To comply with MARPOL Annex VI, stakeholders face multi-criteria decision-making in technology selection. This study provides an Analytic Hierarchy Process (AHP)-based method to support stakeholders in selecting emission abatement technology aligned with their business demands, taking into account a range of sustainability criteria. The analysis reveals that there is no one-size-fits-all solution to technology selection. Low-sulfur fuel oil and LNG are preferable alternative fuels for large-size commercial (long-sea shipping) vessels due to their better capacity storage savings, while a dual-fuel engine offers flexibility in fuel changeover. Electrification offers zero-emission performance, lower noise levels, and peak energy solutions benefiting cruise ships and short-distance or harbor boats, but tugboats need greener diesel to meet performance criteria. From a policy perspective, our model provides insights into the effects of green transition processes in Norway and Singapore on stakeholders' decisions with respect to port infrastructure and land transport at the portside.

Various deterministic models for economic ship speed optimisation exist in the literature, but none considered the time charter contract, and in particular the influence of the redelivery time. This paper studies the economic optimal speed of a ship on a (time) charter contract through the development of an Operational Research (OR) optimisation model. The ship charterer's objective is to maximise the Net Present Value (NPV) of a cash-flow function of the ship's activities over a relevant future horizon H, where H can be interpreted as any possible day within the redelivery time window as specified in the time charter clause. We develop a general time charter contract model PM(H), and three special cases P1, P infinity and PM(H-* infinity), each model mapping onto different contractual contexts, and present algorithms to each of these models for finding optimal ship speeds for any journey structure. While ships on time charter contracts may travel to any series of ports during the charter contract, examining the models' behaviour when the ship repeatedly executes a roundtrip journey allows us to reach some important general insights about the impact of contract type for any journey structure. In particular, economic speeds in PM(H) follow a very different pattern than those in the classic models from the literature, as well as in the recent class of NPV models P(n, m, Go) from Ge et al., (2021). We prove that P1 and P infinity map quite generally to the classes P(1, n, 0) and P(infinity, n, -), respectively, while P(n, m, 0) shows behaviour in approximation equal to the special case PM(H-* infinity). We prove that two main strands of speed optimisation models from the literature, which did not consider the contract type nor used the NPV approach, show equivalence under mild conditions to P1 and P infinity, respectively. These results facilitate matching models to contract types. None of these models, however, matches the general time charter contract model PM(H) introduced in this paper. In general, the paper demonstrates how optimal economic speed is dependent on the (time) charter contract type, and that this should thus be reflected in the speed optimisation model developed.

The evaluation of the performance of a decision-making unit (DMU) can be measured by its own optimistic and pessimistic multipliers, leading to an interval self-efficiency score. While this concept has been thoroughly studied with regard to single-stage systems, there is still a gap when it is extended to two-stage tandem structures, which better correspond to a real-world scenario. In this paper, we argue that in this context, a meaningful ranking of the DMUs is obtained; this outcome simultaneously considers the optimistic and pessimistic viewpoints within the self-appraisal context, and the most favourable and unfavourable weight sets of each of the other DMUs in a peer-appraisal setting. We initially extend the optimistic-pessimistic Data Envelopment Analysis (DEA) models to the specifications of such a two-stage structure. The two opposing self-efficiency measures are merged to a combined self-efficiency measure via the geometric average. Under this framework, the DMUs are further evaluated in a peer setting via the interval cross-efficiency (CE). This methodological tool is applied to evaluate the target DMU in relation to the most favourable and unfavourable weight profiles of each of the other DMUs, while maintaining the combined self-efficiency measure. We, thus, determine an interval individual CE score for each DMU and flow. By treating the interval CE matrix as a multi-criteria decision making problem and by utilising several well-established approaches from the literature, we delineate its remaining elements; we show how these lead us to a meaningful ultimate ranking of the DMUs. A numerical example about the efficiency evaluation of ten bank branches in China illustrates the applicability of our modelling approaches.

We develop a demand postponement mechanism to improve the performance of a single item, periodic review inventory system with advance demand. The focus in the literature has been on how to stimulate customers towards advance demand. Predicting how demand will shift can be problematic, however, and backorders may still occur. We focus on how a firm can address backorders under a given advance demand pattern by a mechanism of compensation from which both the firm and the customers will benefit: the firm may offer a discount to customers for accepting later deliveries at a promised delivery date. Delivery postponement offers are made selectively, i.e. in some periods and to some customers only when there is a benefit for the firm to do so. Customers may decline the offer, but then face the probability of a backorder. In each period, the firm has to decide whether to make delivery postponement offers and for how long, and whether to order from its supplier and how much. We formulate the problem as a Markov Decision Process and solve it by backward induction. Numerical examples illustrate the properties of the state-dependent policies obtained for both uncapacitated and capacitated inventory systems. The postponement mechanism in capacitated systems leads to policies that differ from the threshold policy identified as optimal in the literature. Overall, the approach shows promise to improve system performance more efficiently compared to strategies aiming to increase advance demand in the system.(c) 2021 Elsevier B.V. All rights reserved.

In this article, we examine how an Operational Research (OR) modeling approach can help in identifying how structural components in the supply process of a food product subject to a small probability of almost immediate failure affects the amount of waste arising at the retailer. This process can be viewed as the cumulative effect of various possible causes, including (apparent) product flaws and breakage. This category of waste, in contrast to products that are removed based on reaching their best before or use by date, are also having little potential for redistribtion, and may thus be most targeted in future waste reduction legislative initiatives. We develop some relatively easy to calculate measures to help a retailer with identifying the financial implications of waste production in relation to some supply source characteristics, the financial motivation of its supplier to tackle item deterioration at the retailer level, and how this is affected by the level of logistics collaboration. We also discuss how the model can help in deriving the relative benefit of technological, logistical, supplier selection, and marketing strategies available to the retailer to meet future legislative waste reduction targets, and derive conclusions with respect to the design of legislative instruments. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Many organisations are composed of multiple departments connected either in series or in parallel, which may be further decomposed into a number of functions arranged in a hierarchical structure. Several researchers have successfully used appropriate Data Envelopment Analysis (DEA) modelling techniques to assess complex structures. However, to our knowledge, no-one has yet examined the case of measuring and evaluating a parallel network structure combined with a hierarchical one. This paper discusses the development of a multi-function parallel system with embedded hierarchical network structures. A linear additive decomposition DEA model and a non-linear multiplicative aggregation DEA model are proposed as alternatives to evaluate the operating performance of such a structure. The system, the sub-systems, and the efficiencies of their internal units, as well as their relationships, are identified. The system efficiency of the additive model is shown to be greater than or equal to that of the multiplicative model. To verify the applicability of our proposed models, we consider a hypothetical example of the measurement and evaluation of the performances of several Business Schools across a number of universities. Other envisaged areas of application of our structure could include supporting the evaluation of the supply chain management of a firm, or the determination of the most desirable ship design considering maintenance issues.

In Data Envelopment Analysis (DEA), a variety of approaches have been used in the context of single-stage and basic serial two-stage systems to attain fairness in the evaluation of decision-making units (DMUs). Little work, however, has been done to address this challenge in a generalised two-stage structure featuring additional inputs in the second stage and a proportion of first-stage outputs as final outputs. In this paper, we argue that in this context, fairness is enhanced by increasing measures related to the discriminatory power and the weighting scheme of the method. We describe a mechanism that gives prominence to a more contemporary concept of fairness, incorporating diversity and inclusion of minority opinions. These aspects have, to our knowledge, not yet received explicit attention in the methodological development of DEA. We propose a novel combination of an additive self-efficiency aggregation model, a minimax secondary goal model, and the CRiteria Importance Through Inter-criteria Correlation (CRITIC) method, in order to promote these aspects of fairness, and thus achieve a better degree of cooperation between the stages of a DMU and among DMUs. The additive aggregation model is chosen over the alternative multiplicative approach for a variety of reasons relating to the emphasis on the intermediate products exchanged and the simplification. The minimax model offers peer evaluation in which each DMU aims to evaluate the worst of the others in the best possible light. Application of the CRITIC method to DEA addresses the aggregation problem within the cross-efficiency concept. Practical applications of this approach could include supporting the determination of training needs in job rotation manufacturing, or evaluation of sustainable supply chains. The paper includes a description of a numerical experiment, illustrating the approach.

In this research, we study at which speeds an oceangoing ship should ideally travel on each of a series of legs of a journey as to maximise the Net Present Value (NPV) of the ship. A novel class of models for the ship speed optimisation problem, which we refer to as P(n, m, G(o)), is presented. It is based on incorporating cash-flow functions and is flexible in modelling journey structures of variable composition. By studying properties of optimal leg speeds within this NPV framework, we demonstrate two novel elements of ship speed optimisation: (a) When executing a series of identical journeys, optimal ship speeds from one execution of the journey to the next are shown to change. We refer to this as the chain effect. (b) The ship's optimal speed is in general highly dependent on the decision maker's views on the ship's future profit potential(FPP). We present two efficient algorithms to solve the models. The methodology is applied to case studies based on the literature and the results are compared with classic model formulations. Net Present Value Equivalence Analysis (NPVEA) shows how the proposed framework increases understanding of the applicability and limitations of these classic model formulations. The use of the FPP concept is recommended in speed optimisation and job selection models. (c) 2021 Elsevier Ltd. All rights reserved.

This study is concerned with analysing the past demand data and development of aninventory model with demand arising from deterministic which is known in advance and random sourcessimultaneously. Two different shortages are created for each demand type and in order to prevent modelto backlog the deterministic demand, very high shortage cost is given for deterministic demand. Thenumerical value of the parameters are obtained from a real case which the inventory system of aninformation and technological organization of a university. The main difference of this study from theprevious studies is that the order amount must be in palette quantity for a deterministic and stochasticdemand inventory problem. Under this constraint, an inventory model is developed and tested withseveral datasets. Assuming lead time as constant, the value of deterministic demand present in the systemand impact of palette constraint are investigated. These investigations are compared with the status quoin the case study. It has seen that the palette quantity behaves as safety stock for high level randomdemand. Recommendations based on the impacts of advance demand information, lead time and palletquantity are presented in terms of changing in ordering costs, holding costs and service level.

This paper expands previous work on stock-dependent demand for a retailer with a two-warehouse (OW/RW) situation to the case of deteriorating items and where the retailer seeks to obtain the integrated optimal distribution policy from collaboration with a supplier. Motivated by practical applications and recent literature, a policy is considered whereby products in good order from the retailer's back-room (RW) are frequently transferred to its capacitated main store OW. Because the demand depends on the stock of good products in the OW, the aim is to keep this stock at its full capacity with products in good condition, and this can be done for as long as the RW stock of good products is positive. A firm's objective function is the Net Present Value (NPV) of the firm's future cash-flows. The profit functions are developed for both this continuous resupply policy and the commonly used policy in the OW/RW literature. Numerical examples are included and have been solved with grid search methods. The examples illustrate the benefits of adopting the continuous resupply policy, and also collaboration between the retailer and the wholesaler. Moreover, it is shown how these benefits can be shared by small adjustments to the product's unit price between the firms. (C) 2020 Elsevier Inc. All rights reserved.

This paper develops a strategy to jointly optimize the inventory and distribution for an online sales firm. The firm has to decide how to distribute the products from its warehouse to customers: this can either be done by using a company-owned vehicle, or by outsourcing to a third-party transportation company. The online sales environment includes a flexible delivery option that gives a discount to customers in return. This option is offered when the inventory level in the warehouse is lower than a threshold level. Customers accepting flexible delivery pay a deposit at the time they place the order and pay the remaining reduced price at the time of delivery. By offering the flexible delivery option, the firm aims to reduce the cost of distribution to the customers as well as postpone the timing of paying an outside supplier for stock replenishment. Additionally, this allows the firm to use on hand stock effectively to respond to more urgent customer requests. As the timing of cash-flows are dependent on the customer behaviour and the inventory and distribution strategy, the profit function is the Net Present Value of future cash-flows. We analyze the benefit of flexible delivery to the firm and perform sensitivity analysis with respect to various parameters. The profitability of flexible delivery depends on price setting and customer behaviour. Flexible delivery, in this model, has great potential to reduce transport distances and emissions when firms use their own vehicles.

Transport companies may cooperate to increase their efficiency levels by, for example, the exchange of orders or vehicle capacity. In this paper a new approach to horizontal carrier collaboration is presented: the sharing of distribution centres (DCs) with partnering organisations. This problem can be classified as a cooperative facility location problem and formulated as an innovative mixed integer linear programme. To ensure cooperation sustainability, collaborative costs need to be allocated fairly to the different participants. To analyse the benefits of cooperative facility location and the effects of different cost allocation techniques, numerical experiments based on experimental design are carried out on a UK case study. Sharing DCs may lead to significant cost savings up to 21.6%. In contrast to the case of sharing orders or vehicles, there are diseconomies of scale in terms of the number of partners and more collaborative benefit can be expected when partners are unequal in size. Moreover, results indicate that horizontal collaboration at the level of DCs works well with a limited number of partners and can be based on intuitively appealing cost sharing techniques, which may reduce alliance complexity and enforce the strength of mutual partner relationships.

In this paper, we propose a general agent-based distributed framework where each agent is implementing a different metaheuristic/local search combination. Moreover, an agent continuously adapts itself during the search process using a direct cooperation protocol based on reinforcement learning and pattern matching. Good patterns that make up improving solutions are identified and shared by the agents. This agent-based system aims to provide a modular flexible framework to deal with a variety of different problem domains. We have evaluated the performance of this approach using the proposed framework which embodies a set of well known metaheuristics with different configurations as agents on two problem domains, Permutation Flow-shop Scheduling and Capacitated Vehicle Routing. The results show the success of the approach yielding three new best known results of the Capacitated Vehicle Routing benchmarks tested, whilst the results for Permutation Flow-shop Scheduling are commensurate with the best known values for all the benchmarks tested. (C) 2016 The Authors. Published by Elsevier B.V.

Historically, many organisations have made investment decisions using a conventional ranking and prioritisation process. When prioritising, a fixed score is determined for each investment. This can be a measure of the investment's financial return (e.g. Net Present Value), or possibly a measure of the risk that the investment will mitigate for the organisation (risk score). During prioritisation, the portfolio of investments is ranked by that fixed score, and then those investments that can be executed within the budgetary constraints are selected. Mathematical optimisation using linear programming can improve on prioritisation results and achieve higher value outcomes whilst honouring multiple constraints (e.g. financial, service level, resources, timing, inter-project dependencies, and risk tolerances). Whilst individual organisations have reported significant benefits of using optimisation techniques these results are naturally specific to their operating context. This study seeks to generalise these results and quantify the value obtained by using optimisation techniques on portfolios of asset investments.

This paper develops inventory models to help answer strategic questions concerning whether planning for shortages offers financial benefits. A production-inventory system producing a deteriorating product in batches at a finite production rate with partial backordering is considered. Customers pay a deposit when placing a backorder. Backordered items receive a discount on the sales price. As lost sales may lead to customers not returning, the demand rate may depend on the fraction of lost sales. We develop a cash-flow based profit maximising Net Present Value (NPV) model without the inventory cost parameters commonly used in this context: unit holding cost, unit backorder cost, unit deterioration cost, and unit lost sales cost. The model finds the optimal inventory policy just like NPV models that discount the traditional parameters but has the advantage of not needing to estimate the value of the traditional parameters. It is shown that in models based on discounting the traditional parameters, the parameters are not exogenously determinable but are non-trivial functions of non-financial endogenous system parameters such as the production rate, annual demand rate, and backorder rate. Extensive numerical experiments illustrate how cash-flow NPV models provide insights into the value of planning for shortages and strategic choices about the design of the production-inventory system. It also provides insight into the classical problem of how to interpret unit backorder cost and unit lost sales cost. The study indicates that these insights cannot be reliably obtained from NPV models based on discounting unit backorder costs and unit lost sales costs.

Two main concepts are established in the literature for the Parameter Setting Problem of metaheuristics: Parameter Tuning Strategies (PTS) and Parameter Control Strategies (PCS). While PTS result in a fixed parameter setting for a set of problem instances, PCS are incorporated into the metaheuristic and adapt parameter values according to instance-specific performance feedback. The idea of Instance-specific Parameter Tuning Strategies (IPTS) is aiming to combine advantages of both tuning and control strategies by enabling the adoption of parameter values tailored to instance-specific characteristics a priori to running the metaheuristic. This requires, however, a significant knowledge about the impact of instance characteristics on heuristic performance. This paper presents an approach that semi-automatically designs the fuzzy logic rule base to obtain instance-specific parameter values by means of decision trees. This enables the user to automate the process of converting insights about instance-specific information and its impact on heuristic performance into a fuzzy rule base IPTS system. The system incorporates the decision maker’s preference about the trade-off between computational time and solution quality.

Classic inventory models use average cost functions. It is generally accepted that these models should account for the time value of money. They do so not by considering the timing of cash-flows, but by including opportunity costs. The Net Present Value (NPV) framework has long been used to compare these models with. We formalise NPV Equivalence Analysis (NPVEA) under various payment structures, and apply it to a few classic inventory models. While taking the linear approximation is typically part of the process to find equivalence, the essence is to disregard the parameters of a classic inventory model but instead start from cash-flow structures between firms. It is demonstrated how this leads to different plausible interpretations of, or variations to, classic inventory models, in particular for payment structures that differ from conventional assumptions. We identify situations with negative holding costs, which indicates that more features from the real world must be added into the decision model. We illustrate that in addition to capital costs, firms can enjoy capital rewards. These rewards may not always affect the firm's inventory decisions, but are in general useful for finding the impact of changes to various parameters on the firm's future profits.

While many review articles exist on (deterministic) lot sizing models used in the context of price and quantity discounts, buyer–vendor coordination, supply chain management, and joint economic lot sizing problems, they do not convey the impact of important findings which date back to at least 2002, or, in hindsight, to 1984. As a result, many recent articles still model the financial implications of lot sizing decisions without having the assurance that these models would help the firm(s) involved in maximising the Net Present Value (NPV). This paper therefore reviews these findings, while adding also its own contributions, as to convey the general importance to lot sizing theory. We show that the underlying principles used in the four key articles that have led to a division in modelling approaches are in fact all in line with NPV, and argue that therefore there should not be these discrepancies that currently persist in the literature. We establish the connections between these four strands of the literature using the solution to a simple variation of Harris׳ EOQ model, deriving thereby results from Boyaci and Gallego (2002) and Beullens and Janssens (2011), but showing their general applicability to any type of supply-chain structure. The breath of implications to deterministic lot sizing theory is illustrated using practical examples. We present a stochastic version of the model of Crowther (1964), which is arguably the least understood and applied model, but on the other hand the most important one in realising how these modelling strands can be unified.