A green multi-period inventory routing problem with pickup and split delivery: A case study in flour industry

Mahjoob, M. J.; Fazeli, Seyed Sajjad; Tavassoli, Leyla Sadat; Mirmozaffari, Mirpouya; Milanlouei, Soodabeh

doi:10.1016/j.susoc.2021.04.002

Cited by 19 publications

(3 citation statements)

References 35 publications

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“…The explanation is as: (10) shows that mutation occurs only when the generated uniform random number is less than the mutation threshold, (11) shows that the mutation is conducted by switching two orders. The mutating order is selected by choosing the order inside the vehicle randomly.…”

Section: Methodsmentioning

confidence: 99%

Pickup and delivery problem in the collaborative city courier service by using genetic algorithm and nearest distance

Kusuma

Kallista

2022

Bulletin EEI

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One problem in collaborative pickup delivery problem (PDP) was excessive outsourced jobs. It happened in many studies on the collaborative PDP. Besides, the revenue sharing in it was unclear although important. This work aimed to propose a novel collaborative PDP model which minimizes total travel distance while maintains low outsourced jobs. It proposed several contributions. First, it prioritized internal jobs first rather than full collaborative model. Second, it proposed new revenue sharing model. It adopted cluster-first route-second and mixed pickup and delivery. It was developed by combining the genetic algorithm and nearest distance algorithm where the genetic algorithm was used in the clustering process and the nearest distance was used in the routing process. The simulation result shows that the proposed model was better than the comparing models: (1) combined K-means and genetic algorithm model (KMGA) and (2) combined simulated annealing and last-in first-out (SNLIFO) model. When the number of orders was high (300 units), the total travel distance of the proposed model was 37 percent lower than the KMGA model and 30 percent lower than the SNLIFO model. In average, the outsourcing rate of the proposed model was 70 percent lower than the previous models.

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Section: Methodsmentioning

confidence: 99%

Pickup and delivery problem in the collaborative city courier service by using genetic algorithm and nearest distance

Kusuma

Kallista

2022

Bulletin EEI

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“…In these problems, it is believed that the number of operations with a non-zero processing time is more than the number of machines and jobs [43]. Some methods offered a heuristic algorithm for solving the open-shop problem with two machines and transportation times [44][45][46][47][48][49][50]. In another suggested model, the transportation time between machines, as a fixed one, was described and associated with a delay time between the completion time of one operation and the start time of the following operation on the same job [51].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Simulated Annealing for No-Wait Open-Shop Surgical Case Scheduling Problems

Rahimi

Hejazi

Zandieh

et al. 2023

ASI

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In this paper, the problem of finding an assignment of “n” surgeries to be presented in one of “m” identical operating rooms (ORs) or machines as the surgical case scheduling problem (SCSP) is proposed. Since ORs are among NP-hard optimization problems, mathematical and metaheuristic methods to address OR optimization problems are used. The job or surgical operation ordering in any OR is a permanent part of all sequencing and scheduling problems. The transportation times between ORs are defined based on the type of surgical operations and do not depend on distance, so there is no surgical operation waiting time for transferring. These problems are called no-wait open-shop scheduling problems (NWOSP) with transportation times. The transportation system for the problems is considered a multi-transportation system with no limitation on the number of transportation devices. Accordingly, this study modeled a novel combined no-wait open-shop surgical case scheduling problem (NWOSP-SCSP) with multi-transportation times for the first time to minimize the maximum percentile of makespan for OR as a single objective model. A mixed-integer linear program (MILP) with small-sized instances is solved. In addition to the small-sized model, a novel metaheuristic based on a hybrid simulated annealing (SA) algorithm to solve large-sized problems in an acceptable computational time is suggested, considering the comparison of the SA algorithm and a new recommended heuristic algorithm. Then, the proposed hybrid SA and SA algorithms are compared based on their performance measurement. After reaching the results with a numerical analysis in Nova Scotia health authority hospitals and health centers, the hybrid SA algorithm has generated significantly higher performance than the SA algorithm.

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“…The application of IRP has been found in different industries, including the perishable and food retail industry [4]-[8], the biogas industry [9], the petrochemical industry [10], the spare part manufacturing industry [11], and furniture industry [12]. Several extensions of IRP are also studied, including time-dependent IRP [13], multi-period IRP [14], IRP with time-varying demand [15], multi-vehicle cyclic IRP [16], IRP with split up and deliveries [17], and maritime IRP [18]. As an NP-Available online at: http://e-jurnal.lppmunsera.org/index.php/JSMI Jurnal Sistem dan Manajemen Industri ISSN (Print) 2580-2887 ISSN (Online) 2580-2895 http://dx.doi.org/10.30656/jsmi.v6i2.4813 hard problem, inventory and routing problems are solved using many approaches, including exact methods and metaheuristics.…”

Section: Introductionmentioning

confidence: 99%

Tabu search heuristic for inventory routing problem with stochastic demand and time windows

Maghfiroh¹,

Redi²

2022

j. sist. manaj. ind.

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This study proposes the hybridization of tabu search (TS) and variable neighbourhood descent (VND) for solving the Inventory Routing Problems with Stochastic Demand and Time Windows (IRPSDTW). Vendor Managed Inventory (VMI) is among the most used approaches for managing supply chains comprising multiple stakeholders, and implementing VMI require addressing the Inventory Routing Problem (IRP). Considering practical constraints related to demand uncertainty and time constraint, the proposed model combines multi-item replenishment schedules with unknown demand to arrange delivery paths, where the actual demand amount is only known upon arrival at a customer location with a time limit. The proposed method starts from the initial solution that considers the time windows and uses the TS method to solve the problem. As an extension, the VND is conducted to jump the solution from its local optimal. The results show that the proposed method can solve the IRPSDTW, especially for uniformly distributed customer locations.

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A green multi-period inventory routing problem with pickup and split delivery: A case study in flour industry

Cited by 19 publications

References 35 publications

Pickup and delivery problem in the collaborative city courier service by using genetic algorithm and nearest distance

Pickup and delivery problem in the collaborative city courier service by using genetic algorithm and nearest distance

A Novel Hybrid Simulated Annealing for No-Wait Open-Shop Surgical Case Scheduling Problems

Tabu search heuristic for inventory routing problem with stochastic demand and time windows

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