Search results
1 – 10 of 555Satya Prakash and Indrajit Mukherjee
This study primarily aims to develop and solve an enhanced optimisation model for an assembly product multi-period inbound inventory routing problem (IRP). The many-to-one…
Abstract
Purpose
This study primarily aims to develop and solve an enhanced optimisation model for an assembly product multi-period inbound inventory routing problem (IRP). The many-to-one (inbound) model considers the bill of materials (BOM), supply failure risks (SFR) and customer demand uncertainty. The secondary objective is to study the influence of potential time-dependent model variables on the overall supply network costs based on a full factorial design of experiments (DOE).
Design/methodology/approach
A five-step solution approach is proposed to derive the optimal inventory levels, best sourcing strategy and vehicle route plans for a multi-period discrete manufacturing product assembly IRP. The proposed approach considers an optimal risk mitigation strategy by considering less risk-prone suppliers to deliver the required components in a specific period. A mixed-integer linear programming formulation was solved to derive the optimal supply network costs.
Findings
The simulation results indicate that lower demand variation, lower component price and higher supply capacity can provide superior cost performance for an inbound supply network. The results also demonstrate that increasing supply capacity does not necessarily decrease product shortages. However, when demand variation is high, product shortages are reduced at the expense of the supply network cost.
Research limitations/implications
A two-echelon supply network for a single assembled discrete product with homogeneous vehicle fleet availability was considered in this study.
Originality/value
The proposed multi-period inbound IRP model considers realistic SFR, customer demand uncertainties and product assembly requirements based on a specific BOM. The mathematical model includes various practical aspects, such as supply capacity constraints, supplier management costs and target service-level requirements. A sensitivity analysis based on a full factorial DOE provides new insights that can aid practitioners in real-life decision-making.
Details
Keywords
The development of a decision support tool for the single‐period inventory problem is presented. The support tool allows a consideration of the following factors: empirical…
Abstract
The development of a decision support tool for the single‐period inventory problem is presented. The support tool allows a consideration of the following factors: empirical frequency distributions, theoretical probability distribution functions and managerial probability estimates of total demand over the period; piece‐wise linear (possibly discontinuous) cost functions. Such functions allow for the possibility of “fixed cost” elements and/or “economies/diseconomies” of scale and account for most, if not all, of the purchase cost, holding cost, salvage revenue and shortage cost functions that arise in practice; choice of performance measures; and “what‐if” analysis on the problem parameters. The support tool, which uses the Monte Carlo simulation option of Visual IFPS/Plus, is transparent and constructively simple and thus readily facilitates understanding, acceptance and implementation by management. The support tool runs under the Windows operating system and has easy access to appropriate spreadsheet and word‐processing packages for further processing and report presentation.
Details
Keywords
The spot market has been gradually recognized as an important alternative purchasing source. To maintain a flexible replenishment strategy, call, put and bidirectional option…
Abstract
Purpose
The spot market has been gradually recognized as an important alternative purchasing source. To maintain a flexible replenishment strategy, call, put and bidirectional option contracts, as a risk hedging, are in combined usage with the spot market, respectively. The purpose of this paper is to analyze a finite-horizon replenishment problem with option contracts in the context of a spot market.
Design/methodology/approach
Based on stochastic dynamic programming, the firm’s optimal replenishment policy with either call, put or bidirectional option contracts is always shown to be order-up-to type, characterized by an upper threshold and a lower one. The corresponding policy parameters in different cases are calculated through an approximate algorithm. This research highlights the effectiveness of option contracts on the firm’s operational strategies and overall profitability.
Findings
This study reveals that the firm is better off with option contracts than without them. When the price parameters are the same for different option contracts, bidirectional option contracts are the best choice among these flexible contracts; otherwise, unilateral option contracts might be either better or worse than bidirectional ones. In addition, if low inventory costs and high spot price volatility are confronted, the firm prefers to call option contracts rather than put ones; otherwise, there exists an opposite conclusion.
Originality/value
In addition to highlight the advantage of option contracts over wholesale price contracts, this paper provides interesting observations with respect to the effect of different option contracts on the firm. Many significant insights derived from this research do not only contribute to the provider’s feasible design of the supply contracts, but also contribute to the user’s rational operational strategies for higher profitability.
Details
Keywords
H. Niles Perera, Behnam Fahimnia and Travis Tokar
The success of a supply chain is highly reliant on effective inventory and ordering decisions. This paper systematically reviews and analyzes the literature on inventory ordering…
Abstract
Purpose
The success of a supply chain is highly reliant on effective inventory and ordering decisions. This paper systematically reviews and analyzes the literature on inventory ordering decisions conducted using behavioral experiments to inform the state-of-the-art.
Design/methodology/approach
This paper presents the first systematic review of this literature. We systematically identify a body of 101 papers from an initial pool of over 12,000.
Findings
Extant literature and industry observations posit that decision makers often deviate from optimal ordering behavior prescribed by the quantitative models. Such deviations are often accompanied by excessive inventory costs and/or lost sales. Understanding how humans make inventory decisions is paramount to minimize the associated consequences. To address this, the field of behavioral operations management has produced a rich body of research on inventory decision-making using behavioral experiments. Our analysis identifies primary research clusters, summarizes key learnings and highlights opportunities for future research in this critical decision-making area.
Practical implications
The findings will have a significant impact on future research on behavioral inventory ordering decisions while informing practitioners to reach better ordering decisions.
Originality/value
Previous systematic reviews have explored behavioral operations broadly or its subdisciplines such as judgmental forecasting. This paper presents a systematic review that specifically investigates the state-of-the-art of inventory ordering decisions using behavioral experiments.
Details
Keywords
Misagh Rahbari, Seyed Hossein Razavi Hajiagha, Hannan Amoozad Mahdiraji, Farshid Riahi Dorcheh and Jose Arturo Garza-Reyes
This study focuses on a specific method of meat production that involves carcass purchase and meat production by packing facilities with a novel two-stage model that…
Abstract
Purpose
This study focuses on a specific method of meat production that involves carcass purchase and meat production by packing facilities with a novel two-stage model that simultaneously considers location-routing and inventory-production operating decisions. The considered problem aims to reduce variable and fixed transportation and production costs, inventory holding cost and the cost of opening cold storage facilities.
Design/methodology/approach
The proposed model encompasses a two-stage model consisting of a single-echelon and a three-echelon many-to-many network with deterministic demand. The proposed model is a mixed-integer linear programming (MILP) model which was tested with the general algebraic modelling system (GAMS) software for a real-world case study in Iran. A sensitivity analysis was performed to examine the effect of retailers' holding capacity and supply capacity at carcass suppliers.
Findings
In this research, the number of products transferred at each level, the number of products held, the quantity of red meat produced, the required cold storage facilities and the required vehicles were optimally specified. The outcomes indicated a two percent (2%) decrease in cost per kg of red meat. Eventually, the outcomes of the first and second sensitivity analysis indicated that reduced retailers' holding capacity and supply capacity at carcass suppliers leads to higher total costs.
Originality/value
This research proposes a novel multi-period location-inventory-routing problem for the red meat supply chain in an emerging economy with a heterogeneous vehicle fleet and logistics decisions. The proposed model is presented in two stages and four-echelon including carcass suppliers, packing facilities, cold storage facilities and retailers.
Details
Keywords
Pravin Suryawanshi and Pankaj Dutta
The emergence of risk in today's business environment is affecting every managerial decision, majorly due to globalization, disruptions, poor infrastructure, forecasting errors…
Abstract
Purpose
The emergence of risk in today's business environment is affecting every managerial decision, majorly due to globalization, disruptions, poor infrastructure, forecasting errors and different uncertainties. The impact of such disruptive events is significantly high for perishable items due to their susceptibility toward economic loss. This paper aims to design and address an operational planning problem of a perishable food supply chain (SC).
Design/methodology/approach
The proposed model considers the simultaneous effect of disruption, random demand and deterioration of food items on business objectives under constrained conditions. The study describes this situation using a mixed-integer nonlinear program with a piecewise approximation algorithm. The proposed algorithm is easy to implement and competitive to handle stationary as well as nonstationary random variables in place of scenario techniques. The mathematical model includes a real-life case study from a kiwi fruit distribution industry.
Findings
The study quantifies the performance of SC in terms of SC cost and fill rate. Additionally, it investigates the effects of disruption due to suppliers, transport losses, product perishability and demand stochasticity. The model incorporates an incentive-based strategy to provide cost-cutting in the existing business plan considering the effect of deterioration. The study performs sensitivity analysis to show various “what-if” situations and derives implications for managerial insights.
Originality/value
The study contributes to the scant literature of quantitative modeling of food SC. The research work is original as it integrates a stochastic (uncertain) nature of SC simultaneously coupled with the effect of disruption, transport losses and product perishability. It incorporates proactive planning strategies to minimize the disruption impact and the concept of incremental quantity discounts on lot sizes at a destination node.
Details
Keywords
This paper seeks to construct a model for inventory management for multiple periods. The model considers not only the usual parameters, but also price quantity discount, storage…
Abstract
Purpose
This paper seeks to construct a model for inventory management for multiple periods. The model considers not only the usual parameters, but also price quantity discount, storage and batch size constraints.
Design/methodology/approach
Mixed 0‐1 integer programming is applied to solve the multi‐period inventory problem and to determine an appropriate inventory level for each period. The total cost of materials in the system is minimized and the optimal purchase amount in each period is determined.
Findings
The proposed model is applied in colour filter inventory management in thin film transistor‐liquid crystal display (TFT‐LCD) manufacturing because colour filter replenishment has the characteristics of price quantity discount, large product size, batch‐sized purchase and forbidden shortage in the plant. Sensitivity analysis of major parameters of the model is also performed to depict the effects of these parameters on the solutions.
Practical implications
The proposed model can be tailored and applied to other inventory management problems.
Originality/value
Although many mathematical models are available for inventory management, this study considers some special characteristics that might be present in real practice. TFT‐LCD manufacturing is one of the most prosperous industries in Taiwan, and colour‐filter inventory management is essential for TFT‐LCD manufacturers for achieving competitive edge. The proposed model in this study can be applied to fulfil the goal.
Details
Keywords
Misagh Rahbari, Seyed Hossein Razavi Hajiagha, Mahdi Raeei Dehaghi, Mahmoud Moallem and Farshid Riahi Dorcheh
In this paper, multi-period location–inventory–routing problem (LIRP) considering different vehicles with various capacities has been investigated for the supply chain of red…
Abstract
Purpose
In this paper, multi-period location–inventory–routing problem (LIRP) considering different vehicles with various capacities has been investigated for the supply chain of red meat. The purpose of this paper is to reduce variable and fixed costs of transportation and production, holding costs of red meat, costs of meeting livestock needs and refrigerator rents.
Design/methodology/approach
The considered supply chain network includes five echelons. Demand considered for each customer is approximated as deterministic using historical data. The modeling is performed on a real case. The presented model is a linear mixed-integer programming model. The considered model is solved using general algebraic modeling system (GAMS) software for data set of the real case.
Findings
A real-world case is solved using the proposed method. The obtained results have shown a reduction of 4.20 per cent in final price of red meat. Also, it was observed that if the time periods changed from month to week, the final cost of meat per kilogram would increase by 43.26 per cent.
Originality/value
This paper presents a five-echelon LIRP for the meat supply chain in which vehicles are considered heterogeneous. To evaluate the capability of the presented model, a real case is solved in Iran and its results are compared with the real conditions of a firm, and the rate of improvement is presented. Finally, the impact of the changed time period on the results of the solution is examined.
Details
Keywords
Mohammad Saeid Atabaki, Seyed Hamid Reza Pasandideh and Mohammad Mohammadi
Lot-sizing is among the most important problems in the production and inventory management field. The purpose of this paper is to move one step forward in the direction of the…
Abstract
Purpose
Lot-sizing is among the most important problems in the production and inventory management field. The purpose of this paper is to move one step forward in the direction of the real environment of the dynamic, multi-period, lot-sizing problem. For this purpose, a two-warehouse inventory system, imperfect quality and supplier capacity are simultaneously taken into consideration, where the aim is minimization of the system costs.
Design/methodology/approach
The problem is formulated in a novel continuous nonlinear programming model. Because of the high complexity of the lot-sizing model, invasive weed optimization (IWO), as a population-based metaheuristic algorithm, is proposed to solve the problem. The designed IWO benefits from an innovative encoding–decoding procedure and a heuristic operator for dispersing seeds. Moreover, sequential unconstrained minimization technique (SUMT) is used to improve the efficiency of the IWO.
Findings
Taking into consideration a two-warehouse system along with the imperfect quality items leads to model nonlinearity. Using the proposed hybrid IWO and SUMT (SUIWO) for solving small-sized instances shows that SUIWO can provide satisfactory solutions within a reasonable computational time. In comparison between SUIWO and a parameter-tuned genetic algorithm (GA), it is found that when the size of the problem increases, the superiority of SUIWO to GA to find desirable solutions becomes more tangible.
Originality/value
Developing a continuous nonlinear model for the concerned lot-sizing problem and designing a hybrid IWO and SUMT based on a heuristic encoding–decoding procedure are two main originalities of the present study.
Details
Keywords
Mahdieh Masoumi, Amir Aghsami, Mohammad Alipour-Vaezi, Fariborz Jolai and Behdad Esmailifar
Due to the randomness and unpredictability of many disasters, it is essential to be prepared to face difficult conditions after a disaster to reduce human casualties and meet the…
Abstract
Purpose
Due to the randomness and unpredictability of many disasters, it is essential to be prepared to face difficult conditions after a disaster to reduce human casualties and meet the needs of the people. After the disaster, one of the most essential measures is to deliver relief supplies to those affected by the disaster. Therefore, this paper aims to assign demand points to the warehouses as well as routing their related relief vehicles after a disaster considering convergence in the border warehouses.
Design/methodology/approach
This research proposes a multi-objective, multi-commodity and multi-period queueing-inventory-routing problem in which a queuing system has been applied to reduce the congestion in the borders of the affected zones. To show the validity of the proposed model, a small-size problem has been solved using exact methods. Moreover, to deal with the complexity of the problem, a metaheuristic algorithm has been utilized to solve the large dimensions of the problem. Finally, various sensitivity analyses have been performed to determine the effects of different parameters on the optimal response.
Findings
According to the results, the proposed model can optimize the objective functions simultaneously, in which decision-makers can determine their priority according to the condition by using the sensitivity analysis results.
Originality/value
The focus of the research is on delivering relief items to the affected people on time and at the lowest cost, in addition to preventing long queues at the entrances to the affected areas.
Details