Abstract:In practice, suppliers often provide retailers with forward financing to increase demand or decrease inventory. This paper proposes a new and practical joint replenishment and delivery (JRD) model by considering trade credit. However, because of the complex mathematical properties of JRD, high-quality solutions to the problem have eluded researchers. We design an effective hybrid differential evolution algorithm based on simulated annealing (HDE-SA) that can resolve this non-deterministic polynomial hard probl… Show more
“…Being a multi-objective optimization problem, the ATD can be solved by various algorithms, ranging from multi-objective particle swarm optimization (PSO) [4][5][6], predictive fuzzy control [7], multi-modal dynamic programming [8], the hybrid algorithm of the differential evolution (DE) and simulated annealing (SA) [9] to cellular automata algorithm [10].…”
The automatic train driving (ATD) can greatly improve the efficiency and safety of train operations in urban rail system. This paper mainly establishes a multi-objective optimization model and designs a fuzzy controller for the ATD. Firstly, the relevant parameters of train operations were analyzed, including, safety, punctuality, parking accuracy, passenger comfort and energy consumption. Then, a multi-swarm optimization algorithm was developed to optimize the train operation curve, coupling the particle swarm optimization (PSO) and the cuckoo search (CS) algorithm. Taking the optimized curve as the input, the author finalized the design of the fuzzy controller. The simulation results show that our method can effectively solve the multi-objective problem of the ATD in urban transit system.
“…Being a multi-objective optimization problem, the ATD can be solved by various algorithms, ranging from multi-objective particle swarm optimization (PSO) [4][5][6], predictive fuzzy control [7], multi-modal dynamic programming [8], the hybrid algorithm of the differential evolution (DE) and simulated annealing (SA) [9] to cellular automata algorithm [10].…”
The automatic train driving (ATD) can greatly improve the efficiency and safety of train operations in urban rail system. This paper mainly establishes a multi-objective optimization model and designs a fuzzy controller for the ATD. Firstly, the relevant parameters of train operations were analyzed, including, safety, punctuality, parking accuracy, passenger comfort and energy consumption. Then, a multi-swarm optimization algorithm was developed to optimize the train operation curve, coupling the particle swarm optimization (PSO) and the cuckoo search (CS) algorithm. Taking the optimized curve as the input, the author finalized the design of the fuzzy controller. The simulation results show that our method can effectively solve the multi-objective problem of the ATD in urban transit system.
“…Sets and parameters D i,m demand rate of item i distributed through warehouse m (deliver to retailer i) h R i inventory holding cost of item i in retailer i per unit per unit time integer number that decides the outbound schedule of item i distributed through warehouse m; the upper and lower bounds of f i,m are f UB i;m and f LB i;m ; the matrix for all f i,m is F k i,m integer number that decides the replenishment schedule of item i distributed through warehouse m; the upper and lower bounds of k i,m are k UB i;m and k LB i;m ; the matrix for all k i,m is K T basic cycle time; the upper and lower bounds of T are T max and T min x i,m 0-1 variable, if x i,m ¼ 1, the path of item i distributed through warehouse m exists; otherwise, 0 1. Introduction Over the past few years, the coordinated replenishment problem (CRP) has been heavily studied (Zeng et al, 2016). For a CRP procedure, the cost of placing an order for a number of different items comprises two parts: a major ordering cost incurred whenever an order is placed, and this cost is independent of the number of different items in the order; and a minor ordering cost, which is decided by the number of different items in the order.…”
Purpose
The purpose of this paper is to investigate a new and practical decision support model of the coordinated replenishment and delivery (CRD) problem with multi-warehouse (M-CRD) to improve the performance of a supply chain. Two algorithms, tabu search-RAND (TS-RAND) and adaptive hybrid different evolution (AHDE) algorithm, are developed and compared as to the performance of each in solving the M-CRD problem.
Design/methodology/approach
The proposed M-CRD is more complex and practical than classical CRDs, which are non-deterministic polynomial-time hard problems. According to the structure of the M-CRD, a hybrid algorithm, TS-RAND, and AHDE are designed to solve the M-CRD.
Findings
Results of M-CRDs with different scales show that TS-RAND and AHDE are good candidates for handling small-scale M-CRD. TS-RAND can also find satisfactory solutions for large-scale M-CRDs. The total cost (TC) of M-CRD is apparently lower than that of a CRD with a single warehouse. Moreover, the TC is lower for the M-CRD with a larger number of optional warehouses.
Practical implications
The proposed M-CRD is helpful for managers to select the suitable warehouse and to decide the delivery scheduling with a coordinated replenishment policy under complex operations management situations. TS-RAND can be easily used by practitioners because of its robustness, easy implementation, and quick convergence.
Originality/value
Compared with the traditional CRDs with one warehouse, a better policy with lower TC can be obtained by the new M-CRD. Moreover, the proposed TS-RAND is a good candidate for solving the M-CRD.
“…Chung 14 gave a complete proof on the solution procedure for non-instantaneous deteriorating items with permissible delay in payment. Zeng et al 15 gave an effective hybrid differential evolution algorithm incorporating simulated annealing for joint replenishment and delivery problem with trade credit. Liao et al 16,17 determined optimal inventory policies considering two levels of trade credit.…”
In recent years, offering credit period by the supplier to the retailer has become a usual strategy. Hence, in the present work, an inventory model for noninstantaneous deteriorating items is framed considering money inflation and time discounting, where a permissible delay period is offered by the supplier as an alternative to price discount. Further, the salvage value associated with deteriorated units is considered, and the shortages allowed are partially backlogged. Focus is made on obtaining the optimal replenishment policy by minimizing the total inventory cost. This is achieved by developing mathematical theorems that determines the existence and the uniqueness of the optimal solutions. Moreover, computational algorithm is designed and illustrated using numerical examples and analysis. Various managerial insights obtained from the analysis are also highlighted.
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