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The purpose of this study is to solve the problem of healthcare waste management in developing countries. The buildup of medical waste has attracted the attention of all spheres of society due to the expanding population and developing economy. Timely collection and processing of medical waste are extremely important due to its potential hazards. Although the problem of planning medical waste management has been addressed in developed countries, it persists in several developing countries. This research is motivated by an example of a city in India characterized by a dense population, abundant health-care facilities and a lack of planning for managing large medical waste generated daily.

The authors address the problem of designing the network of collection and processing facilities for medical waste and optimizing the vehicle route that collects and transfers the waste between facilities. Due to distinct topographic restrictions in the considered city, the collection and transfer process needs to be conducted in two echelons – from hospitals to collection centers using smaller vehicles and then to the processing facilities using trucks. This work addresses these two problems as a two-echelon location-routing problem.

A mixed-integer programming model is developed to minimize the cost of opening the facilities and transporting medical waste. Several managerial insights are drawn up to assist planners and decision-makers.

This study follows a case study approach to provide a descriptive and prescriptive approach to hospital waste management in the ancient city of Varanasi. The city has witnessed unplanned growth over the years and is densely populated. The health-care facilities in the city have a large catchment area and attract patients from neighboring districts. The situation analysis based on secondary data and unstructured interviews of the stakeholders suggests that the ad hoc approach prevails in present hospital waste management in the city.

The purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost.

To model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO).

The findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques.

The data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials.

This study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs.

This study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems.

This study focuses on a specific method of meat production that involves carcass purchase and meat production by packing facilities with a novel two-stage model that simultaneously considers location-routing and inventory-production operating decisions. The considered problem aims to reduce variable and fixed transportation and production costs, inventory holding cost and the cost of opening cold storage facilities.

The proposed model encompasses a two-stage model consisting of a single-echelon and a three-echelon many-to-many network with deterministic demand. The proposed model is a mixed-integer linear programming (MILP) model which was tested with the general algebraic modelling system (GAMS) software for a real-world case study in Iran. A sensitivity analysis was performed to examine the effect of retailers' holding capacity and supply capacity at carcass suppliers.

In this research, the number of products transferred at each level, the number of products held, the quantity of red meat produced, the required cold storage facilities and the required vehicles were optimally specified. The outcomes indicated a two percent (2%) decrease in cost per kg of red meat. Eventually, the outcomes of the first and second sensitivity analysis indicated that reduced retailers' holding capacity and supply capacity at carcass suppliers leads to higher total costs.

This research proposes a novel multi-period location-inventory-routing problem for the red meat supply chain in an emerging economy with a heterogeneous vehicle fleet and logistics decisions. The proposed model is presented in two stages and four-echelon including carcass suppliers, packing facilities, cold storage facilities and retailers.

The paper aims to study the challenges and solutions of city logistics in the new retail era. The new retail, which is characterized by omni-channel, fragmented orders and decentralized 2C distribution, is becoming the mainstream of the retail industry worldwide. In order to achieve a comprehensive breakthrough in new retail, the change of order fulfillment mode is the most noteworthy issue. The aim of this paper is to design a city logistics operation model for new retail and verify its feasibility and efficiency.

A physical internet (PI) enabled two-tier city logistics solution is proposed by redefining the key facilities in city logistics with the PI concept. A “Container-as-a-Warehouse” operation mode is designed to provide a more flexible store and transfer solution. A mathematical model of the proposed solution is established. An adaptive large neighborhood search (ALNS) is designed based upon an iterative procedure, which ensures consistent and optimal results.

To quantitatively assess the feasibility of the proposed solution, a computational experiment is designed to compare the performance of the proposed model against the conventional two-tier city logistics operation. The effects of geographical location pattern, utilization of PI-hub as well as the fluctuation of customer orders are analyzed. The results show that the PI-enabled city logistics is more advantageous than the conventional solution.

This study does not consider the impact of new technologies in city logistics; for example, the replenishment problem of unmanned vending machines and the charging problem of electric vehicles.

The proposed PI-enabled solution and analysis results in this paper have positive guiding significance for future practical application.

Based on the concept of PI, this paper proposes an innovative and practical operation model to solve the city logistics challenges.

The vehicle routing problem (VRP) has been widely investigated during last decades to reduce logistics costs and improve service level. In addition, many researchers have realized the importance of green logistic system design in decreasing environmental pollution and achieving sustainable development.

In this paper, a bi-objective mathematical model is developed for the capacitated electric VRP with time windows and partial recharge. The first objective deals with minimizing the route to reduce the costs related to vehicles, while the second objective minimizes the delay of arrival vehicles to depots based on the soft time window. A hybrid metaheuristic algorithm including non-dominated sorting genetic algorithm (NSGA-II) and teaching-learning-based optimization (TLBO), called NSGA-II-TLBO, is proposed for solving this problem. The Taguchi method is used to adjust the parameters of algorithms. Several numerical instances in different sizes are solved and the performance of the proposed algorithm is compared to NSGA-II and multi-objective simulated annealing (MOSA) as two well-known algorithms based on the five indexes including time, mean ideal distance (MID), diversity, spacing and the Rate of Achievement to two objectives Simultaneously (RAS).

The results demonstrate that the hybrid algorithm outperforms terms of spacing and RAS indexes with p-value <0.04. However, MOSA and NSGA-II algorithms have better performance in terms of central processing unit (CPU) time index. In addition, there is no meaningful difference between the algorithms in terms of MID and diversity indexes. Finally, the impacts of changing the parameters of the model on the results are investigated by performing sensitivity analysis.

In this research, an environment-friendly transportation system is addressed by presenting a bi-objective mathematical model for the routing problem of an electric capacitated vehicle considering the time windows with the possibility of recharging.

While in some districts having drinking water is a given reality, there are others where there is a lack of access to this resource. Unfortunately, even today, 10.2% of the world population lives this situation and it could be worse in the coming years, according to UNICEF. Inhabitants in Pamplona Alta at southern Lima, Peru, daily suffer this harsh reality. This social challenge study attempts to define a methodology for an effective logistic planning of water distribution in Torres Minas. Currently, they obtain it from unsanitary and informal vendors. This chapter provides the basis of a new layout of the water distribution network based on clusters to efficiently satisfy water demand. Specifically, we propose the use of orderly delivery points called “bus-stops of water” in a two-echelon distribution system, whose optimization relies on a mixed-integer linear programming (MILP) technique. The objective of these guidelines is minimizing the transactional and transportation cost, while increasing the bargaining power of the community. Results showed a reduction of 52.67% and 26% in transactional and transportation costs, respectively, and a reduction of the associated risks of shortage and contamination of a tight delivery of water. Moreover, we foster the application of this methodology in other similar situations to produce sustainable growth for human settlements; regardless, there is a lack of access to water or a steep geography.

Due to unceasing declination in environment, sustainable agro-food supply chains have become a topic of concern to business, government organizations and customers. The purpose of this study is to examine a problem associated with sustainable network design in context of Indian agro-food grain supply chain.

A mixed integer nonlinear programming (MINLP) model is suggested to apprehend the major complications related with two-echelon food grain supply chain along with sustainability aspects (carbon emissions). Genetic algorithm (GA) and quantum-based genetic algorithm (Q-GA), two meta-heuristic algorithms and LINGO 18 (traditional approach) are employed to establish the vehicle allocation and selection of orders set.

The model minimizes the total transportation cost and carbon emission tax in gathering food grains from farmers to the hubs and later to the selected demand points (warehouses). The simulated data are adopted to test and validate the suggested model. The computational experiments concede that the performance of LINGO is superior than meta-heuristic algorithms (GA and Q-GA) in terms of solution obtained, but there is trade-off with respect to computational time.

In literature, inadequate study has been perceived on defining environmental sustainable issues connected with agro-food supply chain from farmer to final distribution centers. A MINLP model has been formulated as practical scenario for central part of India that captures all the major complexities to make the system more efficient. This study is regulated to agro-food Indian industries.

The suggested network design problem is an innovative approach to design distribution systems from farmers to the hubs and later to the selected warehouses. This study considerably assists the organizations to design their distribution network more efficiently.

The purpose of this paper is to manage the demand uncertainty considered as lower and upper levels for a medium-scale industrial distribution planning problem in a biobjective inventory routing problem (IRP). In order to achieve this, the grey system theory is applied since no statistical distribution from the past data and incomplete information.

This study is investigated with optimizing the distribution plan, which involves 30 customers of 12 periods in a manufacturing company under demand uncertainty that is considered as lower and upper levels for a biobjective IRP with using grey demand parameters as a grey integer programming model. In the data set, there are also some missing demand values for the customers. So, the seven different grey models are developed to eliminat the effects on demand uncertainties in computational analysis using a piece of developed software considering the logistical performance indicators such as total deliveries, total cost, the total number of tours, distribution capacity, average remaining capacity and solution time.

Results show that comparing the grey models, the cost per unit and the maximum number of vehicle parameters are also calculated as the new key performance indicator, and then results were ranked and evaluated in detail. Another important finding is the demand uncertainties can be managed with a new approach via logistics performance indicators using alternative solutions.

The results enable logistics managers to understand the importance of demand uncertainties if more reliable decisions are wanted to make with obtaining a proper distribution plan for effective use of their expectations about the success factors in logistics management.

The study is the first in terms of the application of grey models in a biobjective IRP with using interval grey demand data. Successful implementation of the grey approaches allows obtaining a more reliable distribution plan. In addition, this paper also offers a new key performance indicator for the final decision.

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.

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.

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.

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.

The traveling purchaser problem (TPP) has gained attention in academics to deal with different variants in real business world. This study aims to study a green TPP with quantity discounts and soft time windows (TPPQS), in which a firm needs to purchase products from a set of available markets and deliver the products to a set of customers.

Vehicles are available to visit the markets, which offer products at different prices and with different quantity discount schemes. Soft time windows are present for the markets and the customers, and earliness cost and tardiness may incur if a vehicle cannot arrive a market or a customer within the designated time interval. The environmental impact of transportation activities is considered. The objective of this research is to minimize the total cost, including vehicle-assigning cost, vehicle-traveling cost, purchasing cost, emission cost, earliness cost and tardiness cost, while meeting the total demand of the customers and satisfying all the constraints. A mixed integer programming (MIP) model and a genetic algorithm (GA) approach are proposed to solve the TPPQS.

The results show that both the MIP and the GA can obtain optimal solutions for small-scale cases, and the GA can generate near-optimal solutions for large-scale cases within a short computational time.

The proposed models can help firms increase the performance of customer satisfaction and provide valuable supply chain management references in the service industry.

The proposed models for TPPQS are novel and can facilitate firms to design their green traveling purchasing plans more effectively in today’s environmental conscious and competitive market.