Search results

1 – 10 of over 3000
To view the access options for this content please click here
Article
Publication date: 31 May 2021

Misagh Rahbari, Seyed Hossein Razavi Hajiagha, Hannan Amoozad Mahdiraji, Farshid Riahi Dorcheh and Jose Arturo Garza-Reyes

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

To view the access options for this content please click here
Article
Publication date: 14 November 2018

Evangelia Baou, Vasilis P. Koutras, Vasileios Zeimpekis and Ioannis Minis

The purpose of this paper is to formulate and solve a new emergency evacuation planning problem. This problem addresses the needs of both able and disabled persons who are…

Abstract

Purpose

The purpose of this paper is to formulate and solve a new emergency evacuation planning problem. This problem addresses the needs of both able and disabled persons who are evacuated from multiple pick-up locations and transported using a heterogeneous fleet of vehicles.

Design/methodology/approach

The problem is formulated using a mixed integer linear programming model and solved using a heuristic algorithm. The authors analyze the selected heuristic with respect to key parameters and use it to address theoretical and practical case studies.

Findings

Evacuating people with disabilities has a significant impact on total evacuation time, due to increased loading/unloading times. Additionally, increasing the number of large capacity vehicles adapted to transport individuals with disabilities benefits total evacuation time.

Research limitations/implications

The mathematical model is of high complexity and it is not possible to obtain exact solutions in reasonable computational times. The efficiency of the heuristic has not been analyzed with respect to optimality.

Practical implications

Solving the problem by a heuristic provides a fast solution, a requirement in emergency evacuation cases, especially when the state of the theater of the emergency changes dynamically. The parametric analysis of the heuristic provides valuable insights in improving an emergency evacuation system.

Social implications

Efficient population evacuation studied in this work may save lives. This is especially critical for disabled evacuees, the evacuation of whom requires longer operational times.

Originality/value

The authors consider a population that comprises able and disabled individuals, the latter with varying degrees of disability. The authors also consider a heterogeneous fleet of vehicles, which perform multiple trips during the evacuation process.

Details

Journal of Humanitarian Logistics and Supply Chain Management, vol. 8 no. 4
Type: Research Article
ISSN: 2042-6747

Keywords

To view the access options for this content please click here
Article
Publication date: 4 December 2020

Fatemeh Sabouhi, Ali Bozorgi-Amiri and Parinaz Vaez

This study aims to minimize the expected arrival time of relief vehicles to the affected areas, considering the destruction of potential routes and disruptions due to…

Abstract

Purpose

This study aims to minimize the expected arrival time of relief vehicles to the affected areas, considering the destruction of potential routes and disruptions due to disasters. In relief operations, required relief items in each affected area and disrupted routes are considered as uncertain parameters. Additionally, for a more realistic consideration of the situations, it is assumed that the demand of each affected area could be met by multiple vehicles and distribution centers (DCs) and vehicles have limited capacity.

Design/methodology/approach

The current study developed a two-stage stochastic programming model for the distribution of relief items from DCs to the affected areas. Locating the DCs was the first-stage decisions in the introduced model. The second-stage decisions consisted of routing and scheduling of the vehicles to reach the affected areas.

Findings

In this paper, 7th district of Tehran was selected as a case study to assess the applicability of the model, and related results and different sensitivity analyses were presented as well. By carrying out a simultaneous sensitivity analysis on the capacity of vehicles and the maximum number of DCs that can be opened, optimal values for these parameters were determined, that would help making optimal decisions upon the occurrence of a disaster to decrease total relief time and to maximize the exploitation of available facilities.

Originality/value

The contributions of this paper are as below: presenting an integrated model for the distribution of relief items among affected areas in the response phase of a disaster, using a two-stage stochastic programming approach to cope with route disruptions and uncertain demands for relief items, determining location of the DCs and routing and scheduling of vehicles to relief operations and considering a heterogeneous fleet of capacitated relief vehicles and DCs with limited capacity and fulfilling the demand of each affected area by more than one vehicle to represent more realistic situations.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

To view the access options for this content please click here
Article
Publication date: 17 September 2018

Masoud Rabbani, Pooya Pourreza, Hamed Farrokhi-Asl and Narjes Nouri

This paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW).

Abstract

Purpose

This paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW).

Design/methodology/approach

The objective of this problem is minimization of the total traveling cost and the time window violations. Two meta-heuristic algorithms, namely, simple genetic algorithm (GA) and hybrid genetic algorithm (HGA) are used to find the best solution for this problem. A comparison on the results of these two algorithms has been done and based on the outcome, it has been proved that HGA has better performance than GA.

Findings

A comparison on the results of these two algorithms has been done and based on the outcome, it has been proved that HGA has better performance than GA.

Originality/value

This paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW). The defined problem is a practical problem in the supply management and logistic. The repair vehicle services the customers who have goods, while the pickup vehicle visits the customer with nonrepaired goods. All the vehicles belong to an internal fleet of a company and have different capacities and fixed/variable cost. Moreover, vehicles have different limitations in their time of traveling. The objective of this problem is minimization of the total traveling cost and the time window violations. Two meta-heuristic algorithms (simple genetic algorithm and hybrid one) are used to find the best solution for this problem.

Details

Journal of Modelling in Management, vol. 13 no. 3
Type: Research Article
ISSN: 1746-5664

Keywords

Content available
Article
Publication date: 10 December 2020

Dave C. Longhorn and John Dale Stobbs

This paper aims to propose two solution approaches to determine the number of ground transport vehicles that are required to ensure the on-time delivery of military…

Abstract

Purpose

This paper aims to propose two solution approaches to determine the number of ground transport vehicles that are required to ensure the on-time delivery of military equipment between origin and destination node pairs in some geographic region, which is an important logistics problem at the US Transportation Command.

Design/methodology/approach

The author uses a mathematical program and a traditional heuristic to provide optimal and near-optimal solutions, respectively. The author also compares the approaches for random, small-scale problems to assess the quality and computational efficiency of the heuristic solution, and also uses the heuristic to solve a notional, large-scale problem typical of real problems.

Findings

This work helps analysts identify how many ground transport vehicles are needed to meet cargo delivery requirements in any military theater of operation.

Research limitations/implications

This research assumes all problem data is deterministic, so it does not capture variations in requirements or transit times between nodes.

Practical implications

This work provides prescriptive details to military analysts and decision-makers in a timely manner. Prior to this work, insights for this type of problem were generated using time-consuming simulation taking about a week and often involving trial-and-error.

Originality/value

This research provides new methods to solve an important logistics problem. The heuristic presented in this paper was recently used to provide operational insights about ground vehicle requirements to support a geographic combatant command and to inform decisions for railcar recapitalization within the US Army.

Details

Journal of Defense Analytics and Logistics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2399-6439

Keywords

To view the access options for this content please click here
Article
Publication date: 27 September 2019

Sanjay Jharkharia and Chiranjit Das

The purpose of this study is to model a vehicle routing problem with integrated picking and delivery under carbon cap and trade policy. This study also provides…

Abstract

Purpose

The purpose of this study is to model a vehicle routing problem with integrated picking and delivery under carbon cap and trade policy. This study also provides sensitivity analyses of carbon cap and price to the total cost.

Design/methodology/approach

A mixed integer linear programming (MILP) model is formulated to model the vehicle routing with integrated order picking and delivery constraints. The model is then solved by using the CPLEX solver. Carbon footprint is estimated by a fuel consumption function that is dependent on two factors, distance and vehicle speed. The model is analyzed by considering 10 suppliers and 20 customers. The distance and vehicle speed data are generated using simulation with random numbers.

Findings

Significant amount of carbon footprint can be reduced through the adoption of eco-efficient vehicle routing with a marginal increase in total transportation cost. Sensitivity analysis indicates that compared to carbon cap, carbon price has more influence on the total cost.

Research limitations/implications

The model considers mid-sized problem instances. To analyze large size problems, heuristics and meta-heuristics may be used.

Practical implications

This study provides an analysis of carbon cap and price model that would assist practitioners and policymakers in formulating their policy in the context of carbon emissions.

Originality/value

This study provides two significant contributions to low carbon supply chain management. First, it provides a vehicle routing model under carbon cap and trade policy. Second, it provides a sensitivity analysis of carbon cap and price in the model.

To view the access options for this content please click here
Article
Publication date: 9 November 2015

Christoph H Glock and Taebok Kim

This paper studies a supply chain consisting of multiple suppliers and a single buyer. It considers the case where a set of heterogeneous trucks is used for transporting…

Abstract

Purpose

This paper studies a supply chain consisting of multiple suppliers and a single buyer. It considers the case where a set of heterogeneous trucks is used for transporting products, and develops a mathematical model that coordinates the supply chain. The purpose of this paper is to minimise the costs of producing and delivering a product as well as carbon emissions resulting from transportation. In addition, the authors analyse how imposing a tax on carbon emissions impacts the delivery of products from the suppliers to the buyer.

Design/methodology/approach

It is assumed that heterogeneous vehicles are used for transporting products, which have different performance and cost attributes. A mathematical model that considers both operating costs and carbon emissions from transportation is developed. The impact of vehicle attributes on lot sizing and routing decisions is studied with the help of numerical examples and a sensitivity analysis.

Findings

The analysis shows that considering carbon emissions in coordinating a supply chain leads to changes in the routing of vehicles. It is further shown that if carbon emissions lead to costs, routes are changed in such a way that vehicles travel long distances empty or with a low vehicle load to reduce fuel consumption and therewith emissions.

Research limitations/implications

Several areas for future work are highlighted. The study of alternative supply chain structures, for example structures which include logistics service providers, or the investigation of different functional relationships between vehicle load and emission generation offer possibilities for extending the model.

Originality/value

The paper is one of the first to study the use of heterogeneous vehicles in an inventory model of a supply chain, and one of the few supply chain inventory models that consider ecological aspects.

Details

The International Journal of Logistics Management, vol. 26 no. 3
Type: Research Article
ISSN: 0957-4093

Keywords

To view the access options for this content please click here
Article
Publication date: 14 March 2018

Meilinda F.N. Maghfiroh and Shinya Hanaoka

The purpose of this paper is to investigate the application of the dynamic vehicle routing problem for last mile distribution during disaster response. The authors explore…

Abstract

Purpose

The purpose of this paper is to investigate the application of the dynamic vehicle routing problem for last mile distribution during disaster response. The authors explore a model that involves limited heterogeneous vehicles, multiple trips, locations with different accessibilities, uncertain demands, and anticipating new locations that are expected to build responsive last mile distribution systems.

Design/methodology/approach

The modified simulated annealing algorithm with variable neighborhood search for local search is used to solve the last mile distribution model based on the criterion of total travel time. A dynamic simulator that accommodates new requests from demand nodes and a sample average estimator was added to the framework to deal with the stochastic and dynamicity of the problem.

Findings

This study illustrates some practical complexities in last mile distribution during disaster response and shows the benefits of flexible vehicle routing by considering stochastic and dynamic situations.

Research limitations/implications

This study only focuses day-to-day distribution on road/land transportation for distribution, and additional transportation modes need to be considered further.

Practical implications

The proposed model offers operational insights for government disaster agencies by highlighting the dynamic model concept for supporting relief distribution decisions. The result suggests that different characteristics and complexities of affected areas might require different distribution strategies.

Originality/value

This study modifies the concept of the truck and trailer routing problem to model locations with different accessibilities while anticipating the information gap for demand size and locations. The results show the importance of flexible distribution systems during a disaster for minimizing the disaster risks.

Details

Journal of Humanitarian Logistics and Supply Chain Management, vol. 8 no. 2
Type: Research Article
ISSN: 2042-6747

Keywords

To view the access options for this content please click here
Article
Publication date: 1 January 2013

Ravindra Kumar, Purnima Parida, Bhujang Kanga Durai and Wafaa Saleh

Heterogeneous traffic in Delhi is complex to understand due its typical composition, speed acceleration, cruising, deceleration and idling activity in flow. To arrive at…

Abstract

Purpose

Heterogeneous traffic in Delhi is complex to understand due its typical composition, speed acceleration, cruising, deceleration and idling activity in flow. To arrive at accurate emission factor estimates and implement proper traffic demand management there is need to understand microscopic vehicle operation activity. The vehicular operations are easily quantified by understanding driving cycle of the particular vehicle in real world driving conditions. The purpose of this paper is to present a study on the understanding of driving conditions in India that are heterogeneous in nature.

Design/methodology/approach

To understand the heterogeneity, the driving cycle data were collected using GPS on different types of both motorised and non‐motorized modes of transport, e.g. car, auto rickshaw, bus, motorcycle and cycle rickshaw and bicycle on different traffic corridors in Delhi.

Findings

Research findings show that driving cycles differ for different types of vehicles. Therefore, each mode should be encouraged based on their average speed‐time sequence in any traffic mix. The real‐world driving cycle will be also useful for the understanding of fuel consumption and emissions in real‐world scenarios, in order to control vehicle emissions properly, achieve fuel efficiency and to obtain a more sustainable transport system.

Originality/value

This type of research has not been carried out previously in any Indian city.

Details

World Journal of Science, Technology and Sustainable Development, vol. 10 no. 1
Type: Research Article
ISSN: 2042-5945

Keywords

To view the access options for this content please click here
Book part
Publication date: 20 August 2018

Bartosz Sawik

In this chapter, four bi-objective vehicle routing problems are considered. Weighted-sum approach optimization models are formulated with the use of mixed-integer…

Abstract

In this chapter, four bi-objective vehicle routing problems are considered. Weighted-sum approach optimization models are formulated with the use of mixed-integer programming. In presented optimization models, maximization of capacity of truck versus minimization of utilization of fuel, carbon emission, and production of noise are taken into account. The problems deal with real data for green logistics for routes crossing the Western Pyrenees in Navarre, Basque Country, and La Rioja, Spain.

Heterogeneous fleet of trucks is considered. Different types of trucks have not only different capacities, but also require different amounts of fuel for operations. Consequently, the amount of carbon emission and noise vary as well. Modern logistic companies planning delivery routes must consider the trade-off between the financial and environmental aspects of transportation. Efficiency of delivery routes is impacted by truck size and the possibility of dividing long delivery routes into smaller ones. The results of computational experiments modeled after real data from a Spanish food distribution company are reported. Computational results based on formulated optimization models show some balance between fleet size, truck types, and utilization of fuel, carbon emission, and production of noise. As a result, the company could consider a mixture of trucks sizes and divided routes for smaller trucks. Analyses of obtained results could help logistics managers lead the initiative in environmental conservation by saving fuel and consequently minimizing pollution. The computational experiments were performed using the AMPL programming language and the CPLEX solver.

1 – 10 of over 3000