers by considering the travel time minimization is developed. Each carrier with its limited capacity can have reserved requests which must be served by itself and selective requests which can be served by itself or other vehicles or not served at all. There are various applications of the proposed model in the environment which can help reducing number of vehicles serving to the customers and eliminating empty back hauls. A Genetic Algorithm (GA) is proposed to solve this problem due to its Non-deterministic Polynomial-time hard (NP-hard) nature. In addition, Variable Neighbourhood Search (VNS) method is developed for improving the quality of initial solutions. Some instances are generated at different scales to evaluate the algorithm's performance by comparing the results of an exact optimal solution with that of the proposed algorithm. The obtained results demonstrate the efficiency of the proposed algorithm in providing reasonable solutions within an acceptable computational time. The algorithm is also tested for an online shopping website in Tehran, Iran. The test outcome shows that the proposed model returns a better benefit compared to the manual methods. The results of sensitivity analysis suggest that increasing the fairness coefficient among carries can led to a decrease in the total obtained profit. • Develop a novel carrier collaboration routing problem model with multi-commodity service and sharing fair profit. • Extend the carrier collaboration to general problems with in many-to-one problem pickup and delivery category. • Solve the problem with genetic algorithm with a new specific chromosome representation. • Result of implementation of the model in the real-world and sample instances. • Sensitivity analysis of the model based on fair distribution between participants is conducted. [ABSTRACT FROM AUTHOR]

One of the important issues in the world is the significant growth of waste production, including waste that is not biodegradable in nature. According to the Kerman Municipality, 440 tonnes of municipal waste is collected daily in Kerman consisting of five major parts of paper, plastic, metal, glass, and wet waste. The major problems of municipal solid waste disposal are soil erosion, air pollution, and greenhouse gas emissions. The most important factors related to recycling are waste sorting and the relevant environmental conditions. This study aims to create a sustainable approach by locating the optimal sites to reduce environmental pollution, decrease costs, and improve the service system to the society. Optimal locations for establishing the collecting and sorting centers in the city are specified by the use of geographic information system software, based on criteria consisting of population density, road network, distance to health centers, distance to disposal center, waste sorting culture, land space, and land cost, which were weighted by an analytical hierarchy process. It was noteworthy that the criterion "waste sorting culture", which has a foundation in human sciences and sociology, has been considered by experts in this study to be of the highest importance among other criteria at locating sorting centers. Subsequently, using a symmetric capacitated vehicle routing problem, the number and capacity of each vehicle are determined to serve the specified locations according to the economic, social, and environmental constraints. [ABSTRACT FROM AUTHOR]

This paper studies a multi-trip vehicle routing problem with time windows specifically related to urban waste collection. Urban waste collection is one of the municipal activities with large costs and has many practical difficulties. In other words, waste collection and disposal is a costly task due to high operating expenses (fuel, maintenance, recycling, manpower, etc.) and small improvements in this field can result in tremendous savings on municipal expenditure. In the raised problem, the goal is to minimize total cost including traversing cost, vehicle employment cost, and exit penalty from permissible time windows. In this problem, the waste is deposited at the points indicating the demand nodes, in which each demand shows the volume of generated waste. Considering multiple trips for vehicles and time windows are the most critical features of the problem, so that the priorities of serving some specific places such as hospitals can be observed. Since vehicle routing problems (VRP) belongs to NP-hard problems, an efficient simulated annealing (SA) is proposed to solve the problem. The computational results show that our proposed algorithm has a great performance in a short computational time in comparison with the CPLEX solver. Finally, in order to demonstrate the applicability of the model, a case study is analyzed in Iran, and the optimal policies are presented. [ABSTRACT FROM AUTHOR]

Parcel delivery is a complex logistic service, as it serves many small or mediumsized customers who may send or receive parcels. Modeling such delivery system needs to integrate two different research areas of hub location and vehicle routing. As it totally depends on the network and the linkage of the nodes, in this paper, some door-to-door service providers are taken into account to provide suitable information for modeling parcel deliveries of sparse and wide countries. Since the proposed mixed-integer programming model is NP-hard, a new multi-steps solution method based on a simulated annealing algorithm and local search is presented. The results of the proposed model and the solution method are evaluated based on some small test problems. The performance of the solution method is illustrated by solving a real case with all capital cities of 31 provinces in Iran. [ABSTRACT FROM AUTHOR]

The methods to collect and transport various types of medical waste are vital to the management of healthcare waste. The lack of a safe plan for the timely collection and transportation of such wastes may have an undesirable impact on the environment and public health. The present research addresses a real-life healthcare waste collection vehicle routing problem (HWCVRP) for small medical centers in Iran that produce minor amounts of waste and lack on-site facilities to treat wastes of various kinds. This problem involves social, environmental, and economic objective functions, aiming to achieve sustainable development. The social objective function is optimized based on a novel definition of risk in medical waste collection and designed to reduce public health risk by minimizing the time of waste collection from centers that produce a larger volume of more hazardous waste. Furthermore, the present paper provides a comprehensive estimate of fuel consumption in vehicles to be accurately minimized to diminish the environmental hazards involved in sustainable transportation. Also, the economic objective function is set to minimize the variable and fixed costs of transportation. A self-adaptive multi-objective evolutionary algorithm is generated with numerous effective operators to solve the model. Specific metrics are employed to compare the performance of the proposed meta-heuristic algorithm with those of multiple other evolutionary algorithms. The results reveal the effectiveness of the proposed method in reaching high-quality non-dominated solutions. Ultimately, a real-life case study is used to implement the proposed approach and to evaluate its performance. Image 1 • A real perspective to the triple bottom line of health-care waste collection. • A new social aspect of sustainability based on the risk of different medical waste. • A new multi-objective self-adaptive evolutionary algorithm (MOSAEA). • The results show that MOSAEA dominates recently competitive approaches. [ABSTRACT FROM AUTHOR]

Recent developments in business network have heightened the need for the issue of cost reduction while increasing the productivity. Supply chain network is a fundamental property of industrial practitioners and researchers. Achieving a practical objective, this research study has an attempt to enhance the efficiency of a supply chain by considering simultaneous pick up and split delivery. This attempt would result in total costs minimization and customer service maximization in the form of multi-products and multi-period. In addition, environmental aspect of this system is considered as well as green vehicle routing problem (GVRP). We propose a mixed-integer linear programming model by a ε-constraint approach. This study not only utilizes exact method from GAMS software, but also employs four hybrid and meta-heuristics including GAKA, GAPSO, SA and RDA to develop and evaluate the solutions. In addition, the validation of model is presented for a real-life data. Besides, a case study has been done in order to accumulate the parameters data. This case study is utilized in a food industry located in the north of Iran. • Using VRPSPSD 2 2 Vehicle Routing Problem With Simultaneous Pick-up and Split Delivery. for routing among manufacture and distribution centers into customers. • An innovative bi-objective model considering the environmental aspects and green VRP is developed. • Four tuned meta-heuristic algorithms are employed to find the best solution. • Four metrics used to evaluate the proposed algorithms for the problem. • Case study is utilized in a food industry located in the north of Iran. [ABSTRACT FROM AUTHOR]

• Development of a comprehensive Integrated Solid Waste Management Decision support system. • Application of the Tri-echelon Mixed Fleet size and VRP problem on ISWM. • Implementation on a real case study of Tehran city, Iran. • Uncertainty has been undertaken as a two-stage stochastic approach. The growing challenge of Municipal Solid Waste Management in mega-cities calls for development of practical decision-making support tools to assist authorities in City Logistics and Urban Planning. This study aims to optimize the logistics network and transportation system of ISWM where the complete chain of MSW/residue is formulated as a tri-echelon ISWM logistics network. Assuming various levels of complexity of a realistic ISWM system, a Mixed-Integer Linear Programming (MILP) model is developed to formulate the ISWM system in the framework of the Fleet Size and Mix Vehicle Routing Problem with Time Windows. Addressing uncertainty in ratios of MSW generation, a two-stage stochastic optimization approach is proposed to effectively support the cost-effective ISWM transportation system by determining the optimal fleet size and decomposition, vehicles routes and capacity-allocation to the system components. The proposed approach successfully was applied to a real case of ISWM in southern Tehran, Iran. The numerical experiments showed the usefulness of the approach to minimize the economic costs of the system under uncertainty. Furthermore, the results verified the strength and effectiveness of the method when experiencing larger deviations between the estimated and actual amounts of the uncertain parameter and in cases of unplanned disruptions in the system network. [ABSTRACT FROM AUTHOR]