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The value chain of the Norwegian meat production industry has recently been through major structural changes resulting in increased flows and transportation needs at all levels. The purpose of this paper is to present results of the initial stage of a five‐year research project between the Norwegian Meat Research Centre, Norwegian meat companies and Molde University College. The main goal of the project is to develop a decision support system for the transport of live animals to a slaughterhouse to reduce transportation costs while maintaining high level of livestock welfare and meat quality, as these are three main factors for the profitability of both farmers and industry.

The paper presents a mixed integer programming model that combines vehicle routing and inventory control. We introduce the possibility for multiple routes for a given vehicle on a given day in a multiple‐period planning perspective. Arrival times of the loaded vehicles to the slaughterhouse are controlled by production (slaughter) rate and inventory level at the abattoirs so that the supply of animals for slaughter is steady and production breaks are avoided. Livestock welfare is secured by the route duration constraints.

The model has been formulated and tested on small data sets. The major future challenge is to solve real‐life problems from the involved companies.

The main limitation is the present inability to solve large cases.

The model combining transportation and inventory control in a setting of animal welfare constraints is original.

Preface The functions of business divide into several areas and the general focus of this book is on one of the most important although least understood of these—DISTRIBUTION. The particular focus is on reviewing current practice in distribution costing and on attempting to push the frontiers back a little by suggesting some new approaches to overcome previously defined shortcomings.

The global energy industry transports supplies and personnel via helicopter to offshore locations and is increasingly focusing on optimizing upstream logistics. This paper aims to and achieves a mutually beneficial balance between research and practice by providing generalizable methods to a problem routinely encountered in practice. Overall, the development and execution of the heterogeneous capacitated helicopter routing problem with split deliveries and multiple depots is validated by the networks’ results.

Using a unique sample of deepwater and ultra-deepwater permanent offshore locations in the Gulf of Mexico, transportation networks consisting of 57 locations operated by 19 firms are optimized via a randomized greedy algorithm. The study’s randomized greedy algorithm yields depot assignment, vehicle assignment, passenger assignment and routing. All data processing techniques and iterative algorithm processes are defined and explained.

Results show that the model effectively solves the complex transportation networks consisting of subject firms’ offshore nodes and eligible depots. Specifically, average load factors related to seat capacity and effective vehicle capacity of 87.7 and 95.7% are realized, respectively. The study’s model is a unique contribution to the extant literature and provides researchers and practitioners a practical approach to model development and solution deliverance.

The extant literature encompasses works that inadequately observe the complexity associated with the transportation of personnel. Specifically, this research, unlike many works in the extant literature, uses a heterogeneous versus homogeneous fleet, includes multiple depots versus a single depot and allows split deliveries. Also, the current research ensures all relevant aircraft capabilities and limitations are observed. In particular, the paper takes into account vehicles’ seat capacities, effective capacities via maximum gross takeoff weights and reserve fuel requirements. The current model, which is built upon a heterogeneous capacitated helicopter routing problem with split deliveries and multiple depots (HCHRPSDMD), sufficiently provides a practical approach to model development and solution deliverance while promoting future research endeavors. Future research may use these findings for other geographical regions and similar transportation networks and could adopt firm-specific actual cost parameters instead of the estimated average hourly costs of operating different helicopters. Furthermore, future endeavors may employ other techniques for the derivation of solutions. Future works may be enhanced with actual cost data in lieu of estimations. In the current study, cost data were not available; however, estimations do not inherently proscribe sound interpretations of the models’ outputs. Also, future research endeavors including manual method results may enable comparative results to establish cost variance analysis. Although the current study is, to some extent, limited, the practicality for practitioners and contribution to researchers is comprehensible. Due to the idiosyncrasies and complexity prevalent in modern transportation networks, optimization is and will continue to be a rich opportunity for implementation and research.

As described by previous researchers, energy firms may more efficiently use their contracted aircraft via implementation of a decision-making mechanism for passenger assignment, aircraft selection, depot selection and aircraft routing. Most energy firms possess numerous and spatially segregated offshore facilities and, therefore, are unable to efficiently and effectively make such decisions. Ultimately, the efficient use of firms’ contracted helicopters can enhance profitability via reduced costs without compromising operational performance. Reduced costs are likely to be realized by a potential workforce or workload reduction, reduced flight hours and enhanced bargaining power with commercial helicopter operators. Specifically, enhanced bargaining power may be realized as a result of minimized depots from which the aircraft are operated and an overall reduction of aircraft via increased asset utilization. In essence, the efficient use of commercial helicopters may yield systemic efficiencies that can be shared among all stakeholders, contracting energy firms and commercial helicopter operators. The achievement of operational efficiencies, ultimately, may determine the realization of target performance or solvency of a plethora of firms in the future (Krishnan et al., 2019).

For economies, communities and industries depending on crude oil and natural gas production, people’s livelihoods are significantly impacted due to price fluctuations (Rostan and Rostan, 2020; Solaymani, 2019). Based on a unique set of inputs and outputs, the International Energy Agency region (IEA), which includes the current study’s sample set, was found to achieve greater overall production efficiency relative to the Organization of the Petroleum Exporting Countries (OPEC) and the Organization of Arab Petroleum Exporting Countries (OAPEC) (Ohene-Asare et al., 2018). Therefore, enhanced logistics efficiency within the energy industry’s transportation sector across the globe is reasonably likely. For countries relying on these commodities’ exportation, production efficiency is and will continue to be a priority. With limited resources available in industry and society, efficiency is prone to yield advantageous results for all stakeholders. Furthermore, in the context of this study, a reduction of carbon dioxide and noise pollution in air, above water and on land will contribute to society’s drive to protect the environment and preserve our natural resources for future generations.

The current study represents the lone or one of few research endeavors to evaluate the heterogeneous capacitated helicopter routing problem with split deliveries and multiple depots. Furthermore, research pertaining to transportation via helicopter in the Gulf of Mexico’s offshore basin is unprecedented. Lastly, this work yields actionable knowledge for practitioners while enhancing current and promoting future research endeavors.

The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology.

Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques.

Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate.

Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.

Real time control and scheduling systems determine the vehicle routing plan based on the current status of the system. The status of a system can be represented by different attributes of demand such as location, quantity, and due date. The objective of this article is to propose a real time dynamic vehicle control and scheduling system for multi‐depot physical distribution. To perform the system objectives effectively, the proposed system includes five major modules. These are: global information collection system, depot controller, route planner, vehicle scheduler, vechicle route and time table feedback system. A simulation experiment is described at the end of the article to evaluate the performance of the proposed system. The results indicate that the proposed system is promising and can be implemented in practical operations.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The purpose of this paper is to solve the capacitated location routing problem (CLRP), which is an NP-hard problem that involves making strategic decisions as well as tactical and operational decisions, using a hybrid particle swarm optimization (PSO) algorithm.

PSO, which is a population-based metaheuristic, is combined with a variable neighborhood strategy variable neighborhood search to solve the CLRP.

The algorithm is tested on a set of instances available in the literature and gave good quality solutions, results are compared to those obtained by other metaheuristic, evolutionary and PSO algorithms.

Local search is a time consuming phase in hybrid PSO algorithms, a set of neighborhood structures suitable for the solution representation used in the PSO algorithm is proposed in the VNS phase, moves are applied directly to particles, a clear decoding method is adopted to evaluate a particle (solution) and there is no need to re-encode solutions in the form of particles after applying local search.