Simultaneous Pickup Research Articles

Green reverse supply chain network design considering location-routing-inventory decisions with simultaneous pickup and delivery.

In this work, a bi-objective mixed-integer linear programming model is formulated for the first time to design a green supply chain while considering both forward and reverse flows, location-inventory-routing problem with simultaneous pickup and delivery, scheduling of vehicles, and time window. This model is presented to manage the production and distribution of perishable products and aims to minimize total costs and lost demands simultaneously. To solve the proposed bi-objective model under uncertainty, an approach based on fuzzy theory is developed. The performance of the proposed model and solution approach is evaluated using data from a bread production and distribution industry in Alborz province. The results showed that Hashtgerd center should be set up as the production and separation center and suppliers 1 and 2 (i.e., Seifabad and Mahdasht) should be selected to purchase raw materials when designing this network. The results obtained from the model's implementation in the real world and the sensitivity analysis of coefficients of objective functions indicate the desired efficiency and effectiveness of the proposed model and solution approach.

Capacitated location routing problem with simultaneous pickup and delivery under the risk of disruption

This paper develops a new mathematical model to study a location-routing problem with simultaneous pickup and delivery under the risk of disruption. A remarkable number of previous studies have assumed that network components (e.g., routes, production factories, depots, etc.) are always available and can permanently serve the customers. This assumption is no longer valid when the network faces disruptions such as flood, earthquake, tsunami, terrorist attacks and workers strike. In case of any disruption in the network, tremendous cost is imposed on the stockholders. Incorporating disruption in the design phase of the network will alleviate the impact of these disasters and let the network resist disruption. In this study, a mixed integer programming (MIP) model is proposed that formulates a reliable capacitated location-routing problem with simultaneous pickup and delivery (RCLRP-SPD) services in supply chain distribution network. The objective function attempts to minimize the sum of location cost of depots, routing cost of vehicles and cost of unfulfilled demand of customers. Since the model is NP-Hard, three meta-heuristics are tailored for large-sized instances and the results show the outperformance of hybrid algorithms comparing to classic genetic algorithm. Finally, the obtained results are discussed and the paper is concluded.

Developing a green and bipolar fuzzy inventory-routing model in agri-food reverse logistics with postharvest behavior.

A novel periodical mixed-integer mathematical model in the field of fresh agri-food product's distribution (tomato product for instance) is developed in this paper. Green topics and two significant features of fresh agricultural products, namely freshness and ripeness, are added to the inventory routing problem (IRP) with simultaneous pickup and delivery for perishable products. The objective function tends to optimize the system total interest. In this model, traditional costs such as transportation and holding costs are considered besides up-to-date points such as expired products and customer's dissatisfaction costs. To compute the customer's dissatisfaction costs, the level of deviation from ideal quality should be measured to specify the biological postharvest behavior of fresh products. To determine the fair pricing, the patterns of quality decay have been applied. Considering the environmental effects and recycling requirements of expired crops, the reverse logistics notion has been applied to collect and reuse the wasted products. The level of greenhouse gas (GHG) emission has been controlled to reduce the harmful impressions of this gas and protect the environment. In the optimization procedure, the system total costs are developed by assuming fuzzy quality levels and fuzzy holding costs. Also, a bipolar approach has been applied for fuzzy programming. Finally, a numerical example besides sensitivity analysis and managerial insights is presented. Results show that remarking fuzzy parameters lead to lower profit and different routing and transmission. Also, applying less pollutant vehicles and increasing plant's delivery levels can be noted to reach a green environment and higher level of profit.

A robust possibilistic programming model for production-routing problem in a three-echelon supply chain

PurposeThis 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.Design/methodology/approachAt 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.FindingsThe 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.Originality/valueThis 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.

A bi-objective two-echelon pollution routing problem with simultaneous pickup and delivery under multiple time windows constraint

Pollution emitted by a vehicle can be checked by reducing the distance traveled and through operational adjustments. Optimized route and simultaneous pickup and delivery in a vehicle routing problem (VRP) can achieve the same. This paper investigates a two-echelon pollution routing problem with simultaneous pickup and delivery by considering multiple time windows (2E-PRPSPD-MTW) for customers’ visit. The first echelon consists of depots and intermediate depots (called satellites), whereas the second echelon consists of the satellites and customers. Two fleets (one for each echelon) of heterogeneous vehicles accomplish pickup and delivery operations. The 2E-PRPSPD-MTW is developed in a bi-objective framework, focusing on minimization of both travel time and fuel consumption. The proposed bi-objective 2E-PRPSPD-MTW is an NP-hard problem. So, in order to optimize it, multi-objective variable neighborhood search (MOVNS) is modified and used. In this process, six operators are proposed in order to improve the neighborhood structure. The suggested operators explore the entire solution space to find the near optimal solution. With the aim of providing much richer insights, the efficacy of the proposed method and mathematical formulation is demonstrated through numerical experiment for a number of instances varying from small to large scale.

A sustainable closed-loop location-routing-inventory problem for perishable products

Perishable products may expire if their holding time exceeds their shelf-life. In this study, along with designing a forward flow to distribute perishable products; remained perished products at retailers can be gathered for recycling during distributing fresh products. To mitigate the waste, recycled products are offered to a secondary market. A mathematical model for this Closed-Loop Location-Routing-Inventory Problem (CL-LRIP) is developed by considering multi-compartment trucks, simultaneous pickup and delivery, technology selection, and risk of urban traffic. Based on three sustainability pillars, three objective functions are considered. This way, the interests of the network's three main stakeholders are embedded. The proposed model is solved by the Torabi-Hassini method. Two evolutionary algorithms, including Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and a new hybrid one, are also developed to solve large-sized cases of the NP-complete problem. Statistical tests show the superiority of the hybrid algorithm in the computational time (CT) metric, which is about 0.4 NSGA-II’s CT. The results indicate the importance of closing the network loop for perishable products. Finally, the sensitivity analysis determined that 83.33 % decrease in recycled product’s sale price causes 9.08% increase in costs, 2.77% decrease in environmental side-effects, and 5.16% decrease in social objectives, which are significant.

Reverse Logistic Processes for Glass Container Reuse

The beer industry has been seeking alternatives to reduce the negative impacts of global warming on its manufacturing processes with the hope of attaining a more sustainable development. In accordance, a three-stage environmental plan has been put forth. The first stage proposes a washing and disinfection process for glass bottle reutilization applying lean principles, which in turn comply with current sanitary regulations. Secondly, distribution and recollection routes have been established after considering a vehicle routing problem (VRP) with simultaneous pick-up and delivery, as well as time windows (VRPSPDTW). This VRP involves beer bottle pickup and delivery at different customer locations, with both early and late deadlines, along with pre-established pickup and delivery demands. Tabu Search metaheuristic was adapted for a greedy algorithm in order to bring about positive outcomes. Therefore, a Life Cycle Analysis (LCA) for actual and proposed situations was applied to measure and compare emissions. Finally, with the objective of evaluating its financial impact, a Monte Carlo simulation was applied considering risk and uncertainty impact in all financial outcomes. This uncertainty was appraised through a Weighted Average Capital Cost (WACC) in conjunction with forecasted sales growth. In conclusion, the results showed that the cost-benefit ratio was stable in both the short- and mid-term.

Delivering and picking goods under time window restrictions: an effective evolutionary algorithm

Routing of vehicles and scheduling of deliveries play a crucial role in logistics operations as they affect both the distribution cost and customer satisfaction. That is why researchers have intensively studied this problem in conjunction with the multiple variables and constraints involved in the logistics operations. In this paper, the cases of time windows and simultaneous pickups and deliveries, where goods are simultaneously delivered and collected from customers within a predetermined time slot, are studied. The objective of our research is to create efficient routes that minimize both the number of vehicles and the total distance travelled, as both of them affect the total distribution cost. Considering various plans of routes that are differentiated by the number of routes and the sequence of visitations, can be beneficial for decision-makers, since they have the opportunity to select the plan that better fits their needs. Therefore, in this paper we develop a multiobjective evolutionary algorithm (MOEA) that integrates an improved construction algorithm and a new crossover operator for efficient distribution services. Through the proposed MOEA a set of solutions (route plans), known as Pareto-optimal, is obtained, while single biased solutions are avoided. The proposed algorithm is tested in two well-known datasets in order to evaluate the algorithm’s efficiency. The results indicate that the algorithm’s solutions have small deviation from the best-published and some non-dominated solutions are also obtained.

Designing optimal route for the distribution chain of a rural LPG delivery system

A practical distribution system that arises in the context of delivering liquefied petroleum gas (LPG) through cylinders is considered in this study. To meet all the challenging constraints, the model is explicitly considered as a simultaneous pickup and delivery single commodity truncated vehicle routing problem with the homogeneous fleet of vehicles. The aim of this problem is to find the optimal routes for the set of vehicles locating at the distributing agency (DA), which offers simultaneous pickup and delivery operations over single commodity (i.e. LPG cylinders) to a fixed subset (need not serve all delivery centers) of delivery centers at rural level. The model is designed using zero-one integer linear programming. For proper treatment of the present model, an exact Lexi-search algorithm (LSA) has been developed. A comparative study is performed between the LSA and existing results for the relaxed version of the present model. Further, the efficiency of the LSA is tested through numerical experiments over small and medium CVRP benchmark test instances. The extensive computational results have shown that the LSA is productive and revealed that the real solutions have more consistent than the integral solutions in the presence of truncation constraint.

Simultaneous Pickup and Delivery Traveling Salesman Problem considering the Express Lockers Using Attention Route Planning Network.

This paper presents a simultaneous pickup and delivery route designing model, which considers the use of express lockers. Unlike the traditional traveling salesman problem (TSP), this model analyzes the scenario that a courier serves a neighborhood with multiple trips. Considering the locker and vehicle capacity, the total cost is constituted of back order, lost sale, and traveling time. We aim to minimize the total cost when satisfying all requests. A modified deep Q-learning network is designed to get the optimal results from our model, leveraging masked multi-head attention to select the courier paths. Our algorithm outperforms other stochastic optimization methods with better optimal solutions and O(n) computational time in evaluation processes. The experiment has shown that reinforcement learning is a better choice than traditional stochastic optimization methods, consuming less power and time during evaluation processes, which indicates that this approach fits better for large-scale data and broad deployment.

DESIGN OF WASTE VEGETABLE OIL COLLECTION NETWORKS APPLYING VEHICLE ROUTING PROBLEM AND SIMULTANEOUS PICKUP AND DELIVERY

The growth of industrialization in Mexico has caused an increase in the demand for materials to satisfy the consumption of goods and services of a growing population. Given this scenario, there is a rise of the residual generation with affectations on the ecosystem and population health. Hence, the objective of this research was to design a network for waste vegetable oil collection based on vehicle routing problem with simultaneous pickup and delivery, starting from a distribution centre to 49 restaurants, as the generation sources of waste vegetable oil. The Vehicle Routing Problem Simultaneous Pickup and Delivery with Time Windows was the variant used as a vehicle routing method to solve the problem. The free software VPRPD was the tool used to solve the vehicle routing problem with simultaneous pickup and delivery that allowed to specify time restrictions. This software uses the simulated annealing metaheuristics in its syntax. As a result, it was obtained a total of 8 networks, for a vehicle capacity utilization of 70 percent in the 6 t vehicle and 46 percent in the 8 t vehicle.

A TACTICAL/STRATEGIC LEVEL COST ANALYSIS BASED ON VISIT TIME PREFERENCES FOR VEHICLE ROUTING PROBLEM WITH SIMULTANEOUS PICKUP AND DELIVERY

Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) has been studied for long years and used to model for many real-life applications. The problem is basically to find optimal routes for vehicles that serve customers in a service area to collect as well as deliver goods at the same time. Many logistic companies such as cargo, post, online supermarket, etc. face this problem in their daily operations. Because of high competitions in the sector, companies start to provide visit time preferences that customers can select. Although this is highly preferred by customers, it causes additional costs for companies since it is highly probable that preferences of customers violate optimal routes of vehicles. This problem is called as VRPSPD in Time Windows (VRPSPDTW). In this study, we develop a tactical/strategic level pricing policy based on calculation of additional travel time cost caused by visit time preferences of customers. Idea behind the policy is that solving VRPSPD for same customer set with/without considering time windows in each trial. After conducting many trials for different customer sets, average differences between travel times of VRPSPD and VRPSPDTW are determined. A generic hourly additional prices based on the average differences are calculated. Both problems are modelled as mixed ineger linear programmings and solved with CPLEX 12.9. Differences among travel times vary from 32 to 180 minutes according to experimental settings. Furthermore, results do not only change with time windows, but also different service regions play important role on results.

Integrated optimization of transportation, inventory and vehicle routing with simultaneous pickup and delivery in two-echelon green supply chain network

The economic concern, environmental awareness related legislations, and potential recycling benefits are the main motives that have changed the dimension of supply chain network with environmentally effective transportation and efficient scrap collection system. This paper investigates the two echelon supply chain network of the battery manufacturing industry. This deals with an integrated optimization procedure for solving the green transportation problem and inventory problem in the first echelon, and the capacitated multi depot (Distribution center/Collection center) heterogeneous green vehicle routing problem with simultaneous pickup and delivery in the second echelon. The minimization of the green objective function comprising inventory carrying cost, transportation cost and carbon emission cost are considered in this work. The focus of this paper is to formulate the problem in the Mixed Integer Non-Linear Programming model and to solve the four problem instances (P1 – P4) using GAMS 23.5 and Simulated Annealing Algorithm (SAA) with swap neighbourhood solution method. The experimental results show that the integrated approach with SAA provides a better solution than the sequential approach. The robustness test is carried out with four typical problem instances which show that the proposed SAA provides near optimal solutions. The model analysis is demonstrated with different components of the objective function which will help the top management to take appropriate decisions in the context of cost savings. The performance comparison reveals that the proposed SAA (SAA-1) has given better results than the Simulated Annealing Algorithm with insertion method (SAA-2).

A hyper heuristic for the green vehicle routing problem with simultaneous pickup and delivery

This paper studies the green vehicle routing problem with simultaneous pickup and delivery (G-VRPSPD). It aims to minimize fuel consumption costs while satisfying customer pickup and delivery demands simultaneously. The fuel consumption is directly proportional to green house gas emissions. We mathematically formulate the problem, and develop a hyper-heuristic (HH-ILS) algorithm based on iterative local search and variable neighborhood descent heuristics to effectively solve the problem. Extensive computational experiments are conducted to analyze the impact of the G-VRPSPD and the HH-ILS. We investigate the effect of green objective function on total fuel consumption cost by comparing the G-VRPSPD with the VRPSPD. We perform comparative analysis to investigate the performance of HH-ILS. We also conduct sensitivity analysis to investigate the performance of neighborhood structures, hyper heuristic and local search. The results show that the green objective function has a significant effect on total fuel consumption cost. The HH-ILS algorithm yields competitive results when compared with the mathematical formulation and the state-of-the-art heuristics in the literature.

Dynamic multi-stage failure-specific cooperative recourse strategy for logistics with simultaneous pickup and delivery

With the development of distribution logistics, the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) has garnered attention in recent years due to its higher capacity-usage rate and transportation efficiency. This study proposes a novel dynamic multi-stage failure-specific cooperative (DMS-FSC) recourse strategy for solving the route failure which occurs in the VRPSPD. In the case of simultaneous transportation, the proposed DMS-FSC strategy allows a pair of cooperative vehicles to revisit each other’s customers with unmet demands by dividing the whole transportation route into multiple stages. An extended genetic algorithm with a new multi-stage paired vectors representation scheme is offered to more effectively deal with the proposed DMS-FSC strategy. It explores a more ideal a priori transportation plan for the VRPSPD by incorporating an acceptance criterion based on simulated annealing. Two sets of cases with middle and corner located depots are used to verify the proposed algorithm’s effectiveness in solving the presented problem. Furthermore, a comparison between the DMS-FSC strategy and the baseline strategy, which involves no cooperation, is also made to evaluate the cost savings generated from the proposed DMS-FSC strategy.

A Novel Multi-Objective Model for the Cold Chain Logistics Considering Multiple Effects

This paper focuses on solving a problem of green location-routing with cold chain logistics (GLRPCCL). Considering the sustainable effects of the economy, environment, society, and cargos, we try to establish a multi-objective model to minimize the total cost, the full set of greenhouse gas (GHG) emissions, the average waiting time, and the total quality degradation. Several practical demands were considered: heterogeneous fleet (HF), time windows (TW), simultaneous pickup and delivery (SPD), and a feature of mixed transportation. To search the optimal Pareto front of such a nondeterministic polynomial hard problem, we proposed an optimization framework that combines three multi-objective evolutionary algorithms (MOEAs) and also developed two search mechanisms for a large composite neighborhood described by 16 operators. Extensive analysis was conducted to empirically assess the impacts of several problem parameters (i.e., distribution strategy, fleet composition, and depots’ time windows and costs) on Pareto solutions in terms of the performance indicators. Based on the experimental results, this provides several managerial insights for the sustainale logistics companies.

Forward and reverse logistics vehicle routing problems with time horizons in B2C e-commerce logistics

This research addresses a vehicle routing problem with simultaneous pickup and delivery with time windows from multiple depots (MVRPSPDTW) over a time horizon in the B2C e-commerce logistics system. We consider an e-commerce logistics system with a multi-period, which consists of customers, logistics service providers (LSPs), suppliers, and a decision-making platform. A mixed-integer non-linear programming (MINLP) model is developed and tested on small- and large-scale instances. To handle more realistic large-scale problems, we have used two approaches (i) exact optimisation approach using (i.e. CPLEX tool) and metaheuristic algorithms (i.e. Differential Evolutionary Algorithm (DE), Parallel Differential Evolutionary Algorithm (Par-DE), Genetic Algorithm (GA), and Block-based Genetic Algorithm (BBGA)) to minimise the total transportation cost and penalty due to the delay by logistics service providers. The computation experiment is conducted on the real practical scenario data and the comparative result is demonstrated.

Waiting strategy for the vehicle routing problem with simultaneous pickup and delivery using genetic algorithm

With the development of information and telecommunication technology and the wide adoption of smartphones, consumers gradually change their purchase pattern toward online shopping. They can order products from their smartphones at any moment from any place, and the volume and variety of products delivered to consumers are increasing explosively. Companies in this industry need to set up the operational strategies to accommodate the increasing demand for delivery and return of products, and their focus should be the real-world vehicle routing problems with an additional consideration of the dynamic orders placed over time. This study proposes a waiting strategy for the vehicle routing problem with simultaneous pickup and delivery. This strategy implements an index called the rerouting indicator, which functions as a decision-making threshold to determine the rerouting point for real-time demands. For the most real-world-cases with complex problems, this study proposes a genetic algorithm to solve and validate its accuracy and performance by comparing the computational results. The significance and application of the waiting strategy are validated through several experiments, and the appropriate discretion by a decision maker can demonstrate the value of the proposed strategy.

Using the Callback to eliminate sub-routing in a vehicle routing problem with simultaneous pick-up and delivery of multiple products

The Vehicle Routing Problem with simultaneous pick-up and delivery of multiple products (VRPSPD) is addressed in practice when just collecting the product is not sufficient to meet the client’s requirements. This article aims to apply Callback available in Gurobi software to the elimination of sub-routes in a VRPSPD using lazy constraints. In order to test the efficiency of the proposed method, we first solved the relaxed problem of Mixed Integer Programming (MIP), and then the same problem was solved using the Callback. The performed tests show that the use of Callback is efficient leading to time reductions in runtime to prove the optimality.

A lexicographic-based two-stage algorithm for vehicle routing problem with simultaneous pickup–delivery and time window

Vehicle routing problem with simultaneous pickup–delivery and time window (VRPSPDTW) is computationally challenging as it generalizes the classical and NP-hard vehicle routing problem. According to the state-of-the-art, VRPSPDTW usually has two hierarchical optimization objectives: a primary objective of minimizing the number of vehicles (NV) and a secondary objective of reducing the transportation distance (TD). Given the existing research and our trial results, we find that the optimization of TD is not necessarily a promotion for reducing NV. In this paper, an effective learning-based two-stage algorithm, which has never been studied before, is proposed to solve the VRPSPDTW. In the first stage, a modified variable neighborhood search with a learning-based objective function is proposed to minimize the primary objective with retaining the potential structures. In the second stage, a bi-structure based tabu search (BSTS) is designed to optimize the primary and secondary objectives further. The experimental results on 93 benchmark instances demonstrate that the proposed algorithm performs remarkably well both in terms of computational efficiency and solution quality. In particular, the proposed two-stage algorithm improve several best known solutions (either a better NV or a better TD when NV are the same) from the state-of-the-art. To our knowledge, this is the first learning-based two-stage algorithm for solving VRPSPDTW reaching such a performance. Finally, we empirically analyze several critical components of the algorithm to highlight their impacts on the performance of the proposed algorithm.