Search results

1 – 10 of over 10000
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.

Article
Publication date: 17 January 2022

Leila Hashemi, Armin Mahmoodi, Milad Jasemi, Richard C. Millar and Jeremy Laliberté

In the present research, location and routing problems, as well as the supply chain, which includes manufacturers, distributor candidate sites and retailers, are explored…

Abstract

Purpose

In the present research, location and routing problems, as well as the supply chain, which includes manufacturers, distributor candidate sites and retailers, are explored. The goal of addressing the issue is to reduce delivery times and system costs for retailers so that routing and distributor location may be determined.

Design/methodology/approach

By adding certain unique criteria and limits, the issue becomes more realistic. Customers expect simultaneous deliveries and pickups, and retail service start times have soft and hard time windows. Transportation expenses, noncompliance with the soft time window, distributor construction, vehicle purchase or leasing, and manufacturing costs are all part of the system costs. The problem's conceptual model is developed and modeled first, and then General Algebraic Modeling System software (GAMS) and Multiple Objective Particle Swarm Optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGAII) algorithms are used to solve it in small dimensions.

Findings

According to the mathematical model's solution, the average error of the two suggested methods, in contrast to the exact answer, is less than 0.7%. In addition, the performance of algorithms in terms of deviation from the GAMS exact solution is pretty satisfactory, with a divergence of 0.4% for the biggest problem (N = 100). As a result, NSGAII is shown to be superior to MOSPSO.

Research limitations/implications

Since this paper deals with two bi-objective models, the priorities of decision-makers in selecting the best solution were not taken into account, and each of the objective functions was given an equal weight based on the weighting procedures. The model has not been compared or studied in both robust and deterministic modes. This is because, with the exception of the variable that indicates traffic mode uncertainty, all variables are deterministic, and the uncertainty character of demand in each level of the supply chain is ignored.

Practical implications

The suggested model's conclusions are useful for any group of decision-makers concerned with optimizing production patterns at any level. The employment of a diverse fleet of delivery vehicles, as well as the use of stochastic optimization techniques to define the time windows, demonstrates how successful distribution networks are in lowering operational costs.

Originality/value

According to a multi-objective model in a three-echelon supply chain, this research fills in the gaps in the link between routing and location choices in a realistic manner, taking into account the actual restrictions of a distribution network. The model may reduce the uncertainty in vehicle performance while choosing a refueling strategy or dealing with diverse traffic scenarios, bringing it closer to certainty. In addition, two modified MOPSO and NSGA-II algorithms are presented for solving the model, with the results compared to the exact GAMS approach for medium- and small-sized problems.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 15 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

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

Article
Publication date: 1 December 1999

Tzong‐Ru Lee and Ji‐Hwa Ueng

In a modern business environment, employees are a key resource to a company. Hence, the competitiveness of a company depends largely on its ability to treat employees…

3231

Abstract

In a modern business environment, employees are a key resource to a company. Hence, the competitiveness of a company depends largely on its ability to treat employees fairly. Fairness can be attained by using the load‐balancing methodology. Develops an integer programming model for vehicle routing problems. There are two objectives, first, to minimize the total distance, and second, to balance the workload among employees as much as possible. We also develop a heuristic algorithm to solve the problems. The findings show that the proposed heuristic algorithm performs well to our 11 test cases.

Details

International Journal of Physical Distribution & Logistics Management, vol. 29 no. 10
Type: Research Article
ISSN: 0960-0035

Keywords

Open Access
Article
Publication date: 17 November 2021

Leila Hashemi, Armin Mahmoodi, Milad Jasemi, Richard C. Millar and Jeremy Laliberté

This study aims to investigate a locating-routing-allocating problems and the supply chain, including factories distributor candidate locations and retailers. The purpose…

Abstract

Purpose

This study aims to investigate a locating-routing-allocating problems and the supply chain, including factories distributor candidate locations and retailers. The purpose of this paper is to minimize system costs and delivery time to retailers so that routing is done and the location of the distributors is located.

Design/methodology/approach

The problem gets closer to reality by adding some special conditions and constraints. Retail service start times have hard and soft time windows, and each customer has a demand for simultaneous delivery and pickups. System costs include the cost of transportation, non-compliance with the soft time window, construction of a distributor, purchase or rental of a vehicle and production costs. The conceptual model of the problem is first defined and modeled and then solved in small dimensions by general algebraic modeling system (GAMS) software and non-dominated sorting genetic algorithm II (NSGAII) and multiple objective particle swarm optimization (MOPSO) algorithms.

Findings

According to the solution of the mathematical model, the average error of the two proposed algorithms in comparison with the exact solution is less than 0.7%. Also, the algorithms’ performance in terms of deviation from the GAMS exact solution, is quite acceptable and for the largest problem (N = 100) is 0.4%. Accordingly, it is concluded that NSGAII is superior to MOSPSO.

Research limitations/implications

In this study, since the model is bi-objective, the priorities of decision makers in choosing the optimal solution have not been considered and each of the objective functions has been given equal importance according to the weighting methods. Also, the model has not been compared and analyzed in deterministic and robust modes. This is because all variables, except the one that represents the uncertainty of traffic modes, are deterministic and the random nature of the demand in each graph is not considered.

Practical implications

The results of the proposed model are valuable for any group of decision makers who care optimizing the production pattern at any level. The use of a heterogeneous fleet of delivery vehicles and application of stochastic optimization methods in defining the time windows, show how effective the distribution networks are in reducing operating costs.

Originality/value

This study fills the gaps in the relationship between location and routing decisions in a practical way, considering the real constraints of a distribution network, based on a multi-objective model in a three-echelon supply chain. The model is able to optimize the uncertainty in the performance of vehicles to select the refueling strategy or different traffic situations and bring it closer to the state of certainty. Moreover, two modified algorithms of NSGA-II and multiple objective particle swarm optimization (MOPSO) are provided to solve the model while the results are compared with the exact general algebraic modeling system (GAMS) method for the small- and medium-sized problems.

Details

Smart and Resilient Transportation, vol. 3 no. 3
Type: Research Article
ISSN: 2632-0487

Keywords

Article
Publication date: 13 May 2022

Zeynep Aydınalp and Doğan Özgen

Drugs are strategic products with essential functions in human health. An optimum design of the pharmaceutical supply chain is critical to avoid economic damage and…

Abstract

Purpose

Drugs are strategic products with essential functions in human health. An optimum design of the pharmaceutical supply chain is critical to avoid economic damage and adverse effects on human health. The vehicle-routing problem, focused on finding the lowest-cost routes with available vehicles and constraints, such as time constraints and road length, is an important aspect of this. In this paper, the vehicle routing problem (VRP) for a pharmaceutical company in Turkey is discussed.

Design/methodology/approach

A mixed-integer programming (MIP) model based on the vehicle routing problem with time windows (VRPTW) is presented, aiming to minimize the total route cost with certain constraints. As the model provides an optimum solution for small problem sizes with the GUROBI® solver, for large problem sizes, metaheuristic methods that simulate annealing and adaptive large neighborhood search algorithms are proposed. A real dataset was used to analyze the effectiveness of the metaheuristic algorithms. The proposed simulated annealing (SA) and adaptive large neighborhood search (ALNS) were evaluated and compared against GUROBI® and each other through a set of real problem instances.

Findings

The model is solved optimally for a small-sized dataset with exact algorithms; for solving a larger dataset, however, metaheuristic algorithms require significantly lesser time. For the problem addressed in this study, while the metaheuristic algorithms obtained the optimum solution in less than one minute, the solution in the GUROBI® solver was limited to one hour and three hours, and no solution could be obtained in this time interval.

Originality/value

The VRPTW problem presented in this paper is a real-life problem. The vehicle fleet owned by the factory cannot be transported between certain suppliers, which complicates the solution of the problem.

Details

International Journal of Intelligent Computing and Cybernetics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 1 April 1989

Horst A. Eiselt and Gilbert Laporte

Distribution systems planning frequently involves two majordecisions: facility location and vehicle routing. The facilities to belocated may be “primary facilities”, e.g…

Abstract

Distribution systems planning frequently involves two major decisions: facility location and vehicle routing. The facilities to be located may be “primary facilities”, e.g. factories, but more often, these are lighter “secondary facilities” such as depots, warehouses or distribution centres. Routing decisions concern the optimal movement of goods and vehicles in the system, usually from primary to secondary facilities, and from secondary facilities to users or customers. Studies which integrate the two areas are more often than not limited to the case where all deliveries are return trips involving only one destination. There exist, however, several situations where vehicles visit more than one point on the same trip. In such cases, relationships between location and routing decisions become more intricate. Strategies by which the two aspects of the problem are optimised separately and sequentially are often sub‐optimal. Also of importance is the trade‐off between the cost of providing service and customer inconvenience. A framework is proposed for the study of such combined location‐routing problems. A number of real‐life cases described in the literature are summarised and some algorithmic issues related to such problems are discussed.

Details

International Journal of Physical Distribution & Materials Management, vol. 19 no. 4
Type: Research Article
ISSN: 0269-8218

Keywords

Open Access
Article
Publication date: 30 September 2021

Thakshila Samarakkody and Heshan Alagalla

This research is designed to optimize the business process of a green tea dealer, who is a key supply chain partner of the Sri Lankan tea industry. The most appropriate…

Abstract

Purpose

This research is designed to optimize the business process of a green tea dealer, who is a key supply chain partner of the Sri Lankan tea industry. The most appropriate trips for each vehicle in multiple trip routing systems are identified to minimize the total cost by considering the traveling distance.

Design/methodology/approach

The study has followed the concepts in vehicle routing problems and mixed-integer programming mathematical techniques. The model was coded with the Python programming language and was solved with the CPLEX Optimization solver version 12.10. In total, 20 data instances were used from the subjected green tea dealer for the validation of the model.

Findings

The result of the numerical experiment showed the ability to access supply over the full capacity of the available fleet. The model achieved optimal traveling distance for all the instances, with the capability of saving 17% of daily transpiration cost as an average.

Research limitations/implications

This study contributes to the three index mixed-integer programing model formulation through in-depth analysis and combination of several extensions of vehicle routing problem.

Practical implications

This study contributes to the three index mixed-integer programming model formulation through in-depth analysis and combination of several extensions of the vehicle routing problem.

Social implications

The proposed model provides a cost-effective optimal routing plan to the green tea dealer, which satisfies all the practical situations by following the multiple trip vehicle routing problems. Licensee green tea dealer is able to have an optimal fleet size, which is always less than the original fleet size. Elimination of a vehicle from the fleet has the capability of reducing the workforce. Hence, this provides managerial implication for the optimal fleet sizing and route designing.

Originality/value

Developing an optimization model for a tea dealer in Sri Lankan context is important, as this a complex real world case which has a significant importance in export economy of the country and which has not been analyzed or optimized through any previous research effort.

Details

Modern Supply Chain Research and Applications, vol. 3 no. 4
Type: Research Article
ISSN: 2631-3871

Keywords

Article
Publication date: 20 February 2020

Ashkan Ayough, Behrooz Khorshidvand, Negah Massomnedjad and Alireza Motameni

As a critical problem in sophisticated distribution systems, vehicle routing plays a pivotal role in dealing with time windows and capacities constraints. The purpose of…

Abstract

Purpose

As a critical problem in sophisticated distribution systems, vehicle routing plays a pivotal role in dealing with time windows and capacities constraints. The purpose of this paper is to addresses a new integrated model to incorporate both three-dimensional and time windows aspects of the routing problem. First, capacitated vehicle routing decisions are made subject to a soft time interval to meet the customers’ demands. Afterward, these decisions are entered into the three-dimensional loading problem.

Design/methodology/approach

The problem is solved using generalized algebraic modeling system software in small-size problems. The problem is NP-hard and requires an efficient solution methodology. For this purpose, a hybrid algorithm has been proposed to solve the large-size problems. The efficiency of this algorithm is checked by making comparisons with exact solutions for small and medium size test problems, and with the related literature for large size problems.

Findings

The numerical experiments show that the proposed model covers more effectively the broader aspects of the transportation problem. Furthermore, the proposed algorithm supports competitive and satisfactory results by giving reasonable outputs in comparison with previous studies.

Originality/value

The main purpose of this integration is to achieve minimum total transportation costs, which cannot be guaranteed without applying two referred constraints, simultaneously.

Details

Journal of Modelling in Management, vol. 15 no. 3
Type: Research Article
ISSN: 1746-5664

Keywords

1 – 10 of over 10000