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This paper aims to optimize the halal product distribution by minimizing the transportation cost while ensuring halal integrity of the product. The problem is considered as a capacitated vehicle routing problem (CVRP), based on the assumption that two different types of vehicles are used for distribution: vehicles dedicated for halal product distribution and vehicles dedicated for nonhalal products distribution. The problem is modeled as an integer linear program (ILP), termed CVRP-halal and nonhalal products distribution (CVRP-HNPD). It is solved using tabu-search (TS)-based algorithm and is suitable for application to real-life sized halal product distribution.

Two approaches are used in solving the problem: exact approach (integer-linear program) and approximate approach (TS). First, the problem is modeled as ILP and solved using CPLEX Solver. To solve life-sized problems, a TS-based algorithm is developed and run using MATLAB.

The experiments on numerical data and life-sized instances validate the proposed model and algorithm and show that cost-minimizing routes for HNPD are developed while ensuring the halal integrity of the products.

The proposed model and algorithm are suitable as decision support tools for managers responsible for distribution of halal products as they facilitate the development of minimum cost distribution routes for halal and nonhalal products while maintaining the integrity of halal products. The model and algorithm provide a low transportation cost strategy at the operational level of halal products distribution while fulfilling the halal logistics requirement.

To the best of the author’s knowledge, this is the first study that specifically deals with the CVRP of halal products distribution by proposing CVRP-HNPD model and TS-CVRP-HNPD algorithm. The proposed model and algorithm ensure the integrity of halal products along the distribution chain, from the warehouse (distribution center) to the retailer, while achieving lowest transportation cost.

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.

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.

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.

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

This paper studies a concept for protecting vulnerable population groups during pandemics using direct home deliveries of essential supplies, from a distribution logistics perspective. The purpose of this paper is to evaluate feasible and resource-efficient home delivery strategies, including collaboration between retailers and logistics service providers based on a practical application.

A food home delivery concept in urban areas during pandemics is mathematically modeled. All seniors living in a district of Berlin, Germany, represent the vulnerable population supplied by a grocery distribution center. A capacitated vehicle routing problem (CVRP) is developed in combination with a k-means clustering algorithm. To manage this large-scale problem efficiently, mixed-integer programming (MIP) is used. The impact of collaboration and additional delivery scenarios is examined with a sensitivity analysis.

Roughly 45 medically vulnerable persons can be served by one delivery vehicle in the baseline scenario. Operational measures allow a drastic decrease in required resources by reducing service quality. In this way, home delivery for the vulnerable population of Berlin can be achieved. This requires collaboration between grocery and parcel services and public authorities as well as overcoming accompanying challenges.

Developing a home delivery concept for providing essential goods to urban vulnerable groups during pandemics creates a special value. Setting a large-scale CVRP with variable fleet size in combination with a clustering algorithm contributes to the originality.

The purpose of this paper is to reduce the distribution cost of an Indian cooperative dairy. The reduction of cost was achieved with the application of the clustering method (k-means clustering) and capacitated vehicle routing problem (cheapest link algorithm (CLA)).

Capacitated k-means clustering was used to split delivery locations into similar size groups (i.e. clusters) based on proximity without exceeding a specified total cluster capacity. Each cluster would be served by a local stockist. CLA was then used to find delivery routes from dairy (i.e. depot) to stockist in each cluster and from stockist to all other delivery locations within the cluster.

K-means clustering and CLA suggested optimal delivery routes on which vehicles will run. The complete algorithm was able to provide a solution within 30 s.

Clustering of delivery locations and use of heterogeneous fleet of delivery vehicles can result in considerable savings in daily operational cost.

Most of the research related to the use of demand clustering to improve distribution routes has been theoretical, which do not take into account real-world limitations like vehicle’s specific limitations. The authors tried to address that gap by taking a real-world case of a cooperative dairy and compared the result with existing distribution routes used by dairy. This work can be used by other dairies or distribution companies according to their scenario.

Population growth, urbanisation and the increased use of online shopping are some of the key challenges affecting the traditional logistics model. The purpose of this paper is to focus on the distribution of grocery products ordered online and the subsequent home delivery and click and collect services offered by online retailers to fulfil these orders. These services are unsustainable due to increased operational costs, carbon emissions, traffic and noise. The main objective of the research is to propose sustainable logistics models to reduce economic, environmental and social costs whilst maintaining service levels.

The authors have a mixed methodology based on simulation and mathematical modelling to evaluate the proposed shared logistics model using: primary data from a major UK retailer, secondary data from online retailers and primary data from a consumer survey on preferences for receiving groceries purchased online. Integration of these three data sets serves as input to vehicle routing models that reveal the benefits from collaboration by solving individual distribution problems of two retailers first, followed by the joint distribution problem under single decision maker assumption.

The benefits from collaboration could be more than 10 per cent in the distance travelled and 16 per cent in the time required to deliver the orders when two online grocery retailers collaborate in distribution activities.

The collaborative model developed for the online grocery market incentivises retailers to switch from current unsustainable logistics models to the proposed collaborative models.

Nowadays, global warming, due to large-scale emissions of greenhouse gasses, is among top environmental issues. The purpose of this paper is to present a problem involving the incorporation of environmental aspects into logistics, which provides a comparison between pollution reduction and distance-based approaches.

In green vehicle routing problem (VRP), the aim is to model and solve an optimization problem in order to minimize the fuel consumption which results in reducing energy consumption as well as air pollution. The Gravitational Search Algorithm (GSA) is adapted and used as a powerful heuristic.

Here, it is shown that a set of routes with minimum length is not an optimal solution for FCVRP model since the total distance is not the only effective factor for fuel consumption and vehicle's load plays an important role too. In many cases, a considerable reduction in emissions can be achieved by only an insignificant increase in costs.

Green transportation is a policy toward reducing carbon emissions. This research focussed on routes problem and introduce FCVRP model. GSA is used as a powerful heuristic to obtain high quality routes in a reasonable time. Considering other factors that affecting fuel consumption could make this study more realistic.

When a distribution center receives all the information it needs about the demand from all the retail stores it supplies, a VRP is produced. So the models are valid for use by all goods producers and distributors. The preliminary assessment of the proposed model and method carried out on benchmark problems up to 200 nodes.

Fuel consumption is one of the most influential factors in transportation costs. This paper introduces an innovative decision-making framework to obtain optimum routes in a vehicle routes problem considering air pollution. The results were compared from fuel consumption as well as total travel distance viewpoints.

The use of multiple-capacity rail-guided vehicles (RGVs) has made automated storage and retrieval system (AS/RS) optimization more complex. The paper performs dual-RGV scheduling considering loading/unloading and collision-avoidance constraints simultaneously as these issues have only been considered separately in the previous literature.

This paper proposes a novel model for dual-RGV scheduling with two-sided loading/unloading operations and collision-avoidance constraints. To solve the proposed problem, a hybrid harmony search algorithm (HHSA) is developed. To enhance its performance, a descent-based local search with eight move operators is introduced.

A group of problem instances at different scales are optimized with the proposed algorithm and the results are compared with those of two other high-performance methods. The results demonstrate that the proposed method can efficiently solve realistically sized cases of dual multi-capacity RGV scheduling problems in AS/RSs.

For the first time in the research on dual multi-capacity RGV scheduling in an AS/RS, two-sided loading/unloading operations and collision avoidance constraints are simultaneously considered. Furthermore, a mathematical model for minimizing the makespan is developed and the HHSA is developed to determine solutions.

The purpose of this paper is to explore a real world vehicle routing problem (VRP) that has multi-depot subcontractors with a heterogeneous fleet of vehicles that are available to pickup/deliver jobs with varying time windows and locations. Both the overall job completion time and number of drivers utilized are analyzed for the automated job allocations and manual job assignments from transportation field experts.

A nested genetic algorithm (GA) is used to automate the job allocation process and minimize the overall time to deliver all jobs, while utilizing the fewest number of drivers – as a secondary objective.

Three different real world data sets were used to compare the results of the GA vs transportation field experts’ manual assignments. The job assignments from the GA improved the overall job completion time in 100 percent (30/30) of the cases and maintained the same or fewer drivers as BS Logistics (BSL) in 47 percent (14/30) of the cases.

This paper provides a novel approach to solving a real world VRP that has multiple variants. While there have been numerous models to capture a select number of these variants, the value of this nested GA lies in its ability to incorporate multiple depots, a heterogeneous fleet of vehicles as well as varying pickup times, pickup locations, delivery times and delivery locations for each job into a single model. Existing research does not provide models to collectively address all of these variants.

A variant of Kohonen's algorithm for the self‐organizing map (SOM) is used to solve a continuous location‐routing problem that can be applied to identify potential sites for subsequent selection by a discrete finite set model. The paper aims to show how the algorithm may be customized to fit the problem structure in a way that allows aspects of location and routing to be integrated into the solution procedure.

A set of test instances is used to compare the solutions of the neural network to those obtained by sequential approaches based on a savings procedure.

Compared to the results of the sequential approaches, the neural network yields good results.

Future work may cover the expansion of the neural approach to multi‐depot and multi‐stage problems. Additionally, application of procedures other than the savings procedure should be evaluated with respect to their potential for further enhancing the solution quality of the sequential approaches.

This paper shows that strategic location decisions in practical applications with long‐term customer relationships can be taken using simultaneously generated routing information on an operational level.

The paper provides a new variety of applications for SOM as well as high quality results for the specific type of problem considered.

In a kitting supply system, the occurrence of material-handling errors is unavoidable and will cause serious production losses to an assembly line. To minimize production losses, this paper aims to present a dynamic scheduling problem of automotive assembly line considering material-handling mistakes by integrating abnormal disturbance into the material distribution problem of mixed-model assembly lines (MMALs).

A multi-phase dynamic scheduling (MPDS) algorithm is proposed based on the characteristics and properties of the dynamic scheduling problem. In the first phase, the static material distribution scheduling problem is decomposed into three optimization sub-problems, and the dynamic programming algorithm is used to jointly optimize the sub-problems to obtain the optimal initial scheduling plan. In the second phase, a two-stage rescheduling algorithm incorporating removing rules and adding rules was designed according to the status update mechanism of material demand and multi-load AGVs.

Through comparative experiments with the periodic distribution strategy (PD) and the direct insertion method (DI), the superiority of the proposed dynamic scheduling strategy and algorithm is verified.

To the best of the authors’ knowledge, this study is the first to consider the impact of material-handling errors on the material distribution scheduling problem when using a kitting strategy. By designing an MPDS algorithm, this paper aims to maximize the absorption of the disturbance caused by material-handling errors and reduce the production losses of the assembly line as well as the total cost of the material transportation.